Daniel Lawrence Lu's blog

Line scan camera image processing part 2

Sun, 02 Aug 2026 00:00:00 +0000

Line scan camera image processing part 2

2026-08-02

I further improved my line scan camera image processing!

Earlier, I wrote a blog post about various image processing methods in my previous blog post.

Line scan camera image processing

However, as you may recall from that post, some things are still not satisfactory.

Vertical stripes removal was bad
Speed estimation still isn’t perfect

Thanks to modern advancements in vibe coding, I was able to implement some of the ideas I wanted to implement in that blog post, to significantly perfect those things. (Spoiler alert: still not perfect, but a lot better)

FIGURE 1 Well-aligned Piccadilly Line

EXIF data

Camera: Alkeria Necta N4K2-7C
Software: nectar-preview
Date: 2025-09-13 09:27:13

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1 Speed estimation by human

At the end of the day, doing subpixel correlation between the two channels of the Bayer array is stil super sussy. As mentioned in the previous blog post:

Hmm, I think my speed estimation still isn’t perfect. It could be off by about 10%. For future work, I think I might be able to extract feature correspondences such as SIFT or LightGlue. Trains are full of repeating elements that are supposed to be evenly spaced. I can detect those, and add a cost function to evenly space them, and optimize. Another idea is to use a circle Hough transform to find circles.

Instead of the fancy feature-based methods or Hough transform strategy, I implemented “meatbag dewarping” by using Codex to vibe code a Python GUI app for me, a human, to draw constraining features:

Ellipses, that ought to be circles
Horizontal segments that ought to be the same width
Cut positions to cut up the super long image into chunks (e.g. between train cars)

So I fired up Codex with the following prompt:

In the python directory, let’s implement a new GUI tool to help fine tune speed estimation. Right now, as you may know from reading preview.py, speed estimation is done by subpixel correlation of the two green channels of the Bayer array line scan camera. In most cases, it works well, but in some featureless scenes, noisy scenes, scenes where the sample rate is much higher than the motion, or sometimes frankly I have no idea why, it is off by a little bit. The idea is that the GUI tool will allow me, a human, to carefully label two kinds of constraints: 1. draw ellipses that ought to be true circles, e.g. train wheels, London Underground roundel, etc. 2. draw horizontal segments that ought to be the same length after speed correction. For example, if the subject is a train, I can draw horizontal segments to constrain the width of each train window to be equal, or the width of each train car to be equal. This GUI tool will show a couple of raw, completely uncorrected frames, in a scrollable sliding window. e.g. if it shows 8 frames, then it would start out with 0000.bin to 0007.bin, and then we can increment or decrement the sliding window start/end by 1 by pressing left or right. Then, we can select which of the two tools to use, and draw directly on the raw frames. In a side panel, it shows a list of circle features added thus far, and a list of “horizontal width groups”. The horizontal line segments in each horizontal width segment group shall indicate that in real life they are all the same width. For ellipse drawing, just clicking and dragging the top left and bottom right of the bounding rectangle of the ellipse will work. The user may select on a previously drawn ellipse and resize it as needed. The ellipse shall be thin 1 px in a bright #00ff00 green color for easy visibility, but the draggable handles may be semitransparent and bigger (e.g. 8 px) for ergonomic clicking. For equal-width constraints, clicking and dragging shall highlight a full-height slice, with a thin 1 px vertical line on either side. The user may select, out of the list of equal width constraint groups, to add a new constraint to an existing group, modify an existing constraint, or to add a new group. Next, the user shall also indicate the start, end, and “cutting positions” of the sequence. This will populate a third list of things, which is again editable. Each “cutting position” is simply an x position; the first one indicates the start of the region of interest, the last one the end, and the middle ones are cutting positions. For example, for a train, we may want to add the cutting regions at the couplers in between the train cars. The UI for cut positions will simply show a thin 1 px vertical line that the user may place or modify. Finally, the output of this tool saves to a JSON file in the same directory as the raw data. Don’t worry about actually using the new constraint data in preview.py. All we want to do is to output the JSON. Also, when running the script, if the JSON already exists, it will load it from disk and populate the state of the tool already, so that the user may easily add or modify existing speed correction jsons. To display the raw chunks, we can simply bin the red, two green channels, and blue channel into a half size (e.g. 2048 x 1 rgb, from the original 4096 x 2 bayer) image for fast performance. Implement the GUI tool in a new python script in the python directory and add whatever dependencies you need to pyproject.toml. Make your own judgment about using the most modern, cleanest, minimal dependencies. Use a dark mode.

Anyway, it basically one-shotted the task, although I had to iterate a couple of times to iron out things I didn’t specify clearly, and add new features like the navigator on the bottom.

FIGURE 2 Screenshot of the speed estimation annotation tool

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FIGURE 3 Before: inaccurate speed estimation. The first power car is super stretched out compared to the last, because the train slowed down as it was entering Gare Montparnasse.

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Camera: Alkeria Necta N4K2-7C
Software: nectar-preview
Date: 2023-02-22 10:54:10

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FIGURE 4 After: better speed estimation. This Ouigo train just happens to be full of circular fiducials.

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Camera: Alkeria Necta N4K2-7C
Software: nectar-preview
Date: 2023-02-22 10:54:10

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FIGURE 5 Before: inaccurate speed estimation of accelerating GWR Class 800 at Bath Spa station.

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Camera: Alkeria Necta N4K2-7C
Software: nectar-preview
Date: 2025-09-10 14:47:58

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FIGURE 6 After: seems better now. Still not perfect (maybe I’m just bad at using my own manual annotation tool).

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Camera: Alkeria Necta N4K2-7C
Software: nectar-preview
Date: 2025-09-10 14:47:58

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Anyway, after adding a bunch of speed estimation annotations, I was able to get Codex to greatly improve the original correlation-based approach too, now that there’s a real ground truth metric to evaluate against.

FIGURE 7 This Piccadilly Line had no annotations but came out really nice.

EXIF data

Camera: Alkeria Necta N4K2-7C
Software: nectar-preview
Date: 2025-09-13 09:37:17

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2 Vertical stripes removal

Vertical stripes continued to plague many of my line scan captures. So I kicked off Codex with the following prompt:

Take a look at the preview.py script. The problem we are trying to solve now is the vertical stripes showing up in line scan camera captures. The line scan camera captures one column at a time and has a 2 x 4096 px sensor comprising a Bayer array (one column is red-green and the other is green-blue), and it seems that the columns have different brightnesses, causing the stripe artifacts.

Here’s the description of the problem and the original approach to solve it:

Currently, the “jitter correction” in column_exposure_jitter_correction is still unsatisfactory. For a brief explanation of the algorithm, see the “Getting rid of the vertical stripes” section of /home/dllu/proj/daniel.lawrence.lu/blog/2025-09-21-line-scan-camera-image-processing/index.md. Of course, that is just a documenting blog post for your reference, but you should fix the code in preview.py in this project without touching that blog post.

Here’s what’s wrong with the original approach:

Smooth gradients may naturally occur in trains. This causes incremental steps that keep stacking and result in a big incorrect correction.
The current algorithm is neither aware of overexposed areas and underexposed areas.
The current algorithm is sensitive to tuning, such as the sigma, which varies greatly by the overall exposure of the photos.

Here are some weird camera quirks I know of:

The brightness offset causing the stripes seems to be nonlinear and row position-dependent. The hue of the stripes also changes, suggesting that it may vary between the channels.
The stripes are much more visible in the darker regions.
The two green channels of the Bayer array are different from each other in a weird, nonlinear, and row position-dependent way. As such, it’s possible that one of them is zeroed out (clipping at zero) but the other isn’t. This particularly exacerbates the jitter correction’s problems and the correction may introduce strong striping that wasn’t previously there due to applying an offset to the zeroed out regions.
There’s a brightness offset associated with the overall brightness of the whole line. In other words, if a column contains parts which are naturally very dark, it is likely to be overexposed, and conversely, if a column contains parts which are naturally very bright, the exposure for the whole column is decreased.
The brightness offset effect is particularly strong if parts of the column are clipped. For an example, take a look at the capture /mnt/dataz/pictures/linescan/2026-01-13-09-49-37. The roof of the train is catching the sunlight at such an angle that it has many clipped highlights, which are causing the rest of the image to have strong vertical stripes.
Due to the poor quality of the current algorithm, jitter correction may introduce new vertical stripes that mess with downstream algorithms such as speed estimation (which does a highly sensitive subpixel correlation), so I’ve turned it off by default.

Here are some ideas that we CANNOT do:

We cannot assume that the top or bottom of the image are background. Hence, we cannot simply perform a calibration based on the top row and bottom row. This is because sometimes, the captured subject does in fact extend to the top and bottom edges. Also, even if it doesn’t, it can have subtle influences on the top and bottom of the image, such as casting a shadow. Furthermore, the photometric calibration we do has a nonlinear dependency on both the row position and the brightness, so typically calibrating based on a single row is not sufficient. Finally, as many of the captures involve trains, train bodies themselves often contain flat regions that are important to eliminate vertical stripes on.
We cannot blindly assume that high frequency stripes are all noise. High frequency, high contrast vertical stripes may naturally occur in trains, for example, in ventillation grills.

Here are some ideas that we might try:

Feel free to try out any lightweight machine learning models, for example, to run segmentation models or whatever. This machine is equipped with a powerful NVIDIA GB10 which has 128 GB of unified memory shared between the GPU and the host. You may add dependencies such as pytorch to the pyproject.toml, if it is helpful. We have $HF_TOKEN in the environment in case you need to download models from HuggingFace.

Here are some suggestions for captures that we can iterate on:

/mnt/dataz/pictures/linescan/18-10-06-13-52-16-utc contains a Spanish AVE Class 102 “Pato” which has both smooth gradients and overexposed areas.
/mnt/dataz/pictures/linescan/18-10-18-02-01-33-utc contains a Shanghai maglev which is very underexposed and noisy. The speed estimation has failed badly due to the noisy data even though the train is in fact moving at a nearly constant speed during the capture.
/mnt/dataz/pictures/linescan/2026-01-13-09-49-37 contains a Hong Kong MTR which has overexposed elements on the roof.
BONUS: /mnt/dataz/pictures/linescan/2026-01-13-09-32-44 contains a rotating line panorama. As such, it has no fixed (horizontally striped) background. Yet, vertical stripe artifacts are still visible, e.g. when inspecting the bottom right of the image, we see subtle stripes caused by large changes in brightness in the scene near the top caused by high contrast edges between tall buildings and the sky.

(note that the ones starting with 18 should have FLIP_OUTPUT_180 = False and the ones starting with 2026-01 and later should have FLIP_OUTPUT_180 = True)

While implementing:

You can treat the various capture directories as scratch spaces to dump debug artifacts or visualizations as needed. However, take care to avoid modifying or overwriting the raw data (*.bin and capture_settings.txt, if it exists) and best not to overwrite existing artifacts.
Feel free to refactor the code or add arguments to make your experiments more ergonomic instead of the original approach which is to manually modify the list of hardcoded globs and the hardcoded settings.
Instead of iterating on the whole capture, which may be more than 100,000 px wide and very slow to process, you should hone in on a couple of known bad chunks for rapid iteration. In the same vein, avoid loading the full capture data into memory which would be likely to cause OOM. Best to cherry pick some known chunks and use them as a quick test harness. As vertical stripe correction happens prior to speed estimation and skew correction, it is fruitful for your test harness to iterate rapidly on known chunks without running speed estimation, skew correction, or high quality sampling. Bear in mind that the final success criteria still requires these to be enabled, however.
Use your vision capabilities as much as possible to inspect the images for any remaining artifacts or obvious issues. Deploy subagents if necessary to quickly find relevant parts of large captures.
We are now on the branch jitter-correction. Create a new commit whenever you’ve made meaningful progress towards eliminating the vertical stripes.
Don’t modify this document.
Jot down your experiments, ideas, thoughts, and results by creating new Markdown documents in python/experimental-docs. Create the docs in chronological order, e.g. 2026-07-26-blah-blah-blah.md, and ensure that each document is coherent by appending new results in chronological order within it.
Read the earlier experiments and ideas (if any) in python/experimental-docs and inspect the current state of the code by looking at the commits ahead of master. The current state may have changed (hopefully improved) from the description given in this document.

This took Codex several iterations to implement correctly.

First, after an hour, it triumphantly concluded that it was perfected, but in fact it had caused a serious regression. Then, I told it that it had caused a regression, and it spent another six hours, and declared victory after erasing all the detail. Then it implemented a weird multi pass approach to erase all the detail and then rescue detail later.

Whatever, at least it’s better than before.

FIGURE 8 Initial image with tons of horrible stripes.

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FIGURE 9 Codex’s first attempt: it’s way worse!

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FIGURE 10 Codex’s second attempt: detail in wheels is gone and splotches ahve appeared.

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FIGURE 11 Final version. Some stripes remain, ugh!!!

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3 DNG export

It’s nice to process photos in Darktable using stuff like:

Global tone mapping with AgX
color calibration
sharpening, defringing, and denoising
tone curves

The line scan camera outputs 14 bit raws so it’s not bad. To that end, I vibe coded the ability to output reasonable-looking DNGs.

FIGURE 12 Line scan photo in Darktable.

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FIGURE 13 Exported pic

EXIF data

Camera: Alkeria Necta N4K2-7C
Software: darktable 5.5.0+1479~g9e36df0344
Date: 2025-07-05 18:16:22

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4 The overall correction pipeline

As you know, the pipeline is intentionally designed in these phases:

vertical stripes removal
speed estimation
skew correction

To understand this, we need to clarify:

Raw data before speed estimation: horizontal axis is time
Resampled data after speed estimation: horizontal axis is space

The vertical stripes happen in the raw image data as each column just physically has different brightness in the time domain.

Speed estimation estimates a unitless ratio (pixel/pixel) that maps the time domain pixels to the space domain pixels. Vertical stripes really mess with the speed estimation, which uses subpixel correlation between columns to estimate speed, so it’s really sensitive to subtle differences in brightness.

Finally, skew correction needs to happen after speed estimation and vertical stripes correction, because first of all the skew is due to a physical tilt in the camera, so it happens in the space domain, and secondly, the stripes also introduce false edges that mess with the Hough transform.

4.1 AI shenanigans

While giving Codex several sample pictures, I expected that correcting the vertical stripes better would impact the speed estimation. In particular, if it weren’t done correctly, speed estimation by subpixel correlation would be borked.

So I told it to try to make sure that the speed estimation works.

But instead, it did a few stupid booboos:

it added an artificial cap to the max and min speeds. This happened to work for the test cases I mentioned but broke other captures.
it disabled vertical stripes removal in speed correction and skew correction. This eliminated “regressions” compared to not having vertical stripes correction in the first place, but failed to bring the improvements of the vertical stripes correction.

Also, gpt-5.6-sol max is generally basically blind. Relying on its vision capabilities is extremely flaky. It cannot notice obvious flaws in the image.

FIGURE 14 Shanghai Transrapid: Before, speed estimation completely derped due to the noisy nature of the image, even though the train was moving at a constant speed.

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FIGURE 15 After: it works better. Sorry, my camera was tilted beyond the skew-correction Hough Transform’s max slope.

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Camera: Alkeria Necta N4K2-7C
Software: nectar-preview
Date: 2018-10-18 10:01:33

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Trip to Pismo Beach

Sun, 19 Jul 2026 00:00:00 +0000

Trip to Pismo Beach

2026-07-19

In May, we went to Pismo Beach!

FIGURE 1 Pismo Beach.

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Camera: FUJIFILM GFX100S
Lens: GF500mmF5.6 R LM OIS WR
Aperture: f/5.6
Shutter: 1/900 s
ISO: ISO 100
Software: darktable 5.5.0+967~g46d32311e8
Date: 2026-05-09 15:50:07

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1 2026-05-08: Arrival

Upon arriving in San Luis Obispo, we went to eat Peruvian food at Kiko Peruvian Cuisine.

FIGURE 2 Ceviche.

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Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/1.7
Shutter: 1/105 s
ISO: ISO 1600
Software: Digital Camera GFX100S Ver2.13
Date: 2026-05-08 19:49:41

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2 2026-05-09: Avila Ranch, SLO, and then Pismo

2.1 Avila Ranch

We went to Avila Ranch so that our baby can meet some animals. Baby is too young to feed the animals though, and I didn’t get any good pics of the animals. There were goats, sheep, chickens, and other amazing creatures big and small! There was also a really cute grocery store.

FIGURE 3 Onions at Avila Ranch

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Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/42 s
ISO: ISO 1250
Software: Digital Camera GFX100S Ver2.13
Date: 2026-05-09 11:03:32

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2.2 Lunch and coffee in SLO

We went back to SLO. We visited the Madonna Inn to behold its really unique architecture at the recommendation of some other guests we met during breakfast at our hotel.

FIGURE 4 Restaurant in the Madonna Inn.

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Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/4.0
Shutter: 1/15 s
ISO: ISO 2000
Software: darktable 5.5.0+1479~g9e36df0344
Date: 2026-05-09 11:55:02

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Then we went to eat lunch. We got Peruvian again, this time at Mistura.

FIGURE 5 Lunch

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Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/4.0
Shutter: 1/18 s
ISO: ISO 3200
Software: Digital Camera GFX100S Ver2.13
Date: 2026-05-09 12:55:03

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FIGURE 6 Nikkei sushi

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Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/4.0
Shutter: 1/27 s
ISO: ISO 3200
Software: Digital Camera GFX100S Ver2.13
Date: 2026-05-09 12:53:08

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2.3 Pier and beach

Then we went back to Pismo Beach to enjoy the pier and the actual beach.

FIGURE 7 Babby on the pier. He kept touching all the bird poop on the railings.

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Camera: FUJIFILM GFX100S
Lens: GF500mmF5.6 R LM OIS WR
Aperture: f/7.1
Shutter: 1/480 s
ISO: ISO 100
Software: darktable 5.5.0+967~g46d32311e8
Date: 2026-05-09 16:25:16

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FIGURE 8 Babby had a great time on the beach playing with the sand.

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Camera: FUJIFILM GFX100S
Lens: GF500mmF5.6 R LM OIS WR
Aperture: f/5.6
Shutter: 1/400 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.13
Date: 2026-05-09 17:11:18

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2.4 Dinner

We went to eat Italian food at Rosa’s Italian Restaurant.

FIGURE 9 Mussels

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Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/6.4
Shutter: 1/42 s
ISO: ISO 2500
Software: Digital Camera GFX100S Ver2.13
Date: 2026-05-09 18:28:24

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2.5 Nighttime neon photography

FIGURE 10 Board walk plaza

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Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/42 s
ISO: ISO 320
Software: darktable 5.5.0+967~g46d32311e8
Date: 2026-05-09 20:05:17

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FIGURE 11 Pomeroy Ave

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Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/1.7
Shutter: 1/42 s
ISO: ISO 160
Software: darktable 5.5.0+967~g46d32311e8
Date: 2026-05-09 20:10:08

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FIGURE 12 Splash Cafe

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Lens: GF55mmF1.7 R WR
Aperture: f/3.6
Shutter: 1/42 s
ISO: ISO 200
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Date: 2026-05-09 20:12:23

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FIGURE 13 Cool Cat Cafe

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Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/3.6
Shutter: 1/42 s
ISO: ISO 500
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Date: 2026-05-09 20:12:42

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FIGURE 14 Pier at dusk

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Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/1.7
Shutter: 1/42 s
ISO: ISO 320
Software: darktable 5.5.0+967~g46d32311e8
Date: 2026-05-09 20:22:41

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FIGURE 15 Brad’s Fish and Chips

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Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/1.7
Shutter: 1/250 s
ISO: ISO 400
Software: darktable 5.5.0+967~g46d32311e8
Date: 2026-05-09 20:30:20

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FIGURE 16 Esteem Surf Co

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Shutter: 1/42 s
ISO: ISO 800
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Date: 2026-05-09 20:34:56

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3 2026-05-10: Heading back

In the morning, we went to Dinosaur Caves Park.

FIGURE 17 California Towhee

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Aperture: f/5.6
Shutter: 1/400 s
ISO: ISO 200
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Date: 2026-05-10 11:28:26

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Then we grabbed lunch in San Luis Obispo again. We went to Shake Shake Fresh Noodle which is surprisingly delicious.

FIGURE 18 Noodle

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Camera: Apple iPhone 14 Pro Max
Lens: iPhone 14 Pro Max back triple camera 6.86mm f/1.78
Aperture: f/1.8
Shutter: 1/60 s
ISO: ISO 80
Software: 26.5
Date: 2026-05-10 12:11:18

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Then we went home!

DGX Spark as a daily driver

Wed, 15 Jul 2026 00:00:00 +0000

DGX Spark as a daily driver

2026-07-15

I bought an NVIDIA DGX Spark! It’s actually quite a competent general purpose computer while being super tiny and power efficient. It basically just has an ARM CPU, a decent NVIDIA GPU (with the same number of CUDA cores as the RTX 5070), 128 GB of unified memory, and a 4 TB SSD.

FIGURE 1 My small form factor PC next to the DGX Spark.

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Lens: 125
Aperture: f/NaN
Shutter: 1.7 s
ISO: ISO 100
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Date: 2026-05-16 14:06:47

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DISCLAIMER: I work at NVIDIA but I bought this with my own money and the opinions are my own. I didn’t get anything for writing this review. When I bought my DGX Spark, it only cost me $4000, but its price has since increased to $4700.

The DGX Spark runs “DGX OS” but it is in fact just plain old Ubuntu 24.04 with some additions. If you want, you can just install another Linux distribution easily (Fedora works well), although there may be a couple of weird bugs with the Realtek Ethernet driver so the NVIDIA version of the Linux kernel has a couple of patches. Unlike some other ARM devices, the DGX Spark is all ACPI rather than device tree based, so regular Linux builds for arm64 work just fine.

Generally, it is a power-efficient and quiet box (it gets slightly audible under load, but no annoying whine, and is silent when idle). It’s really nice how it doesn’t heat up the room or make noise. Stuff like web browsing and general programming work are super fast.

The CPU has a Geekbench 6 score roughly on par with an M3 Pro 14 core and an AMD Ryzen 9 7950X: 2983 single core and 18183 multi core. Weirdly enough, my results were slightly worse than Geerlingguy’s Dell Pro Max with the same GB10. Perhaps it’s just thermals or random fluctuations. At any rate, it’s better than my SFFPC with its AMD Ryzen 9 7945HX, which scored 2869 and 16973.

FIGURE 2 Fastfetch system specs.

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1 Can it run Crysis?

It turns out, it can totally run Crysis. The DGX Spark is quite decent at gaming in fact.

FIGURE 3 Yes, it can run Crysis!

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Lens: SIGMA 70mm F2.8 DG MACRO A018
Aperture: f/2.8
Shutter: 1/70 s
ISO: ISO 1600
Software: darktable 5.5.0+1479~g9e36df0344
Date: 2026-07-15 22:18:24

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This is Crysis Remastered running at 3200 x 1800 resolution with everything set to High and DLSS set to Performance. The game has a platinum rating on ProtonDB, but like other people running NVIDIA graphics, I had to set the Proton version to Experimental for it to work.

The Steam Snap for arm64 is maintained by Canonical and specifically targets gaming on DGX Spark. It’s probably the best avenue to play most games. It uses FEX to translate x86_64 to arm64, fulfilling a similar role as Rosetta 2 on Apple devices. It also bundles DLSS, RTX, and stuff so you can expect to play modern AAA titles with ray tracing turned on.

Obviously, the “bang for the buck” isn’t great compared to dedicated gaming machines. After all, the DGX Spark is quite expensive. You probably wouldn’t want to buy the DGX Spark just for gaming. My small form factor PC with a dedicated NVIDIA graphics card is better at gaming at a lower price.

The GPU of the DGX Spark is somewhat comparable to the RTX 5070 but with much slower memory bandwidth and much lower power, and there’s some overhead from FEX (whereas the performance penalty of Proton is usually rather tiny, or sometimes games are even faster in Proton than on Windows), so you can expect maybe two-thirds of the performance.

Many modern Steam games with great graphics work just fine out of the box, including:

Cyberpunk 2077
Portal RTX
Indiana Jones and the Great Circle
Running Train

and many more! Generally, modern games on common game engines like Unreal, Unity, and Godot have a very high chance of just working, with bells and whistles like RTX and DLSS working too.

FIGURE 4 Cyberpunk 2077 benchmark results.

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FIGURE 5 Running Train.

EXIF data

Camera: Apple iPhone 14 Pro Max
Lens: iPhone 14 Pro Max back triple camera 6.86mm f/1.78
Aperture: f/1.8
Shutter: 1/121 s
ISO: ISO 100
Software: 26.6
Date: 2026-07-10 15:07:46

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The main categories of games that don’t work are:

Games with aggressive anticheat that don’t work on Linux, such as League of Legends
Some older games that need tinkering to get working (see the software that needed tinkering section)
Non-Steam games have a lower chance of working

2 Local LLM inference

The main reason for most people to buy the DGX Spark is probably to dabble with local LLM inference. I am happy to report that the DGX Spark has aged like fine wine in this regard. It just keeps getting better!

Whereas early reviews lambasted it for apparently mediocre performance (bottlenecked by the memory bandwidth), a litany of novel tricks have enabled it to be literally several times faster now than when it first came out!

NVFP4 gave a huge boost. It wasn’t originally supported on DGX Spark when it first came out, to much chagrin, but it became available soon afterwards.
NVFP4 KV-cache quantization brought a big time-to-first-token speedup.
Multi-token prediction gives a free speedup of up to 2x on many models.
NVIDIA Model Optimizer gave another big boost to quantized models, resulting in a release on 2026-06-30 of a version of Qwen 3.6 27B that was way faster and better performing than earlier quantizations.
DFlash uses a diffusion-based drafter to predict an entire block of future tokens at once, further improving upon speculative decoding.

FIGURE 6 NVIDIA released a new quantization of Qwen 3.6 27B on 2026-06-30.

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There’s a Spark Arena leaderboard with recipes and benchmarks that’s pretty useful.

Anyway, right now, you can expect around 30-40 tokens per second (single concurrency, single Spark) with an NVFP4 quantization of Qwen 3.6 27B dense model, which is highly capable for everyday programming. It’s quite useful for general coding and agentic automation. At concurrency level 4 (four agents running in parallel, for example) you can expect a total token output rate of around 100 per second.

For the Qwen 3.6 35B mixture-of-experts model, you can expect much faster performance, up to around 170 tok/s decode, so that’s incredibly useful for quick chatting and automation, although perhaps slightly worse at coding.

3 Software that needed tinkering

3.1 Darktable

Darktable’s arm64 build works out of the box, but is extremely sluggish. It seems there’s some issue with the NVIDIA driver (at least on version 580, as tested) where transferring data from the GPU to CPU is very slow in Darktable’s OpenCL pipeline. This affects both in-app interactivity as well as export speed.

I created the pull request “prefault OpenCL readback destinations” which fixes the issue. The pull request “preheat host memory for OpenCL” also fixes it, in a perhaps slightly nicer way (this PR got reverted, so the fix still isn’t available in mainline Darktable).

3.2 Rhinoceros 3D

Rhinoceros 3D is a 3D modeling application for Windows x86_64 and for macOS (on Apple Silicon). I bought a license, and used this for my other projects such as my line scan camera case. Unfortunately, the latest version is well-known for not working on Linux, with a salty thread on the Rhino forums about it.

Getting the Windows version to work is generally considered more straightforward than the macOS version.

Anyway, I asked Codex to get it working, and it actually did! Here are the AI-generated instructions:

Rhino setup instructions

FIGURE 7 Rendering my concept car “Pupple”.

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FIGURE 8 NVIDIA Optix is working.

Download

Please note: Wine does not accept AI-generated code, and I’m not knowledgeable enough about the fixes here, so I can’t really upstream my patched fork.

3.3 Need for Speed Porsche Unleashed

Need for Speed Porsche Unleashed is one of my favourite video games of all time. I used to play it a ton in elementary school and have great nostalgia when I play it again. There is something special about just driving through a scenic European street without any distractions like “influencer voices”, experience confetti, billboards and ramps, and so on. At the same time, the pretty atmosphere is chill compared to super hardcore racing sims on laser-scanned tracks surrounded by nothing but tires, chain link fences, and ads.

FIGURE 9 The garage view has the wrong aspect ratio depending on window size but oh well.

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FIGURE 10 Porsche Unleashed running at 6144x3456 resolution.

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It turns out it just works well with mainline Wine for arm64 with FEX as the ARM64EC compatibility backend. I installed Verok’s patch and used the OpenGL3 renderer which works great! Now the graphics are even better than the original game (which ran only at really low resolutions of like 1024x768).

One weird quirk: GB10 has 10 fast Cortex-X925 cores and 10 slow Cortex-A725 cores. I had to explicitly pin it to the 10 fast cores rather than the 10 slow cores, otherwise the framerates would be really low. I guess perhaps the game from 1999 uses so few resources that the scheduler doesn’t realize it needs to be running in real time.

On Windows 11, I had an incredibly hard time getting Verok’s patch to work, because Windows kept flagging it as malware. I had to jump through an insane amount of hoops. Every time I was about to right click to add it as an exception, the OS silently deleted it. It was super frustrating. Getting it working on the DGX Spark was quite painless in comparison. I don’t actually know if it contains any real malware, so please proceed at your own risk (I personally think it’s safe).

3.4 StarCraft II

Like Rhino 3D, I asked Codex to help me get it working. SC2 works great on regular x86_64 machines in Proton or Wine, but on arm64, it crashed upon startup. Anyway, here are the AI-generated instructions to get it working:

SC2 setup instructions

FIGURE 11 Settings menu in Starcraft II.

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FIGURE 12 Framerate is 85 fps.

Download

As with Rhino, Wine does not accept AI-generated code, and I’m not knowledgeable enough about the fixes here, so I can’t upstream my patched fork.

4 A custom shelf

The DGX Spark comes with four USB-C ports (of which one is the power supply). I wanted to augment my DGX Spark with:

more USB ports for my keyboard, mouse, microphone, and stuff
a spare NVMe M.2 2280 SSD I have lying around
an SD card reader

so I bought a Mac Mini M4 dock by Hagibis which has all of the above with a fast USB4 connection with a fairly reputable ASMedia ASM2464PD chip and a decent-looking heatsink. That is in turn daisy-chained to a four-port USB 3.0 switchable dock connected to my mouse, keyboard, audio, and microphone, which can also be easily switched to my MacBook (for work).

The four USB-C ports of the DGX Spark ended up being occupied by:

power
my four hard drive USB 3.2 ZFS raidz2 array with four 24 TB drives
Hagibis dock (SSD connection)
Hagibis dock (everything else)

To set up my DGX Spark nicely with the dock and to give it a floating appearance, I designed a custom mini shelf.

FIGURE 13 DGX Spark and USB hubs on shelf.

EXIF data

Camera: FUJIFILM GFX100S
Lens: SIGMA 70mm F2.8 DG MACRO A018
Aperture: f/7.1
Shutter: 1/6.5 s
ISO: ISO 1600
Software: darktable 5.5.0+1479~g9e36df0344
Date: 2026-07-16 21:54:07

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FIGURE 14 Drawing of the shelf.

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FIGURE 15 Shelf fits the bottom radius of the DGX Spark perfectly.

EXIF data

Camera: Apple iPhone 14 Pro Max
Lens: iPhone 14 Pro Max back triple camera 6.86mm f/1.78
Aperture: f/1.8
Shutter: 1/30 s
ISO: ISO 640
Software: 26.5
Date: 2026-05-26 20:31:06

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DWG drawing

I got it made by SendCutSend and it cost me about $65.

The bottom of the DGX Spark is a silicone foot that is pretty grippy. The Hagibis Mac Mini dock is stuck on via a thermal pad. I also stuck an adhesive rubber sheet at the bottom of the whole thing to prevent it from sliding around. This way, I can easily plug and unplug stuff, insert the SD card, and so on, without worrying about things sliding.

Theoretically, the shelf also slightly improves cooling, although probably to a minimal degree. Many USB4 SSD enclosures get quite hot, so the thermal pad mated to a big aluminum thingy probably helps. I also set the Hagibis dock to be upside-down with the fins on top (the fins are what’s directly mated to the SSD, and would otherwise be at the bottom of the Mac Mini with very little airflow).

Aesthetically, in the end, it still doesn’t look super clean. I guess the Mac Mini dock just doesn’t match the look of the DGX Spark perfectly after all, and my other USB switcher is very ugly. Oh well!

Incidentally, my speaker stands for my KEF Q150 were also custom made by SendCutSend. Those cost me over $110. They are just a rounded rectangle with two bends.

5 Conclusion

So anyway the whole thing is great (price tag notwithstanding):

it handles my “main PC” use cases, such as light occasional gaming, CAD needs (Rhino 3D), and photography workflow
generates LLM tokens decently for simple coding tasks and automation
power-efficient and quiet
looks nice
whatever doesn’t run out of the box, I can just get AI agents to get it working

I wrote this blog post because a lot of people have the impression that the DGX Spark is a one-trick pony that can’t do anything else besides LLM inference. I often see people saying that one of the pros of the AMD Strix Halo platform is its ability to play games. I’ll admit that not all games work, and it is perhaps not the “year of the Linux desktop” for many people yet, not to mention Linux on ARM. But we are getting much closer than many realize.

Incidentally, this is my second Linux ARM machine, the other one being my Macbook Air M2 running Asahi Linux.

Nowadays, getting an SFFPC built with 128 GB of RAM and a 4 TB SSD is going to cost a lot anyway, not to mention having the 128 GB of memory being fully accessible by the GPU.

To be honest, I haven’t even trained any models on it yet. I have a list of great project ideas that could use training. I’ll do that eventually…

FIGURE 16 Size comparison with the Steam Machine (which has a similar 150 mm-ish square footprint). So the amount of gaming performance per unit volume is several times better on the Spark, commensurate with its three or four times higher price tag!

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Standalone CNC line scan camera

Fri, 12 Jun 2026 00:00:00 +0000

Standalone CNC line scan camera

2026-06-12

I use my line scan camera to take cool pictures of trains and other stuff.

FIGURE 1 Piccadilly line.

Download

For more information about how it works and the algorithms I use to process the images, see: line scan camera image processing.

But heretofore I had to plug it into my laptop, which was kinda annoying for many reasons.

I have to carry a laptop around
The Macbook Air I was using has only a 256GB SSD which necessitates bringing an external SSD
On the Macbook Air, there were some bugs with sometimes the camera negotiating only a 480 MB/s USB2 link instead of the 5 Gbps one, leading to dropped frames
It just doesn’t look cool and stylish like a custom made standalone camera
Screen isn’t bright enough

FIGURE 2 Dongle hell with the Macbook.

EXIF data

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Lens: iPhone 14 Pro Max back triple camera 6.86mm f/1.78
Aperture: f/1.8
Shutter: 1/147 s
ISO: ISO 64
Software: 26.0
Date: 2025-09-13 10:21:19

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FIGURE 3 A dropped frame can really ruin a capture. Here, the end of car 5519, near 5521, was missing a slice.

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1 The camera

OK here it is!

FIGURE 4 Left side.

EXIF data

Camera: FUJIFILM GFX100S
Lens: SIGMA 70mm F2.8 DG MACRO A018
Aperture: f/3.5
Shutter: 1/80 s
ISO: ISO 2000
Software: darktable 5.5.0+1479~g9e36df0344
Date: 2026-06-12 14:45:42

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FIGURE 5 Right side.

EXIF data

Camera: FUJIFILM GFX100S
Lens: SIGMA 70mm F2.8 DG MACRO A018
Aperture: f/3.5
Shutter: 1/85 s
ISO: ISO 2000
Software: darktable 5.5.0+1479~g9e36df0344
Date: 2026-06-12 14:46:35

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FIGURE 6 Front side.

EXIF data

Camera: FUJIFILM GFX100S
Lens: SIGMA 70mm F2.8 DG MACRO A018
Aperture: f/5.0
Shutter: 1/34 s
ISO: ISO 2000
Software: darktable 5.5.0+1479~g9e36df0344
Date: 2026-06-12 14:47:31

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FIGURE 7 Rear side.

EXIF data

Camera: FUJIFILM GFX100S
Lens: SIGMA 70mm F2.8 DG MACRO A018
Aperture: f/5.0
Shutter: 1/80 s
ISO: ISO 2000
Software: darktable 5.5.0+1479~g9e36df0344
Date: 2026-06-12 14:48:29

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Here are slightly older pics with worse 3D printed battery holder, bezel, and no wifi hat.

FIGURE 8 Trying it out.

EXIF data

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Lens: GF55mmF1.7 R WR
Aperture: f/11.0
Shutter: 1/42 s
ISO: ISO 640
Software: darktable 5.3.0+959~gf86f0af591
Date: 2026-02-10 16:30:27

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FIGURE 9 Rear side.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/11.0
Shutter: 1/42 s
ISO: ISO 640
Software: darktable 5.3.0+959~gf86f0af591
Date: 2026-02-10 16:29:59

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1.1 Bill of materials

Here’s the BOM (prices are approximate):

Component	Approx. Cost
Alkeria Necta N4K2-7C	$2000
NVIDIA Jetson Orin Nano Super	$250
Intel 670P 2TB SSD	$55 back in 2022, probably like $300 now lol
CNC case	$110
Three-key Bluetooth macro pad	$20
7 inch 1080P 2000 nit touchscreen	$70
Short USB-A to USB-C cables	$5
Short USB-A to right angle micro USB 3 cable	$5
Short HDMI cable	$15
USB-C PD to barrel plug adapter	$7
Anker power bank	$60

Table 1

FIGURE 10 Block diagram of how it’s connected. The SVG was generated by AI but it looks right to me.

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1.2 Case design

I modelled the case in Rhinoceros 3D.

I ordered it from JLC CNC for about RMB 831, which is like $110.

FIGURE 11 JLCCNC order screenshot

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FIGURE 12 Sendcutsend would have cost ten times as much.

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FIGURE 13 3D printed prototype

EXIF data

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Lens: SIGMA 70mm F2.8 DG MACRO A018
Aperture: f/8.0
Shutter: 1/15 s
ISO: ISO 3200
Software: darktable 5.3.0+959~gf86f0af591
Date: 2026-01-04 21:28:28

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FIGURE 14 A pile of 3D printed rejects.

EXIF data

Camera: Apple iPhone 14 Pro Max
Lens: iPhone 14 Pro Max back triple camera 6.86mm f/1.78
Aperture: f/1.8
Shutter: 1/40 s
ISO: ISO 320
Software: 26.3
Date: 2026-01-04 22:00:22

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FIGURE 15 Transferring the guts from the old 3D printed prototype to the shiny metal one.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/16.0
Shutter: 1/42 s
ISO: ISO 1000
Software: Digital Camera GFX100S Ver2.13
Date: 2026-01-17 08:23:35

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1.3 Setting up the Jetson

I picked the Jetson because it has a low price and great compute compared to the similarly priced Raspberry Pi 5.

I wanted to only use the NVME M2 2280, and apparently the only way to do so is to use the NVIDIA SDK Manager, which requires an x86_64 Ubuntu 24.04 machine. It was really annoying. I couldn’t do it from my Arch Linux machine (must be Ubuntu), another Jetson (not x86), or my Windows machine, but was eventually able to do it by actually using an Ubuntu desktop. The process was also really finicky with its USB cable requirements. I had to plug the cable into the back of the PC.

Another unanticipated issue was that the Jetson doesn’t keep time when powered off due to not having an RTC battery. Sometimes, my photos end up being out of order.

FIGURE 16 My friend soldering an RTC battery

EXIF data

Camera: FUJIFILM GFX100S
Lens: SIGMA 70mm F2.8 DG MACRO A018
Aperture: f/2.8
Shutter: 1/70 s
ISO: ISO 125
Software: Digital Camera GFX100S Ver2.13
Date: 2026-06-11 20:19:53

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Actually, you can just bridge R560 with solder. Some people online painstakingly solder a 560 mΩ SMD resistor which is super difficult. I think the actual resistance is supposed to be 1 kΩ and the “R560” is just the name of the part and not the resistance.

FIGURE 17 Pinout

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FIGURE 18 It works.

EXIF data

Camera: FUJIFILM GFX100S
Lens: SIGMA 70mm F2.8 DG MACRO A018
Aperture: f/2.8
Shutter: 1/160 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.13
Date: 2026-06-11 21:04:15

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1.4 Touchscreen

FIGURE 19 Touchscreen specs

EXIF data

Date: 2026-06-12 15:54:27

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The good news is that the touchscreen is super bright (2000 nits), so it is easily legible in daylight unlike the Macbook.

Also, it is powered by 5V USB (you can also power it by a barrel plug if you want), so I could just plug it into the Jetson and it just works.

1.5 Capture UI

It’s implemented in C++ with Dear ImGui.

GitHub code

I set up a systemd thing to launch it automatically upon boot.

FIGURE 20 Live debugging in the field.

EXIF data

Camera: Apple iPhone 14 Pro Max
Lens: iPhone 14 Pro Max back triple camera 6.86mm f/1.78
Aperture: f/1.8
Shutter: 1/1321 s
ISO: ISO 80
Software: 26.3
Date: 2026-01-15 09:53:27

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I set up a keybinding to toggle capture on/off with the spacebar so that I can control it wirelessly with a Bluetooth macro pad, to avoid shaking the camera when capturing.

2 Future ideas

I’d do these differently next time:

Add screw threads to the CNC case
My wifi hat protrudes weirdly because my screws aren’t long enough and again, no screw threads. So I had to use standoffs.

Another idea is to add a regular video camera (i.e. an area-scan camera) to the thing. That would help measure the speed of the train as well as help me see which part of the scene the line scan camera is looking at.

Right now, the tripod quick release plate (a Peak Design plate) is attached by a single M4 screw. I’d like to use two screws instead attached to the Alkeria camera for better strength.

3 Other people's line scan cameras

3.1 Adam Magyar

Adam Magyar’s “Einstein’s Camera” is perhaps the most famous one.

FIGURE 21 Adam Magyar and his line scan camera.

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His monochrome line scan camera takes really clean images even in low light, but is huge and needs a backpack. To be fair, it was over a decade ago.

3.2 Seth Itow

My former colleague Seth created his handheld battery-powered line scan camera independently after being inspired by my previous stuff.

FIGURE 22 Seth’s line scan camera.

Download

4 FAQ

Do you actually carry it with a strap attached to those Peak Design anchor links?

Good heavens no! It’s just a meme.

What lenses do you use?

In customizing my line scan camera I mentioned that I converted it to Sony E mount (manual only, no electronic contacts).

I use a Cheecar 50mm f/1.2 and a Samyang 85mm f/1.8 CS, both manual focus for Sony E mount (APS-C). The line is 28mm long so an APS-C lens covers it while being affordable.

I also have a variety of full frame lenses that I occasionally use on both this and my Fuji GFX camera, including:

Voigtlander Ultron 35mm f/1.7
Voigtlander Apo Skopar 90mm f/2.8
Voigtlander Apo Lanthar 125mm f/2.5

My Cheecar and Samyang have an IR-block filter, but the Voigtlanders don’t. Actually, I had one for the 90mm, but I smashed it accidentally.

FIGURE 23 Before vs after putting on the IR-block filter.

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How long does the battery last?

The Anker battery lasts about 1 hour. It is nice because it has a display indicating the amount of charge remaining, and it has a built-in USB-C cable capable of outputting PD 20 V and more than the 35 W needed for the setup.

Switching to Markdown

Sun, 31 May 2026 00:00:00 +0000

Switching to Markdown

2026-05-31

Back in 2014, I wrote a custom Markdown-like language. At the time I did so because I needed features like math equations that weren’t super standardized back then. Now, I’ve finally decided to admit defeat and just switch to Markdown. I’m still using my custom static site generator, dllup-rs, but I no longer have to worry about maintaining my own parser and stuff.

The advantages of using Markdown are many:

AI agents can read it natively
built-in syntax highlighting in most text editors
no need to maintain my horrible vibe-coded parser and AST
I got muscle memory for typing markdown due to vibe coding so much, e.g. triple backticks instead of triple tildes for code snippets

But I’m keeping my static site generator, which has:

images using <figure> and special handling for my high resolution photography nerd use case, like showing EXIF data and downloading in various sizes including the 100 megapixel original
serverside KaTeX and precomputed image dimensions since I really really hate my website jumping around during loading
various extensions that even highly extended Markdown variants don’t have, like my custom “big button” and citations/references

The downside of course is that it is kinda cool that back as a student I hand-wrote some really concise Python for my personal markup language and static site generator. Now, with my vibe-coded static site generator and a generic markup language, perhaps a little bit of the personality and “nerd cred” are gone. But it runs a lot faster and has way more features as described in “amazing website improvements”. At least all my blog posts are hand written and my CSS styles are mostly hand designed.

Trip to Ridgecrest

Sat, 11 Apr 2026 00:00:00 +0000

Trip to Ridgecrest

2026-04-11

At the end of March 2026, we went to Ridgecrest, to visit nearby Trona Pinnacles, Alabama Hills, and other attractions. We went with our friend, my cousin in law, and their dog Emily.

FIGURE 1 Some kinda dried desert plant thing.

EXIF data

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Lens: GF55mmF1.7 R WR
Aperture: f/4.0
Shutter: 1/1400 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.13
Date: 2026-03-27 11:44:53

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1 2026-03-26: Drive to Ridgecrest

We ate lunch at Harris BBQ while supercharging our car.

FIGURE 2 A cat at Harris BBQ, where we ate lunch.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF110mmF2 R LM WR
Aperture: f/2.0
Shutter: 1/170 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.13
Date: 2026-03-26 12:08:28

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FIGURE 3 Inyokern Station, a gas station, but I stopped there because there were Tesla superchargers next to it.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF110mmF2 R LM WR
Aperture: f/5.6
Shutter: 1/640 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.13
Date: 2026-03-26 16:47:10

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For dinner, we wanted to dine at the restaurant called Mon Reve, but it was booked out for two weeks. So we randomly picked another restaurant, Saigon Flavor.

FIGURE 4 Emily.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/2.8
Shutter: 1/180 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.13
Date: 2026-03-26 18:18:09

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FIGURE 5 Pho.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/1.7
Shutter: 1/42 s
ISO: ISO 1000
Software: Digital Camera GFX100S Ver2.13
Date: 2026-03-26 19:16:29

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2 2026-03-27: Trona Pinnacles

We went to Trona Pinnacles!

First we had breakfast at McDonalds. Then we bought takeout sandwiches at some strange online-only restaurant mysteriously embedded within Denny’s.

FIGURE 6 Tesla Model S.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/8.0
Shutter: 1/500 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.13
Date: 2026-03-27 11:34:29

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FIGURE 7 Tesla Model S.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/850 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.13
Date: 2026-03-27 11:35:03

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FIGURE 8 Tesla Model S.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/8.0
Shutter: 1/340 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.13
Date: 2026-03-27 11:35:56

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FIGURE 9 A sticker-encrusted stop sign at a railroad crossing.

EXIF data

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Lens: GF55mmF1.7 R WR
Aperture: f/2.8
Shutter: 1/2900 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.13
Date: 2026-03-27 11:43:00

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FIGURE 10 I like trains.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/1000 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.13
Date: 2026-03-27 11:45:23

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FIGURE 11 Car.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/850 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.13
Date: 2026-03-27 12:06:08

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FIGURE 12 Car.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/900 s
ISO: ISO 100
Software: darktable 5.3.0+959~gf86f0af591
Date: 2026-03-27 12:06:43

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FIGURE 13 The pinnacles!!!

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/850 s
ISO: ISO 100
Software: darktable 5.3.0+959~gf86f0af591
Date: 2026-03-27 12:06:54

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FIGURE 14 Some pointy rocks.

EXIF data

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Lens: GF55mmF1.7 R WR
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Software: darktable 5.3.0+959~gf86f0af591
Date: 2026-03-27 12:44:46

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2.1 Ballarat Ghost Town

We went to check out a ghost town.

FIGURE 15 Model S near a dried up lake.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/1100 s
ISO: ISO 100
Software: darktable 5.3.0+959~gf86f0af591
Date: 2026-03-27 14:25:32

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FIGURE 16 Ballarat Trading Post.

EXIF data

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Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/600 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.13
Date: 2026-03-27 14:48:01

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There was a really friendly and extremely skinny dude in the gift shop, who told us about the history of the place. He also told us about how he goes to Trona and buys bottled water as it is the only source of hydration (no potable water in the ghost town), but he would yank out the dollar bills from the vending machine to avoid having to pay money. His family are still gold miners. I guess gold mining is not super profitable today.

The trading post was in fact built in the 1980s, long after the town was abandoned, by some guy who built it all by hand apparently.

FIGURE 17 A Vocho abandoned in the desert.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/8.0
Shutter: 1/220 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.13
Date: 2026-03-27 14:46:13

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FIGURE 18 It was very hot, exceeding 313 K.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/4.0
Shutter: 1/52 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.13
Date: 2026-03-27 14:49:35

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FIGURE 19 Abandoned honky tonk.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/4.0
Shutter: 1/56 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.13
Date: 2026-03-27 14:50:59

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2.2 Dinner

For dinner, we went to Tokyo House, because they are okay with dogs. It’s just typical small-town American-Japanese cuisine with rolls and “hibachi”.

FIGURE 20 Tokyo House.

EXIF data

Camera: Apple iPhone 14 Pro Max
Lens: iPhone 14 Pro Max back triple camera 6.86mm f/1.78
Aperture: f/1.8
Shutter: 1/40 s
ISO: ISO 1000
Software: 26.4
Date: 2026-03-27 18:30:58

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2.3 Trona Pinnacles again but at midnight

We went to Trona Pinnacles again at 3 am to see the stars!

FIGURE 21 Disco doge Emily

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FIGURE 22 Trona Pinnacles

EXIF data

Software: darktable 5.3.0+959~gf86f0af591

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3 2026-03-28: Alabama Hills

For breakfast we had extreme American diner food at Kristy’s Family restaurant. This was probably the tastiest meal of the trip, and an extreme calorie bomb.

FIGURE 23 Breakfast.

EXIF data

Camera: Apple iPhone 14 Pro Max
Lens: iPhone 14 Pro Max back triple camera 6.86mm f/1.78
Aperture: f/1.8
Shutter: 1/60 s
ISO: ISO 160
Software: 26.4
Date: 2026-03-28 11:41:04

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Then we headed north to Alabama Hills!

FIGURE 24 A painted face on some rocks.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/8.0
Shutter: 1/400 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.13
Date: 2026-03-28 13:47:29

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FIGURE 25 An arch.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/8.0
Shutter: 1/170 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.13
Date: 2026-03-28 14:33:58

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FIGURE 26 Some round rocks.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/8.0
Shutter: 1/420 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.13
Date: 2026-03-28 14:31:21

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FIGURE 27 The same view as Tony Stark’s Jericho Missiles.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/680 s
ISO: ISO 100
Software: darktable 5.5.0+967~g46d32311e8
Date: 2026-03-28 15:39:57

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Then I went to charge the Tesla again and found a Vocho.

FIGURE 28 Vocho

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF110mmF2 R LM WR
Aperture: f/2.0
Shutter: 1/1800 s
ISO: ISO 100
Software: darktable 5.3.0+959~gf86f0af591
Date: 2026-03-28 17:52:28

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FIGURE 29 Tesla Superchargers

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF110mmF2 R LM WR
Aperture: f/5.6
Shutter: 1/300 s
ISO: ISO 100
Software: darktable 5.3.0+959~gf86f0af591
Date: 2026-03-28 17:59:04

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FIGURE 30 A house in Inyokern

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF110mmF2 R LM WR
Aperture: f/5.6
Shutter: 1/320 s
ISO: ISO 100
Software: darktable 5.5.0+967~g46d32311e8
Date: 2026-03-28 18:00:58

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For dinner, we went to Casa Corona, a Mexican restaurant.

FIGURE 31 Dinner

EXIF data

Camera: Apple iPhone 14 Pro Max
Lens: iPhone 14 Pro Max back triple camera 6.86mm f/1.78
Aperture: f/1.8
Shutter: 1/60 s
ISO: ISO 250
Software: 26.4
Date: 2026-03-28 19:02:41

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4 2026-03-29: Drive back

We had breakfast at Beanster’s Espresso. All the other customers were really hardcore Marines.

Then we drove back! We had In N Out for lunch.

FIGURE 32 Sleepy babby.

EXIF data

Camera: Apple iPhone 14 Pro Max
Lens: iPhone 14 Pro Max back triple camera 6.86mm f/1.78
Aperture: f/1.8
Shutter: 1/121 s
ISO: ISO 80
Software: 26.4
Date: 2026-03-29 09:48:39

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Uploading to Wikimedia Commons with AI

Wed, 25 Mar 2026 00:00:00 +0000

Uploading to Wikimedia Commons with AI

2026-03-25

Uploading to Wikimedia Commons is fun and rewarding. I love taking photos of stuff, and sharing my photos with the world is nice. Otherwise, it would be kind of sad that my 100 megapixel photos just languish on my computer, never to be seen by anyone other than my family and me. I’ve uploaded tons of photos which you can see at user:dllu on Commons.

However, uploading to Commons has some pain points. Finding the right categories is a big annoyance, because the rather fine-grained categories on Commons are both a blessing and a curse. It’s really easy to make mistakes (by putting a file in a parent category instead of a more specific one) or to forget certain types of categories. Sometimes I even forget what I took a picture of. Also, there are a lot of easily automatable steps like adding the geolocation, tags based on camera parameters, and so on.

I wrote a Hacker News comment complaining about all the friction:

As someone who has uploaded lots of photos to Wikimedia Commons, I think one main challenge is coming up with an encyclopedic description and the right categories. Searching for categories is remarkably difficult, manual, and unintuitive. For example, I recently uploaded a photo of a Caltrain at San Jose Diridon station. So I put “Category:San Jose Diridon station” as a category. But this was in fact incorrect, since there’s a more specific category called “Category:Trains at San José Diridon station”. On Wikimedia Commons, the UI to add a category only tells you if a category exists or not, but doesn’t let you search nicely. There are also location/time categories like “Category:December 2023 in California”. Probably less than 1% of photos taken during December 2023 in California get added to such categories, even when GPS and EXIF data contain information about the location and date of capture. As for writing a description, it should be comprehensive, descriptive, yet succinct, in an encyclopedic tone, and with links to relevant categories on Wikimedia Commons or to relevant pages on Wikipedia as necessary. Again, making the relevant links is mildly tedious. As a result, when uploading any single photo, I would need to open dozens of tabs to verify the right pages and categories.

FIGURE 1 Caltrain photo on Wikimedia Commons.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/42 s
ISO: ISO 640
Software: Digital Camera GFX100S Ver2.10
Date: 2023-11-21 16:55:05

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Fortunately, we have the technology to solve this!

Generally, the tedious task of searching for categories can be solved by AI by using a vision language model to inspect the photo, along with its metadata, and to search Commons for relevant categories. Obviously, nobody wants to pollute the nice Commons project with inaccurate AI slop, so it is important that the script shows a UI for me, a human, to review and edit the suggested caption and categories.

I wrote a big prompt for Codex to add a script to upload to Wikimedia Commons into my pupphoto project. It is a collection of scripts to help me organize my photos, including scripts to upload photos to this very blog.

pupphoto

I'd like to write a Python script to upload a photo to Wikimedia Commons and populate its description page with the appropriate categories, description, and so on, by querying the appropriate openai api endpoints as well as wikimedia commons endpoints.

* For category search, we should downsize the image to a canonical size, and use openai vision apis (i.e. https://developers.openai.com/api/docs/guides/images-vision#analyze-images ) to analyze it (we can also pass in relevant information such as GPS lat long from EXIF data, if available, and date/time) and propose some keywords to search. Then, it should search for those keywords and find a list of categories along with the as a structured dag (some categories are children of others). Finally, we should use the vision api again, given the image and relevant data, and also pass in the structured dag in a comprehensible format (e.g. json), and ask the model to determine the most appropriate categories. For the vision API, we can use gpt-5.4 with "high" detail level. Please put some thought into how best to format the prompt in order to get the model to propose the best categories. We should generally go for a "leaf node" in the dag for the most specific category, instead of a vague parent category. For example, a photo of a new Caltrain in San Jose station taken in 2023 may have the categories "Stadler KISS of Caltrain", "Trains at San José Diridon station", and "Caltrain in 2023". It should NOT go into both a child and parent category at the same time, e.g. it should NOT go into the category "San José Diridon Station platforms", which is a parent category of "Trains at San José Diridon station". It should also not get the category "2023 in California" which is an ancestor of "Caltrain in 2023".
* We should also automatically search for categories based on photographic equipment, technique, and so on, based on the EXIF data, to get categories such as "Taken with Fujifilm GFX100S", "Lens focal length 55 mm", "Exposure time 1/250 sec", and so on. We may do this based on some hardcoded rules. To generate the hardcoded rules, you may first perform some searches to ensure that you have the right list of such categories. For example we can first generate a list of all "Lens focal length %dmm" categories by searching the Commons API.
* For the caption, we should also use the openai vision API and get it to write a concise caption. Please carefully construct the prompt to the model so that it produces a concise, factual, encyclopaedic, neutral, and objective caption. 
* We should also generate an appropriate filename for the whole image, again using the vision API. You may consolidate vision API calls with the caption one and the categories with good prompting (e.g. ask it to output title on the first line and caption on the second line). This should save us a lot of monetary costs compared to repeatedly uploading the image.
Please append a common suffix (configurable in config.toml).

Next, you should open a simple UI. It is probably easiest to serve a mini webapp and open a browser. The UI shall display the image and have a textarea for the caption as well as an editable list of categories. It shall also have a button to save and proceed to uploading.

Only when the user presses the button shall it actually create the Wikitext content for the description page and perform the actual upload. The Wikitext content should have a template like
```
=={{int:filedesc}}==
{{Information
|description =
{{en|1 = %s}}
|date = %s
|source = {{own}}
|author = %s
|other fields = {{Information field | name = Raw file SHA1 sum | value = %s}}
}}
{{Location|%s|%s}}

=={{int:license-header}}==
%s

%s
```

* For the Raw file SHA1 sum, please consult how the filename is formed from the sha1sum of the raw file in import.py. If the filename of the photo matches it, you can add the that "information field" template with the raw file sha1sum. Otherwise, skip it.
* For the location template, only add it if the file contains valid EXIF. Please respect the "remove_gps_if_banned" logic and avoid adding the location if it belongs to a banned area.
* For the license, it should be configurable in the config.toml, but probably default to '{{self|cc-by-sa-4.0}}'
* For the author name, it should also be configurable in the config.toml.
* For the Commons API user and password, as well as the OpenAI API key, they should be configurable in config.toml.

Upon clicking the save button, it should show a loading spinner until the upload is complete, and then redirect to the newly uploaded file's Wikimedia Commons page.
Then, the script shall quit.
We should make it possible to run multiple of these upload jobs (say, 10) by running multiple instances of this script in parallel (probably easiest to use a randomized port).

Happily, with Codex using gpt-5.4, it nearly one shotted it, minus some small bugs (such as parsing the GPS lat/long from EXIF data).

FIGURE 2 Screenshot of the upload tool.

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In most cases, it works remarkably well.

However, I soon found that sometimes the AI screws up when there are no GPS coordinates. This is really annoying because sometimes my camera just inexplicably doesn’t connect to the bluetooth app on my phone (which provides the GPS coordinates).

FIGURE 3 The world-famous Holyrood Palace quadrangle gets a rather generic set of categories and caption in the absence of GPS data.

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So I vibe coded the ability to give the AI a hint.

FIGURE 4 GPT 5 needs to take a hint.

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FIGURE 5 Much better now. Commons Link.

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By the way, the upload-commons.py script is combo wombo with my other project, sriv, which I mentioned in an earlier blog post, sriv: simple rust image viewer. In my bindings.toml for sriv, I just put:

"ctrl+w" = "cd /home/dllu/proj/pupphoto && uv run python upload_commons.py {file}"

So now, whenever I am scrolling through my images in sriv, I can just upload whichever ones I think look nice.

Trip to Hong Kong, Zhuhai, Chongqing, Chengdu, and Shanghai

Tue, 03 Feb 2026 00:00:00 +0000

Trip to Hong Kong, Zhuhai, Chongqing, Chengdu, and Shanghai

2026-02-03

From January to February of 2026, Joanne, baby, and I went to China!

1 2026-01-15: Arrival in Hong Kong

I arrived in the early morning in Hong Kong at around 3 am. Joanne and baby had already arrived a month earlier and were in Zhuhai. After wasting some hours in the airport, I took the airport express to downtown to take photos by myself! First, I went to Tsim Sha Tsui to get dawn cityscapes across Victoria Harbor.

FIGURE 1 Foggy morning.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/20 s
ISO: ISO 125
Software: darktable 5.3.0+959~gf86f0af591
Date: 2026-01-15 07:06:40

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Then, without further ado, I took out my 500mm lens!

FIGURE 2 A pilot vessel.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF500mmF5.6 R LM OIS WR
Aperture: f/5.6
Shutter: 1/350 s
ISO: ISO 3200
Software: Digital Camera GFX100S Ver2.13
Date: 2026-01-15 07:10:54

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FIGURE 3 Radar domes.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF500mmF5.6 R LM OIS WR
Aperture: f/5.6
Shutter: 1/60 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.13
Date: 2026-01-15 07:11:19

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FIGURE 4 A colourful ferry with skyscrapers rendered grey by the smog.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF500mmF5.6 R LM OIS WR
Aperture: f/5.6
Shutter: 1/240 s
ISO: ISO 800
Software: darktable 5.4.0
Date: 2026-01-15 07:20:47

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FIGURE 5 A variety of air conditioner units.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF500mmF5.6 R LM OIS WR
Aperture: f/5.6
Shutter: 1/200 s
ISO: ISO 1600
Software: Digital Camera GFX100S Ver2.13
Date: 2026-01-15 07:37:15

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I ate a breakfast of piggy bun (a plain bun which does not contain pork) and then went to take line scan photos. Due to technical difficulties caused by vibe coding my capture code, I had to disassemble my line scan camera and repair it in the field. I soon fixed it and captured a couple of pics of the MTR Airport Express.

FIGURE 6 MTR train. This one is not the Airport Express but the actual Airport Express image turned out really bad quality.

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Afterwards, I had lunch at a random Tsui Wah location (curry beef), and then took the bus to Zhuhai to be reunited with my wife and child!

2 2026-01-16: Day trip to Foshan

We hired a seven seater car for the day to go to Foshan. My wife, child, mother in law, and aunt in law all came. On the ride there, we saw some amazing viaduct construction projects. Coming from California, where a single bridge/viaduct for the California High Speed Rail took decades, billions, and tons of hand wringing, it was awe-inspiring to see such immense structures being built.

FIGURE 7 Extreme bridge construction.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/2.0
Shutter: 1/850 s
ISO: ISO 400
Software: darktable 5.4.0
Date: 2026-01-16 08:24:35

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FIGURE 8 Extreme viaduct construction.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/2.8
Shutter: 1/500 s
ISO: ISO 400
Software: darktable 5.4.0
Date: 2026-01-16 08:24:55

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2.1 Zumiao Temple

We arrived at the Foshan Zumiao to watch a martial arts performance and lion dance. After all, Foshan is known for its martial arts as that’s where Ip Man comes from.

FIGURE 9 Drummer and percussion players for the lion dance.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF500mmF5.6 R LM OIS WR
Aperture: f/8.0
Shutter: 1/400 s
ISO: ISO 640
Software: Digital Camera GFX100S Ver2.13
Date: 2026-01-16 10:11:25

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FIGURE 10 Temple rooftop detail.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF500mmF5.6 R LM OIS WR
Aperture: f/5.6
Shutter: 1/400 s
ISO: ISO 125
Software: Digital Camera GFX100S Ver2.13
Date: 2026-01-16 10:12:07

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FIGURE 11 Lion.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF500mmF5.6 R LM OIS WR
Aperture: f/5.6
Shutter: 1/400 s
ISO: ISO 320
Software: Digital Camera GFX100S Ver2.13
Date: 2026-01-16 10:32:50

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FIGURE 12 Zumiao.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 1/200 s
ISO: ISO 100
Software: darktable 5.4.0
Date: 2026-01-16 11:14:41

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FIGURE 13 Some turtles.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF500mmF5.6 R LM OIS WR
Aperture: f/5.6
Shutter: 1/400 s
ISO: ISO 640
Software: Digital Camera GFX100S Ver2.13
Date: 2026-01-16 11:20:28

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FIGURE 14 Intricate detailing on the roof.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF500mmF5.6 R LM OIS WR
Aperture: f/5.6
Shutter: 1/400 s
ISO: ISO 250
Software: Digital Camera GFX100S Ver2.13
Date: 2026-01-16 11:21:41

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FIGURE 15 A bas relief.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/210 s
ISO: ISO 100
Software: darktable 5.4.0
Date: 2026-01-16 11:36:15

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2.2 Lunch in Lingnan Tiandi

Then, we went to the neighboring commercial area/old town of Lingnan Tiandi and had lunch at Darongshu Efu.

FIGURE 16 A cute bear.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/50 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.13
Date: 2026-01-16 11:50:47

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FIGURE 17 Extreme raw and undercooked fish.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/50 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.13
Date: 2026-01-16 12:12:23

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FIGURE 18 Steamed egg with truffle for the baby.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/4.5
Shutter: 1/42 s
ISO: ISO 250
Software: Digital Camera GFX100S Ver2.13
Date: 2026-01-16 12:15:22

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FIGURE 19 Fish lantern.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/1.7
Shutter: 1/950 s
ISO: ISO 100
Software: darktable 5.4.0
Date: 2026-01-16 12:57:29

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After lunch, we had dessert.

FIGURE 20 Double skin milk.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/3.2
Shutter: 1/42 s
ISO: ISO 125
Software: Digital Camera GFX100S Ver2.13
Date: 2026-01-16 13:03:20

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FIGURE 21 Another fish lantern.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/1.7
Shutter: 1/200 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.13
Date: 2026-01-16 13:41:15

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2.3 Liangyuan

In the afternoon, we went to Liangyuan. It was the abode of an influential family. This garden was very amazing, and I took some truly wonderful photos of my family which I will not be sharing here.

FIGURE 22 A cool rock.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/1.7
Shutter: 1/600 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.13
Date: 2026-01-16 14:25:45

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FIGURE 23 Pond.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/8.0
Shutter: 1/125 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.13
Date: 2026-01-16 14:47:24

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FIGURE 24 Garden.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/200 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.13
Date: 2026-01-16 15:00:48

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2.4 Back to Zhuhai

We went back to Zhuhai and ate home cooked dinner. Then we strolled around the neighbourhood.

FIGURE 25 Old buildings near our apartment.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/42 s
ISO: ISO 200
Software: darktable 5.4.0
Date: 2026-01-16 17:48:36

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3 2026-01-17: Day trip to Macao

My line scan camera parts arrived (I’ll be writing more about this in a separate blog post), so I spent some time in the morning transplanting my line scan camera guts into the new CNC aluminum enclosure.

FIGURE 26 Old 3D printed plastic enclosure on the left; new enclosure on the right.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/16.0
Shutter: 1/42 s
ISO: ISO 1000
Software: Digital Camera GFX100S Ver2.13
Date: 2026-01-17 08:23:35

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FIGURE 27 Fully assembled.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/8.0
Shutter: 1/100 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.13
Date: 2026-01-17 09:45:16

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3.1 Lunch: Omakase

Before heading to Macao, we went to a restaurant called Pan in Zhuhai. The omakase was really amazing and only cost about $50. It is easily on the same level as $100+ omakases elsewhere. Of course, rent and labour are really cheap in China, but also they saved a little by sourcing some seafood from Dalian. Unfortunately, I didn’t bring my sushi lens.

FIGURE 28 Real snapper (tai).

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/4.0
Shutter: 1/42 s
ISO: ISO 800
Software: darktable 5.3.0+959~gf86f0af591
Date: 2026-01-17 11:51:28

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FIGURE 29 Bluefin tuna akami. It’s Spanish farmed bluefin tuna, somewhat less expensive than the Japanese wild ones but still quite good.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/4.0
Shutter: 1/42 s
ISO: ISO 640
Software: darktable 5.3.0+959~gf86f0af591
Date: 2026-01-17 12:03:03

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FIGURE 30 Exterior of the restaurant. The interior (not pictured) is also amazing, very spacious, clean, and modern.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/210 s
ISO: ISO 100
Software: darktable 5.4.0
Date: 2026-01-17 12:40:32

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3.2 Trip to Macao

We went to Macao for a coffee expo.

FIGURE 31 Galaxy Chill Coffee.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/1.7
Shutter: 1/75 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.13
Date: 2026-01-17 14:23:49

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FIGURE 32 Some cups of coffee.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/1.7
Shutter: 1/80 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.13
Date: 2026-01-17 14:42:19

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FIGURE 33 Fellow Espresso Series 1. We didn’t end up buying this.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/1.7
Shutter: 1/50 s
ISO: ISO 100
Software: darktable 5.3.0+959~gf86f0af591
Date: 2026-01-17 15:11:07

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We had several cups of coffee. Then we went back to Zhuhai, caffeinated.

FIGURE 34 Smoggy skyline of Macao seen from the shuttle bus.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/4.0
Shutter: 1/1700 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.13
Date: 2026-01-17 16:06:35

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3.3 Dinner: porridge hot pot

Porridge hot pot is where you dunk thinly sliced meat and stuff into a pot of simmering rice congee. The congee itself is plain but the idea is that the starch in the congee softens up the meat and unlocks extra flavor. This is a markedly different kind of hot pot than the spicy kind most people are familiar with.

FIGURE 35 Porridge hot pot.

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Date: 2026-01-17 18:49:01

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FIGURE 36 Fish skin salad (it’s a side dish, not meant to be dunked into the porridge).

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Date: 2026-01-17 18:52:48

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4 2026-01-18: Flight to Chongqing

FIGURE 37 View from our apartment in the early morning.

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Date: 2026-01-18 07:33:09

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FIGURE 38 A cool mall interior.

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Date: 2026-01-18 17:32:37

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FIGURE 39 Spicy chicken.

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Date: 2026-01-18 19:29:21

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5 2026-01-19: Extreme Chongqing sightseeing

First we woke up and ate noodles.

FIGURE 40 Noodles.

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Date: 2026-01-19 09:00:33

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5.1 Liberation Monument

Then we went to get coffee, then we headed for the liberation monument. It was very rainy.

FIGURE 41 Liberation monument.

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Date: 2026-01-19 11:39:44

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We went to get snacks. It was in a ludicrously noisy food court. There’s a kind of food called 钵钵鸡 (”boboji” or “bobo chicken”), which consists of a variety of skewers that you can choose from, and it’s served cold in spicy chili oil broth. Despite its name, you are not required to choose chicken skewers, and it is quite common to get boboji without any chicken in it. We also got some tangyuan, some tiny fried dumplings, and random other foods.

FIGURE 42 A variety of skewers on display for boboji.

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Date: 2026-01-19 11:51:31

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5.2 Mountain City Alley

Then we walked to the Mountain City Alley (shanchengxiang).

FIGURE 43 A fish tank in a random store on the way there.

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Date: 2026-01-19 12:50:43

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FIGURE 44 Some guy’s balcony and kitchen with an amazing variety of cured meats.

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Date: 2026-01-19 12:52:56

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FIGURE 45 Rainy view of the Nanjimen railway bridge.

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Date: 2026-01-19 13:27:45

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5.3 Lunch: Steamed combination

For lunch, we ate at a random touristy restaurant and got the “steamed combo”. To our surprise, after a while, a chubby chef came out holding an immense bamboo basket that was about one meter in diameter. It contained many tasty dishes. Outermost ring clockwise from top:

Meatballs in clear broth
Squash
Potato with pickled vegetables
Chili with mountain yam (剁椒山药)
Peanuts
Pork belly (烧白)
Glutinous rice with jujube (八宝饭)
Osmanthus cake (桂花糕)
Steamed pork with sticky rice (粉蒸肉)
Glutinous rice balls

Inner ring clockwise from top:

Braised pork intestines
Cured pork belly (腊肉)
Braised beef with beans (红烧牛肉)
Chinese sausage

Center dish: Fish cake (鱼糕).

FIGURE 46 Steamed combo.

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Date: 2026-01-19 14:01:25

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5.4 Shibati

Then we went to see the Eighteen Stairs (shibati). It’s a “traditional style architecture” village.

FIGURE 47 Some of the eighteen steps.

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Date: 2026-01-19 16:13:05

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FIGURE 48 Nanjimen bridge looms over Shibati.

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Date: 2026-01-19 16:28:13

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5.5 Liziba

Then we went to look at the monorail going through a building at Liziba Station. I didn’t bring my line scan camera.

FIGURE 49 Monorail.

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Date: 2026-01-19 17:19:38

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5.6 Hotpot

For dinner we went to eat authentic Chongqing hot pot!

FIGURE 50 Hotpot.

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Date: 2026-01-19 18:18:18

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FIGURE 51 The Chaotianmen bridge.

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Date: 2026-01-19 18:20:53

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FIGURE 52 View across the river.

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Date: 2026-01-19 19:31:17

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6 2026-01-20: Extreme Chongqing sightseeing

This day was not as extreme as the previous day, due to tiredness and poor weather. We had a basic breakfast of takeout in the hotel, and then some delicious pastries and an extremely intense matcha ice cream in the mall.

FIGURE 53 Horse decorations at the mall for the year of the horse.

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Date: 2026-01-20 11:52:36

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Then, we had a quick lunch of peking duck in the mall, as well as some dumplings and a “dry steamed beef” (which was kind of mediocre to be honest). But it was quite affordable.

FIGURE 54 Peking duck.

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Then we went to Xiahaoli. It’s another traditional-themed village.

FIGURE 55 Qiansimen bridge (千厮门大桥).

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Date: 2026-01-20 15:23:38

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FIGURE 56 A really lame man-made waterfall with “inspirational slogans” plastered everywhere. But the village itself was very charming.

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Date: 2026-01-20 15:55:44

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Generally, the mountainous terrain of Chongqing made it impossible to use a stroller, so we left it in the hotel, and then I had to carry the baby and the umbrella at the same time. It was a great workout for my biceps.

7 2026-01-21: Chengdu

FIGURE 57 Morning view from our Chongqing hotel before departing.

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Date: 2026-01-21 07:57:04

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We took the high speed rail to Chengdu. Somehow, we were always in a rush, so I didn’t take any pictures. Even after arriving, we were also in a rush to get to the hotel, so I didn’t take any pictures either. Plus we had a lot of luggage and our hands were sore from carrying a baby.

FIGURE 58 A three-wheeled car at Chongqing station.

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Date: 2026-01-21 08:52:30

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FIGURE 59 Plastic surgery center across from our hotel. There’s a nicer hotel (Atour S) on top.

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Date: 2026-01-21 13:08:48

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7.1 Lunch: Peking duck

We weren’t super satisfied with yesterday’s shopping mall-tier peking duck, so we went to an actual nice restaurant.

FIGURE 60 Fish.

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FIGURE 61 Chef carving the peking duck.

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FIGURE 62 Scrumptious slices of duck.

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Date: 2026-01-21 14:06:08

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7.2 Coffee in Yulin

We went for a stroll in the old Yulin neighborhood of Chengdu. There, we found a lot of old people just chilling or playing Chinese chess or mahjong outside, as well as some very expensive and bougie coffee shops.

We had some coffee at the Carpark coffee shop.

FIGURE 63 Carpark coffee shop.

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FIGURE 64 A chill neighborhood with lots of old people relaxing outside.

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There was a block that was full of shops selling dried and cured meats and seafood with lots of old people buying stuff. Very cool.

FIGURE 65 A lady just casually sitting next to an open fire while fresh pork hangs outside.

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Date: 2026-01-21 16:13:23

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FIGURE 66 A leviathan of a spicy dried fish (30 kg), along with some pork bellies and stuff.

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Then, we went to the IFS mall.

FIGURE 67 IFS mall.

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Date: 2026-01-21 16:48:09

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For dinner, we had takeout noodles in the hotel.

8 2026-01-22: Pandas and Dujiangyan

We went to see pandas! After a quick breakfast in the hotel, we headed off to the panda reserve. We hired a car for the whole day to drive us and our American friends around.

8.1 Pandas

FIGURE 68 A Volkswagen Santana police car. The Santana used to be super ubiquitous but stringent emissions control and improved quality of newer Chinese cars have rendered it obsolete and it is pretty rare to see one on the streets nowadays.

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Date: 2026-01-22 09:56:17

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FIGURE 69 Extreme panda merchandise peddling outside the panda reserve.

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Date: 2026-01-22 10:00:04

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FIGURE 70 Panda.

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Date: 2026-01-22 10:30:13

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FIGURE 71 Panda (pooping).

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Date: 2026-01-22 10:45:04

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8.2 Lunch: Traditional food

We had lunch at a traditional restaurant. Our driver recommended a spot but we checked the reviews and they were consistently really, really bad. So we found our own place.

It was alright. It was located in a village. The food was not bad but it was quite expensive (by Chinese standards). The restaurant was a collection of huts and the server basically never came to check on us, and since we were by ourselves in a hut we couldn’t flag down a server.

FIGURE 72 Steamed egg with morels.

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Date: 2026-01-22 12:26:12

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FIGURE 73 Super expensive chicken soup made with old village chicken.

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Date: 2026-01-22 12:26:33

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FIGURE 74 Cured pork belly with leeks.

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Date: 2026-01-22 12:41:44

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8.3 Dujiangyan

Dujiangyan (都江堰) is an ancient irrigation system built 2000 years ago. It diverts a river to provide water to Chengdu. The civil engineering project was of a truly amazing scale and it is quite cool to see.

First, we hired a tour guide and went up the Yulei mountain to get a good view. There was a really long escalator going up and a pagoda, both relatively recently built (after the 2008 Wenchuan earthquake) but in a traditional style.

FIGURE 75 View of Dujiangyan.

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Date: 2026-01-22 14:17:42

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FIGURE 76 Pagoda at the top of Yulei mountain.

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FIGURE 77 View of Dujiangyan city from the pagoda.

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Then we hiked down the mountain with the tour guide, who explained to us the amazing history of the irrigation project. It is really cool how public infrastructure and civil engineers are so well respected in Chinese culture and even revered as gods.

On the way, we also saw the Erwang (two kings) temple. The temple complex was really big, but I was holding the baby so I didn’t get many good shots.

FIGURE 78 Petting this good boy at the temple brings good luck.

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FIGURE 79 View of the waterworks.

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FIGURE 80 One of the trees in the garden. There were also some 1800+ year old ginkgo trees (not this one).

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FIGURE 81 The southern bridge.

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The ancient town (mostly rebuilt after the 2008 earthquake as well) was also quite fun. There were tons of interesting shops selling cool traditional goods.

Anyway, we were tired from hiking an extreme amount of stairs, so we called it a day and headed back. During the drive back, our driver, who was ethnically Tibetan, started blasting some Tibetan pop songs. I hadn’t heard this kind of music before so it was an interesting experience.

Upon arriving back at the hotel, our driver wanted to park on the sidewalk, which was a common thing to do, albeit in contravention of certain traffic laws. However, an old man pedestrian, who was waiting at the traffic light, didn’t want to budge to make way, as was his right. Our driver squeezed the minivan past him, got out, shouted some bad words and started engaging in a bout of fisticuffs with the dude. It was very surprising and both mildly horrifying and very hilarious. We didn’t want any drama while carrying a baby so we just slunk back into the hotel.

9 2026-01-23: More Chengdu sightseeing

We went to Tianfu square, People’s park, Wide and Narrow alleys, and Chunxi pedestrian shopping street.

9.1 Tianfu square

We took the metro to Tianfu square.

FIGURE 82 Flowers in the square.

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Date: 2026-01-23 10:14:33

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FIGURE 83 Sichuan Science and Technology Museum.

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9.2 People's Park

We walked over to the People’s Park.

FIGURE 84 People’s Park

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We had some tea in the park.

9.3 Wide and Narrow Alleys

For lunch, we ate traditional Sichuan food. We found a restaurant in the Wide and Narrow Alleys, which is another “traditional style” area. Those of you who have had Sichuan food in the US are probably familiar with such staples as Kung Pao Chicken and Mapo Tofu. These are actually authentic, traditional dishes. So we got them.

FIGURE 85 Kung Pao Chicken.

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Date: 2026-01-23 12:49:57

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FIGURE 86 Mapo Tofu.

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FIGURE 87 An interesting dish we’ve never had before. It’s a mild tasting pile of white chicken pudding. It’s very soft and slippery, like egg whites, unlike any chicken I’ve ever had, but has a chickeny flavor. Whatever it is, it is probably quite rich in protein.

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Date: 2026-01-23 12:59:33

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Then we walked around the Wide and Narrow Alleys.

FIGURE 88 Ding’s Coffee. The dark-skinned character represents their founder, who is ethnically Chinese, but just has slightly darker skin than usual.

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Date: 2026-01-23 13:45:43

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I sent a pair of postcards to my parents and my sister. My sister only received hers two months later.

9.4 Chunxi pedestrian street

FIGURE 89 The bling bling “gold palace”.

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FIGURE 90 Shopping street.

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Date: 2026-01-23 15:54:22

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9.5 Dinner: Hotpot

We went to a relatively low-key hot pot spot popular with the locals. Since we were travelling with American friends who didn’t want to eat too spicy, and we already had fairly spicy food for lunch (not to mention Chongqing hotpot a few days ago), we opted for the mild “sour soup” and the completely non-spicy pork bone broth. The server was in complete shock when we told her we didn’t want the spicy broth. This was quite unheard of!

FIGURE 91 Hot pot.

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Date: 2026-01-23 18:40:20

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9.6 River stroll

After hotpot, we went for a stroll to take photos of extreme bling bling LEDs along the river.

FIGURE 92 River.

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Date: 2026-01-23 20:08:30

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FIGURE 93 Hejiangting (合江亭)

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Date: 2026-01-23 20:47:40

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FIGURE 94 Anshun bridge (安顺桥)

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Date: 2026-01-23 21:28:41

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10 2026-01-24: Chengdu temples

We went to the Wenshu Monastery (文殊院). It was Laba festival so they had free porridge. The monastery is associated with good grades so many students came to get their porridge.

FIGURE 95 Wenshu Monastery

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Date: 2026-01-24 12:04:59

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For lunch, we had tasty local food at the recommendation of a friend. It was very cheap and delicious.

FIGURE 96 Cured pork belly served in half of a fake antique vase.

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Date: 2026-01-24 13:25:14

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Then, we went to see a show. It was great!

FIGURE 97 Show.

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Date: 2026-01-24 15:01:25

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FIGURE 98 Last view from our hotel.

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Date: 2026-01-24 18:24:24

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11 2026-01-25: Flight to Shanghai

Just like that, our Sichuan trip was over.

We flew to Shanghai. Due to carrying luggage plus baby, I didn’t take any photos of the travel. Also, I accidentally forgot my CPAP (used for my extreme snoring problem and sleep apnea) on the ride share to the airport in Chengdu, which was very annoying.

We had some nondescript food on the plane.

Anyway, we arrived in Shanghai, just in time for dinner.

FIGURE 99 Crab noodle. Extremely delicious. Dozens of small river crabs were steamed and shucked to make this shockingly umami bowl of crab.

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Date: 2026-01-25 18:55:04

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A funny thing happened. We originally wanted to go to a certain restaurant, which was located on the upper floor of a mall, so we went upstairs, got seated, and ate, and then wanted to pay. In China, it is normal to pay with Meituan coupons which typically offer some discount. So we looked up the name of the restaurant, and bought the coupon. We were about to step out when the staff member came running after us, saying that we hadn’t paid yet. Apparently, we accidentally dined at a different restaurant than the one we were originally planning to dine at, and never realized this throughout our entire meal. Actually, Joanne found that the menu on the app doesn’t match the physical menu in the restaurant, but we thought it was just because they hadn’t updated their web content, rather than us sitting at the wrong restaurant.

Anyway, the restaurant staff called up the other restaurant, which was located adjacent to the one where we ate, and they were able to refund us. Then we paid the correct restaurant. The restaurant we ate at was in fact higher rated and probably more delicious than the one we originally wanted to go to.

12 2026-01-26: Shanghai fun

We went to do some casual shopping and sightseeing. First we went to check out some camera shops but there was nothing I wanted to buy. Then we got some extraordinarily delicious Fujian food at a restaurant called Xizuo 席作 · 福建会馆.

We got:

squid
oyster omelette
taro in pork broth
vegetable
Hokkien seafood noodle

FIGURE 100 Squid.

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Date: 2026-01-26 13:21:52

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FIGURE 101 Oyster omelette.

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Date: 2026-01-26 13:30:47

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FIGURE 102 Hokkien seafood noodle.

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Date: 2026-01-26 13:40:42

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The food was incredibly flavorful. The seafood noodles had a really rich broth. The squid was spicy.

Then, we went to a cool indie clothes store. The store was located in an upstairs apartment in an old-school heritage building in Shanghai. We couldn’t find it at first since it was just a residential block.

FIGURE 103 Clothes store.

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Date: 2026-01-26 16:43:58

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In the evening, we went to visit my friend Yan. He was the owner of the Cessna in my flight on a friend’s Cessna, but now he lives in Shanghai.

FIGURE 104 Takeout Shanghainese food.

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Date: 2026-01-26 19:07:03

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13 2026-01-27: Free day

Our hotel is near Xujiahui so we went for a walk there.

FIGURE 105 Xujiahui.

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Date: 2026-01-27 11:33:57

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FIGURE 106 Xujiahui.

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Date: 2026-01-27 11:35:09

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FIGURE 107 A government building.

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Date: 2026-01-27 15:13:37

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FIGURE 108 Police cars outside the Shanghai Stadium.

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Date: 2026-01-27 16:03:22

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FIGURE 109 Longhua temple pagoda.

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Date: 2026-01-27 16:24:47

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Due to my extreme snoring, and the fact that we accidentally lost the CPAP in Chengdu, I had to stay at a cheap different hotel from Joanne and the baby.

FIGURE 110 Cheap hotel.

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Date: 2026-01-27 22:19:27

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The hotel was only around $30 per night.

14 2026-01-28: Longhua temple

In the morning, I went to get coffee. I found this spot called Black Cat Coffee that has a really amazing Kees van der Westen Speedster espresso machine! This machine costs like $20k.

Due to the fact that the previous customer ordered like 20 lattes, which the barista had to prepare one at a time meticulously with his single group head machine, he invited me to sit down inside. Unfortunately, the coffee shop’s cat, who is called Yang Rou (lamb meat) and is in fact an orange cat instead of a black one, was feeling unwell that day so he did not show up.

FIGURE 111 Black Cat Coffee.

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Date: 2026-01-28 08:32:52

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Then we went for a walk around Longhua temple.

FIGURE 112 Baby at the Longhua pagoda.

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Shutter: 1/1600 s
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Date: 2026-01-28 11:21:21

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For lunch, we had fish hot pot with my parents, aunt, and uncle.

FIGURE 113 Steamed fish hot pot.

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Shutter: 1/200 s
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Date: 2026-01-28 12:58:09

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FIGURE 114 Longhua memorial of the unknown martyrs.

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Date: 2026-01-28 15:05:10

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15 2026-01-29: Lunch with aunt and Qibao

For lunch, we hung out with my aunt and we had some incredibly tasty Cantonese food.

FIGURE 115 Cantonese roast duck.

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Date: 2026-01-29 12:31:28

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FIGURE 116 Glass noodles.

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ISO: ISO 250
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Date: 2026-01-29 12:36:16

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In the afternoon, my parents and I went to Qibao old town.

FIGURE 117 Baby touching some silver taels.

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Date: 2026-01-29 14:20:35

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FIGURE 118 One of the stone bridges in Qibao old town.

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Date: 2026-01-29 14:30:28

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In the evening I went for a walk near my parents’ apartment.

FIGURE 119 Black stone mansion, a historical building and landmark.

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Date: 2026-01-29 16:59:23

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FIGURE 120 Another cool historical building.

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Date: 2026-01-29 17:01:02

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FIGURE 121 A modern building with a tree growing through it.

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ISO: ISO 500
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Date: 2026-01-29 17:07:03

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16 2026-01-30: Tea and Italian food

We had tea with a cousin of mine. He’s super accomplished, has grandkids, and was formerly the district head of a district in Shanghai.

FIGURE 122 Confectionery during tea.

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Date: 2026-01-30 14:37:09

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For dinner, we took my parents to a fancy Italian restaurant. We had:

Prosciutto with burrata
Endives salad
Calamari
Ossobuco
Crab spaghetti
Grilled bluefin tuna (the special of the day, and super expensive!)

FIGURE 123 Seared bluefin tuna.

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Date: 2026-01-30 19:28:14

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The bluefin tuna came on a really hot sizzling plate. My mom said the tuna looked raw, so she pressed each piece of tuna firmly into the plate to cook it thoroughly. We accidentally fed the baby some ossobuco, not realizing that it was both kind of spicy and contained alcohol.

17 2026-01-31: Family

On this day, due to important family events, I didn’t bring my GFX camera. In the afternoon, I went on a great city walk along Huaihai road with my dad, who showed me a lot of marvelous architecture and told me lots of great stories about the history.

For dinner, my aunt, uncle, parents, and we went to eat a truly delicious traditional Suzhou meal, including:

Squirrel fish
Crab roe xiaolongbao
Small eels
Noodles
Traditional Suzhou desserts
Vegetables cooked in cooking wine
Lion’s head meatballs

FIGURE 124 Squirrel fish.

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ISO: ISO 80
Software: 26.3
Date: 2026-01-31 19:21:59

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FIGURE 125 Amazing Suzhou desserts.

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Lens: iPhone 14 Pro Max back triple camera 6.86mm f/1.78
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Shutter: 1/111 s
ISO: ISO 80
Software: 26.3
Date: 2026-01-31 19:19:21

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FIGURE 126 Crab roe xiaolongbao.

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Shutter: 1/111 s
ISO: ISO 50
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Date: 2026-01-31 19:18:25

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I accidentally gave baby some of the vegetables cooked in cooking wine again, which had a strong alcohol taste when I tried it myself later. Oops!

18 2026-02-01: Flight to Zhuhai

FIGURE 127 Yep it’s an airplane.

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Date: 2026-02-01 10:55:54

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19 2026-02-02: Picnic at the Huangyang wetlands park

We went to Huangyang wetlands park. I brought my 500mm lens (which I didn’t bring on the Chongqing, Chengdu, and Shanghai leg due to trying to minimize weight while travelling with babby), and found some wildlife.

FIGURE 128 Some people were fishing despite signs saying not to fish in the protected wildlife refuge.

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Lens: GF500mmF5.6 R LM OIS WR
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Shutter: 1/680 s
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Software: Digital Camera GFX100S Ver2.13
Date: 2026-02-02 14:35:55

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FIGURE 129 A fishing boat.

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Date: 2026-02-02 14:07:25

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FIGURE 130 A kingfisher.

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Date: 2026-02-02 13:49:06

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FIGURE 131 A pair of bulbuls.

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Lens: GF500mmF5.6 R LM OIS WR
Aperture: f/6.4
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ISO: ISO 320
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Date: 2026-02-02 15:48:10

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FIGURE 132 A guy with a large amount of ornaments on his bike and blaring loud music.

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Shutter: 1/640 s
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Date: 2026-02-02 16:26:35

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20 2026-02-03: Went to the mall

FIGURE 133 A mall.

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Shutter: 1/210 s
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Date: 2026-02-03 16:16:23

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21 2026-02-04: Photography session with babby

We went to a photo studio with the baby to take some Chinese New Year pics. I’m not sharing them on my public blog though.

Then, for dinner, we had food with relatives.

FIGURE 134 Steamed fish.

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Shutter: 1/125 s
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Date: 2026-02-04 19:21:22

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FIGURE 135 Stir fried beef cubes.

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Date: 2026-02-04 19:42:11

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FIGURE 136 Steamed chicken in leaf.

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Shutter: 1/125 s
ISO: ISO 1250
Software: Digital Camera GFX100S Ver2.13
Date: 2026-02-04 19:47:35

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22 2026-02-06: Going to Guangzhou

We departed for Guangzhou on a weekend getaway!

We went with our friend and her eight-year-old son.

Due to our place in Zhuhai being really far from the train station, we decided to take a bus to get there. Unfortunately, tickets for the bus we wanted to take were all sold out and we had to wait three hours for the next one. Most displeasing!

FIGURE 137 Doumen bus stop.

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ISO: ISO 100
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Date: 2026-02-06 13:26:34

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Anyway, we got on the bus, held babby for two hours, and arrived at Guangzhou South station. Then, we took the metro to the hotel. For dinner, we walked over to a sushi restaurant in a mall.

Guangzhou is one of the old cities with a lot of old school architecture and it is really cool to see these classical buildings.

FIGURE 138 A barber shop.

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Aperture: f/2.2
Shutter: 1/42 s
ISO: ISO 1250
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Date: 2026-02-06 18:25:29

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FIGURE 139 A super badass chandelier.

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ISO: ISO 640
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Date: 2026-02-06 18:26:47

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The sushi was “Chinese-style sushi”. This means:

a lot of mango
a lot of salmon
a lot of mayo
domestically grown foie gras

FIGURE 140 Foie gras sushi.

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ISO: ISO 100
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Date: 2026-02-06 19:28:22

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FIGURE 141 Crab stick, mayo, and mango sushi.

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Shutter: 1/56 s
ISO: ISO 100
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Date: 2026-02-06 19:12:54

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Funnily enough, the restaurant gives each table an hourglass with a guarantee that if your food doesn’t come on time, it’s free. Actually, the hourglass is just for show; the guarantee is based on the actual timestamp on the receipt, but they do honour it. Our foie gras sushi was about 30 seconds late so we got it for free.

After dinner, we went to look at the sights, like Canton Tower. Since I’ve been there before, and we had to carry the baby, I didn’t take many photos.

FIGURE 142 A Sabertooth APC.

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Aperture: f/1.7
Shutter: 1/42 s
ISO: ISO 2000
Software: Digital Camera GFX100S Ver2.13
Date: 2026-02-06 20:56:54

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FIGURE 143 Huacheng square.

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Shutter: 1/42 s
ISO: ISO 1250
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Date: 2026-02-06 20:59:09

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23 2026-02-07: Guangzhou sightseeing

We went on an extreme city walk!!!

Itinerary:

river
Beijing road
Sacred Heart Cathedral
Shangxiajiu pedestrian street
Yongqing Fang

The river walk pics turned out very mediocre because of boring lighting and we weren’t walking along the most interesting part of the river. There were many old people jogging along the river.

FIGURE 144 River walk.

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Shutter: 1/500 s
ISO: ISO 100
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Date: 2026-02-07 09:55:14

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FIGURE 145 An alley near Beijing road.

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ISO: ISO 160
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Date: 2026-02-07 10:11:45

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23.1 Sacred Heart Cathedral

We went to the Sacred Heart Cathedral.

FIGURE 146 Super high resolution panorama of the front view.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/680 s
ISO: ISO 100
Software: darktable 5.5.0+967~g46d32311e8
Date: 2026-02-07 11:25:32

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FIGURE 147 Interior of the Sacred Heart Cathedral.

EXIF data

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Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/42 s
ISO: ISO 1250
Software: darktable 5.5.0+967~g46d32311e8
Date: 2026-02-07 11:30:08

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23.2 Pedestrian street

We went to the Shangxiajiu pedestrian street. For lunch, we ate at a random Hong Kong cha chaan teng.

FIGURE 148 Egg shrimp meat rice.

EXIF data

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Lens: GF55mmF1.7 R WR
Aperture: f/3.2
Shutter: 1/42 s
ISO: ISO 125
Software: Digital Camera GFX100S Ver2.13
Date: 2026-02-07 12:52:03

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FIGURE 149 A cool old building.

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Lens: GF55mmF1.7 R WR
Aperture: f/5.0
Shutter: 1/80 s
ISO: ISO 100
Software: darktable 5.5.0+967~g46d32311e8
Date: 2026-02-07 12:11:13

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FIGURE 150 Shangxiajiu pedestrian street.

EXIF data

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Lens: GF55mmF1.7 R WR
Aperture: f/8.0
Shutter: 1/56 s
ISO: ISO 100
Software: darktable 5.5.0+967~g46d32311e8
Date: 2026-02-07 12:25:50

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23.3 Yongqing Fang

Yongqing Fang (永庆坊) is a complex of preserved/rebuilt old school Lingnan-style architecture, canals, and bridges.

FIGURE 151 A pair of stone bridges.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/125 s
ISO: ISO 100
Software: darktable 5.5.0+1479~g9e36df0344
Date: 2026-02-07 15:19:04

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FIGURE 152 A Cantonese opera.

EXIF data

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Lens: GF55mmF1.7 R WR
Aperture: f/6.4
Shutter: 1/100 s
ISO: ISO 100
Software: darktable 5.5.0+1479~g9e36df0344
Date: 2026-02-07 15:23:18

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23.4 Heading back

We took the metro back to Guangzhou South station. We took the brand new Line 22, which is affectionately nicknamed “the big subway” because the trains go super fast (160 km/h) and the stations are spread very far.

FIGURE 153 Metro station of Guangzhou line 22.

EXIF data

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Lens: GF55mmF1.7 R WR
Aperture: f/7.1
Shutter: 1/42 s
ISO: ISO 1250
Software: darktable 5.3.0+959~gf86f0af591
Date: 2026-02-07 15:50:36

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For the return trip we took the train instead, which is vastly more comfortable, goes at 200 km/h, is entirely grade separated, and offers great views along the way.

FIGURE 154 The armed police at Guangzhou South station had a booth where you can get first aid and free chrysanthemum tea.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/1.7
Shutter: 1/280 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.13
Date: 2026-02-07 16:31:15

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FIGURE 155 Interior of Guangzhou South Station.

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Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/8.0
Shutter: 1/42 s
ISO: ISO 200
Software: darktable 5.3.0+959~gf86f0af591
Date: 2026-02-07 17:03:34

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FIGURE 156 Platforms. I love trains!!!

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/42 s
ISO: ISO 100
Software: darktable 5.5.0+1479~g9e36df0344
Date: 2026-02-07 17:09:36

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Upon arriving back in Zhuhai, we took a Didi back to Doumen and then ate some “sauna chicken” (桑拿鸡), a traditional Cantonese dish (from Shunde) where the chicken is steamed over boiling broth and the juices drip down into the broth below and then later you get to drink the extremely tasty broth.

FIGURE 157 Random shot of the raw chicken before it gets subjected to an authentic Finnish experience making it cooked.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/1.7
Shutter: 1/340 s
ISO: ISO 1600
Software: Digital Camera GFX100S Ver2.13
Date: 2026-02-07 20:09:17

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24 2026-02-08: 500mm day in Zhuhai

On this day I took my 500mm lens and gamboled about Zhuhai.

Here’s the obligatory meme photo.

FIGURE 158 Zhuhai opera house with a random traditionally-styled restaurant in front of it.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF500mmF5.6 R LM OIS WR
Aperture: f/5.6
Shutter: 1/56 s
ISO: ISO 100
Software: darktable 5.3.0+959~gf86f0af591
Date: 2026-02-08 08:30:45

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FIGURE 159 Pylon of the Baishi bridge with Macau Tower in the background.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF500mmF5.6 R LM OIS WR
Aperture: f/8.0
Shutter: 1/400 s
ISO: ISO 160
Software: darktable 5.5.0+967~g46d32311e8
Date: 2026-02-08 13:31:33

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FIGURE 160 Pylon of the Baishi bridge with the Grand Lisboa in the background.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF500mmF5.6 R LM OIS WR
Aperture: f/5.6
Shutter: 1/500 s
ISO: ISO 100
Software: darktable 5.5.0+1479~g9e36df0344
Date: 2026-02-08 13:20:29

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25 2026-02-11: Stroll around the neighbourhood

In the remaining days, I was mostly working and chilling with family rather than doing any sort of extreme sightseeing, so some days are entirely devoid of photos.

On 2026-02-11, we wanted to go see a light show, but its admission cost money and didn’t look too great so we decided not to go anymore.

FIGURE 161 Sunset at a wetlands preserve.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/850 s
ISO: ISO 100
Software: darktable 5.3.0+959~gf86f0af591
Date: 2026-02-11 17:47:24

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Instead, we went to eat barbequed skewers.

FIGURE 162 Grilled oysters.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/1.7
Shutter: 1/42 s
ISO: ISO 125
Software: Digital Camera GFX100S Ver2.13
Date: 2026-02-11 18:37:25

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After eating barbeque, we walked back and took pictures of the night streets.

FIGURE 163 A random street.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/2.8
Shutter: 1/42 s
ISO: ISO 1250
Software: darktable 5.3.0+959~gf86f0af591
Date: 2026-02-11 19:41:00

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FIGURE 164 A random truck selling snacks.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/1.7
Shutter: 1/42 s
ISO: ISO 100
Software: darktable 5.3.0+959~gf86f0af591
Date: 2026-02-11 19:50:05

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26 2026-02-14: Valentine's day omakase

First, I went to buy a coffee, and on the way, I took a picture of a cat in a shop, as well as a vegetable garden.

FIGURE 165 Cat.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/7.1
Shutter: 1/500 s
ISO: ISO 100
Software: darktable 5.5.0+967~g46d32311e8
Date: 2026-02-14 09:20:52

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FIGURE 166 Small vegetable garden.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/8.0
Shutter: 1/220 s
ISO: ISO 100
Software: darktable 5.5.0+1479~g9e36df0344
Date: 2026-02-14 09:03:52

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Then, we had omakase again at the same place we had last time. The Valentine’s day special omakase comprises the following dishes:

amuse-bouche

snow crab, ebi-imo

appetizers

scallion hamachi
mehikari, a small fish
ankimo liver
abalone with liver sauce
red salmon dashi
shirako (fish sperm) with dashi

sushi

hotate (scallop)
botan ebi
shima aji
tuna o toro
saba (mackerel)
uni (sea urchin)
anago (conger eel)
temaki (hand roll)

soup

miso soup

dessert

assorted seasonal fruits

FIGURE 167 Shrimp

EXIF data

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Lens: GF55mmF1.7 R WR
Aperture: f/1.7
Shutter: 1/80 s
ISO: ISO 100
Software: darktable 5.3.0+959~gf86f0af591
Date: 2026-02-14 13:22:03

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FIGURE 168 Shima Aji

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/1.7
Shutter: 1/80 s
ISO: ISO 100
Software: darktable 5.3.0+959~gf86f0af591
Date: 2026-02-14 13:28:51

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FIGURE 169 O-toro

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Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/1.7
Shutter: 1/90 s
ISO: ISO 100
Software: darktable 5.3.0+959~gf86f0af591
Date: 2026-02-14 13:31:18

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FIGURE 170 Mackerel pressed sushi

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Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/1.7
Shutter: 1/85 s
ISO: ISO 100
Software: darktable 5.3.0+959~gf86f0af591
Date: 2026-02-14 13:59:31

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After sushi, we went to hang out with a cousin. From their place there’s a great view of the recently completed Huangmaohai sea-crossing bridge, which is notable for being extremely big.

FIGURE 171 Huangmaohai sea-crossing bridge

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1.9 s
ISO: ISO 100
Software: darktable 5.3.0+959~gf86f0af591
Date: 2026-02-14 18:48:43

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27 2026-02-15: Rooftop 500mm shots

I randomly climbed onto the rooftop of our apartment building.

FIGURE 172 Ferris wheel of Daxin shopping mall.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF500mmF5.6 R LM OIS WR
Aperture: f/5.6
Shutter: 1/400 s
ISO: ISO 100
Software: darktable 5.3.0+959~gf86f0af591
Date: 2026-02-15 11:54:35

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FIGURE 173 A random school. The slogan reads “educating people for the Communist Party; educating talent for the country”.

EXIF data

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Lens: GF500mmF5.6 R LM OIS WR
Aperture: f/5.6
Shutter: 1/400 s
ISO: ISO 100
Software: darktable 5.5.0+967~g46d32311e8
Date: 2026-02-15 11:55:02

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FIGURE 174 Colourful houses in Doumen district.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF500mmF5.6 R LM OIS WR
Aperture: f/5.6
Shutter: 1/450 s
ISO: ISO 100
Software: darktable 5.3.0+959~gf86f0af591
Date: 2026-02-15 12:08:20

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28 2026-02-16: Hiking

For the last day, we hiked up a hill called 尖峰山 (Jian Feng Shan, literally “pointy peak mountain”). It was super tiring because it was very hot and humid and we were carrying a baby. The views from the top weren’t even that great.

Then, the next day, on the 17th, it was Chinese New Year! But instead of celebrating we just took a cab to Hong Kong airport and caught a plane back to California.

Autonomous cars

Wed, 24 Dec 2025 00:00:00 +0000

Autonomous cars

2025-12-24

In the nearly 10 years of living in the Bay Area, I’ve taken many photos of autonomous vehicles, as well as human-driven vehicles and prototypes used for autonomous vehicle research! So here’s a list of some of my original photos.

1 Apple

FIGURE 1 Apple Lexus vehicle with a Velodyne HDL-64E and a bunch of cameras.

EXIF data

Camera: SONY ILCE-7R
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/8.0
Shutter: 1/200 s
ISO: ISO 100
Software: darktable 2.3.0+893~g0b45f4abb
Date: 2017-09-13 17:47:47

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FIGURE 2 A 2018 prototype. This has to win some sort of award for the craziest sensor arrangement.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/4.0
Shutter: 1/1250 s
ISO: ISO 100
Software: darktable 5.3.0+959~gf86f0af591
Date: 2018-03-29 20:06:34

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FIGURE 3 A 2020 prototype.

EXIF data

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Lens: GF50mmF3.5 R LM WR
Aperture: f/8.0
Shutter: 1/100 s
ISO: ISO 100
Software: Digital Camera GFX 50R Ver1.00
Date: 2020-02-11 11:00:52

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FIGURE 4 Rear of the 2020 prototype.

EXIF data

Camera: FUJIFILM GFX 50R
Lens: GF50mmF3.5 R LM WR
Aperture: f/8.0
Shutter: 1/150 s
ISO: ISO 100
Software: Digital Camera GFX 50R Ver1.00
Date: 2020-02-11 11:01:05

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2 Argo

Argo was a self driving car startup that got acquired by Ford but it was shut down in 2022.

FIGURE 5 Dual-Velodyne turret on top of an Argo Ford Fusion at CVPR 2019 in Long Beach.

EXIF data

Camera: FUJIFILM GFX 50R
Lens: GF45mmF2.8 R WR
Aperture: f/2.8
Shutter: 1/105 s
ISO: ISO 3200
Software: Digital Camera GFX 50R Ver1.00
Date: 2019-06-17 12:00:08

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FIGURE 6 Argo vehicle at CVPR 2022 in New Orleans.

EXIF data

Camera: FUJIFILM GFX 50R
Lens: GF23mmF4 R LM WR
Aperture: f/4.0
Shutter: 1/80 s
ISO: ISO 1600
Software: darktable unknown-version
Date: 2022-06-23 11:53:18

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3 Cruise

Cruise was the self driving car subsidiary of GM. In December 2024, they announced that they were halting the effort, but recently Cruise cars can still be seen undergoing testing, so maybe they are continuing their development in secret.

FIGURE 7 Early Cruise prototype in 2017 with the Velodyne HDL-32 instead of the VLP-32.

EXIF data

Camera: SONY ILCE-7R
Lens: E 20mm F2
Aperture: f/8.0
Shutter: 1/200 s
ISO: ISO 200
Software: ILCE-7R v3.20
Date: 2017-09-08 16:29:32

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FIGURE 8 Another Cruise with the HDL-32. Sorry for the bad framing, but at least this photo is relatively sharp and detailed.

EXIF data

Camera: SONY ILCE-7R
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/8.0
Shutter: 1/320 s
ISO: ISO 100
Software: ILCE-7R v3.20
Date: 2017-09-12 17:34:46

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FIGURE 9 Two Cruise vehicles in 2017. Notice their shock-mounted Velodyne VLP-32 pucks.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/5.6
Shutter: 1/250 s
ISO: ISO 100
Software: ILCE-7RM2 v3.30
Date: 2017-10-10 23:42:29

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FIGURE 10 Unmarked Cruise Chevy Bolt.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/5.6
Shutter: 1/500 s
ISO: ISO 100
Software: darktable 2.3.0+893~g0b45f4abb
Date: 2017-10-18 17:59:41

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FIGURE 11 By 2019 they had switched to Hesai lidars on top.

EXIF data

Camera: FUJIFILM GFX 50R
Lens: GF45mmF2.8 R WR
Aperture: f/8.0
Shutter: 1/220 s
ISO: ISO 100
Software: darktable 3.0.0rc2+101~g12a3e26fc
Date: 2019-10-20 15:34:07

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FIGURE 12 During the pandemic, they tried delivering food with Cruise cars.

EXIF data

Camera: FUJIFILM GFX 50R
Lens: GF50mmF3.5 R LM WR
Aperture: f/6.4
Shutter: 1/250 s
ISO: ISO 100
Software: darktable 3.1.0+2444~g536df44ad
Date: 2020-08-04 16:46:12

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FIGURE 13 Cruise Origin vehicle at CVPR 2022 in New Orleans.

EXIF data

Camera: FUJIFILM GFX 50R
Lens: GF23mmF4 R LM WR
Aperture: f/4.0
Shutter: 1/105 s
ISO: ISO 1600
Software: darktable unknown-version
Date: 2022-06-23 11:52:38

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4 Luminar

Luminar was a lidar company but they recently announced their bankruptcy.

FIGURE 14 Luminar Chrysler Pacifica demo vehicle at Stanford University in 2019.

EXIF data

Camera: FUJIFILM GFX 50R
Lens: GF45mmF2.8 R WR
Aperture: f/8.0
Shutter: 1/60 s
ISO: ISO 400
Software: Digital Camera GFX 50R Ver1.00
Date: 2019-01-15 10:49:06

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FIGURE 15 Luminar booth at CES 2020.

EXIF data

Camera: FUJIFILM GFX 50R
Lens: GF45mmF2.8 R WR
Aperture: f/8.0
Shutter: 1/80 s
ISO: ISO 100
Software: darktable 3.0.0rc2+101~g12a3e26fc
Date: 2020-01-10 14:32:52

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FIGURE 16 Luminar Toyota Highlander at CES 2020.

EXIF data

Camera: FUJIFILM GFX 50R
Lens: GF45mmF2.8 R WR
Aperture: f/8.0
Shutter: 1/60 s
ISO: ISO 320
Software: Digital Camera GFX 50R Ver1.00
Date: 2020-01-10 14:32:28

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5 Ouster

Ouster is a manufacturer of lidars for autonomous vehicles and other markets.

FIGURE 17 Subaru Crosstrek XV with three Velodyne VLP-16 lidars for testing.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/3.2
Shutter: 1/100 s
ISO: ISO 200
Software: ILCE-7RM2 v3.30
Date: 2017-10-10 21:44:14

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FIGURE 18 Subaru with three Ouster OS1 prototypes.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/2.0
Shutter: 1/100 s
ISO: ISO 400
Software: ILCE-7RM2 v3.30
Date: 2017-11-16 23:02:00

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FIGURE 19 Subaru with only one Ouster OS1.

EXIF data

Camera: SONY ILCE-7RM2
Lens: ----
Aperture: f/0.0
Shutter: 1/800 s
ISO: ISO 100
Software: GIMP 2.99.1
Date: 2018-06-15 00:49:06

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FIGURE 20 Subaru with Ouster OS1.

EXIF data

Camera: SONY ILCE-7RM2
Lens: ----
Aperture: f/0.0
Shutter: 1/640 s
ISO: ISO 100
Software: darktable 2.5.0+379~g6f0b7ddeb
Date: 2018-06-15 00:35:35

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FIGURE 21 Chrysler Pacifica demo vehicle.

EXIF data

Camera: FUJIFILM GFX 50R
Lens: 0 mm f/unknown
Aperture: f/NaN
Shutter: 8.0 s
ISO: ISO 100
Software: Digital Camera GFX 50R Ver1.00
Date: 2020-01-03 00:08:34

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FIGURE 22 Ouster booth at CES 2020.

EXIF data

Camera: FUJIFILM GFX 50R
Lens: GF23mmF4 R LM WR
Aperture: f/4.0
Shutter: 1/25 s
ISO: ISO 400
Software: darktable 3.0.0rc2+101~g12a3e26fc
Date: 2020-01-09 10:32:19

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6 Tesla

I had the pleasure to see a Cybercab prototype at Santana Row in 2024.

FIGURE 23 Cybercab at Santana Row.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/4.0
Shutter: 1/15 s
ISO: ISO 500
Software: darktable 4.8.1
Date: 2024-11-10 14:45:30

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FIGURE 24 Cybercab at Santana Row.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/4.0
Shutter: 1/15 s
ISO: ISO 400
Software: Digital Camera GFX100S Ver2.12
Date: 2024-11-10 14:44:34

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FIGURE 25 Cybercab at Santana Row.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/8.0
Shutter: 1/17 s
ISO: ISO 1000
Software: Digital Camera GFX100S Ver2.12
Date: 2024-11-10 14:42:41

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7 Uber

I worked somewhat near the Uber garage at 737 Harrison St, before they shut down in 2019. They started out as the Advanced Technology Center (ATC) in 2016 with the acquisition of Otto, before they rebranded to the Advanced Technology Group (ATG) sometime in 2017. Following a fatal accident in 2018, their efforts were soon shut down.

7.1 Ford Fusion

FIGURE 26 Uber ATC Ford Fusion in 2016.

EXIF data

Camera: SONY ILCE-7R
Lens: FE 70-300mm F4.5-5.6 G OSS
Aperture: f/5.6
Shutter: 1/800 s
ISO: ISO 400
Software: ILCE-7R v3.20
Date: 2016-11-06 15:22:39

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FIGURE 27 Detail of the sensor pod on top of a Ford Fusion. There’s a Velodyne HDL-64 and an array of really nice, large aperture cameras.

EXIF data

Camera: SONY ILCE-7R
Lens: ----
Aperture: f/0.0
Shutter: 1/2500 s
ISO: ISO 400
Software: ILCE-7R v3.20
Date: 2017-02-01 17:56:55

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7.2 Volvo XC90

FIGURE 28 Uber ATC Volvo XC90 in 2016.

EXIF data

Camera: SONY ILCE-7R
Lens: ----
Aperture: f/0.0
Shutter: 1/800 s
ISO: ISO 100
Software: ILCE-7R v3.20
Date: 2016-12-07 17:09:44

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FIGURE 29 Uber ATG Volvo XC90 in 2017.

EXIF data

Camera: SONY ILCE-7R
Lens: FE 55mm F1.8 ZA
Aperture: f/5.0
Shutter: 1/800 s
ISO: ISO 125
Software: ILCE-7R v3.20
Date: 2017-04-11 19:37:56

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FIGURE 30 Uber ATG Volvo XC90 in 2017.

EXIF data

Camera: SONY ILCE-7R
Lens: FE 55mm F1.8 ZA
Aperture: f/8.0
Shutter: 1/250 s
ISO: ISO 200
Software: ILCE-7R v3.20
Date: 2017-04-11 19:40:46

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FIGURE 31 Uber ATG Volvo XC90 in 2017.

EXIF data

Camera: SONY ILCE-7R
Lens: FE 55mm F1.8 ZA
Aperture: f/5.6
Shutter: 1/640 s
ISO: ISO 100
Software: ILCE-7R v3.20
Date: 2017-04-14 19:40:55

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FIGURE 32 The safety driver cheekily made a hand gesture, not knowing the imminent demise of the Uber self driving car effort.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/5.6
Shutter: 1/640 s
ISO: ISO 100
Software: ILCE-7RM2 v3.30
Date: 2017-12-19 21:18:57

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7.3 Otto trucks

Following the acquisition of Otto, they had several kinds of trucks: Volvo and Peterbilt.

FIGURE 33 Volvo trucks at their 737 Harrison depot in 2017.

EXIF data

Camera: SONY ILCE-7R
Lens: E 20mm F2
Aperture: f/8.0
Shutter: 1/1600 s
ISO: ISO 400
Software: ILCE-7R v3.20
Date: 2017-03-14 19:46:19

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FIGURE 34 Volvo trucks at their 737 Harrison depot in 2017.

EXIF data

Camera: SONY ILCE-7R
Lens: FE 55mm F1.8 ZA
Aperture: f/8.0
Shutter: 1/160 s
ISO: ISO 100
Software: ILCE-7R v3.20
Date: 2017-04-14 20:07:46

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8 Waymo

8.1 Chrysler Pacifica

FIGURE 35 Waymo Chrysler Pacifica in 2017. Compared to the Velodyne-equipped other self driving car efforts, the Pacifica with Waymo’s in-house lidar was already really polished and well-integrated even in 2017.

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Date: 2017-11-19 23:46:59

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8.2 Jaguar I-Pace

FIGURE 36 Jaguar I-Pace in 2021.

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FIGURE 37 Jaguar I-Pace in 2023.

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FIGURE 38 Jaguar I-Pace in 2021.

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Date: 2023-11-21 12:40:06

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FIGURE 39 Interior of Jaguar I-Pace in 2024.

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Date: 2024-11-09 15:33:23

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8.3 Zeekr RT

These started showing up on public streets in 2025. I took a few photos at their Bayshore depot on Toland St.

FIGURE 40 Front view of several Zeekr RTs.

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Date: 2025-12-20 15:34:45

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FIGURE 41 Oblique view of a Zeekr RT.

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FIGURE 42 With reflections from a puddle!

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9 Woven Planet

The successor to Toyota Research Institute, they have a lot of different types of vehicles. I saw a TRI car at the CVPR 2019 conference before they rebranded to Woven Planet.

FIGURE 43 TRI at CVPR 2019.

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Date: 2019-06-17 12:15:32

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The most commonly seen ones are the data collection pods on top of Lyft cars (typically Hyundais or Toyota Priuses).

FIGURE 44 Woven Planet pod on top of a Prius.

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Date: 2023-11-21 12:47:02

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10 Zoox

10.1 Toyota Highlander

FIGURE 45 Early Zoox prototype in 2018.

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Date: 2018-08-17 22:45:52

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FIGURE 46 Highlander making its way down the twisty Lombard Street in 2019.

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FIGURE 47 Another Highlander doing the same thing.

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The following photos were taken in December 2025 at their Mission depot on Division and Bryant.

FIGURE 48 A row of Highlanders in 2025.

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FIGURE 49 A Toyota Highlander.

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10.2 VH

VH stands for “Vaporware Horseshit”. Apparently, Jalopnik writer Jason Torchinsky dissed Zoox in 2013 by calling them that, and then they embraced the moniker and called their self driving cars VH-1, VH-2, etc.

FIGURE 50 The VH chassis on display at CVPR 2019 in Long Beach.

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FIGURE 51 VH rig at CVPR 2019 in Long Beach.

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FIGURE 52 VH at CVPR 2022 in New Orleans.

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FIGURE 53 VH at CVPR 2022 in New Orleans.

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FIGURE 54 VH on the streets of San Francisco in 2025.

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Date: 2025-12-28 13:50:03

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11 Other autonomous vehicles

These are AV companies whose vehicles I’ve only seen a handful of times.

11.1 AEye

AEye is a lidar company making pulsed 1550 nm lidar. A year ago, the same car destroyed a camera so I was very worried.

FIGURE 55 AEye at CES 2020.

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Date: 2020-01-10 15:56:56

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FIGURE 56 Another AEye vehicle at CES 2020.

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Date: 2020-01-10 08:26:20

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11.2 AutonomouStuff

AutonomouStuff is a supplier of sensors and stuff for robotics and self driving cars.

FIGURE 57 Demonstration vehicle at CES 2020. In the background you can see another demo vehicle from the Israeli lidar company Innoviz.

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Date: 2020-01-09 10:12:17

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11.3 AutoX

AutoX was a startup founded by professor Jianxiong Xiao. I remember using his SFMedu MATLAB code during grad school to learn about bundle adjustment! The company renamed itself Tensor and pivoted to making bespoke self driving cars.

FIGURE 58 AutoX Chrysler Pacifica at CES 2020.

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Date: 2020-01-10 13:41:06

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11.4 DiDi

I saw their car at the CVPR 2019 conference in Long Beach.

FIGURE 59 DiDi Lincoln MKZ at CVPR 2019. To be honest this seems like a kit from AutonomouStuff.

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Date: 2019-06-17 11:56:19

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12 Mapper.ai

Mapper.ai was founded around 2015-2016 and then got acquired by Velodyne in 2019. They often drove around in a Honda Civic to collect HD mapping data.

FIGURE 60 Mapper.ai Honda Civic.

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Date: 2017-12-19 21:26:30

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FIGURE 61 Mapper.ai Honda Civic.

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Date: 2017-11-06 20:31:17

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12.1 Quanergy

Quanergy was a lidar company that went all-in on their solid state Quanergy S3 sensor, but went bankrupt in 2023.

FIGURE 62 Quanergy car at CES 2020.

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Date: 2020-01-10 13:12:54

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12.2 Motional

Motional is an autonomous driving company majority owned by Hyundai.

FIGURE 63 Motional at CVPR 2022.

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12.3 TuSimple

TuSimple is a self driving truck company but they wound down their US operations and left the US in 2023. Then in 2024, they pivoted away from self-driving trucks towards AI for video games, animation, and content creation. Pretty wild pivot to be honest.

FIGURE 64 TuSimple at CVPR 2019.

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Date: 2019-06-17 12:19:45

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Trip to British Columbia

Tue, 23 Dec 2025 00:00:00 +0000

Trip to British Columbia

2025-12-23

In June 2025, Joanne, baby, and I went to British Columbia, Canada! We went to Vancouver, Abbotsford, and Williams Lake. This trip was mostly for visiting family and friends, so we didn’t end up doing a ton of sightseeing. Nonetheless, here are the photos!

FIGURE 1 The Fraser River as seen from BC Highway 20.

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Software: Digital Camera GFX100S Ver2.13
Date: 2025-06-21 15:32:58

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1 2025-06-13: Arrival in Vancouver

We visited our friends who live in Burnaby. They have a baby roughly the same age as our baby so it was fun watching them interact with each other. Actually, the babies don’t really care about each other, but it was fun for the adults.

FIGURE 2 Vancouver Skytrain in the rare original 1986 livery making its way between high rise condominiums.

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Date: 2025-06-13 14:57:04

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For lunch, we ate really delicious takeout sushi. The rectangular seared salmon oshizushi are a specialty of Vancouver.

FIGURE 3 Takeout sushi.

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Date: 2025-06-13 14:01:05

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For dinner, we ate some grilled meat.

FIGURE 4 Meat

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Date: 2025-06-13 18:17:13

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2 2025-06-14: Richmond

One cool thing about Vancouver is the amount and quality of Hong Kong food, particularly cha chaan teng food in Richmond.

Joanne and I both love tasty HK food so we ate at Cha Kee. I got a fried pork cutlet combo with scrambled eggs and toast and a large iced HK milk tea, and Joanne got a rice noodle combo (also with the eggs and toast) with a hot HK milk tea.

FIGURE 5 Cha chaan teng.

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Date: 2025-06-14 09:46:56

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In the afternoon I had some important engagements with friends so there are no photos.

FIGURE 6 View from our hotel in downtown Vancouver.

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Date: 2025-06-14 17:49:40

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For dinner, we ate takeout sushi again.

FIGURE 7 Takeout sushi.

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ISO: ISO 160
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Date: 2025-06-14 16:59:07

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3 2025-06-15: Visiting parents in Abbotsford

For breakfast, we had tasty cheung fun at Hei Hei Rice Roll. I got the fancy truffle seafood one!

FIGURE 8 Truffle seafood cheung fun.

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Date: 2025-06-15 10:11:46

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FIGURE 9 Interior of the food court.

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Date: 2025-06-15 09:50:28

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In the afternoon, we drove over to Abbotsford to visit my parents. My parents were very thrilled to see our baby.

FIGURE 10 Playing the piano at my parents’ house.

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FIGURE 11 The backyard of my parents’ house.

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Date: 2025-06-15 15:17:08

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4 2025-06-16: Hanging out in Vancouver

For breakfast, we got food at a cha chaan teng again, but it was a different one.

FIGURE 12 Satay beef instant noodles with HK milk tea.

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Date: 2025-06-16 10:00:39

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FIGURE 13 From a photographic standpoint, this photo is unremarkable, but this very clean Porsche 986 Boxster reminded me of the video game Need For Speed Porsche Unleashed.

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Date: 2025-06-16 10:18:10

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We went to T&T Supermarket to look at (but not buy) spot prawns.

FIGURE 14 Spot prawns.

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Date: 2025-06-16 10:43:32

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For lunch, we got fancy food at Mott 32.

FIGURE 15 Fancy truffle siu mai. There was a quail egg with a runny yolk inside each of these.

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Date: 2025-06-16 13:03:14

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FIGURE 16 Fancy har gow.

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Date: 2025-06-16 13:03:19

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FIGURE 17 Fancy roe fried rice.

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Date: 2025-06-16 13:04:05

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Then we just kinda strolled around downtown.

FIGURE 18 Fairmont Hotel Vancouver. There’s coincidentally also a yellow Porsche in this photo but it’s a different model than the one above (this one is a 997 Cabriolet, which costs a lot more).

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Date: 2025-06-16 14:27:37

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FIGURE 19 1090 West Georgia.

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Date: 2025-06-16 17:54:01

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For dinner, I ate a roast beef ramen at Beest while Joanne wanted a takeout noodle from a different restaurant called “Flower and Horse in Spring” so I picked it up.

5 2025-06-17

We went to hang out with our Burnaby friends again. We had takeout sushi again from the same spot.

FIGURE 20 Takeout sushi.

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Date: 2025-06-17 12:41:37

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We got bubble tea!

FIGURE 21 Bubble tea.

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Date: 2025-06-17 16:52:05

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Then, for dinner, we dined with another friend at a restaurant called Joyful Seafood Restaurant.

FIGURE 22 Spicy clams.

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Date: 2025-06-17 18:37:36

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6 2025-06-18: Arrival in Abbotsford

We spent the day at home at my parents’ house so no pictures were taken.

7 2025-06-19: Drive to Williams Lake

It was a long drive (about 7 hours) to Williams Lake.

During the drive, we saw some truly amazing views, but I couldn’t take pictures because I was busy driving.

FIGURE 23 A Volkswagen Type 2 van being used as an advertisement at Kumsheen Raft Resort.

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Date: 2025-06-19 13:08:21

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FIGURE 24 Six Mile Lake.

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Date: 2025-06-19 14:47:50

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FIGURE 25 South Cariboo Recreation Centre with lots of Canada geese.

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Date: 2025-06-19 15:58:07

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8 2025-06-20: Williams Lake

On this day, we hung out with family.

FIGURE 26 Williams Lake Visitor Centre.

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Date: 2025-06-20 09:24:37

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In the evening, we had dinner at Lvy Vietnamese Pho. It was one of the only Vietnamese restaurants in Williams Lake but was quite authentic.

FIGURE 27 Vietnamese papaya salad.

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Shutter: 1/42 s
ISO: ISO 160
Software: Digital Camera GFX100S Ver2.13
Date: 2025-06-20 15:27:36

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9 2025-06-21: Scout Island and Riske Creek

We went to Scout Island, a nature preserve. Most of the photos I took were of family but here are some scenery photos. On the island, we ran into a jolly British guy who told us about local attractions. He said that Riske Creek is quite nice.

FIGURE 28 Signal Point, a rocky bit of stuff sticking out of the hill.

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Date: 2025-06-21 10:38:37

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FIGURE 29 The eponymous lake.

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Date: 2025-06-21 10:52:20

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For lunch, we went to Mr Mike’s Steakhouse.

FIGURE 30 There was a nice mural on the outside of the steakhouse.

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Date: 2025-06-21 12:17:02

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FIGURE 31 Steak and fries and a broccolini for good health.

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Date: 2025-06-21 12:50:29

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Then we drove over to Riske Creek.

FIGURE 32 A famous bridge on BC Highway 20.

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Date: 2025-06-21 15:24:42

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FIGURE 33 Chilcotin Lodge.

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Date: 2025-06-21 16:00:27

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FIGURE 34 Our rented Jeep.

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Date: 2025-06-21 16:00:38

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We drove along Farwell Canyon Road a little bit, but didn’t make it as far as the Farwell Canyon, which would have offered some spectacular views, because everyone was tired, especially the baby.

FIGURE 35 Some vehicles at the Toosey First Nation Band reservation.

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Date: 2025-06-21 16:07:20

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FIGURE 36 BC Highway 20 bridge again (extremely high resolution image!!!).

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10 2025-06-22: Drive back to Abbotsford

We had breakfast at a restaurant called The Osprey’s Nest. We had eggs benedicts. It was really good actually.

FIGURE 37 Osprey’s Nest sculpture.

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Date: 2025-06-22 10:22:43

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FIGURE 38 Some geological formations and trees.

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Date: 2025-06-22 13:48:45

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Along the way, we saw trains running along tracks perched most precariously along the edges of the Fraser River.

FIGURE 39 A really long train along Fraser River.

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Date: 2025-06-22 14:53:11

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FIGURE 40 Train!!!

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Date: 2025-06-22 15:06:41

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FIGURE 41 Goldpan park was closed due to elevated risk of landslides, so I could only get a picture of it from the outside.

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Date: 2025-06-22 15:34:29

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FIGURE 42 Rock sheds protecting this train track along the Thompson River. This place is called White Canyon.

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Date: 2025-06-22 16:11:22

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The next day (2025-06-23), we rested at my parents’ house and then in the morning of the 24th, we drove to Vancouver in the very early morning to catch our flight back to San Francisco.

Trip to Hawaii

Sat, 20 Dec 2025 00:00:00 +0000

Trip to Hawaii

2025-12-20

In June 2025, we went to Hawaii! We went to the Big Island, which is also known as Hawaiʻi Island. This was the first time we took a plane with our baby!

1 2025-06-02: Arrival and extreme food eating

Upon getting off the plane at Kona airport, we went to get some delicious poke! We went to Pau Hana Poke which is near the airport and well-reviewed. It was quite good. We could immediately tell the difference between actual Hawaiian poke and the ones you find in the contiguous US states. Hawaiian poke has firmer fish in bigger chunks, and more savory sauces.

FIGURE 1 Poke.

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Date: 2025-06-02 10:25:35

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Then we checked into our hotel, which was Kona Coast Resort. The good thing about this resort is that it is very affordable and has huge rooms. However, they gave us a room that was upstairs and it was really hard to carry the stroller and luggage up to the third floor via the precarious and exposed outdoor staircase. The room was also quite old.

FIGURE 2 Living room of our hotel.

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Date: 2025-06-02 12:19:55

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FIGURE 3 View from our hotel living room.

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Date: 2025-06-02 15:37:57

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Then, after relaxing for a while, we went to the supermarket across the street for some more poke.

FIGURE 4 Spam at a supermarket.

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Date: 2025-06-02 14:35:42

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Soon, it was time for dinner! We booked a fancy restaurant called Canoe House far from our hotel, so we had to drive an hour there. Canoe House was located in an upscale resort called Mauna Lani, Auberge Collection, which cost several times more than our hotel.

FIGURE 5 Driving to dinner.

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Date: 2025-06-02 16:30:57

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FIGURE 6 Tasty drinks.

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Date: 2025-06-02 17:18:09

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FIGURE 7 Lobster.

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Date: 2025-06-02 17:43:43

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After dinner, we strolled around the area to enjoy the view.

FIGURE 8 Beach.

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Date: 2025-06-02 18:31:05

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FIGURE 9 Sunset.

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Date: 2025-06-02 18:32:18

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2 2025-06-03: Coffee plantation tour

In the morning, we went to get really old-school Japanese breakfast at Teshima’s.

I got a combo breakfast with some fried mahi-mahi, fried eggs, rice, pickles, and miso soup. It was a delightful comfort food meal, and very affordable. Generally, the execution and the vibes were very home-style and rustic, which is quite nice.

FIGURE 10 Interior of Teshima’s.

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Date: 2025-06-03 08:34:44

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FIGURE 11 Fried Mahi-Mahi.

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Date: 2025-06-03 08:45:17

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After breakfast, we went to some timeshare presentation thingy because they promised us a free vacation. It was an utter waste of time.

For lunch, we went to a place called Da Poke Shack. This was an abysmal experience. Expensive, bad service, small portions. The line was a 30-minute wait. They ran out of rice. It cost $45 for 2 tiny rice-less poke bowls (in other words, just a scoop of fish). The poke tasted the same as supermarket poke and seemed inferior to the other poke restaurant we tried on the first day.

Then, we went to the Mountain Thunder coffee plantation. The weather and the view were amazing on the mountain because the lush foliage was enveloped in mist. But I didn’t get a lot of good pictures since it started raining hard and we had to juggle umbrellas and the baby.

FIGURE 12 Coffee machines.

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Date: 2025-06-03 14:49:58

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FIGURE 13 Our rented Jeep.

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Date: 2025-06-03 15:05:13

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FIGURE 14 Gift shop.

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Date: 2025-06-03 15:05:36

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For dinner, we got some pho and other Asian-inspired dishes at a restaurant called TK Noodle & hot pot. We were pleasantly surprised by the very large portions, featuring two gigantic beef ribs at a reasonable price.

FIGURE 15 Pho.

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Date: 2025-06-03 17:44:15

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3 2025-06-04: Day trip to Hilo

We drove to the other side of the big island, Hilo!

Along the very long drive, the view was amazing, but I didn’t get any pictures since I was driving.

FIGURE 16 Some shops in Hilo.

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Date: 2025-06-04 12:30:31

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We went to a flea market and farmer’s market, which was nice as there were lots of interesting fruits.

FIGURE 17 Fruits.

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Date: 2025-06-04 13:11:59

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We had lunch at Ken’s House of Pancakes.

FIGURE 18 Big plate of meat at Ken’s House of Pancakes. We didn’t actually get any pancakes there.

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Date: 2025-06-04 14:24:06

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After lunch, we went to the Hawai‘i Tropical Botanical Garden. The botanical garden was wonderful, but we were very tired from pushing the stroller on the really steep boardwalk.

FIGURE 19 Trees at the botanical garden.

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Date: 2025-06-04 15:37:42

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FIGURE 20 Plant.

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Date: 2025-06-04 15:57:10

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FIGURE 21 Some coastal scenery.

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Date: 2025-06-04 15:45:56

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Afterwards, I went to Akaka Falls. Joanne and baby were too tired so they rested in the car.

FIGURE 22 Akaka Falls.

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Date: 2025-06-04 16:54:32

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FIGURE 23 Some bamboo at Akaka Falls park.

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Date: 2025-06-04 17:01:50

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Then we drove back. It was a long drive (over 2 hours each way).

FIGURE 24 Scenery on the drive back.

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Date: 2025-06-04 18:43:47

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Sometime during this long drive, our baby developed an aversion to the car seat and henceforth cried very badly every time we put him in the car seat for the next two months. Very sad.

For dinner, we got takeout Japanese food (a chirashi bowl).

FIGURE 25 Takeout chirashi.

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Date: 2025-06-04 20:16:08

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The quality was average, but the price was quite affordable compared to the Bay Area.

4 2025-06-05: Petroglyphs and more coffee

In the morning, we went to the Kaloko-Honokōhau National Historical Park.

Generally, the park offered some nice views, but I seriously underestimated how difficult the terrain was. We couldn’t make it very far with our stroller, and then it started pouring, so we had to go home. We never made it to the beach. Still, we saw some cool plants, a few petroglyphs, remnants of an ancient village, and some volcanic rocks.

FIGURE 26 A nice tree.

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Date: 2025-06-05 09:45:33

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FIGURE 27 There’s a stick figure petroglyph.

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Date: 2025-06-05 09:51:17

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For lunch we bought some bento boxes.

FIGURE 28 Lunch tonkatsu bento box.

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Date: 2025-06-05 11:58:59

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Then we went to another coffee plantation, Greenwell Farms. The tour guide was extremely knowledgeable and entertaining at the same time. I learned a lot about coffee! Apparently, the roots come from a hardier plant but the fruits come from a better-tasting species, so they graft them together.

FIGURE 29 Grafted coffee plants.

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Date: 2025-06-05 14:32:40

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FIGURE 30 Baby coffee plant grafts.

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Date: 2025-06-05 14:47:46

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For dinner, we ate an extreme loco moco from Ono Loa.

FIGURE 31 Baby in the hotel.

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Date: 2025-06-05 16:11:08

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5 2025-06-06: Flight back

Before boarding our flight, we got even more poke!

FIGURE 32 Poke.

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Date: 2025-06-06 11:38:48

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Ultimately, because of bad weather, the short duration of our trip, and the fact that it was difficult travelling with a baby, we didn’t get to go to Volcano national park and many other amazing attractions. Oh well! Hopefully, next time, when baby is bigger, we can bring him to funner activities like the Volcano National Park, the Hakalau Forest National Wildlife Refuge, and so on.

Trip to Zhuhai, Guangzhou, and Macau (2024-09)

Sun, 14 Dec 2025 00:00:00 +0000

Trip to Zhuhai, Guangzhou, and Macau (2024-09)

2025-12-14

In September 2024, Joanne and I went to China! We went to Zhuhai, Guangzhou, and Macao. This is the sequel to trip to Japan and Hong Kong. I was going to publish this blog post at the same time as that one, but due to extreme procrastination, here we are over a year later!

We took the bus from Hong Kong to Zhuhai via the Hong Kong-Zhuhai-Macau bridge.

FIGURE 1 The Jinwan Arts Center in the Jinwan district of Zhuhai.

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Date: 2024-09-16 22:10:03

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1 Saturday 2024-09-14: Arriving in Zhuhai

We arrived in Zhuhai in the afternoon. The first thing to do was to set up our cellular service. The signal in the parking lot was really bad and all the public wifi was unusable because literally every wifi hotspot requires you to have a +86 phone number in order to access it. Stringent laws in China require internet access to be tied to your real identity.

FIGURE 2 Welcome sign upon exiting the Zhuhai terminus of the HZBM.

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It was a long drive from the bridge terminus to our apartment, located in the Doumen suburbs.

FIGURE 3 Cabinet of stuff at my mother in law’s house.

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Date: 2024-09-15 10:33:47

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FIGURE 4 Home-cooked fish and chicken drumsticks and takeout roast pork belly.

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Date: 2024-09-14 16:58:23

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2 Sunday 2024-09-15: Food and shopping

Upon waking up, we had a quick breakfast at home of takeout cheung fun (腸粉).

Then we went to get lunch with some friends! Oops, we accidentally went to the wrong restaurant. This one is empty because I think they haven’t opened yet, but it looks really cool without anyone in it, so I snapped a pic.

FIGURE 5 An empty restaurant.

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Date: 2024-09-15 10:54:56

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The actual restaurant we went to had a rather average offering of dim sum, sour vegetable fish (酸菜鱼), and other dishes that I won’t bore you with. After lunch, we wanted to get coffee. But the coffee shop we went to was entirely reserved by a famous celebrity! So we couldn’t get coffee. We were very sad and did not even get an autograph.

FIGURE 6 Celebrity tent at the coffee shop.

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Date: 2024-09-15 12:17:33

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Then we went to sightsee at Beishancun (北山村, lit. North Mountain Village). The village comprises a lot of charming old-school buildings and various restaurants and tea shops serving delicious food. To save money, we took the public bus there.

FIGURE 7 View from the bus when crossing a bridge.

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FIGURE 8 A charming table and stools in one of the old buildings in Beishancun.

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FIGURE 9 A Weiqi (also known as Go) club in Beishancun.

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Date: 2024-09-15 13:55:39

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Then we got dessert!

FIGURE 10 Matcha cheesecake and swiss roll.

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Date: 2024-09-15 14:30:04

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In the afternoon, we went shopping. We went to the Huafa mall, an awesome and huge mall!

FIGURE 11 Miniso popup in the mall atrium.

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FIGURE 12 Spacious atrium.

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Date: 2024-09-15 16:08:31

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FIGURE 13 Another view in the mall.

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Date: 2024-09-15 17:45:37

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FIGURE 14 Zeekr 007, an awesome electric car.

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Date: 2024-09-15 16:26:39

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The most puzzling thing is that the mall atrium is very exposed to the outside. Yet, they have air conditioning and they keep pumping cold air in. It doesn’t work very well, since all the cold air escapes through the vast opening, and the mall remains hot and humid in Zhuhai’s tropical climate. Still, who needs actual cool air when you can simply look cool?

FIGURE 15 Exterior of the mall.

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Date: 2024-09-15 18:06:52

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FIGURE 16 Exterior of the mall. As far as I know, neither this mall nor Zhuhai has anything to do with the Olympics and I have no idea how they got the rights to use those five rings.

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Date: 2024-09-15 18:28:42

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For dinner, we ate Vietnamese food (sorry, no pics) and then went home.

3 Monday 2024-09-16: Wetlands, haircut, and Jinwan Arts Center.

First things first, we got a delicious cheung fun (腸粉) a few minutes walk in the neighborhood of our apartment. The tasty cheung fun are super thin and wrinkly and soak up all the delicious special soy sauce. In contrast, the ones we get in the US are often too smooth and thick.

FIGURE 17 Hole-in-the-wall establishment featuring stone-milled cheung fun.

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Date: 2024-09-16 07:53:32

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FIGURE 18 Cheung fun.

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Date: 2024-09-16 07:54:45

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After chilling for a while in the morning, and buying a tube of toothpaste, we went for a hike in the Huangyang river wetlands reserve!

FIGURE 19 An observation deck in the wetlands park. There’s a cool white bird on the left.

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Lens: GF20-35mmF4 R WR
Aperture: f/8.0
Shutter: 1/300 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.12
Date: 2024-09-16 13:50:07

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FIGURE 20 More marshes.

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Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/8.0
Shutter: 1/300 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.12
Date: 2024-09-16 13:52:37

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Then it was time for a haircut!

FIGURE 21 The incredibly clean and modern salon.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 1/15 s
ISO: ISO 125
Software: darktable 4.8.1
Date: 2024-09-16 14:10:57

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A really high quality haircut in this extremely fancy salon, plus hair washing and impeccable service, cost a mere fraction of the cheapest Supercuts haircut that you can find in the US.

FIGURE 22 A performance arts center.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 1/150 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-16 15:50:22

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FIGURE 23 A random hotel and a big clothes store.

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Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 1/420 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-16 15:51:21

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We got dinner with Joanne’s cousins. The food was mediocre. The restaurant was full of cigarette smoke, which made me extremely annoyed because Joanne was pregnant. We ordered a morel soup, which, to our dismay, arrived with a single, solitary morel mushroom on top. This “crab island” was quite creatively plated, however.

FIGURE 24 Crab island.

EXIF data

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Lens: GF20-35mmF4 R WR
Aperture: f/7.1
Shutter: 1/27 s
ISO: ISO 1250
Software: Digital Camera GFX100S Ver2.12
Date: 2024-09-16 19:16:41

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3.1 Jinwan Arts Center

I saw this cool Zaha Hadid building on social media and decided to check it out after dinner. I was expecting a serene building, but what I found was a bustling night festival full of people. Kids were running all over the place with nets trying to catch shrimp in the water surrounding the venue. A bunch of people were waving multicolored LED lights around at night.

FIGURE 25 Jinwan Arts Center.

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Aperture: f/1.8
Shutter: 1/14 s
ISO: ISO 1666
Software: MediaTek Camera Application
Date: 2024-09-16 21:31:07

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Still, after the crowds subsided at 10 pm, I managed to get a few good shots in.

FIGURE 26 Jinwan Arts Center.

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Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/4.0
Shutter: 1/10.0 s
ISO: ISO 1600
Software: darktable 4.8.1
Date: 2024-09-16 21:45:17

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FIGURE 27 This lady was video chatting on her phone.

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Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/4.0
Shutter: 1/3.1 s
ISO: ISO 1600
Software: darktable 4.8.1
Date: 2024-09-16 21:50:32

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FIGURE 28 Jinwan Arts Center.

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Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/8.0
Shutter: 3.5 s
ISO: ISO 100
Software: darktable 5.1.0+453~gd91a096b66-dirty
Date: 2024-09-16 22:07:03

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FIGURE 29 Jinwan Arts Center.

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Lens: GF20-35mmF4 R WR
Aperture: f/8.0
Shutter: 4.5 s
ISO: ISO 100
Software: darktable 5.1.0+453~gd91a096b66-dirty
Date: 2024-09-16 22:10:03

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4 Tuesday, 2024-09-17: Rest day and Mid Autumn Festival

We decided to get cheung fun again in the morning, but at a different shop.

FIGURE 30 Cheung fun.

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Shutter: 1/50 s
ISO: ISO 822
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Date: 2024-09-17 08:44:42

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FIGURE 31 Cheung fun shop.

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Shutter: 1/50 s
ISO: ISO 359
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Date: 2024-09-17 08:28:41

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For lunch, we ate a simple lunch of homemade wontons. In the afternoon, Joanne’s cousins came over to celebrate Mid Autumn Festival!

FIGURE 32 Giant mooncake.

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Aperture: f/1.8
Shutter: 1/50 s
ISO: ISO 840
Software: MediaTek Camera Application
Date: 2024-09-17 11:57:02

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My mother in law cooked a dazzling array of home-cooked foods, including:

Steamed clams
Steamed crabs
Steamed fish
Steamed prawns
Roast poultry
Meat
Stir fried wood ear mushroom and shrimp
Bitter melon soup with pork
Cucumber salad

FIGURE 33 Mid Autumn Festival feast.

EXIF data

Camera: Nothing A015
Aperture: f/1.8
Shutter: 1/100 s
ISO: ISO 492
Software: MediaTek Camera Application
Date: 2024-09-17 18:26:53

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FIGURE 34 Full moon during Mid Autumn Festival.

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Camera: FUJIFILM GFX100S
Lens: 125
Aperture: f/NaN
Shutter: 1/220 s
ISO: ISO 800
Software: darktable 4.8.1
Date: 2024-09-17 18:25:38

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After dinner, I complained that we hadn’t gone out much during the day, so Joanne took pity and we went to take photos of a viral LED-encrusted meme photo spot, called the Zexing food center (泽兴美食城).

FIGURE 35 Zexing food center.

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Aperture: f/5.6
Shutter: 1/15 s
ISO: ISO 100
Software: darktable 5.1.0+453~gd91a096b66-dirty
Date: 2024-09-17 19:27:18

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5 Wednesday, 2024-09-18: Trip to Guangzhou

We took a ride share to the Zhuhai train station and caught an intercity train to Guangzhou.

FIGURE 36 Military recruits (the sash they are wearing says “glorious enlistment”).

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Aperture: f/5.6
Shutter: 1/75 s
ISO: ISO 1600
Software: darktable 4.8.1
Date: 2024-09-18 09:27:12

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FIGURE 37 The platforms are empty because you are not allowed to go up to the platform until a few minutes before your train’s departure. Until then, you have to wait in the waiting area downstairs, which is vast and contains tons of seats.

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Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 1/60 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-18 09:29:01

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The CRH-6A rolling stock was really nice and quiet as it zipped through the country at 200 km/h. Over 95% of the line is elevated on viaducts and is completely grade separated.

FIGURE 38 View from the train.

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We passed by a lot of fisheries raising “crispy meat grass fish” (脆肉鲩), a specialty of the region. This fish is the same species as the regular grass fish, but it is raised on a high-protein diet of beans, so it develops firm flesh that remains firm even after fully cooking. When you slice up this fish into thin slices and eat it in hot pot, it’s really good with a unique texture. It is quite an interesting juxtaposition to see these fisheries nestled amongst high rise buildings.

FIGURE 39 Fisheries seen from the train.

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Soon, we arrived at the Guangzhou South Station, one of the biggest railway stations (it was the biggest in Asia when it opened in 2010).

FIGURE 40 Police buggies in Guangzhou South Station.

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Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 1/26 s
ISO: ISO 2500
Software: darktable 4.8.1
Date: 2024-09-18 10:53:39

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FIGURE 41 An LED panel displays a political slogan about how Xi Jinping Thought ushers in the new era of Communism with Chinese Characteristics.

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Joanne’s friend came to pick us up and we headed straight into the city! We went for a bougie lunch at Guangzhou Jiujia, a famous restaurant since 1935. Guangzhou is the center of Canton and has, by definition, the most authentic Cantonese food. Cantonese food is known for its white cut chicken and roast meats, so we ordered those. It did not disappoint. The white cut chicken was the most delicious chicken I have ever eaten — incredibly flavorful and with a surprisingly firm texture.

FIGURE 42 Chicken.

EXIF data

Camera: FUJIFILM GFX100S
Lens: 125
Aperture: f/NaN
Shutter: 1/180 s
ISO: ISO 2000
Software: Digital Camera GFX100S Ver2.12
Date: 2024-09-18 12:40:23

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FIGURE 43 Roast pork and sausage stuffed with salted egg yolk.

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Camera: FUJIFILM GFX100S
Lens: 125
Aperture: f/NaN
Shutter: 1/180 s
ISO: ISO 2500
Software: Digital Camera GFX100S Ver2.12
Date: 2024-09-18 12:50:40

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FIGURE 44 Beef chow fun (干炒牛河).

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Lens: 125
Aperture: f/NaN
Shutter: 1/125 s
ISO: ISO 3200
Software: Digital Camera GFX100S Ver2.12
Date: 2024-09-18 12:55:20

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5.1 Liwanhu

Then we went to the Liwanhu Park (荔湾湖). It has a nice lake with a bunch of cool old buildings next to it. Very fun to walk down the old alleys.

FIGURE 45 Liwanhu.

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FIGURE 46 A heritage building.

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Lens: GF20-35mmF4 R WR
Aperture: f/8.0
Shutter: 1/240 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-18 14:09:08

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FIGURE 47 A souvenir shop.

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Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/8.0
Shutter: 1/15 s
ISO: ISO 250
Software: darktable 4.8.1
Date: 2024-09-18 14:18:26

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FIGURE 48 A cute cat.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/4.0
Shutter: 1/27 s
ISO: ISO 125
Software: Digital Camera GFX100S Ver2.12
Date: 2024-09-18 14:17:53

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5.2 Chenjiaci

After the lake, we went to Chen clan academy (Chenjiaci, 陈家祠). It is the humble abode of a prominent clan. The buildings are really cool but I had a hard time getting good photos due to the large number of people and very harsh sunlight.

FIGURE 49 Chenjiaci.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/8.0
Shutter: 1/240 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-18 15:53:24

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FIGURE 50 Chenjiaci.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/8.0
Shutter: 1/240 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-18 16:12:23

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In the afternoon, we checked into our hotel. It was so cheap, but amazingly clean and modern!

FIGURE 51 Hotel room.

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FIGURE 52 A random street outside our hotel.

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For dinner, we went to Chaofa, a beef hot pot restaurant. They specialize in Chaoshan beef hot pot (潮汕牛肉火鍋), where extremely fresh slices of beef are dunked in a clear beef broth. This maximizes the tasty beef flavor without overpowering it with spices and seasoning.

FIGURE 53 Whimsical architecture and Mid Autumn Festival moon outside the Chaofa restaurant.

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FIGURE 54 Big plate of beef.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/4.0
Shutter: 1/28 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.12
Date: 2024-09-18 19:20:58

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5.3 Canton Tower

At night, our friend took us to Canton Tower, a hyperboloid of one sheet that is one of the tallest buildings in the world at 604 m.

FIGURE 55 Canton Tower.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/8.0
Shutter: 2.6 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-18 20:15:28

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FIGURE 56 Canton Tower.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/4.0
Shutter: 1.7 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-18 20:19:51

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Canton Tower is entirely covered in colorful LED lights. Most annoyingly, it often plays advertisements. So I had to wait until each ad is over to take photos when it is displaying a more or less aesthetic color scheme.

FIGURE 57 It’s playing an advertisement lol.

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There is a full(ish) moon since it’s the day after Mid Autumn Festival, but due to cloudy weather and the fact that I didn’t bring a super long lens, I didn’t get any good shots of the “eye of Sauron” type photo where a giant moon is placed right on top of the tower.

6 Thursday, 2024-09-19: Line scan photography and back to Zhuhai

In the morning, we ate the complimentary hotel breakfast. It was actually pretty good, and included many classical Cantonese dishes like congee, noodles, curry fishballs, tea eggs, siu mai, and so on.

Then, I went to do line scan photography!

FIGURE 58 CRH400AF high speed train.

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FIGURE 59 CRH6A intercity train.

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FIGURE 60 Air conditioner generator car.

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FIGURE 61 One of the cars of a Lanzhou–Guangzhou “green-skinned” train (绿皮车). Lanzhou is really, really far away (about 2300 km), so this train has come a long way!

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FIGURE 62 Railway tracks where I did the line scan photography.

EXIF data

Camera: Nothing A015
Aperture: f/1.8
Shutter: 1/2793 s
ISO: ISO 102
Software: MediaTek Camera Application
Date: 2024-09-19 11:03:09

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I also had a little bit of time to just walk around and explore.

FIGURE 63 I chanced upon this electronics bazaar. It was past closing time for them though.

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FIGURE 64 A random hotel, not the one we stayed at.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/8.0
Shutter: 1/340 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-19 12:00:13

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Then, we ate chicken rice for lunch and went back to Zhuhai. To be honest this chicken rice was even tastier than the one we had in Hong Kong and was also a lot cheaper (delivered)!

FIGURE 65 Takeout chicken rice. Super cheap, super delicious.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/4.0
Shutter: 1/60 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-19 13:07:04

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6.1 Back in Zhuhai

Back in Zhuhai, we went to a mall called UT Place.

FIGURE 66 Interior of a Luxeed S7, a luxury electric car.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/10.0
Shutter: 1/10.0 s
ISO: ISO 3200
Software: Digital Camera GFX100S Ver2.12
Date: 2024-09-19 16:15:30

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FIGURE 67 Wet market across the street from the mall. This lady came up to me angrily demanding to know why I took a picture but I told her I was a tourist (she was very sceptical).

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 1/15 s
ISO: ISO 200
Software: darktable 4.8.1
Date: 2024-09-19 17:22:12

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For dinner, we ate barbecue meat at a restaurant called Xita Laotaitai (west tower old lady).

FIGURE 68 Grilled meat.

EXIF data

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Lens: GF20-35mmF4 R WR
Aperture: f/4.0
Shutter: 1/27 s
ISO: ISO 250
Software: Digital Camera GFX100S Ver2.12
Date: 2024-09-19 18:30:55

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7 Friday, 2024-09-20: Doumen neighbourhood hanging out

On this day we just kinda hung out near our apartment. We had to take care of some errands.

On this day, I used my Voigtlander Ultron 35mm f/1.7 VM, so you can enjoy these unprocessed, imperfect vintage-look photos full of vignetting and soft corners!

FIGURE 69 Jing An sign at the Zexin food centre.

EXIF data

Camera: FUJIFILM GFX100S
Lens: 35
Aperture: f/NaN
Shutter: 1/160 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.12
Date: 2024-09-20 13:39:44

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FIGURE 70 Daxin mall.

EXIF data

Camera: FUJIFILM GFX100S
Lens: 35
Aperture: f/NaN
Shutter: 1/125 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.12
Date: 2024-09-20 14:20:53

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FIGURE 71 A random hotel.

EXIF data

Camera: FUJIFILM GFX100S
Lens: 35
Aperture: f/NaN
Shutter: 1/100 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.12
Date: 2024-09-20 13:52:56

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FIGURE 72 Chagee.

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Camera: FUJIFILM GFX100S
Lens: 35
Aperture: f/NaN
Shutter: 1/480 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.12
Date: 2024-09-20 14:46:00

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8 Saturday, 2024-09-21: Going to Macao

We went to Macao. It was pouring.

FIGURE 73 Rainy drive to Macao.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/4.0
Shutter: 1/500 s
ISO: ISO 1600
Software: Digital Camera GFX100S Ver2.12
Date: 2024-09-21 13:55:26

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Fortunately, the weather cleared up upon our arrival. I looked outside the balcony and lo and behold, a Lamborghini Aventador, just casually hiding amongst a bunch of relatively cheap cars!

FIGURE 74 Lamborghini Aventador.

EXIF data

Camera: FUJIFILM GFX100S
Lens: 125
Aperture: f/NaN
Shutter: 1/180 s
ISO: ISO 1250
Software: Digital Camera GFX100S Ver2.12
Date: 2024-09-21 17:14:59

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Then we went for dinner!

FIGURE 75 Crowded alleys on a rainy night.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/4.0
Shutter: 1/27 s
ISO: ISO 125
Software: Digital Camera GFX100S Ver2.12
Date: 2024-09-21 18:30:05

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We had fancy and delicious Thai food.

FIGURE 76 Lucky bunnies.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/4.0
Shutter: 1/27 s
ISO: ISO 250
Software: Digital Camera GFX100S Ver2.12
Date: 2024-09-21 19:55:05

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FIGURE 77 An apartment building.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/4.0
Shutter: 1/15 s
ISO: ISO 1600
Software: darktable 4.8.1
Date: 2024-09-21 20:27:47

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8.1 Fireworks

At night, we went to see fireworks!

FIGURE 78 Fireworks.

EXIF data

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Lens: GF55mmF1.7 R WR
Aperture: f/16.0
Shutter: 8.0 s
ISO: ISO 100
Software: darktable 5.1.0+453~gd91a096b66-dirty
Date: 2024-09-21 21:40:57

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FIGURE 79 Fireworks.

EXIF data

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Lens: GF55mmF1.7 R WR
Aperture: f/16.0
Shutter: 8.0 s
ISO: ISO 100
Software: darktable 5.1.0+453~gd91a096b66-dirty
Date: 2024-09-21 21:41:17

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FIGURE 80 Fireworks.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/16.0
Shutter: 8.0 s
ISO: ISO 100
Software: darktable 5.1.0+453~gd91a096b66-dirty
Date: 2024-09-21 21:41:49

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FIGURE 81 Fireworks.

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Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/16.0
Shutter: 5.0 s
ISO: ISO 100
Software: darktable 5.1.0+453~gd91a096b66-dirty
Date: 2024-09-21 21:45:03

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FIGURE 82 Fireworks.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/16.0
Shutter: 5.0 s
ISO: ISO 100
Software: darktable 5.1.0+453~gd91a096b66-dirty
Date: 2024-09-21 21:49:31

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9 Sunday, 2024-09-22: Cars and Macao sightseeing

There was an exotic car show at the Wynn, so we went to check it out.

FIGURE 83 De Tomaso Pantera.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/42 s
ISO: ISO 500
Software: darktable 4.8.1
Date: 2024-09-22 11:42:35

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FIGURE 84 Koenigsegg Jesko.

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Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/42 s
ISO: ISO 1000
Software: darktable 4.8.1
Date: 2024-09-22 11:45:34

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FIGURE 85 Porsche 911 GT1 Straßenversion.

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Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/2.8
Shutter: 1/42 s
ISO: ISO 160
Software: darktable 4.8.1
Date: 2024-09-22 12:06:11

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Then we went around to look at various buildings.

FIGURE 86 Morpheus Hotel.

EXIF data

Software: darktable 4.8.1

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FIGURE 87 Golden spacesuit at the Omega store.

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Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/1.7
Shutter: 1/170 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-22 13:38:29

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FIGURE 88 Big Ben replica.

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Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/170 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-22 13:48:41

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FIGURE 89 Eiffel Tower replica.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/220 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-22 13:49:39

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For dinner, we ate sushi!

FIGURE 90 Sushi.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/2.5
Shutter: 1/42 s
ISO: ISO 1250
Software: Digital Camera GFX100S Ver2.12
Date: 2024-09-22 14:47:37

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10 Monday, 2024-09-23: Flight back

The next day, we flew back to San Francisco. We had a transfer in Taoyuan airport in Taiwan.

FIGURE 91 Airplane food.

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Flight on a friend's Cessna (2018)

Mon, 10 Nov 2025 00:00:00 +0000

Flight on a friend's Cessna (2018)

2025-11-10

On 2018-04-14, I rode with my friend Yan on his Cessna 172M. Since he hadn’t flown for a while, he had to take off and land a couple of times before I could get on. How exciting!

Here’s the Pilot’s Operating Handbook for the plane:

Pilot’s Operating Handbook

Incidentally, this is the same plane that had an emergency landing in Watsonville a few years later in 2022. My friend was neither piloting nor on board the plane at the time of the incident.

FIGURE 1 The Cessna 172M.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/5.6
Shutter: 1/640 s
ISO: ISO 100
Software: ILCE-7RM2 v3.30
Date: 2018-04-15 00:27:29

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FIGURE 2 Moffett Federal Airfield.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/5.6
Shutter: 1/800 s
ISO: ISO 100
Software: darktable 2.5.0+277~g42cfe8aeb
Date: 2018-04-14 23:12:29

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FIGURE 3 The Googleplex.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/5.6
Shutter: 1/800 s
ISO: ISO 100
Software: darktable 2.5.0+379~g6f0b7ddeb
Date: 2018-04-14 23:13:41

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FIGURE 4 Facebook headquarters.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/5.6
Shutter: 1/800 s
ISO: ISO 100
Software: darktable 2.5.0+277~g42cfe8aeb
Date: 2018-04-14 23:16:29

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FIGURE 5 Salt evaporation ponds.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/5.6
Shutter: 1/800 s
ISO: ISO 100
Software: darktable 2.5.0+277~g42cfe8aeb
Date: 2018-04-14 23:18:17

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FIGURE 6 San Francisco International Airport.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/5.6
Shutter: 1/500 s
ISO: ISO 100
Software: darktable 2.5.0+277~g42cfe8aeb
Date: 2018-04-14 23:25:46

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FIGURE 7 The Mission district in San Francisco. In the middle you can see the headquarters of Ouster, where I used to work.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/5.6
Shutter: 1/500 s
ISO: ISO 100
Software: darktable 2.5.0+277~g42cfe8aeb
Date: 2018-04-14 23:31:28

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FIGURE 8 Skyline of San Francisco.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/5.6
Shutter: 1/500 s
ISO: ISO 100
Software: darktable 2.5.0+379~g6f0b7ddeb
Date: 2018-04-14 23:31:49

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FIGURE 9 Golden Gate Bridge.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/8.0
Shutter: 1/500 s
ISO: ISO 200
Software: darktable 2.5.0+343~gd67844ec1
Date: 2018-04-14 23:33:50

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FIGURE 10 Golden Gate Bridge.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/8.0
Shutter: 1/500 s
ISO: ISO 200
Software: darktable 2.5.0+343~gd67844ec1
Date: 2018-04-14 23:34:54

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FIGURE 11 Golden Gate Bridge.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/8.0
Shutter: 1/500 s
ISO: ISO 200
Software: darktable 2.7.0+1123~g8555f5feb
Date: 2018-04-14 23:39:50

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FIGURE 12 Alcatraz Island with San Francisco in the background.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/8.0
Shutter: 1/500 s
ISO: ISO 200
Software: darktable 2.7.0+1123~g8555f5feb
Date: 2018-04-14 23:42:20

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FIGURE 13 Alcatraz.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/8.0
Shutter: 1/500 s
ISO: ISO 200
Software: darktable 2.5.0+277~g42cfe8aeb
Date: 2018-04-14 23:42:32

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FIGURE 14 Coit Tower on Telegraph Hill.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/8.0
Shutter: 1/500 s
ISO: ISO 100
Software: darktable 2.5.0+277~g42cfe8aeb
Date: 2018-04-14 23:43:54

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FIGURE 15 Downtown San Francisco.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/8.0
Shutter: 1/500 s
ISO: ISO 100
Software: darktable 2.5.0+379~g6f0b7ddeb
Date: 2018-04-14 23:44:05

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FIGURE 16 Downtown San Francisco but now with a more vintage postprocessing.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/8.0
Shutter: 1/500 s
ISO: ISO 100
Software: darktable 2.7.0+1123~g8555f5feb
Date: 2018-04-14 23:45:03

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FIGURE 17 Bernal Hill.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/8.0
Shutter: 1/500 s
ISO: ISO 160
Software: darktable 2.5.0+277~g42cfe8aeb
Date: 2018-04-14 23:46:40

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FIGURE 18 Emirates Airbus A380 and British Airways 747 on the tarmac at SFO.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/8.0
Shutter: 1/500 s
ISO: ISO 200
Software: darktable 2.5.0+277~g42cfe8aeb
Date: 2018-04-14 23:51:20

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FIGURE 19 Stanford University.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/8.0
Shutter: 1/500 s
ISO: ISO 200
Software: darktable 2.5.0+277~g42cfe8aeb
Date: 2018-04-14 23:59:33

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FIGURE 20 Apple Park. This photo is, as of writing, the lead photo on the Apple Inc. Wikipedia article and is therefore one of the most famous photos I’ve taken.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/6.3
Shutter: 1/500 s
ISO: ISO 100
Software: darktable 2.5.0+277~g42cfe8aeb
Date: 2018-04-15 00:05:32

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FIGURE 21 NVIDIA Endeavor building, before the new Voyager building was built.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/6.3
Shutter: 1/500 s
ISO: ISO 100
Software: darktable 2.5.0+277~g42cfe8aeb
Date: 2018-04-15 00:09:38

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Mamiya Apo 200mm f/2.8 on Fuji GFX

Fri, 31 Oct 2025 00:00:00 +0000

Mamiya Apo 200mm f/2.8 on Fuji GFX

2025-10-31

The Mamiya Apo 200mm f/2.8 is a medium format lens for Mamiya M645 system. It covers a much bigger image circle than the Fujifilm GFX cameras, though it is smaller than that covered by the RB/RZ lenses such as the Apo-Sekor-Z 350mm.

FIGURE 1 The lens mounted on my GFX 50R.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/11.0
Shutter: 1/250 s
ISO: ISO 400
Software: ILCE-7RM2 v3.30
Date: 2019-10-25 09:20:33

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FIGURE 2 Optical construction.

EXIF data

Software: Adobe Photoshop CC 2019 (Macintosh)

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I found that there’s a “for SIGMA” tripod ring on Amazon that matches the diameter of the Fotasy M645-GFX adapter perfectly, so much so that the tripod foot is precisely at the center of gravity and you can balance the lens on just that.

FIGURE 3 Adapter and tripod ring.

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FIGURE 4 Mounted on my tabletop tripod.

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FIGURE 5 Portrait orientation shooting.

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My general impression is that the lens is fairly sharp stopped down to f/4 and beyond, but when wide open, it has noticeable spherical aberration, giving it a “glowy” look. Also, despite its APO designation, it has quite a bit of chromatic aberration, with purple fringing wide open, and green outlines around bokeh balls. I feel that the Leica Apo Telyt-R 180mm f/3.4 is quite a bit sharper than this lens and has way less CA, although that lens has some soft vignetting which the Mamiya does not have.

In my opinion, the Mamiya Apo 200mm shines on the 50 megapixel GFX cameras, where the slightly reduced sharpness is less of a concern.

In terms of contrast, being an older lens, it does sometimes have some haze or veiling flare, but this is not too big of a concern usually.

Anyway, since my Leica Apo Telyt-R 180mm f/3.4 does roughly the same thing as this lens while being sharper and more compact, I sold the Mamiya to a Pulitzer Prize-winning journalist, Kim Komenich.

Without further ado, here are some unprocessed full resolution photos, some on GFX 50R and some on GFX 100S!

1 Shots on GFX 50R

FIGURE 6 Firetruck.

EXIF data

Camera: FUJIFILM GFX 50R
Lens: 0 mm f/unknown
Aperture: f/NaN
Shutter: 1/220 s
ISO: ISO 400
Software: Digital Camera GFX 50R Ver1.00
Date: 2019-10-26 17:57:12

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FIGURE 7 St Anne of the Sunset church in San Francisco.

EXIF data

Camera: FUJIFILM GFX 50R
Lens: 0 mm f/unknown
Aperture: f/NaN
Shutter: 1/420 s
ISO: ISO 400
Software: Digital Camera GFX 50R Ver1.00
Date: 2019-10-27 17:52:57

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FIGURE 8 Old Volvo P1800.

EXIF data

Camera: FUJIFILM GFX 50R
Lens: 0 mm f/unknown
Aperture: f/NaN
Shutter: 3.2 s
ISO: ISO 100
Software: Digital Camera GFX 50R Ver1.00
Date: 2020-01-02 02:16:33

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FIGURE 9 Ferry Building. This was shot at f/8 and you can see the nice 8-pointed sunstars.

EXIF data

Camera: FUJIFILM GFX 50R
Lens: 0 mm f/unknown
Aperture: f/NaN
Shutter: 15.0 s
ISO: ISO 100
Software: Digital Camera GFX 50R Ver1.00
Date: 2019-12-31 23:22:10

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FIGURE 10 Golden Gate Bridge.

EXIF data

Camera: FUJIFILM GFX 50R
Lens: 0 mm f/unknown
Aperture: f/NaN
Shutter: 1/2.5 s
ISO: ISO 100
Software: Digital Camera GFX 50R Ver1.00
Date: 2019-11-17 17:06:09

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FIGURE 11 Zoox self driving car prototype.

EXIF data

Camera: FUJIFILM GFX 50R
Lens: 0 mm f/unknown
Aperture: f/NaN
Shutter: 1/340 s
ISO: ISO 200
Software: Digital Camera GFX 50R Ver1.00
Date: 2019-11-17 15:01:20

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FIGURE 12 Cityscape of San Francisco.

EXIF data

Camera: FUJIFILM GFX 50R
Lens: 0 mm f/unknown
Aperture: f/NaN
Shutter: 30.0 s
ISO: ISO 100
Software: Digital Camera GFX 50R Ver1.00
Date: 2019-11-23 17:54:45

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2 Shots on GFX 100S

FIGURE 13 Porsche 964 RS America.

EXIF data

Camera: FUJIFILM GFX100S
Lens: 200
Aperture: f/NaN
Shutter: 1/680 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver1.32
Date: 2022-12-21 11:25:37

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FIGURE 14 Porsche 964 RS America.

EXIF data

Camera: FUJIFILM GFX100S
Lens: 200
Aperture: f/NaN
Shutter: 1/900 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver1.32
Date: 2022-12-21 11:23:07

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FIGURE 15 Lotus Exige.

EXIF data

Camera: FUJIFILM GFX100S
Lens: 200
Aperture: f/NaN
Shutter: 12.0 s
ISO: ISO 200
Software: Digital Camera GFX100S Ver1.32
Date: 2022-12-21 19:48:48

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FIGURE 16 Tesla Model S.

EXIF data

Camera: FUJIFILM GFX100S
Lens: 200
Aperture: f/NaN
Shutter: 1/3500 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver1.32
Date: 2022-12-21 11:33:17

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Mamiya Apo-Sekor-Z 350mm f/5.6 on Fuji GFX

Sat, 25 Oct 2025 00:00:00 +0000

Mamiya Apo-Sekor-Z 350mm f/5.6 on Fuji GFX

2025-10-25

The Mamiya Apo-Sekor Z 350mm f/5.6 is a medium format lens for Mamiya RB/RZ 67. It covers a much bigger image circle than the Fujifilm GFX 100S.

FIGURE 1 The lens mounted on my Fuji camera.

EXIF data

Camera: Apple iPhone 11 Pro Max
Lens: iPhone 11 Pro Max back triple camera 4.25mm f/1.8
Aperture: f/1.8
Shutter: 1/60 s
ISO: ISO 160
Software: 15.6.1
Date: 2022-10-21 23:40:29

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It can be acquired quite cheaply these days — only $300 on eBay! However, you’ll need an expensive adapter, such as the Fotodiox Pro RB/RZ to GFX adapter, which costs more than the lens, bringing the total to about $700.

From the manual, you can find the lens construction diagram, MTF, and chromatic aberration plots. The MTF honestly looks great even by modern standards, so let’s see if it lives up to the hype!

FIGURE 2 Specifications of the lens.

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FIGURE 3 Modulation transfer function (MTF) chart.

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FIGURE 4 Chromatic aberration chart.

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In fact, the 350mm is the sharpest out of the Mamiya RB/RZ Apo lenses, having more than an MTF of 0.8 at 40 lp/mm according to the MTF plot. On the Fuji GFX, we’re only using the middle part of the MTF, up to 27 mm.

Generally, I found that it is indeed very sharp and apochromatic!

However, the main drawback of this lens is the poor ergonomics. The lens doesn’t have a focusing helicoid, and the helicoid is on the adapter. But the lens is super heavy, so if your hand is supporting the lens at its center of mass, it would be far from the helicoid, and if your hand were on the helicoid, it would feel very out of balance. This makes the manual focus experience unpleasant to use on the GFX.

Furthermore, to keep the aperture open at the desired aperture, you need to press down on an aperture lever. On the RB/RZ bodies, it would electronically set the aperture at the moment of taking the photo (as it engages the leaf shutter), but when adapted on mirrorless cameras, you’ll have to keep pressing that lever to keep the aperture open at the desired setting. In practice, the lens is sharp enough (and quite slow) that I never need to stop down.

Another quirk is that the adapter with helicoid is prone to sagging with the heavy lens, causing shots at infinity to not have even sharpness. This can typically be fixed with a long lens support, adding to the weight and complexity of the kit.

Finally, it is a slow, long, manual lens, obviously without image stabilization, so it is easy to have blurry photos due to camera shake.

So without further ado, here are some full resolution, unprocessed, uncropped 100 megapixel photos!

FIGURE 5 Airplane approaching the moon.

EXIF data

Camera: FUJIFILM GFX100S
Lens: 350
Aperture: f/NaN
Shutter: 1/500 s
ISO: ISO 1000
Software: Digital Camera GFX100S Ver2.11
Date: 2024-02-23 17:48:57

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FIGURE 6 Ford truck. You can see that even on the chrome Ford logo, it is sharp and clean without any hint of purple fringing or chromatic aberrations.

EXIF data

Camera: FUJIFILM GFX100S
Lens: 350
Aperture: f/NaN
Shutter: 1/500 s
ISO: ISO 160
Software: Digital Camera GFX100S Ver2.11
Date: 2024-02-27 12:24:17

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FIGURE 7 Basketball court.

EXIF data

Camera: FUJIFILM GFX100S
Lens: 350
Aperture: f/NaN
Shutter: 1/500 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.11
Date: 2024-02-27 12:15:05

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FIGURE 8 Mourning dove.

EXIF data

Camera: FUJIFILM GFX100S
Lens: 350
Aperture: f/NaN
Shutter: 1/500 s
ISO: ISO 250
Software: Digital Camera GFX100S Ver2.11
Date: 2024-02-27 12:25:12

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FIGURE 9 Houses.

EXIF data

Camera: FUJIFILM GFX100S
Lens: 350
Aperture: f/NaN
Shutter: 1/500 s
ISO: ISO 160
Software: Digital Camera GFX100S Ver2.11
Date: 2024-02-27 12:20:26

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FIGURE 10 This was shot on Sony A7R II with the RB/RZ to Nikon F adapter and Nikon F to Sony E adapter. However, due to lens sag, the top of the tower is sharp but the bottom is blurry.

EXIF data

Camera: SONY ILCE-7RM2
Lens: ----
Aperture: f/0.0
Shutter: 1/1000 s
ISO: ISO 200
Software: ILCE-7RM2 v3.30
Date: 2018-03-14 16:27:16

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Trip to the UK

Thu, 23 Oct 2025 00:00:00 +0000

Trip to the UK

2025-10-23

Joanne, baby, and I went to the UK! We went to London, Edinburgh, Cambridge, and Bath.

1 Monday, 2025-09-01: Arrival

We arrived in the afternoon. Originally, the plan was to take the Piccadilly line directly to the hotel, but there was an outage due to signalling failures to Heathrow, so we ended up taking the Elizabeth line to Farringdon Station and then transferred to the Thameslink.

FIGURE 1 The Standard London, the hotel at which we were staying for the first three nights.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 1/100 s
ISO: ISO 100
Software: darktable 5.3.0+362~g711a0ee45f
Date: 2025-09-02 11:27:36

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Then we checked into the Standard London and had steak for dinner at Flat Iron King’s Cross.

FIGURE 2 Meats expectations. It’s a wagyu bavette steak cooked medium rare with sides of creamed spinach, bone marrow mashed potatoes, and green salad to make me feel healthier.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/4.0
Shutter: 1/13 s
ISO: ISO 3200
Software: Digital Camera GFX100S Ver2.13
Date: 2025-09-01 18:52:49

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2 Tuesday, 2025-09-02: King's Cross and Camden

Our hotel was right across from St Pancras Station, which offers a great view of this amazing piece of architecture. Apparently, when it was first built, it was criticized for being too ostentatious for a station. It’s way more fancy than King’s Cross station, next to it, which was built merely a decade earlier with a much more austere look.

FIGURE 3 View from our hotel room.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/11.0
Shutter: 1/28 s
ISO: ISO 100
Software: darktable 5.3.0+362~g711a0ee45f
Date: 2025-09-04 08:48:31

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FIGURE 4 St Pancras Station.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 1/300 s
ISO: ISO 100
Software: darktable 5.3.0+362~g711a0ee45f
Date: 2025-09-02 09:47:41

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For breakfast, we ate at Halfcup King’s Cross.

FIGURE 5 Salmon benedict but it’s on some kind of cylindrical croissant rather than a typical English muffin.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/4.0
Shutter: 1/27 s
ISO: ISO 640
Software: darktable 5.3.0+362~g711a0ee45f
Date: 2025-09-02 10:08:20

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Then, I went for a brief walk to take photos of train stations.

FIGURE 6 St Pancras Station.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 1/50 s
ISO: ISO 100
Software: darktable 5.3.0+362~g711a0ee45f
Date: 2025-09-02 11:26:21

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FIGURE 7 There was a guy who had climbed up the clock tower of King’s Cross Station to protest the Iranian government.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/6.4
Shutter: 1/160 s
ISO: ISO 100
Software: darktable 5.3.0+362~g711a0ee45f
Date: 2025-09-02 11:34:38

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We wanted to start off the trip in a relaxing fashion so we walked to nearby Coal Yard for food and shopping.

FIGURE 8 Coal Yard has a cool mix of modern and old architecture.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/7.1
Shutter: 1/150 s
ISO: ISO 100
Software: darktable 5.3.0+362~g711a0ee45f
Date: 2025-09-02 12:28:02

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For lunch, we ate at Dishoom, which was described as the “most overrated and underrated restaurant in London” by Vittles magazine.

FIGURE 9 Interior of Dishoom King’s Cross.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 1/15 s
ISO: ISO 200
Software: darktable 5.3.0+362~g711a0ee45f
Date: 2025-09-02 12:40:51

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In my opinion, the butterflied prawns were delicious, but the okra fries were greasy and soggy, and curry was average (not bad, but doesn’t really stand out from all the other curries elsewhere).

FIGURE 10 Lunch at Dishoom.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/4.0
Shutter: 1/27 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.13
Date: 2025-09-02 13:04:20

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In the afternoon, we walked to Camden market. Camden is a highly diverse neighborhood. We saw many shops selling useless trinkets like fake Labubus and we didn’t end up buying anything.

FIGURE 11 Camden Town.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/8.0
Shutter: 1/42 s
ISO: ISO 100
Software: darktable 5.3.0+362~g711a0ee45f
Date: 2025-09-02 16:52:16

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FIGURE 12 Camden Locks.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/6.4
Shutter: 1/100 s
ISO: ISO 100
Software: darktable 5.3.0+362~g711a0ee45f
Date: 2025-09-02 16:59:37

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FIGURE 13 Camden market.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/85 s
ISO: ISO 100
Software: darktable 5.3.0+362~g711a0ee45f
Date: 2025-09-02 17:02:15

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For dinner, we wanted something light so we got a rice noodle at Holy Sheep.

FIGURE 14 Rice noodle.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/2.0
Shutter: 1/42 s
ISO: ISO 320
Software: Digital Camera GFX100S Ver2.13
Date: 2025-09-02 17:47:07

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FIGURE 15 A street intersection near Camden underground station.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/120 s
ISO: ISO 100
Software: darktable 5.3.0+362~g711a0ee45f
Date: 2025-09-02 18:21:47

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Then we took the Underground back. It was so noisy that my Apple Watch gave me a notification warning me of high noise exposure. Hopefully my baby’s hearing isn’t permanently ruined.

FIGURE 16 London Underground 1995 Stock train serving the Northern Line.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/1.7
Shutter: 1/42 s
ISO: ISO 200
Software: darktable 5.3.0+362~g711a0ee45f
Date: 2025-09-02 18:30:22

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After we got back to the hotel, I snuck out for some nighttime photography.

FIGURE 17 Nighttime view of the Standard London.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 7.5 s
ISO: ISO 100
Software: darktable 5.3.0+362~g711a0ee45f
Date: 2025-09-02 20:48:07

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FIGURE 18 Entrance to St. Pancras Station. It was surreal seeing the station roof through the archway, like a portal to a different world.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 9.0 s
ISO: ISO 100
Software: darktable 5.3.0+362~g711a0ee45f
Date: 2025-09-02 20:49:47

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FIGURE 19 St. Pancras Station.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/8.0
Shutter: 2.3 s
ISO: ISO 100
Software: darktable 5.3.0+362~g711a0ee45f
Date: 2025-09-02 20:56:01

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FIGURE 20 A rare Intercity 225 at King’s Cross Station.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/8.0
Shutter: 2.6 s
ISO: ISO 100
Software: darktable 5.3.0+362~g711a0ee45f
Date: 2025-09-02 21:13:02

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3 Wednesday, 2025-09-03: Day trip to Cambridge

From London, you can go to either Cambridge or Oxford by train in roughly one hour. Since our hotel was near King’s Cross Station, where the train goes to Cambridge, as opposed to Paddington Station, where the train goes to Oxford, we picked Cambridge.

First, we had a full English breakfast and a salmon benedict from Nono’s, which was near our hotel. It was affordable but the quality was mediocre. The Hollandaise on the salmon benedict was much worse than yesterday’s, and the full English breakfast had only one egg and no black pudding. But overall, it was still serviceable.

FIGURE 21 Full English breakfast.

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Date: 2025-09-03 10:22:05

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Then, we went to catch the train at King’s Cross station.

FIGURE 22 Main concourse. If you look closely, you can see Platform $9 \frac{3}{4}$ .

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Date: 2025-09-03 10:51:14

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Upon arriving at Cambridge, we went to Fancett’s for lunch. We ordered the three course lunch (but with different courses for the two of us), including:

Imam bayıldı, minted yoghurt, fresh pomegranate & lavosh crispbreads
A greek salad of feta, Isle of Wight tomato, charentais melon & crispy capers
Thinly sliced roast pork loin, fine French beans, confit shallot & anchovy
Trout & smoked haddock fishcake, with a chives & roe butter sauce

It was very tasty.

FIGURE 23 Greek salad and imam bayıldı.

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ISO: ISO 250
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Date: 2025-09-03 12:34:28

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FIGURE 24 Fish cake.

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ISO: ISO 800
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Date: 2025-09-03 12:53:45

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FIGURE 25 Roast pork loin.

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Aperture: f/4.0
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ISO: ISO 400
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Date: 2025-09-03 12:53:49

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With our stomachs full, it was time for sightseeing! We took a bus to the city centre. The bus driver asked us “What can I get for you?” which was surprising. Apparently, they have to manually input the destination, and the fare is calculated that way rather than being a flat fee.

FIGURE 26 A marketplace.

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Date: 2025-09-03 14:10:15

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We didn’t go into King’s College because the entrance cost was high and it looked like it was under construction.

FIGURE 27 King’s College

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Date: 2025-09-03 14:16:50

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Here are two photos of Great St Mary’s church, a 15th century church in the late perpendicular Gothic style.

FIGURE 28 Exterior of Great St Mary’s.

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Shutter: 1/120 s
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Date: 2025-09-03 14:54:59

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FIGURE 29 Interior of Great St Mary’s.

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Date: 2025-09-03 14:23:07

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Then we walked into the university!

FIGURE 30 Gate of Honour, Gonville and Caius College.

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Date: 2025-09-03 14:27:23

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FIGURE 31 View of Trinity Bridge and punts from Garret Hostel Bridge.

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ISO: ISO 100
Software: Digital Camera GFX100S Ver2.13
Date: 2025-09-03 14:42:21

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We went to check out Corpus Christi College. It was raining quite heavily so we ducked into the chapel. After waiting a while, the rain subsided temporarily, so I could get the following photos.

FIGURE 32 Chapel of Corpus Christi College.

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Date: 2025-09-03 15:23:08

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FIGURE 33 The New Court of Corpus Christi College.

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Date: 2025-09-03 15:33:29

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FIGURE 34 Pembroke College Library.

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ISO: ISO 100
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Date: 2025-09-03 15:51:24

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Afterwards, we ran out of time so we took the train back to London. It was a long walk back to the train station while raining and pushing a baby stroller. Back in London, we had dinner at Dim Sum Duck. We had to queue for a long time but it was delicious and actually better quality than Bay Area Cantonese restaurants.

FIGURE 35 Duck.

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Shutter: 1/42 s
ISO: ISO 2500
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Date: 2025-09-03 19:30:42

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On the walk back to the hotel, it was blue hour, so I snapped two more pictures of train stations. Thanks to my superior camera with a f/1.7 lens that’s super sharp wide open, I could handhold these nearly nighttime shots with amazing image quality.

FIGURE 36 St. Pancras Station on the way back to the hotel.

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Aperture: f/1.7
Shutter: 1/42 s
ISO: ISO 160
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Date: 2025-09-03 19:55:00

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FIGURE 37 King’s Cross Station.

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ISO: ISO 800
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Date: 2025-09-03 19:56:47

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4 Thursday, 2025-09-04: Going to Edinburgh

We woke up and took the train to Edinburgh! We booked first class on the LNER Azuma. With first class, you can get a free meal, plus the seats are slightly wider, which is nice for the long journey.

FIGURE 38 Ploughman’s grazing box.

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Shutter: 1/125 s
ISO: ISO 800
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Date: 2025-09-04 09:42:43

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It was a four and a half hour ride on the LNER Azuma from King’s Cross to Edinburgh Waverley. On the way, we saw a lot of sheep. However, I didn’t get many good photos since I was holding the baby.

Upon arriving, we had a late lunch at White Horse Oyster & Seafood Bar. We ate a bunch of Caledonian oysters, a crab meat Scotch egg, a scallop topped with caviar, a bunch of steamed mussels, and a salmon crudo. It was quite delicious.

FIGURE 39 Scallop with caviar.

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Shutter: 1/42 s
ISO: ISO 1250
Software: Digital Camera GFX100S Ver2.13
Date: 2025-09-04 15:15:02

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Then we checked into the Hoxton Edinburgh. It’s a recently opened hotel that took over the site of what used to be the Grosvenor Hotel. The building is a Georgian building with beautiful renovation inside.

4.1 Afternoon stroll from Haymarket

We wanted to take the first day in Edinburgh easy since we had two nights there, so we just took a short stroll from the hotel. St Mary’s Episcopal Cathedral was very close to our hotel.

FIGURE 40 St Mary’s Episcopal Cathedral.

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Aperture: f/5.6
Shutter: 1/210 s
ISO: ISO 100
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Date: 2025-09-04 17:00:41

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FIGURE 41 Interior of the St Mary’s Episcopal Cathedral.

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Shutter: 1/15 s
ISO: ISO 800
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Date: 2025-09-04 17:03:32

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Then, we went to Dean Bridge to get a photo of Dean Village.

FIGURE 42 Dean Village.

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Aperture: f/5.6
Shutter: 1/450 s
ISO: ISO 100
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Date: 2025-09-04 17:22:23

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Afterwards, we walked towards Princes Street, taking photos of various houses and buildings along the way.

FIGURE 43 Some random houses.

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Aperture: f/5.6
Shutter: 1/180 s
ISO: ISO 100
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Date: 2025-09-04 17:35:25

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FIGURE 44 The Caledonian Hotel, which looks quite nice but was too expensive.

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Aperture: f/5.6
Shutter: 1/350 s
ISO: ISO 100
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Date: 2025-09-04 17:41:03

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From Princes Street we had a good view of Edinburgh Castle. To be honest, Princes Street itself was very uninspiring as it was all full of modern shops.

FIGURE 45 Edinburgh castle.

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Aperture: f/5.6
Shutter: 1/480 s
ISO: ISO 100
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Date: 2025-09-04 17:53:53

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Then we were too tired so we took the tram back to the hotel. We had dinner at the Italian restaurant in the hotel, called Patatino. They have really pretty plates but it was very dark.

FIGURE 46 Salmon crudo, presumably made with real Scottish salmon.

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Aperture: f/4.0
Shutter: 1/12 s
ISO: ISO 3200
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Date: 2025-09-04 19:22:12

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5 Friday, 2025-09-05: Edinburgh sightseeing

I woke up with a fever and diarrhea. This was very bad because I was extremely looking forward to eating a delicious Scottish breakfast consisting of Lorne sausage, Tattie scone, black pudding, and Haggis. In fact, we spent £44 on just that, but I was too sick to eat any of it.

Joanne went and bought me medicine. After eating paracetamol and loperamide, I instantly recovered, so in the afternoon we went sightseeing!

We took the tram to Princes Street and then walked to the Royal Mile. I was very excited to see St Giles’ Cathedral, but unfortunately, the west side of it with the plaza was completely sealed off due to filming. I could only get this crappy photo of the east side, which was very backlit in the afternoon as the sun was in the west.

FIGURE 47 St Giles’ Cathedral.

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Aperture: f/5.6
Shutter: 1/680 s
ISO: ISO 100
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Date: 2025-09-05 14:55:10

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FIGURE 48 View of the Royal Mile; Tron Kirk is the tall spire in this photo.

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Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 1/240 s
ISO: ISO 100
Software: darktable 5.3.0+362~g711a0ee45f
Date: 2025-09-05 14:59:03

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FIGURE 49 Tron Kirk.

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Shutter: 1/350 s
ISO: ISO 100
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Date: 2025-09-05 15:02:02

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Then, I went to check out the Palace of Holyroodhouse while Joanne and baby rested in the palace cafe.

FIGURE 50 Palace of Holyroodhouse.

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Lens: GF20-35mmF4 R WR
Aperture: f/8.0
Shutter: 1/300 s
ISO: ISO 100
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Date: 2025-09-05 15:31:15

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FIGURE 51 Quadrangle of Holyrood Palace.

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Aperture: f/5.6
Shutter: 1/110 s
ISO: ISO 100
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Date: 2025-09-05 15:33:24

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No photos were allowed in the interior of the palace, unfortunately. Generally, I was surprised at how shabby it was, and by the straightforward layout. Unlike other palaces where you have to go through many layers of antechambers and halls to get to stuff like the throne room and the king’s bedroom, this palace was quite compact and you are almost immediately in the throne room upon ascending the stairs. Some rooms, like Mary Queen of Scots’ room, had really narrow staircases going up. The wooden flooring creaked a lot and the tapestries were all faded.

The history was fascinating, though, and the audio tour was great!

FIGURE 52 Holyrood Abbey.

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Aperture: f/8.0
Shutter: 1/85 s
ISO: ISO 100
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Date: 2025-09-05 15:49:24

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6 Saturday, 2025-09-06: York

On our way back from Edinburgh to London, we stopped in York for several hours. At Edinburgh Waverley station, we saw some British Rail Class 43 (HST) trains, but we took an LNER Azuma instead.

FIGURE 53 British Rail Class 43, one of the most badass diesel trains ever, and the speed record holder for diesel trains. It’s currently serving ScotRail.

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Lens: GF20-35mmF4 R WR
Aperture: f/8.0
Shutter: 1/27 s
ISO: ISO 320
Software: Digital Camera GFX100S Ver2.13
Date: 2025-09-06 09:39:38

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Our train to York was delayed by half an hour due to track maintenance. After getting a basic noodle at a Chinese restaurant to help me recover from my extreme gastrointestinal distress, we headed straight for the Shambles!

The Shambles is a historic street full of old houses. In practice, it was actually full of tourists like us, and most of the shops were peddling generic British souvenirs and gifts like Paddington bears and Harry Potter merchandise.

FIGURE 54 The Shambles.

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Shutter: 1/90 s
ISO: ISO 100
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Date: 2025-09-06 14:30:57

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FIGURE 55 If I had brought a longer lens, I might have gotten a great shot of the York Minster towering over the street, but with a wide angle lens, it looks small and insignificant.

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Lens: GF20-35mmF4 R WR
Aperture: f/8.0
Shutter: 1/120 s
ISO: ISO 100
Software: darktable 5.3.0+362~g711a0ee45f
Date: 2025-09-06 14:39:46

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Then, we went to take photos of the York Minster. The architecture was truly amazing, but I underestimated the amount of buildings and trees surrounding it at close distance, so it was hard to get a photo without either extreme perspective distortion or occlusion.

FIGURE 56 The York Minster with extreme perspective distortion.

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Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 1/250 s
ISO: ISO 100
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Date: 2025-09-06 14:46:27

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FIGURE 57 The York Minster occluded by a tree and building.

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Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 1/180 s
ISO: ISO 100
Software: darktable 5.3.0+362~g711a0ee45f
Date: 2025-09-06 14:47:46

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FIGURE 58 The River Ouse.

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Aperture: f/6.4
Shutter: 1/320 s
ISO: ISO 100
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Date: 2025-09-06 14:56:48

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Afterwards, we headed back to York Station to appreciate the fine Victorian architecture. The station is really great with awesome wrought iron arches. However, there were lots of noisy and smelly diesel trains despite the electrification of the East Coast Main Line.

FIGURE 59 View from the pedestrian bridge.

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Aperture: f/5.6
Shutter: 1/20 s
ISO: ISO 100
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Date: 2025-09-06 15:30:17

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After getting back to London King’s Cross, we saw an Intercity 225 in a unique livery.

FIGURE 60 Intercity 225.

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Lens: GF20-35mmF4 R WR
Aperture: f/9.0
Shutter: 1/27 s
ISO: ISO 100
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Date: 2025-09-06 18:11:47

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FIGURE 61 King’s Cross station.

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ISO: ISO 100
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Date: 2025-09-06 18:15:51

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In the evening, we checked into the Chateau Denmark hotel, and had dinner at Din Tai Fung which was right next to our hotel at Tottenham Court Road.

FIGURE 62 Xiaolongbao at Din Tai Fung.

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Lens: GF55mmF1.7 R WR
Aperture: f/1.7
Shutter: 1/42 s
ISO: ISO 640
Software: Digital Camera GFX100S Ver2.13
Date: 2025-09-06 19:59:37

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7 Sunday, 2025-09-07: British Museum

In the morning, I went to do laundry. I found a laundromat, and walked 20 minutes to get there, but despite being usually open 7 days a week, it was unexpectedly closed due to a power outage. So I had to walk really far to another laundromat, near King’s Cross. There, I dropped off our laundry load for full service laundry.

Then, we had breakfast at Dishoom again, but at their Covent Garden location this time. It was pretty good I thought.

FIGURE 63 Akuri at Dishoom, a spicy scrambled egg with onions and stuff.

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Aperture: f/5.6
Shutter: 1/42 s
ISO: ISO 2500
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Date: 2025-09-07 11:15:27

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FIGURE 64 British Museum.

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Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 1/100 s
ISO: ISO 100
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Date: 2025-09-07 12:11:34

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We headed straight for the complaint tablet to Ea-nāṣir, which is famous on the internet due to numerous parodies and memes about it.

FIGURE 65 The famous complaint tablet to Ea-nāṣir.

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Lens: GF55mmF1.7 R WR
Aperture: f/2.8
Shutter: 1/42 s
ISO: ISO 500
Software: Digital Camera GFX100S Ver2.13
Date: 2025-09-07 12:39:01

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Having seen this meme tablet, we went for lunch at the Wolseley. Actually, originally, we wanted to get afternoon tea, but we didn’t read the fine print that says that they only serve afternoon tea after 3 pm, so with our 1:30 pm reservation we could only get lunch. It was Sunday, so I ate a Sunday roast. By now, my gastrointestinal disease had basically completely subsided, so I scarfed down the extreme amounts of meat, fried potatoes, horseradish, and Yorkshire pudding with no trouble.

FIGURE 66 Sunday roast.

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Lens: GF55mmF1.7 R WR
Aperture: f/4.0
Shutter: 1/42 s
ISO: ISO 640
Software: Digital Camera GFX100S Ver2.13
Date: 2025-09-07 14:15:21

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Then, we walked back towards the hotel, along Piccadilly street, Regent Street, and other cool streets. Along the way, we saw the Royal Academy of Arts, so we went in. The main gallery was under renovation, so there were very few people, but they had a special exhibit on Kiefer/Van Gogh that cost £19 per person. Since Joanne loves Van Gogh, we paid the £38 and went in. Actually, there were only three small rooms with very few Van Gogh works (and also very few Kiefer works, but each one was quite large), so we came away feeling a little bit ripped off. In comparison, the amazing National Gallery is free and has a vastly larger collection. Still, art is art, and the art was good.

FIGURE 67 Royal Academy of Arts.

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Aperture: f/5.6
Shutter: 1/200 s
ISO: ISO 100
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Date: 2025-09-07 15:03:28

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FIGURE 68 Regent Street.

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Aperture: f/5.6
Shutter: 1/125 s
ISO: ISO 100
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Date: 2025-09-07 15:56:06

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FIGURE 69 Our cute baby.

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Aperture: f/5.6
Shutter: 1/27 s
ISO: ISO 100
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Date: 2025-09-07 15:54:42

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Later, I went back to the laundromat to pick up the laundry. Since it was near King’s Cross, and the weather was good, I went up to the rooftop of the Standard Hotel, the first hotel where we stayed, to take photos. It was funny going up with my big bag of laundry, but I got some good photos of, well, train stations.

FIGURE 70 King’s Cross station.

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Lens: GF20-35mmF4 R WR
Aperture: f/6.4
Shutter: 1/210 s
ISO: ISO 100
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Date: 2025-09-07 16:57:20

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FIGURE 71 St Pancras Station.

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Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 1/150 s
ISO: ISO 100
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Date: 2025-09-07 16:59:17

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FIGURE 72 St Pancras Station.

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Lens: GF20-35mmF4 R WR
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Shutter: 1/125 s
ISO: ISO 100
Software: darktable 5.3.0+362~g711a0ee45f
Date: 2025-09-07 16:59:28

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In the evening, we went to Chinatown and ate at The Eight Restaurant. London’s Chinatown is very nice and walkable and very clean compared to San Francisco’s.

FIGURE 73 Chicken rice at The Eight.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/28 s
ISO: ISO 3200
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Date: 2025-09-07 18:48:53

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FIGURE 74 Chinatown.

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Date: 2025-09-07 19:23:36

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8 Monday, 2025-09-08: National Gallery

We went to the National Gallery. It was full of amazing artworks, and the architecture itself was beautiful also. As with other art museums in Europe, there were many paintings of Mary and baby Jesus, and while holding our own baby in our hands we felt a real connection to all the depictions of babies in art.

FIGURE 75 National Gallery.

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Date: 2025-09-08 10:13:42

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FIGURE 76 A dome inside the National Gallery.

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Date: 2025-09-08 11:17:58

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FIGURE 77 Another dome inside the National Gallery.

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Date: 2025-09-08 11:28:27

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For lunch, we went to The Barbary in Covent Garden. The restaurant consists of a bar around a central kitchen and serves “exotic dishes inspired by the 16th-century Barbary Coast”.

FIGURE 78 Octopus.

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Date: 2025-09-08 12:52:15

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FIGURE 79 Pork cheek. It was very tender and flavorful but the sauce was rather sweet.

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Date: 2025-09-08 13:00:00

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We walked around Covent Garden and checked out some shops. We went to the Apple Market (a vintage flea market) in Covent Garden Market which was pretty cool. There was a guy selling vintage model cars and I saw lots of cool models, but they were very expensive.

FIGURE 80 Apple Market.

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Date: 2025-09-08 14:35:56

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Then, we went towards Westminster to check out the key landmarks of London. On the way, we passed by Trafalgar Square and the National Gallery again.

FIGURE 81 National Gallery and Trafalgar Square.

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8.1 Westminster

FIGURE 82 Westminster Palace and Big Ben.

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Date: 2025-09-08 17:09:44

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FIGURE 83 Big Ben.

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Date: 2025-09-08 17:34:26

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For dinner, on the way back, we dined at Rasa Sayang, a Malaysian restaurant in Chinatown. I ate the beef rendang and Joanne had the laksa. It was pretty decent, and very authentic.

9 Tuesday, 2025-09-09: Bath

In the morning, we took the Elizabeth Line to Paddington Station and caught a GWR train to Bath!

We checked into the Queensberry hotel. This hotel was amazing! It’s situated in a Georgian townhouse (well, most of Bath is comprised of Georgian townhouses) and it was extremely classy and pretty. In contrast with the Hoxton Edinburgh, which was also in a Georgian townhouse, the Queensberry was more old-school but was a lot more airy and had somewhat finer attention to detail.

FIGURE 84 Our room. Pardon the mess, we put down our stuff immediately because we were tired.

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Date: 2025-09-09 12:10:59

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FIGURE 85 The beautiful bathroom.

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Then it was time to go sightseeing!

FIGURE 86 The Circus, a round array of townhouses surrounding a roundabout. Apparently, they have these sunken front gardens known as “areas” for drainage.

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Date: 2025-09-09 12:47:46

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For lunch, we dined at the recently opened Emberwood restaurant located in the Francis Hotel. We had the pork belly, coal-roasted Cornish scallops, and Cornish hake.

FIGURE 87 Bath Abbey.

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Date: 2025-09-09 14:04:40

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FIGURE 88 The Roman Baths.

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FIGURE 89 Bath Abbey as seen from the Roman Baths.

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FIGURE 90 Bath Abbey as seen from Bath City Parade Gardens.

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Date: 2025-09-09 16:28:17

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FIGURE 91 Pulteney Bridge over River Avon.

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Date: 2025-09-09 16:36:11

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For dinner, we went back to the Queensberry Hotel and dined at the Michelin Starred restaurant, The Olive Tree. Since we didn’t want to linger too long with the baby, we opted for the cheapest meal, the £100 three course menu. It was amazing!

Jersey royal potato with herring roe. This was a two centimeter diameter tiny little potato that was fried crispy and topped with herring roe. Very tasty little amuse-bouche to start with.
Cornish crab, Isle of Wight Tomato, Westcombe ricotta, ginger, basil. This was served in a stemmed bowl as though it were ice cream and quite delicious.
Black treacle and Rye bread, Ivy House clotted cream, roasted yeast, lamb fat. Very interesting spreads for the bread. The lamb fat had a slightly gritty texture but was extremely rich and melted in your mouth. The roasted yeast had a unique umami flavour, and the clotted cream had a wonderful milky fragrance.
Newlyn Market Lobster, cooked over Binchotan, sweetcorn, lovage, lemon verbena. The main course did not disappoint with its delicious lobster.
Oakchurch Farm Strawberries, sheep’s yogurt, Tahitian vanilla, rose, pink peppercorn. This dessert was almost like a sorbet with a nice acidity to it.
To finish: wine gummies and chocolate.

Overall, it was great and we enjoyed the meal a lot. However, baby started fussing, so we had to take turns holding him while eating.

FIGURE 92 Bread and three kinds of “butter”: clotted cream, roasted yeast, and lamb fat.

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Date: 2025-09-09 18:31:19

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FIGURE 93 Lobster.

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Date: 2025-09-09 18:38:10

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10 Wednesday, 2025-09-10: Back to London

The next day, it was very rainy, so we didn’t get to do much at all. We revisited some of the nice attractions from yesterday, but I didn’t get any good photos due to the rain and due to having to push the baby around. We had a casual lunch at Antica, an Italian restaurant situated near the Pulteney Bridge. We ate the Grigliata di Pesce, a gigantic tower consisting of grilled swordfish, sea bass, king prawns, mussels, and potatoes.

Then, we went and got matcha and coffee at WatchHouse. The matcha was very reminiscent of the colour of the bath water in the Roman Baths, but tasted much better.

FIGURE 94 Matcha and coffee.

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Date: 2025-09-10 12:59:47

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Afterwards, without much ado, we headed to the Bath Spa station for the train back to London. We were a couple of hours early, but due to the rain, Joanne didn’t feel like exploring further with the baby. I walked back to the hotel to retrieve our luggage that we had left there in the morning.

FIGURE 95 St John the Evangelist’s Church.

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Date: 2025-09-10 14:03:03

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After lugging all my luggage back to Bath Station, I met a really rude station attendant by the name of Wayne who refused to let me go upstairs to where Joanne was waiting. Joanne had gone up to the platform level earlier since there was no lounge on the ground level. Somehow, Attendant Wayne most humiliatingly accused me of wanting to get on an earlier train when I wanted nothing of the sort. We had advance tickets with seat reservations with a specific time. He was unsympathetic when I told him about my wife and helpless infant child encumbered with a pram and heavy luggage, saying most snidely “you have a phone don’t you? why don’t you just call them to come down?”

When I was finally able to go up, to add injury to insult, I accidentally dropped my line scan camera by tugging on the USB cable too hard while it was resting on a slippery bench, and my brand new IR cut filter was smashed to pieces. The line scan photo didn’t even turn out that well as the top and bottom of the train were cut off, and due to the overcast weather, there wasn’t enough light and the image turned out rather grainy.

FIGURE 96 Line scan photo of British Rail Class 800 in GWR service.

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FIGURE 97 Bath Spa station.

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Date: 2025-09-10 13:57:52

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In the afternoon, we arrived in Paddington Station.

FIGURE 98 Paddington Station.

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Date: 2025-09-10 17:54:26

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FIGURE 99 Paddington Station. You can see the statue of the famous Paddington Bear just casually sitting next to the guy in a blue suit.

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Date: 2025-09-10 17:56:40

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Due to the Tube strike, we could only take a taxi to our hotel. We waited about an hour at the taxi stand due to a ridiculously long queue. In the evening, we finally checked into our hotel at the London Marriott Hotel Kensington. We had the misfortune of being assigned room 147 which has no lift access and could only be accessed by climbing a flight of stairs. However, we were traveling with a baby with a stroller, and it was extremely inconvenient. Apparently, all the better rooms were taken because we arrived in the evening due to the Tube strike, so we had to make do with this inferior room despite paying just as much as anyone else. The porter was too busy idly chatting with other people to help us with our luggage, and the poor receptionist had to help us carry one of our suitcases up this flight of stairs out of sympathy. Thereafter, we had to carry the stroller up and down every day.

For dinner, we went to No. 10 Restaurant, which was actually quite decent. Their duck blood tasted rather good.

11 Thursday, 2025-09-11: Natural History Museum

For breakfast, we went to Cempaka for Malaysian breakfast. I had a tasty Nasi Lemak. It was extremely authentic. Then we went to the Natural History Museum. The exhibits were decent but the best part about it is the amazing architecture.

FIGURE 100 Facade of the Natural History Museum.

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Date: 2025-09-11 11:22:22

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FIGURE 101 Whale skeleton.

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Date: 2025-09-11 11:34:11

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For lunch, we went to a random sushi restaurant called Cacciari’s Asia. It was better than expected, actually.

In the afternoon, I wanted to visit the river Thames for more sightseeing. At first, we considered going to the Victoria and Albert museum, but the weather was nice so we figured we might as well do some more daytime sightseeing while we had the chance. With the Tube strike still going on, we had to take a bus. This turned out to be a terrible decision since the bus was very crowded and slow and somehow at Westminster the bus driver abruptly announced that the bus was going to terminate there and we had to get off.

FIGURE 102 Westminster Abbey

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Date: 2025-09-11 17:25:58

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FIGURE 103 Regent Street.

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Date: 2025-09-11 19:03:53

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We gave up trying to go sightseeing, walked to Chinatown and grabbed a quick dinner at The Eight again (I got the beef curry with tornado egg rice, and Joanne got the three kinds of barbecue combo). Then we took the bus back.

FIGURE 104 Extreme bus crowding.

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Date: 2025-09-11 19:22:08

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At one point, we had to transfer buses, but the buses were so crowded that we gave up. Taxis were impossible to hail, so we called an Uber instead. We returned to the hotel late at night, extremely grumpy, and profoundly disappointed in the poor quality of transportation in London.

12 Friday, 2025-09-12: V&A Museum, Tower Bridge, and Downtown

For breakfast, we ate at La Terrazza in the Hotel Xenia across the street.

Then, we went to the V&A Museum. I was amazed by the architecture. The cast courts reminded me of the novel Piranesi.

FIGURE 105 Main entrance of the Victoria and Albert Museum.

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Date: 2025-09-12 10:42:21

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FIGURE 106 Cast Court, containing casts (replicas) of many famous statues from around the world.

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Date: 2025-09-12 11:13:35

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FIGURE 107 Pond of the V&A Museum courtyard.

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Date: 2025-09-12 11:22:20

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After enjoying the museum, we decided to check out Tower Bridge since the Tube was back in service and I felt like that was one of those things you’ve got to do when in London since it’s a famous landmark.

FIGURE 108 View from Tower Bridge.

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Date: 2025-09-12 12:14:25

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FIGURE 109 Tower Bridge.

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Date: 2025-09-12 12:15:06

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In retrospect, to fully appreciate Tower Bridge, it was probably better to cross one of the other bridges like London Bridge with a view of Tower Bridge instead. Then, for lunch, we headed to M.Manze Tower Bridge location. To our disappointment, despite being called “Tower Bridge location”, it was a long walk, about 20 minutes from the bridge with a stroller.

FIGURE 110 Pie and mash, with stewed eels in the background.

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Date: 2025-09-12 12:52:58

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I really wanted to try the jellied eels, but they ran out and only had stewed eels. Oh well. The stewed eels were mild tasting and very, very soft, so they were quite easy to eat. You could even separate the meat from the bone with just your spoon. They were so mild that I didn’t feel the need to protonate the amines by dousing them in vinegar at all. The liquor (a weird name for parsley sauce) was also quite mild tasting for something so brilliantly green coloured. The pies were pretty decent and had hearty meat filling. The mash was buttery and soft. This was the cheapest meal of our trip by far. Overall, it was alright — not as bad as feared, but not super flavourful and amazing either. Maybe not worth the long walk, to be honest, but definitely good to try.

In the afternoon, we took the Tube a couple more stops and ended up somewhere near the Lloyd Building and Leadenhall Market, both of which were buildings I wanted to visit.

FIGURE 111 Lloyd building.

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Date: 2025-09-12 13:55:03

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FIGURE 112 The Lloyd building retains the original facade in the corner, which you can see in the bottom right here.

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Date: 2025-09-12 14:06:11

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FIGURE 113 Leadenhall Market.

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Date: 2025-09-12 14:00:07

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FIGURE 114 Ceiling of Leadenhall Market.

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Date: 2025-09-12 13:59:27

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Then, we went to Harrods. I was expecting an amazing beautiful mall but the inside was labyrinthine and claustrophobic, and quite depressing as it was utterly devoid of windows. The Food Halls in Harrods looked amazing, though, but I didn’t get any pictures since it was extremely crowded.

FIGURE 115 I was very disappointed with the poor quality kerning between the “A” and the “T” here.

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Date: 2025-09-12 15:33:37

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For dinner, we went to Med Salleh Viet - Earl’s Court. I was sceptical about whether London Vietnamese restaurants could compare with the amazing authentic Vietnamese food we have in San Jose, but this turned out to be wonderfully delicious. Joanne got the pho and I got the black pepper beef.

FIGURE 116 Black pepper beef.

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Date: 2025-09-12 18:17:01

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13 Saturday, 2025-09-13: Line scan photography and Kensington/Hyde Park

In the morning, we had a casual breakfast of a pastry, matcha, and coffee from Blank Street, and then I went to take line scan photos of trains! The 1973 Stock is getting retired next year, so I specifically sought a spot to take line scan photos of it. I managed to find a tiny gap in the fence at BP Car Wash just West of Baron’s Court.

FIGURE 117 Photo of the line scan camera in action.

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Software: 26.0
Date: 2025-09-13 10:21:19

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FIGURE 118 Piccadilly Line 1973 Stock.

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Date: 2025-10-24 23:37:20

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FIGURE 119 District Line S7 Stock.

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Overall I’m pretty happy with these shots. Even with a cracked IR cut filter, it still worked and didn’t seem to impact the image quality too much.

FIGURE 120 Piccadilly Line at Earl’s Court.

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Date: 2025-09-13 10:58:10

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We grabbed a quick lunch at Addie’s Thai Restaurant. Then, we walked to Kensington Gardens and Hyde Park. It was raining very hard during the walk, but fortunately, the weather cleared up as soon as we arrived in the park.

We saw a lady feed the birds even though signs explicitly prohibited doing so.

FIGURE 121 Swans.

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Date: 2025-09-13 14:30:07

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FIGURE 122 Royal Albert Hall.

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Date: 2025-09-13 15:04:35

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For dinner, we dined at Macellaio RC South Kensington. We kind of missed the Florentine bistecca we had in Italy, so here was an opportunity to eat it again! We also got some really fatty but unique appetizers: a beef fat candle, and pork lard!

FIGURE 123 Meats expectations.

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Date: 2025-09-13 18:07:26

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14 Sunday, 2025-09-14: Flight back

We got a taxi to Paddington Station and took the Heathrow Express to the airport.

For the long flight back, we basically had to just take turns holding baby for the entire duration since he did not like the very uncomfortable bassinet, but, as with the flight to the UK, he was very well behaved for the most part, and we got many compliments from fellow passengers.

FIGURE 124 Babby cheeks so squishyyy

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sriv: simple rust image viewer

Wed, 22 Oct 2025 00:00:00 +0000

sriv: simple rust image viewer

2025-10-22

I vibe-coded an image viewer in Rust!

FIGURE 1 Screenshot

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github

For years, I’ve been using simple X image viewer sxiv, and when the developer abandoned it, I switched to nsxiv. Overall, I love it because of its extremely minimalistic UI without any toolbars or GUI elements, vim-like keybindings, overall speed, and support for custom keybindings to execute custom commands. I have some commands to upload photos to my website, and to edit photos in darktable, which are super handy.

But several issues with nsxiv started to become annoying.

It’s built on imlib2, but it crashes for large images. Since I have a 100 megapixel camera and also do line scan photography which regularly produces images over 100,000 px wide, I needed something that could handle large images.
Thumbnailing was single threaded and really slow. With my 100 megapixel photos, it would take about a second for each thumbnail to generate. I’m not super sure about how nsxiv manages its thumbnail cache, but it would sometimes regenerate thumbnails that I’m pretty sure I’ve already generated in the past.
It requires X11 so it doesn’t work natively in Wayland and I would occasionally get some random X-related quirks like X11 error: BadValue when using certain tiling window managers like i3.
Configuration is via Xresources which can get reset when using XWayland.

So I created sriv which addresses these issues.

minimalistic UI with vim-like keybindings and looks and feels almost identical to sxiv and nsxiv.
gpu-accelerated image viewing
parallel thumbnail generation
supports images more than 32768 or 65536 px wide or whatever arcane limit that imlib2 has
works in wayland natively thanks to nannou using wgpu/winit
custom keybindings are via ~/.config/sriv/bindings.toml; configuration of other settings like background color is coming soon

I also added some optimizations like eagerly trying to prefetch the full size image when you are browsing around in thumbnail mode.

Finally, I implemented CLIP-powered semantic search across the image library! Now I can just press / and type in what I need to find. This is incredibly useful for finding photos in my library of tens of thousands of images.

I used Codex CLI to vibe code this and the codebase is just under 3000 lines of code (still less than nsxiv‘s 5000 lines of C, although I’m standing on the shoulders of giants by pulling in enormous crates as dependencies).

Feel free to raise any issues or suggestions on GitHub!

Amazing website improvements

Sun, 19 Oct 2025 00:00:00 +0000

Amazing website improvements

2025-10-19

I made some amazing improvements to my website! I completely revamped the backend and vibe coded it in Rust using Codex CLI.

dllup-rs

As you may know, I have my own markup language called dllup so the dllup-rs project contains a full AST and parser of this markup language.

Previously I wrote the website stuff 10 years ago. It was a jumbled mess of about 600 lines of unmaintainable Python. Now, it’s a vastly more jumbled mess of almost 5000 lines of unmaintainable Rust!

But honestly though, the vibe coding experience was fine. It basically just did what I told it to. If I noticed any inefficiencies when running the code, I could just tell it to fix it, and it would do so. What a time to be alive!

1 Structural changes

1.1 Everything is a blog post

Previously, I had sections like “programming”, “engineering”, and “design”. It made sense at the time when I arrogantly considered myself a “renaissance man” during college that could design stuff, program stuff, and engineer stuff.

But I haven’t updated those sections in a long time while I’ve been putting both engineering and programming projects as blog posts so I might as well just migrate all the old stuff as well.

Don’t worry, old links still work as they redirect to the new links with HTTP 301.

1.2 Search engine optimized URLs

Previously, blog posts just had the date as the URL, like y2018m06d18. This was very SEO-unfriendly and also it was impossible to remember which blog post URL corresponds to what content.

Now, y2018m06d18 is 2018-06-18-chicken-rice-in-san-francisco which is a lot more semantic and nice.

2 Styling changes

2.1 Dark mode

The website now supports dark mode using your default browser preference.

2.2 Other tweaks

The rotating DLLU logo is now always big and centered and I removed some nav stuff after simplifying the website structure.

The table of contents always appears under the DLLU logo.

I also slightly adjusted the font (from Source Sans Pro to Inter, just because I had been using the former for a decade) and spacing.

3 Image handling

One of my greatest passions in life is photography and my philosophy to photography is: more pixels = more better. As such, I’ve invested a huge amount into getting the maximum image quality.

However, it is quite difficult to share my 100 megapixel files, so I had to engineer a bunch of stuff for my blog.

3.1 Multiple image sizes

Previously, I had either:

local images that were usually 600 px wide
remote images

And then I used retina.js to support high DPI displays by having another copy of an image twice the resolution.

But that only worked for local images and not remote ones, and sometimes I just forgot to resize the image manually ahead of time, so this was quite annoying. Now, with HTML supporting the srcset attribute in the img tag for years, it is easy to add multiple sizes.

<img
  src="image-600.jpg"
  srcset="
    image-600.jpg   600w,
    image-1200.jpg 1200w,
    image-2000.jpg 2000w"
  sizes="600px"
  width="600" height="450"
  alt="">

Then I added fetching all remote images, automatic resizing of all images and uploading to Backblaze B2 which makes generating all the sizes easy.

Another thing is that previously clicking on the image loaded the full size. This was annoying since you cannot share a 100 megapixel image easily — for example, on Discord, it wouldn’t unfurl the preview, and it may even crash Safari on iPhone.

Now, I have a list of download links for various sizes, so you can download lower resolutions to share.

3.2 Wide images

Previously, all images were the same width. But I often take wide images, like trains.

FIGURE 1 Shinkansen N700 Series, Himeji, Japan, 2017

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Now, these are shown wider (mostly on desktop).

3.3 Hosting on Backblaze B2

I store all the images in a directory and upload them to Backblaze B2 with rclone. Then I serve the bucket from behind cloudflare at i.dllu.net. So far, this has worked pretty well to avoid the Hacker News hug of death.

3.4 EXIF data

For my photos, many people are curious about the EXIF data. I implemented automatic EXIF data embedding in photos which makes it nice.

FIGURE 2 Florence cathedral. The f-number on this one is NaN since it’s a manual lens (the Leica Apo Telyt-R 180mm f/3.4) that doesn’t transmit EXIF data.

EXIF data

Camera: FUJIFILM GFX100S
Lens: 180
Aperture: f/NaN
Shutter: 18.0 s
ISO: ISO 100
Software: darktable 4.6.1
Date: 2023-02-17 18:09:40

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3.5 Page no longer jumps around

While computing the sizes, I also store the image aspect ratio and set the width and height attributes of the image so that the page doesn’t jump around.

Also, I set the decoding="async" loading="lazy" attributes so that they don’t all start loading at once, preventing people’s browsers from choking when loading my long blog posts with hundreds of photos.

4 Math

I now use KaTeX to render math on the server side instead of using mathjax-node-cli to generate SVG equations that are embedded as <img> elements.

The SVG equations worked really nicely actually. I’m quite proud of being able to use the offsets and sizing in em units from the SVG directly to ensure that they flow beautifully in text.

But it was difficult to achieve dark mode toggle, and the file sizes were slightly bigger, so I ended up switching to KaTeX. KaTeX also supports some accessibility stuff nicer and also can wrap around in text.

The drawback is that now it looks uglier in Lynx.

FIGURE 3 Screenshot in Lynx with ugly KaTeX equations.

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With SVG equations in <img> tags, I set the alt text to be the LaTeX math, so it looked nice in Lynx, and you could simply select it and copy it to copy the LaTeX code for it. Oh well.

5 Tree-sitter code snippets

Now I’m using tree-sitter to syntax highlight code snippets using the inkjet crate. Previously, syntax highlighting was handled by Pygments.

fn highlight_with_inkjet(language: Option<&str>, code: &str) -> Option<String> {
    let mut highlighter = Highlighter::new();
    let theme = Theme::from_helix(ONEDARKER).ok()?;
    let formatter = ThemedHtml::new(theme);
    let lang = language.and_then(Language::from_token).unwrap_or_else(|| {
        Language::from_token("plaintext").unwrap_or(Language::from_token("none").unwrap())
    });
    highlighter.highlight_to_string(lang, &formatter, code).ok()
}

6 RSS feed and sitemap.xml

I also added an RSS feed and sitemap.xml.

RSS feed

7 To-dos

Currently, some things don’t work.

some of my old embedded JavaScript projects don’t seem to be rendering well (presumably due to changes in my CSS)
sometimes wide images occlude the table of contents
some transparent diagrams don’t show up well in dark mode
some image captions still have hardcoded EXIF data
some SVG files are really tiny
sometimes the rotating DLLU logo on top doesn’t rotate all the way

I’ll be fixing all the issues in due time. Please let me know if you find any other bugs.

Line scan camera image processing

Sun, 21 Sep 2025 00:00:00 +0000

Line scan camera image processing

2025-09-21

I use my line scan camera to take cool pictures of trains and other stuff.

But there’s a lot that goes into properly processing the images.

FIGURE 1 A cool tram.

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FIGURE 2 A cool train, the Renfe AVE Class 102, nicknamed Pato because of its duck bill-like appearance.

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FIGURE 3 Cool diesel locomotive.

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FIGURE 4 Nice CRH6A intercity electric multiple unit.

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FIGURE 5 Awesome CR400AF. Super fast.

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FIGURE 6 Piccadilly line.

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1 Principle of operation

The way it works is that the camera has a single column of pixels (or in this case, two columns), that scans at a super high speed. The camera is stationary, but as a train moves past it, it gets scanned.

This is essentially also how a photo finish camera works.

Since the background is static, it gets repeated for every column of the image, giving it its distinctive striped look.

FIGURE 7 Principle of operation of a line scan camera, which produces an image where the horizontal dimension is time rather than space. source: cmglee on Wikimedia Commons

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FIGURE 8 A 1953 photo finish. source

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Line scan cameras are very suitable for capturing trains, since I can capture the full length of the train with minimal perspective distortion. This is super nice for train nerds who want to make models of the trains. Also, as you keep the camera running, you can get incredibly high resolution photos that span over 100,000 pixels wide.

By the way, film photo finish cameras and strip cameras behave almost the same as line scan cameras but with one subtle distinction, which is that you have to pull the film across a strip that’s somewhat wider than a single column of pixels. This is because film is less sensitive than modern digital image sensors. However, you’ll need to know the approximate speed of the subject and pull the film across at roughly the right speed.

2 About the camera

I’m using an Alkeria Necta N4K2-7C. It has a 4096×2 Bayer array image sensor. I’m saving its raw data in 16 bit binary arrays.

FIGURE 9 My camera.

EXIF data

Camera: FUJIFILM GFX100S
Lens: 125
Aperture: f/NaN
Shutter: 12.0 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-12 18:31:00

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FIGURE 10 Image sensor of my camera.

EXIF data

Camera: SONY ILCE-7RM2
Lens: ----
Aperture: f/0.0
Shutter: 1/200 s
ISO: ISO 100
Software: ILCE-7RM2 v3.30
Date: 2018-11-30 09:43:59

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FIGURE 11 Waiting for a subway train to roll by in Brooklyn, New York.

EXIF data

Software: Google
Date: 2021-09-19 15:57:17

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3 Detecting the region of interest

Sometimes, I keep the line scan camera running for a while, and it generates tons of boring data of the background. To detect moving things, I compute an “energy function” that’s defined as

\begin{aligned}\text{energy}(x, y) = \frac{ \frac{\partial \mathbf{I}}{\partial x} }{ 0.1 \text{max}_\mathbf{I} + \sqrt{\left(\frac{\partial \mathbf{I}}{\partial x}\right)^2 + \left(\frac{\partial \mathbf{I}}{\partial y}\right)^2} }\end{aligned}

where $\text{max}_\mathbf{I}$ is the maximum pixel value of the image, and the partial derivatives form the image gradient.

FIGURE 12 Example energy image.

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This is because, for a static background, it will be full of horizontal stripes. By weighing the $x$ -direction (time direction) gradient against the total gradient norm, we can find areas where it’s a more vertical-ish structure rather than a horizontal structure. However, doing this by itself risks noisy gradients in empty (but noisy) areas where the gradient direction is completely random. The maximum pixel value term ensures that whatever gradient we see is salient.

The image is divided into chunks and the score of a chunk is the 99th percentile energy.

Finally, chunks containing moving objects are defined to be ones where the score is at least 1.5× that of the minimum score.

This heuristic took me longer than I would like to admit to figure out. Previously, I came up with heuristics that worked well on one capture but couldn’t generalize well to other captures. Sometimes, the background will contain slowly moving foliage waving in the wind, that would screw up other methods of detection. That resulted in a lot of wasted time because time spent processing empty regions seriously slows down iteration speed when developing the later steps.

4 Speed estimation

The most common question I get is, how do I estimate the speed of the subject? If I don’t do it properly, it will appear stretched out, squished, or flipped.

Typically, I just set the camera to scan as fast as possible while maintaining a decent exposure, so the scan rate is independent of the subject. Faster subjects will appear squished, and slower subjects will appear stretched out.

For most of my earlier works, I just eyeballed it. A good rule of thumb is to look for round things such as the wheels and “no smoking” signs. But now I have a fully automated technique that works fairly robustly.

The key idea is to exploit the fact that the line scan camera actually has two lines in a Bayer array, where one line is red, green, red, green, and the second line is green, blue, green, blue. By comparing the two green channels, we are able to figure out how fast stuff is moving.

The problem is that the data is very noisy, and salient features are sparse. Here’s the general approach:

Divide image into chunks.
Compute the absolute difference between the 2 green channels of each chunk for various small shifts (from -7 to +7). This gives us a cost array for each chunk.
Perform subpixel peak interpolation in the cost array using an iteratively reweighted Gaussian, mean shift style. This gives us a shift estimate per chunk.
Fit a robust spline to the shift estimates.

FIGURE 13 Interpolating to find the peak using mean shift.

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FIGURE 14 Sample spacing and fitted spline.

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As you can see, the data is noisy, but we have surprisingly decent granularity for this very subpixel case where we were scanning slower than needed so the spacing is like 0.5.

The value of the spline is actually the sample spacing. It tells us how close together or far apart the sample points in the original time series we should be using. This leads us to the next section.

FIGURE 15 Uncorrected left end. It’s squished!!!

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FIGURE 16 Uncorrected right end. It’s slightly squished but not nearly as much.

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FIGURE 17 Left end of New York subway train.

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FIGURE 18 Right end of New York subway train.

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Hmm, I think my speed estimation still isn’t perfect. It could be off by about 10%. For future work, I think I might be able to extract feature correspondences such as SIFT or LightGlue. Trains are full of repeating elements that are supposed to be evenly spaced. I can detect those, and add a cost function to evenly space them, and optimize. Another idea is to use a circle Hough transform to find circles.

5 Resampling

From the spline that gives us the sample spacing, we can basically generate the samples as such:

samples = []
sample_position = 0.0
while sample_position < raw_width:
    samples.append(sample_position)
    sample_position += spline(sample_position)

However, there are a few gotchas:

If the spline is negative-valued, it means the subject is going the other way, i.e. the image is flipped. In this case, I start with sample_position set to raw_width and go from right to left.
If the spline goes to zero, we are doomed because the while loop will never terminate. I clamp the steps to at least 0.1 and throw an error if the spline has both positive and negative values.
This is sort of a naive integration compared to the trapezoidal rule or something. However, given that the spline moves very slowly, it is fine.

Now, for each sample position, we also store the sample width, which is the value of the spline. If we were to simply extract a single column from the raw data, we would be throwing away a lot of data, and the result wouldn’t be antialiased. Instead, it is better to pick a window of width proportional to the sample spacing. I chose a Hann window.

FIGURE 19 Naively selecting columns instead of using a windowing function.

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FIGURE 20 Using a rectangular window.

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Not only is the first image very grainy, but the rapidly blinking LED display showing the characters for 筲箕灣 is completely illegible without proper sampling.

FIGURE 21 Upsampling using a rectangular window.

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FIGURE 22 Upsampling using a Hann window.

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As you can see, the rectangular window performs very poorly when upsampling and introduces horrible jagged artifacts. The Hann window does better. Some other windows like the Sinc are even better supposedly.

6 Demosaicing

Recall that the camera has two lines forming a Bayer array.

If we simply create an image of half resolution (i.e. 2048 pixels tall instead of 4096), by grouping each RGGB group into one pixel, we would have some nasty fringing problems since the red and blue pixels are offset.

FIGURE 23 Fringing due to bad demosaicing.

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FIGURE 24 Better.

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Instead we should write out the image with careful attention to offsets, interpolating as necessary. Note that the horizontal offsets must be done after speed estimation, because, before speed estimation, the $x$ -axis is time, and after speed estimation, the $x$ -axis is space. But the 2-pixel wide Bayer array is physically a spatial offset.

I implemented a basic interpolation scheme that uses bilinear interpolation. This fixes most of the fringing, although we can do even better. That will be left for future work.

Unlike a traditional Bayer array, here we have the possibility that the green channels cover 100% of the pixels, so we can potentially do better than traditional demosaicing algorithms. But there’s currently an annoying problem, which is that the two green channels on my line scan camera don’t match.

7 Getting rid of the vertical stripes

Vertical stripes in the image are common and are due to two main reasons:

Clock jitter. The exposure time of each column may be randomly slightly off for some reason.
I’ve noticed that when a dark object shows up, like the coupling between train cars, the whole slice of the image there is brighter (and vice versa; if there’s something really bright, then the whole slice becomes darker).

To fix this, I use linear regression to fit a basic model of the form:

\begin{aligned}\text{model}(\mathbf{x}) = a + b\mathbf{x} + c\mathbf{k}\end{aligned}

where $a$ , $b$ , and $c$ are scalar parameters of the model, $\mathbf{x}$ is a 2048-element vector containing the luminance value of the column (mean over the 4 channels), and $\mathbf{k}$ is the row index (aka the 2048-element vector of $\begin{bmatrix}0, 1, \cdots, 2047\end{bmatrix}$ .

You can compose models as such:

\begin{aligned}\text{model}_1 (\text{model}_2 (\mathbf{x})) &= a_1 + b_1(a_2 + b_2 \mathbf{x} + c_2 \mathbf{k}) + c_1 \mathbf{k}\end{aligned}

This gives us a new model $\text{model}_{12}$ with parameters:

\begin{aligned}a_{12} &= a_1 + b_1 a_2\\ b_{12} &= b_1 b_2\\ c_{12} &= c_1 + b_1 c_2\end{aligned}

The associative property of the composition operator is left as an exercise for the reader.

There is also the identity model, i.e. one that does nothing, which is

\begin{aligned}a = 0, b = 1, c = 0\end{aligned}

and there’s also the inverse:

\begin{aligned}\text{model}^{-1}: a_{\text{inv}} = -\frac{a}{b}, b_{\text{inv}} = \frac{1}{b}, c_{\text{inv}} = -\frac{c}{b}\end{aligned}

so the set of these models forms a mathematical group.

I fit a model to each consecutive pair of columns using weighted least squares, where we assign each row element a weight based on a Gaussian. The weight would be:

\begin{aligned}\mathbf{w} = \text{exp}\left( -\frac{\text{model}(\mathbf{x}_1) - \mathbf{x}_2}{\sigma^2} \right)\end{aligned}

In other words, the residual would be

\begin{aligned}\text{residual} = \text{diag}(\mathbf{w}) (\text{model}(\mathbf{x}_1) - \mathbf{x}_2)\end{aligned}

After fitting this model, we redo the steps again several times, where the $\mathbf{w}$ vector is recalculated each time. This is known as iteratively-reweighted least squares and is pretty good at rejecting outliers.

FIGURE 25 The first plot shows the current column’s luminance and previous column’s luminance, as well as the previous column corrected by the model. The second plot shows the weight. The third plot shows the weighted initial and final error.

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This all gives us relative models between the previous column and the current column, but we want global models that tell us how to correct each column overall. We could set the global models by just composing them forever, but they would soon start to drift arbitrarily far away from the identity model.

You could prevent them from drifting away by solving a band-diagonal linear system where you have residuals of two types:

prior residual, penalizing the difference between each model from the identity
relative model residual, penalizing the difference between the delta between adjacent models and the relative model we computed

This can be solved in $O(n)$ . However, it is a bit of work to implement. In practice, you can mitigate most high frequency stripes by just doing exponential smoothing, which basically acts as a high-pass filter

\begin{aligned}\text{global}_{i + 1} = (1 - \lambda) \text{compose}(\text{global}_i, \text{relative}_i) + \lambda \text{identity}\end{aligned}

for some small $\lambda$ , in this case hardcoded to be 0.02.

FIGURE 26 Before. You can see rather subtle stripes in the dark area.

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FIGURE 27 After.

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Previously, I also had some success by directly fitting the model to line up each column with the first column. However, it doesn’t work for captures where the background isn’t static (e.g. rotating line scan panoramas, and pointing the line scan camera out of a moving train).

By the way, I should point out that getting rid of vertical stripes should be done before speed estimation, since it happens in the time domain at capture time. If a train were speeding up, it would appear stretched out at first, and squished at the end, and the striping would affect the end a lot more than the start.

8 Denoising

I implemented a patch-based denoiser, also known as block matching. It works by making the observation that you often have repeated textures in a line scan photo of a train. Technically, you also have lots of self-similarity in general photos, so patch-based denoising is a common method for denoising in general. However, one important distinction is that most denoisers only look in a small neighborhood around the current patch, but mine looks along the entire row.

What I do is, for each row, we process it independently. From each 3×3 pixel patch, we can construct a feature vector of size 27 (9 times 3 channels, RGB). Then, we collect all these features and sort them by mean value. Now, for each position along the row, we search in the window of size 128 in the sorted vector. The sorted vector will have similar-looking patches nearby, but we further weigh them by Gaussian similarity to the current patch. Then, we compute the weighted average of the center pixel of each of those patches.

Another trick is to realize that the noise is Poisson-distributed which has a standard deviation that scales with the square root of the signal. But if I just square root the input data first, then we just need to compare it to a constant.

This works decently, but is incredibly slow. Let me know if you think of any faster ways to do it. A KD tree in feature space would die from the curse of dimensionality. Perhaps a hash table? To keep things lightweight, we can limit the population in each cell.

FIGURE 28 Noisy watch.

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FIGURE 29 Denoised watch.

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FIGURE 30 Noisy passenger.

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FIGURE 31 Denoised passenger.

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The good thing about the patch-based denoiser is that unique features like this passenger remain virtually unchanged.

Previously, I also tried using a total variation denoiser, processing each row and column independently. It worked decently but would often destroy fine detail in textures.

9 Skew correction

If the camera isn’t perfectly upright, the resulting image may be slightly skewed. I’m planning on implementing automatic skew correction. But here are two caveats:

skew detection must be done after speed estimation
proper sampling should happen after skew detection, since the skew transformation introduces generation loss and we can sample directly from the raw data instead.

So basically we’d need to generate a quick, poorly sampled version, run skew detection on it, and then sample it properly afterwards. We can implement skew detection using a Hough transform. Generally, I do a decent job of keeping the camera upright, so we just need to correct for very small skews, so a Hough transform is suitable (since the complexity scales with the number of bins of the histogram). We can also use the energy function from the region of interest detector to primarily care about vertical structures.

FIGURE 32 Hough transform lines for skew detection superimposed over the vertical structure energy image.

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10 Color calibration

I kinda just eyeballed this color calibration matrix.

\begin{aligned}\begin{bmatrix} 0.9& -0.3& -0.3 \\ -0.8 & 1.6 & -0.3 \\ -0.5 & -0.5 & 2.0 \\ \end{bmatrix}\end{aligned}

But to be honest it looks fairly decent.

FIGURE 33 People’s skin tones look fine to me.

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I also implemented DNG output so that I can postprocess the colors in Darktable.

11 Implementation details

The code is implemented in Python using numpy.

the code

Due to the large size of the data (4096 rows and hundreds of thousands of columns), it is sometimes impossible to fit all of it in memory, so the code takes several passes and outputs in chunks. Actually, it is probably okay to fit it in a few gigabytes of RAM, but you’d have to chunk up the storage (there’s no way a contiguous numpy array of 4096 by 100,000 could be allocated).

11.1 Vibe coding experience

I tried using AI to help with a lot of the implementation. However, the results were mixed.

AI would often accidentally make things quadratic for no reason when a linear time algorithm would suffice. For example, when trying to implement spline-based resampling, ChatGPT 5 came up with horribly slow (but vectorized) code that constructed a giant tensor with a mask across the entire width of the image for every single sample. Since there are 100,000 samples, and each mask was 100,000 columns wide, you can imagine it would take millennia to run. I ended up reimplementing it from scratch by hand. Then Grok 4 implemented weighted least squares regression by materializing the entire weight vector with np.diag instead of simply pre-multiplying each row of A and x with the square root of the weight before doing np.linalg.solve(A, x). Again, with 100,000 elements, making the square matrix with np.diag would have instantly run out of memory.

Both Grok 4 Expert and ChatGPT 5 Thinking also completely failed to implement the band-diagonal least squares for my vertical stripes strategy, but as mentioned, the exponential smoothing trick works okay for now.

However, for some other stuff, AI was quite helpful. It created a class that dynamically loads chunks from disk but provides the API to index and slice it. That was neat. AI was also incredibly good at helping with Matplotlib’s arcane syntax.

12 Other people's line scan photography of trains

12.1 Adam Magyar

Adam Magyar uses a black and white digital line scan camera for his “Stainless” project, and another derived from a scanner for his “Urban Flow” project.

His camera must have much better sensitivity than mine since he managed to capture fairly clean images even for underground trains (whereas I generally require sunlight for mine). Apparently, he had to scout out many subway stations to find ones where the lights don’t flicker at 60 Hz.

12.2 KR64's blog

At kr64.seesaa.net you can find a mind boggling collection of high quality line scan photos of trains from all across Japan.

They probably do this full time as the variety of trains is far greater than I can ever hope to achieve. I believe they use a film slit scan camera.

Unfortunately, their website has a bunch of technical issues and often goes down. I would be happy to help them out but my Japanese is very poor and I don’t see any way to contact them.

All about automotive lidar

Fri, 29 Aug 2025 00:00:00 +0000

All about automotive lidar

2025-08-29

Here I’ll provide a comprehensive overview of automotive lidar technology. Lidar is used for autonomous vehicles and robotics because it’s a cool technology.

FIGURE 1 Waymo Jaguar I-Pace with several lidars.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/2.5
Shutter: 1/2500 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.10
Date: 2023-11-21 12:40:07

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FIGURE 2 A Chrysler Pacifica Hybrid with 8 Ouster lidars.

EXIF data

Camera: FUJIFILM GFX 50R
Lens: 0 mm f/unknown
Aperture: f/NaN
Shutter: 8.0 s
ISO: ISO 100
Software: Digital Camera GFX 50R Ver1.00
Date: 2020-01-03 00:08:34

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1 What lidar does

A lidar is a sensor which measures things by bouncing light off an object. These things include:

distance, by measuring how much time it takes for light to bounce back
bearing, by shining the light or pointing the detector in a particular direction
reflectivity, by measuring how much light has bounced back
speed, by measuring the Doppler shift in the reflected light.
ambient, by measuring the amount of light in the environment in a particular direction

FIGURE 3 Ambient, intensity, and range channels of a really old Ouster OS1-64.

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In general, we are most interested in the first two: distance and bearing. The other ones are very nice to have as well, particularly surface reflectivity, which allows detection of lanes on the ground for example. Some lidars also have the ability to measure ambient light, like a camera.

By measuring distance in many directions, an autonomous vehicle can see its environment. Each measurement corresponds to a 3D point in space, so a vehicle can create a 3D model of the world around itself. This allows it to know exactly where it is, where obstacles are, and how to plan a path to avoid obstacles.

Distance and bearing measurements can be converted into 3D Cartesian points. For example, given range $r$ and bearing $\theta$ , $\phi$ , the 3D point is:

\begin{aligned}\mathbf{p} = \begin{bmatrix}x\\y\\z\end{bmatrix} = \begin{bmatrix} r \sin\theta \cos\phi\\ r \sin\theta \sin\phi\\ r \cos\theta \end{bmatrix}\end{aligned}

In contrast, a camera only measures bearing and ambient light intensity. Each pixel of a photo is a measurement of how much light there is in that particular direction. But generally, a camera has much higher bearing resolution than a lidar.

FIGURE 4 A point cloud accumulated from an Ouster OS1-64 lidar.

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2 Measuring distance

Measuring distance is also known as ranging. Basically, it just measures how close something is. There are in general two ways of doing this:

Measuring the time somehow, exploiting the fact that light travels at a constant speed (the speed of light)
Parallax

For measuring the time, there are again two ways:

Direct time of flight, where we directly measure the time
Modulated lidar, where we modulate some attribute of the outgoing light, e.g. amplitude, frequency, or polarization

2.1 Direct time of flight pulsed lidar

Direct detection pulsed lidar fires one or more laser pulses. Then, we simply measure the time to see the reflection from the pulse.

\begin{aligned}d = \frac{ct}{2}\end{aligned}

where $c$ is the speed of light ( $3\times 10^8$ m/s). The division by 2 is because the range is half of the round trip distance.

Imagine if we have a stopwatch that measures in, say, a nanosecond resolution. If we measure 1000 nanoseconds, then it means the round trip distance was 300 m, which means that the range is 150 m.

This involves measuring the time series data of how much light is seen at any point in time. Since electronics typically run at 1 GHz or so, the time series is discretized on the order of 1 ns, which corresponds to a range of 15 cm. To further improve the ranging accuracy, an interpolation filter is a standard technique in signal processing. Typically ranging accuracy at the centimeter level is possible.

After getting the time series data, the peaks in the series are found, and these correspond to the range.

FIGURE 5 A simplified time series plot of number of photons vs time.

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Usually, it is better to have stronger, shorter pulses. Diode lasers can produce pulses on the order of a couple of nanoseconds, and fiber lasers can produce even shorter pulses with much higher peak energy.

In practice, the laser pulse has some finite duration and shape (rather than being an infinitely short impulse function), so the peak is found in the cross correlation of the outgoing pulse’s shape with the return data, rather than the raw time series data itself. It is possible to send a randomly shaped pulse (or sequence of pulses), and cross correlate the return data against that. This provides much greater resistance against noise, interference, and crosstalk, and is known as a matched filter.

We should note, however, that the shape of the return pulse could be distorted or “smeared out”. This can be due to, for example, hitting a very slanted surface. One strategy to overcome this is to try correlating it with a bunch of different pulse shapes. This technique may be called template matching, dictionary matching, matched filter bank, or model-based detection.

2.1.1 Photodiodes used in pulsed lidar

In order to get a time series of the amount of light per unit time at a super high rate, we need a really fast sensor that can operate at 1 GHz. Usually one of these two types of sensors is used:

Linear-mode avalanche photodiodes (APD)
Geiger-mode avalanche photodiodes, also known as single-photon avalanche photodiodes (SPAD)

Other types of sensors such as CCD sensors are not fast enough for this application.

A photodiode is a diode that also has the photoelectric effect.

A diode is like a one-way valve for electricity. Just like a one way valve for, say, water, if you try to force things sufficiently in the opposite direction, it will break down, resulting in a huge gush of water. Likewise, if you apply a strong voltage in the reverse direction, it’s called a reverse bias, and a sufficiently strong voltage will cause a sudden spike in electrical current. This is called avalanche breakdown.

Meanwhile, some metals produce an electric current when light shines on them, in an effect known as the photoelectric effect.

Avalanche photodiodes have a reverse bias, meaning that a voltage is applied in the opposite direction of the one-way valve. If the reverse voltage exceeds a certain amount known as the breakdown voltage, it stops acting like a diode. Suddenly, a large current can flow through the device.

Linear-mode APDs have a reverse bias slightly below the breakdown voltage. Here, the current is linearly related to the voltage, but the gain is very high, so that even changing a small voltage results in a large change in the current. Hence, it is a very sensitive way of measuring light intensity.

Geiger-mode avalanche photodiodes (GMAPDs) or single-photon avalanche diodes (SPADs) have such a strong reverse bias that even getting hit by a single photon can make them break down, resulting in a large current spike. The output of a SPAD can be directly connected to a voltage discriminator so that the spike becomes a digital signal from logic 0 to 1.

FIGURE 6 I-V diagram of avalanche photodiodes

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In the above I-V diagram, we see the relationship between the voltage (V) and the current (I). The breakdown voltage $V_{BD}$ is labelled. As you can see, where the linear-mode APD operates, the current is linearly proportional to the voltage. The Geiger-mode APD operates where the slope is effectively infinitely steep.

Note on terminology: Typically the word avalanche photodiode (APD) refers to linear-mode APDs. Meanwhile, GMAPDs and SPADs operate in the same way but the term SPAD often refers to silicon devices sensitive to near infrared (850 nm to 940 nm) and GMAPD often refers to InGaAs devices sensitive to longer wavelengths (1064 nm to 1550 nm).

SPADs have the following advantages:

CMOS compatibility: Silicon SPADs can be made with the complementary metal-oxide-semiconductor (CMOS) process, the same way as computer CPUs and the like. Since they output digital signals, you can fabricate them on the same chip that is used to process the signals. Hence the whole detection pipeline can be made cheaply on a silicon application-specific integrated circuit (ASIC). In contrast, the output of an APD is an analog signal, so a high-speed analog-to-digital converter (ADC) is required. This is very expensive and introduces extra noise. Silicon SPADs also benefit from the immense scaling potentials of the CMOS process, allowing very large arrays to be fabricated at a very fine manufacturing node. Hence, SPADs can be used to make very high resolution, dense arrays, as opposed to APDs which are relatively large and expensive discrete components.
Higher gain: SPADs have a higher gain than linear mode APDs. In fact, the gain of a SPAD is essentially infinite, allowing it to detect even a single photon.
Lower temperature dependence: SPADs are less sensitive to temperature than APDs, for which different temperatures can change the sensitivity of the sensor and also affect the dark current.
Better timing jitter: SPADs output such a sharp spike that you can measure the timing very accurately and reliably.

Meanwhile, APDs have these advantages:

No dead time and quenching: A linear mode APD essentially continually outputs an analog signal, so there is no need to recharge. In contrast, after a SPAD fires, it takes a while to recharge. During a SPAD avalanche event, it can be destroyed by its own huge current, so the current must be quenched with a resistor to discharge it. After quenching, it needs to recover to its original biasing condition. The reverse bias voltage is typically supplied by a capacitor, which needs to take time to charge back up again. Hence, there is a dead time ranging from around a few nanoseconds (silicon SPADs) to a microsecond (GMAPDs). By avoiding all this, APDs can have simpler circuitry.
Dynamic range per detector: APDs output a continuous analog output that you can digitize however finely you want, which gives better dynamic range per detector (meanwhile a single SPAD has a dynamic range of only 1 bit, it’s either 0 or 1).
No range walk: Linear mode APDs avoid intensity-dependent range walk and saturation issues, which I discuss in more detail below.

If the return signal from a pulse is very strong, a SPAD array can be saturated at the very beginning of the pulse. If the pulse length is long, ranging may be biased when measuring the range of retroreflective materials. This is also known as range walk.

SPADs are so sensitive that they can be triggered by single photons, but this also makes them sensitive to ambient illumination. Therefore saturation is a concern.

In contrast, the continuous signal from an APD can be digitized with many bits.

To prevent SPADs from being drowned out by ambient light, the probability of detection of any single SPAD must be kept very low. Some techniques include:

SPADs are usually made really small
a tight band-pass filter can reject most ambient light
sometimes an attenuating filter (e.g. a neutral density filter, which attenuates all wavelengths equally) is needed to attenuate the signal even further

2.1.2 SPAD macropixels

FIGURE 7 The Sony IMX479 SPAD sensor is physically a 105×1,568 pixel array, with a total of approximately 164,000 pixels, but it combines many pixels into macropixels, so the final output is only 520 macropixels. This allows it to have amazing dynamic range and produce this beautiful image. Note that the lower image is the raw ambient image output from the lidar rather than a separate photo taken by a camera.

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Instead of a single SPAD per pixel, several SPADs can be combined into a single “macropixel”. This trade-off results in lower spatial resolution, but the benefit is that it mitigates most of the drawbacks of SPADs.

Dynamic range increases from 1 bit to as many bits as you have SPADs in the macropixel.
Dead time of any individual SPAD is mitigated since it is unlikely that all the SPADs will fire at once, meaning that there are always available ones. Of course, in some circumstances (such as retroreflectors) it is still possible for all the SPADs in a macropixel to be saturated.
SPADs can be made individually smaller, making it unlikely for all of them to fire at once, resulting in better resilience against saturation.

2.1.3 Multi-shot ranging

Even with a macropixel, ranging with SPADs can be noisy as there may only be as many photons measured as there are SPADs in the macropixel. To increase signal strength, the lidar can fire many shots and aggregate the time series data from each shot. This is known as multi-shot ranging.

As an additional bonus, making multiple low-energy shots is somewhat safer than a single high-energy shot as the peak laser energy is less.

The tradeoff is that it takes a longer time to make a measurement, during which you could suffer from motion blur.

2.1.4 Silicon photomultipliers

Silicon photomultipliers are a group of SPADs whose outputs are combined into a single analog signal. This has some advantages:

Just like the SPAD macropixel, by combining many SPADs, the dead time of any individual SPAD is a smaller concern.
It can be more sensitive than regular linear mode APDs.
Without the need for digital logic, the chip is simpler and possibly denser than a digital SPAD macropixel.

However, an ADC is still required to digitize the signal.

2.2 Amplitude modulated lidar

Instead of firing pulses, an amplitude modulated lidar continually modulates the laser amplitude at some radio frequency, say, 1 GHz. In other words, it is just a fast blinking light that turns on and off rapidly.

Meanwhile, there are two detectors that turn on and off at the same rate but are out of phase. That is, when detector 1 is on, detector 2 is off, and vice versa.

The range can be estimated by checking the ratio of the light falling in two detectors, for ranges up to a multiple of the modulation wavelength. For example, at 1 GHz, the wavelength is 15 cm.

To resolve the range absolutely, the sensor changes the modulation frequency slightly, say, to 1.05 GHz, giving a range estimate modulo a different wavelength. The unknown multiples can then be found as a least common multiple problem.

The advantage of this type of amplitude-modulated lidar is that it is very cheap. There is no need for high-speed timing electronics to count photons at a high speed. Instead, a simple oscillator is sufficient to make the lights and detectors blink at 1 GHz.

Since the detectors just need to measure intensity rather than timing information, they do not need to be very fast, and basic CMOS or CCD sensors will suffice.

This type of lidar is used in RGBD sensors such as the Kinect V2. However, the ranging accuracy is much poorer than needed for automotive purposes, so this type of lidar is not typically used for automotive.

2.3 Frequency modulated lidar

A frequency modulated lidar has a laser that can change in frequency rapidly.

Now, the laser beam goes through a beam splitter, and part of it is sent out, where it hits something, and bounces back. Then, you can combine the part that didn’t go out with the part that bounced back.

When you combine two waves of similar but slightly different frequency, you’ll end up with something called beat. When the waves line up, they will double their strength, and when they are out of phase, they cancel each other out. Then, you can use a photodiode to measure the time series of the combined wave in order to determine the beat frequency, which in turn tells you the range.

\begin{aligned}\cos(2\pi f_1t)+\cos(2\pi f_2t) = 2 \underbrace{\cos\left(2\pi\frac{f_1+f_2}{2}t\right)}_\text{carrier} \underbrace{\cos\left(2\pi\frac{f_1-f_2}{2}t\right)}_\text{envelope}\end{aligned}

Here’s a plot that shows this effect. The main thing is that the beat frequency is proportional to the difference in frequency, so you can measure it relatively easily with a photodiode.

FIGURE 8 When combining two frequencies, you get a wave of the sum of the two frequencies multiplied by the difference of the two frequencies. Source: Ansgar Hellwig on Wikimedia Commons

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Frequency modulated lidar is known as frequency modulated continuous wave (FMCW) since the laser beam is always on (a continuous wave) that doesn’t turn off. The principle of using the beat to determine the range is known as optical heterodyne detection. Here, “heterodyne” means comparing two slightly different frequencies (as opposed to “homodyne”, where you have the same frequency).

With FMCW lidar, you can also measure the speed of things by measuring the Doppler shift.

The main tradeoff is that you’ll need an expensive fiber laser that can do frequency modulation with highly linear chirps, increasing the overall cost.

2.4 Parallax lidar

A parallax lidar works by triangulation, that is, similar to coincidence rangefinding.

This does not use any timing information at all. A linear photodetector is placed physically offset from the laser. The detector measures the incident angle of the reflected light and obtains the range by triangulation.

FIGURE 9 Parallax rangefinding, figure from “Low cost laser distance sensor” by K. Konolige et al.

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This is rarely or never used in automotive applications but is instead found in robotic vacuum cleaners and other low-speed, low-cost applications. A famous example is the “Low cost laser distance sensor” by Kurt Konolige et al. Many robotic vacuum cleaner sensors are based on this.

A structured light depth camera, also known as active stereo, is a special case of parallax rangefinding. Instead of a single laser beam, it projects a bunch of different dots at once, and instead of a 1D line scan sensor, it has a regular 2D sensor. But the depth measurement is again based on triangulation. Structured light depth cameras are used in the early versions of the Kinect as well as many Intel Realsense cameras.

Parallax rangefinding measures disparity, which is the inverse of range, so the uncertainty in range is quite high and grows quadratically with range. As such, it is less suitable for advanced robotics and autonomous cars.

3 Discerning bearing

As mentioned in our introduction, lidar sensors combine distance readings with bearing to produce 3d points. Now that we’ve covered distance, we are ready to discuss how to figure out the directions (bearing) of things.

Lidars can either:

discern bearing for both tx (the outgoing laser beam) and rx (the detector), or
discern bearing only for rx but not tx (i.e. a flash lidar), or
discern bearing only for tx but not rx (for example, some optical phased array lidars only steer the laser beam, but have a “staring” detector that doesn’t distinguish angle).

Discerning bearing is also known as “imaging”. People may describe a system as having both imaged rx and tx, for example.

Generally, having imaged rx and tx is vastly better, since you are only pointing your laser beam where you’re looking, so you get more range and efficiency, and meanwhile the imaged receiver rejects off-angle background light.

There are two main approaches for discerning bearing:

an array of elements already pointing in different directions
beam steering, by pointing either your detector or laser in various directions

FIGURE 10 Animation showing arrays vs steering

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As with the methods for measuring distance, each method has advantages and disadvantages.

The advantages of arrays are that they don’t have any moving parts, each array element can be a lot cheaper, potentially leading to overall cheaper cost, and that they can produce a much greater quantity of points. The advantages of beam steering are that it works with high quality but expensive laser sources such as fiber lasers and that the scan pattern may be configurable. Being able to use high quality lasers also unlocks the ability to use ranging modalities unavailable to array-based lidars, such as FMCW.

Note that if you rely on steering very few (even one) lasers, the number of points per second is limited by the speed of light. It takes light a microsecond to travel 300 m round trip, meaning that a single beam lidar is limited to about a million points per second at a range of 150 m. Meanwhile, array-based lidars can easily pump out several million points per second.

3.1 Arrays for discerning bearing

The simplest way to determine direction is to just have an array of elements pointed in various directions.

Basically, you’ll need cheap and small array elements in order to have an array.

Laser type	Performance	Cost	Array?
VCSEL	Low	Low	Solid state 2D arrays of hundreds of lasers are possible
Edge-emitting diodes	Mid	Mid	Discrete 1D arrays of dozens of lasers are typical
Fiber	High	High	No, typically used as single laser + beam steering

Table 1 Comparison of lasers

Sensor type	Size	Cost	Array?
SPAD	Small	Low	Solid state 2D arrays of even millions of SPADs are possible
APD	Mid	Mid	Discrete 1D arrays of dozens of APDs are typical

Table 2 Comparison of receivers

3.1.1 Discrete arrays

With discrete arrays, you have discrete components like edge-emitting laser diodes and avalanche photodiodes that are pointed in different directions. Some early lidars, like the Velodyne VLP 16, literally have 16 circuit boards, each with one laser diode on them, and another 16 circuit boards, each with one APD on them. Then, these 32 circuit boards are glued into place.

FIGURE 11 The inside of the Velodyne VLP 16. Source: xtech

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FIGURE 12 Detail of the array of 16 PCBs in the Velodyne VLP 16. Source: xtech

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The reason for doing that is because, due to the simple design of the lens, it was necessary to arrange the lasers and detectors along a curved arc. Interestingly, a Google (now Waymo) patent US8836922B1 describes using a flexible substrate to achieve the curve.

FIGURE 13 Curved detector array in the receive block in US8836922B1.

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3.1.2 Solid state arrays

Solid state arrays put lasers or detectors on a single chip. The obvious benefit is vastly simpler manufacturing and consistency. High performance edge-emitting laser diodes shoot lasers to the sides so you can’t just put a bunch of them in an array on a chip, so you’ll have to make do with lower power VCSELs.

FIGURE 14 The Ouster L3 chip, a SPAD array. Each square is a macropixel with many SPADs. Source: Ouster blog post

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FIGURE 15 Instead of 32 circuit boards, there are just two in this Ouster lidar: one containing the chip full of lasers, and one containing the chip full of detectors. Source: How Ouster Digital Lidar Works

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Since the laser array or detector array is now flat, the optical design will be somewhat more complex. You’ll need the lens to be image space telecentric since your flat array of lasers all produces parallel beams.

FIGURE 16 Ouster lidars have relatively complex multi-element lenses compared to single-element lenses on early Velodyne lidars. Source: How Ouster Digital Lidar Works

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For lasers, only VCSELs are compatible with this method. As for detectors, SPADs are also vastly more amenable to solid state arrays, although APD arrays are also available (but with fewer elements). This is because, as discussed earlier, SPADs are compatible with typical chipmaking technologies and they output digital signals rather than analog ones, so you can fabricate them on a single chip, whereas APDs would typically require discrete components.

With large arrays, a lidar could sequentially fire small parts of an array instead of all of it at once. This is called electronically scanning. In effect, it is similar to scanning, except there are fixed elements already pointed in different directions rather than the same element being made to point in different directions. Electronically scanning has the advantage of less pixel crosstalk/blooming (more on this later) as well as being able to output more power per beam without running into thermal or safety limits.

3.2 Scanning and beam steering methods

3.2.1 Spinning

Perhaps the most straightforward way to do beam steering is to just spin the whole lidar, which gives you 1D angular discernment. The first advantage is that this gives you a 360 degree field of view. This also has the advantage of being highly compatible with arrays, so you can have a vertical array while spinning horizontally. Spinning lidars have basically only one moving part.

FIGURE 17 Size comparison between some spinning lidars.

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An encoder is used to measure the angle of the turret.

The challenges of spinning are that:

You need to send power and data between the spinning turret and the stationary base somehow. The earliest spinning multi-beam lidar, the Velodyne HDL-64E, used a mercury-wetted slip ring (Mercotac 305). This is very efficient, but expensive, fragile, and somewhat environmentally unfriendly. Later spinning lidars transmit power wirelessly through a transformer, and data wirelessly through an optical link.
The cylindrical window can degrade optical performance. Some lidars have compensator optics to suppress aberrations from the cylindrical window. The Quanergy M8 had a variant with an octagonal window instead. Some lidars spin externally (such as the Waymo Laser Bear Honeycomb, Velodyne HDL-64E, and Velodyne HDL 32). But spinning externally makes it less robust against the environment.
Thermal dissipation. The spinning turret houses most of the energy-intensive lasers but it has no direct contact with the outside except through the bearing.
Despite having only one moving part, the spinning turret is a rather large and heavy part, and some early spinning lidars like Velodynes tended to fail a lot when the bearing was damaged or wore out. This is an especially big problem if the turret isn’t well-balanced.

3.2.2 Spinning mirror

Using a spinning polygonal mirror is one of the oldest and most reliable ways to scan a laser beam, which is again a 1D scanning method. This is used in, for example, laser printers.

FIGURE 18 Animation of a laser beam being reflected by a rotating hexagonal mirror.

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As with spinning lidars, an encoder is used to measure the angle of the polygonal mirror.

Compared to spinning the whole turret, this has the main drawback of having a much narrower field of view (about 120 degrees is typical, as opposed to 360 degrees). However, it has the advantage of having a lighter moving part without having to deal with power transmission and heat dissipation and stuff.

3.2.3 Oscillating mirrors/galvos

This is a flat mirror that oscillates in angle to steer the beam, which can be either 1D or 2D.

FIGURE 19 Animation of a laser beam being reflected by an oscillating mirror.

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Typically, a lightweight mirror is connected to a galvanometer in what’s called a mirror galvanometer (galvo). A galvanometer is one of the most basic ways to measure electrical current: it consists of a spring, a magnet, and a solenoid. When a current passes through the solenoid, it creates a magnetic field, which causes a torque to be applied as it tries to align itself with the magnet. The spring resists this force, so the amount it ends up turning is dependent on the current.

Nowadays, fast galvos are incredibly good and are used in all sorts of applications, like laser light shows, engraving, and so on.

Compared to spinning mirrors, this is somewhat less reliable, since reciprocating motion is typically less reliable than constant rotation.

Unlike spinning mirrors, you can have a single mirror that’s actuated in two axes (a 2D galvo) that allows you to steer your beam in both directions with a single mirror.

3.2.4 MEMS mirror

A MEMS (micro-electromechanical system) mirror is simply a mirror that is really small, typically an oscillating mirror. Because it is so small, it is typically considered “solid state” even if it is physically a moving part. Like macroscopic oscillating mirrors, MEMS scanners may be either 1D or 2D.

The primary advantage of MEMS is low cost and relatively better reliability. After all, the rate at which your moving part wears out is strongly dependent on the mass and moments of inertia of that moving part, so keeping it as light as possible makes it more resilient.

There are, however, a couple of drawbacks:

The optical aperture may be limited by the tiny size of the mirror. With lasers, the bigger the aperture, the more it stays collimated (and hence the less it spreads out), so you want the aperture to be as large as possible usually.
Cooling a tiny mirror may be hard. Your entire laser output is bouncing off a tiny surface with tiny thermal pathways. Hence, the mirror should be kept as reflective as possible.

3.2.5 Optical phased arrays

A phased array has many array elements whose phase is slightly offset. As the contributions from each element interfere, a beam is formed where they interfere constructively, and everywhere else, destructive interference causes it to cancel out.

FIGURE 20 Phased array animation. Source: Chetvorno on Wikimedia Commons.

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Phased arrays are common in radar. However, the fundamental physical problem of phased arrays is that the element size must be close to the size of the wavelength, and the wavelength of light (about a micron) is way smaller than the wavelength of radio waves (ranging from millimeters to many meters). If your array spacing is too big, your beam would have very poor collimation and tons of side lobes.

You can use phased arrays for both the transmitter and receiver. For the receiver, you would have an array of optical antennae which are tiny nanophotonic detectors that can each measure the phase and amplitude.

So far, due to the physical challenges with phased arrays, all the existing products (such as the Quanergy S3) are very low quality. Israeli startup Oryx Vision tried optical antennae and unfortunately shut down in 2019.

3.2.6 Baraja SpectrumScan

This uses a frequency sweep laser with a fixed prism. Prisms have dispersion, which means that the index of refraction changes with wavelength (hence turning sunlight into a rainbow), so by changing the wavelength of the laser, the angle is changed. This allows it to scan in 1D. Baraja uses a MEMS mirror for the other axis.

FIGURE 21 Baraja lidar.

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This requires using a high quality fiber laser or tunable diodes that can do large frequency sweeps, which can be costly.

3.2.7 Risley prisms

A prism is a triangular piece of glass that can bend light. Risley prisms are a pair of two prisms that can rotate along the optical axis. When the prisms are lined up, they both bend light the same way, and the beam gets bent a lot. When they are opposite of each other, they cancel each other out, and the beam goes through straight without bending.

FIGURE 22 Animation of how Risley prisms work.

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FIGURE 23 Simplified diagram showing how Risley prisms work.

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Basically, when you have two prisms, one with angle $\theta$ and one with angle $\omega \theta$ , the $x, y$ direction of the beam is proportional to:

\begin{aligned}x &= \sin\theta + \sin\omega\theta\\ y &= \cos\theta + \cos\omega\theta\end{aligned}

which results in this scan pattern if the two prisms are going at constant speeds with a fixed speed ratio $\omega$ :

Speed ratio between prisms: -0.743

The Livox lidars are notable for using Risley prisms. You can make other scan patterns by varying the speed of the prisms, and by putting an array of multiple lasers (e.g. the Livox Horizon’s 6 lasers) instead of one laser.

FIGURE 24 Livox Mid-70. Source: Livox website.

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The advantage of Risley prisms is that, like polygonal mirrors, it’s cheap and robust to have things spinning at a constant speed. However, the disadvantage is a very narrow field of view, and a weird scanning pattern. For some applications, the scan pattern can be an advantage, for example surveying applications where the lidar can be stationary for long periods of time, gradually covering a dense area.

3.3 Combining two 1D methods

Many lidars combine two 1D methods, e.g.:

Horizontally spinning turret + vertical array (e.g. Velodyne pucks, Ouster OS series, Hesai Pandar)
Horizontally spinning turret + vertically spinning polygonal mirror (e.g. Leica BLK360)
Horizontal spinning mirror + vertical spinning or oscillating mirror (e.g. Luminar Iris, Seyond Falcon)
Horizontal spinning mirror + array (e.g. Hesai AT512)

4 Choice of wavelength and eye safety

For lidars, two choices of wavelength are popular:

near infrared, e.g. 850 nm, 865 nm, 905 nm, 940 nm
1550 nm

The main advantage of near IR is that silicon is sensitive in that region, allowing much cheaper, more sensitive silicon detectors, as well as cheap laser sources. In contrast, with 1550 nm, you would need InGaAs semiconductors for your detectors, which are less sensitive and very expensive.

Meanwhile, the main advantage of 1550 nm is that eye safety regulations allow devices to output vastly more power at 1550 nm than in the near IR regime. As a result, 1550 nm lidars tend to have longer range in general.

FIGURE 25 Maximum permissible exposure according to IEC60825. Source: Hankwang from English Wikipedia

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You can see in the above chart that you are allowed to output hundreds of times more power in the steady state scenario (red curve) at 1550 nm compared to, say, 905 nm. The reason is that the human eyeball focuses near IR light to small spots on the retina, so intense light may damage the retina. On the other hand, 1550 nm light is not focused and is attenuated by water, but at high enough intensities, it will damage the cornea instead.

In practice, manufacturers carefully tune the power of the lasers to be just below the eye safety threshold for both 1550 nm lidars and near IR lidars. That is to say, 1550 nm lidars do in fact output up to 1,000,000 times more pulse energy than 905 nm ones.

Paradoxically, 1550 nm lidars may be more dangerous overall, because of the following reasons:

If you have many lidars around, the beams from each 905 nm lidar will be focused to a different spot on your retina, and you are no worse off than if there was a single lidar. But if there are many 1550 nm lidars around, their beams will cumulatively heat up your cornea, potentially exceeding the safety threshold.
1550 nm lidars more often rely on beam steering since it is impractical to have an array of expensive fiber lasers. However, if the beam steering were to fail, the laser beam may be fixed in one direction. This can cause laser energy levels thousands of times stronger than the safety threshold in a particular direction, even when the lidar would be under the threshold when it’s scanning properly.
1550 nm lidars are known to damage cameras. For example, both AEye lidars and Luminar lidars on the Volvo EX90 are known to destroy cameras. This is an especially worrisome problem with pulsed 1550 nm lidar, but FMCW lidars have a continuous wave with lower peak power and may be slightly safer.

Eye safety aside, 1550 nm is also somewhat more attenuated by both water and water vapor, so they are likely to perform worse in poor weather. In fog, Mie scattering of the water droplets may also impact 1550 nm lidar more, as fog droplets are about 1.5 microns, and the scattering is more as the size of the sphere approaches the wavelength. That said, 1550 nm lidars do have better range to begin with, thanks to outputting a lot more power, so even with attenuation, they are still competitive in rainy situations.

5 Laser sources

There are basically three commonly used types of lasers:

Vertical cavity surface emitting laser (VCSEL)
Edge-emitting laser diodes
Fiber lasers

Laser type	Typical wavelength(s)	Beam quality (M²)	Coherence / FMCW-ready	Power per element	Cost	Array?
VCSEL	850–940 nm	Very good, circular	Low–mid linewidth	Low (mW-tens mW)	Low	Excellent: monolithic 2D arrays (10²–10⁵ emitters), fine pitch, easy eye-safety
Edge-emitting diodes	905 nm, 1350-1550 nm	Good (often elliptical)	Mid–high	Mid (100 mW–W class with bars)	Mid	Good: 1D bars/arrays (dozens–hundreds)
Fiber/ECDL	1550 nm	Excellent	High (kHz–100 kHz LW) best for FMCW	High (W class via fiber amps)	High	Poor as dense arrays; usually single source + split/steer

Table 3 Summary of laser types

Vertical cavity surface-emitting lasers (VCSELs) are very cheap and you can make a bunch of them on a chip in a chip-scale solid state array. They are called “vertical cavity” because the beam comes out perpendicular to the chip. You make them by depositing several layers of material on the chip. The main drawback is that they are low peak power.

Edge-emitting laser diodes are a mature technology and are cheap enough to be used in 1D arrays.

Fiber lasers produce high quality light that is highly coherent. But they are quite expensive so you can probably just afford one or two per lidar. Some lidars split one laser between many lidar heads, as in the case of Baraja’s lidar. Not only are fiber lasers more coherent, they can output millions of times greater power than edge-emitting diode lasers and VCSELs as well as much shorter pulses. Having shorter pulses is very advantageous for pulsed lidar as it improves the range resolution. Some fiber lasers can also vary the wavelength in highly linear chirps, allowing use in FMCW lidars.

The development of these lasers is highly driven by the telecommunications industry where they are used in fiber optics, so the lidar industry sort of profits from that for free.

6 Common lidar problems

6.1 Beam angle calibration

Most spinning + array lidars need a calibrated list of angles, one per beam. Some manufacturers, like Ouster, provide a JSON metadata file containing the elevation and azimuth angles of each of the 128 beams, which is calibrated per lidar. Some manufacturers simply give a nominal set of beam angles for a lidar model that is assumed to be the same for each individual lidar, but in practice, each lidar varies slightly due to manufacturing tolerances. Early Velodynes had very bad beam angles as each of the many circuit boards was individually glued in place and manually aligned.

Here are some ways lidar measurements could have bad beam angles:

Accidentally forgetting to use the lidar-specific beam angles, or the manufacturer doesn’t provide them
All the beams are offset by some angle even with lidar-specific beam angles, e.g. bad factory calibration, the lidar got bumped, or due to thermal expansion

This would typically manifest as the ground curving slightly, or the trajectory of the robot curving up or down even when it is expected to be flat.

The well-known KITTI dataset is known to have bad beam angles, and some publications have to manually calibrate them in order to achieve good results. For example, in IMLS-SLAM by J. E. Deschaud:

The drift we get on the KITTI benchmark is not as good as the results we obtained with the Velodyne HDL32. This is due to three facts. First, we found a distortion of the scan point clouds because of a bad intrinsic calibration (we did a calibration of the intrinsic vertical angle of all laser beams of 0.22 degrees using the training data). Second, we found big errors in GPS data (used as ground truth) with, for example, more than 5 m in the beginning of sequence 8.

6.2 Range offsets

Lidars sometimes have different range offsets for each laser. This can happen when using discrete arrays where each laser-detector pair is a separate component that needs to be individually calibrated.

FIGURE 26 Two views of a flat wall in 2011_09_26/2011_09_26_drive_0084_extract/velodyne_points/data/0000000035.txt from the KITTI dataset. Due to uncalibrated range offsets for some of the lasers of the Velodyne HDL-64E used, points from certain beams are offset by several centimeters.

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6.3 Pixel crosstalk/blooming

Blooming affects many lidars. Think of pointing a camera at the sun. There would be huge lens flare and brightness all around the sun. In effect, the light from the sun is “smeared” out onto neighboring pixels. Likewise, when there’s a strong lidar return, there could be spurious returns next to the shiny object.

With array lidars, neighboring detectors sometimes pick up on the return meant for a different detector. This is called crosstalk. However, even single beam lidars can suffer from blooming just due to the fact that the beam has some divergence and that the optics are imperfect.

FIGURE 27 An early prototype of the now-defunct Argo lidar illuminates the scene a whole column at a time, making it susceptible to blooming in the form of vertical columns. Source: Argo AI on YouTube

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FIGURE 28 Spurious bloom returns around a retroreflector for an early Ouster OS1 prototype from 2022. Note that later firmware upgrades mitigated the issue. Source: Ouster marketing data.

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This effect typically can’t be easily calibrated away, and is usually handled in lidar firmware.

6.4 Intensity-dependent range bias

This typically affects SPAD lidars like early Ouster lidars and the now-defunct Argo (formerly Princeton Lightwave) lidar. The reason is that when the return is very strong, all the SPADs get saturated at the beginning of the pulse.

FIGURE 29 For specular reflections, there’s a spike in the point cloud from this early prototype of the Argo lidar. Source: Argo AI on YouTube

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FIGURE 30 Subtle range bias on highly reflective painted stripes of a pedestrian crossing for an early Ouster OS1 prototype from 2022. Source: Ouster marketing data.

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Typically, a pulse is a few nanoseconds long, which means up to a few meters in physical length of the light pulse. Even a slight saturation effect can cause the peak of the time series to be biased significantly. Very advanced signal processing techniques are needed to compensate for this.

6.5 Encoder hysteresis

Hysteresis in an encoder would typically manifest as some kind of lag, e.g. if it’s rotating clockwise, it could output measurements with a slightly different offset than when it’s at the same angle but rotating counter clockwise. Some lidars, such as the Luminar Iris, use encoders for an oscillating beam scanner for vertical beam scanning. It also has a mode where part of the point cloud is an “up-scan” and the other part is a “down-scan”, and the two are superimposed. Often, the point cloud of the up- and down-scans do not align well, even when the vehicle is stationary, suggesting that there may be hysteresis in the encoder.

This may manifest as double-layer point clouds in the ground.

6.6 Encoder physical offset

The encoder used in many spinning lidars is a circular ring with a bunch of ticks engraved on it at regular intervals.

However, it is possible that the encoder is physically offset to the side, because the ring is often just glued in place by humans. This results in a sinusoidal error.

\begin{aligned}f(\theta, \epsilon) = \tan^{-1}\left(\frac{\sin\theta}{\cos\theta + \epsilon}\right) \approx \theta - \epsilon \sin \theta\end{aligned}

FIGURE 31 Diagram of offset encoder and plot of measured angle vs true angle.

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This effect can cause a straight corridor to appear consistently curved to one side.

6.7 Multiple lidars in a box

Some lidars are packaged in such a way that there are two or more separate lidars in a box. For example, the Livox Mid 100 comprises three Mid-40s arranged side by side.

FIGURE 32 Livox Mid 40 and 100. Source: Livox website.

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FIGURE 33 Luminar Hydra has two separate lidars side by side. Source: Luminar via Venturebeat.

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Sometimes, physically jostling the lidar can cause the multiple separate lidars to become misaligned. It would then be necessary to treat them as separate lidars and calibrate their orientations accurately.

7 FAQ

7.1 LiDAR vs lidar

Should it be capitalized as “LiDAR” instead of “lidar”?

No! We should use the lowercase because it’s a commonly used word just like radar. When radar was some sort of highly exotic military technology, it made sense to use all caps for the acronym “radio detection and ranging”, but by now it is so common that we should use lowercase. Many other words started out capitalized when new and exotic, but became lowercase once commonplace:

laser, Light Amplification by Stimulated Emission of Radiation
scuba, Self-Contained Underwater Breathing Apparatus
taser, Thomas A. Swift’s Electric Rifle

Now that most phones and some cars are equipped with lidar, it’s a good time to just use lowercase. Perhaps the main barrier to doing so is Apple’s autocorrect.

7.2 Are rectangular lidars solid state?

No, not necessarily. Whether or not something is solid state is based on whether it has macroscopic moving parts in it, not based on its shape.

Livox lidars are often mistakenly assumed to be solid state, but they are in fact mechanically scanning with some using Risley prisms and some using mirrors.

Nonetheless, for some reason, many people somehow classify them as solid state lidars simply because they’re rectangular. For example:

Tangram Vision put it under “Solid State” even though it says it uses a “rotating mirror to change where it scans”.
SLAM Handbook made the same mistake. Fortunately, they fixed it after I created a GitHub issue.

FIGURE 34 They may be rectangular, but they aren’t solid state.

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Date: 2019-05-20 16:36:14

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Likewise, Luminar lidars are often assumed to be solid state, but they are not. The Luminar Hydra uses galvos and the Luminar Iris uses polygonal mirrors.

Solid state lidars are often perceived to be more durable and reliable. Lidar manufacturers have taken note of this market bias in customers, and marketed accordingly. For example, the Velodyne HDL-64 was marketed as solid state (even though it is externally spinning) in their 2016 press release announcing the VLP-32A.

Based on his experience during this challenge, Hall recognized the limitations of stereovision and developed the HDL-64 Solid-State Hybrid LiDAR sensor.

As justification, however, one might consider that it has an array of 64 lasers to distinguish vertical bearing, so perhaps it could be called 50% solid state as mechanical scanning is used only for the horizontal direction! In contrast, Luminar and Livox lidars use mechanical scanning for both directions, despite being a single non-spinning box.

FIGURE 35 Velodyne booth at CES 2020. In the back corner you can see David Hall.

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Date: 2020-01-09 13:17:30

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FIGURE 36 Velodyne formerly marketed these as “Solid-State Hybrid”.

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Asahi Linux with Sway on the MacBook Air M2

Sun, 01 Dec 2024 00:00:00 +0000

Asahi Linux with Sway on the MacBook Air M2

2024-12-01

I bought a MacBook Air M2. As of writing, it’s very affordable with the 16 GB RAM, 256 GB SSD, 13.6” model available for $750. As of writing, also Asahi Linux doesn’t support anything newer than M2.

buy on amazon

FIGURE 1 Pic of my laptop.

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Date: 2024-12-01 19:14:32

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I had previously used:

2011-2015: MacBook Air 13.3” with Intel Core i5 1.8 GHz, 8 GB of RAM, and 256 GB SSD (aftermarket upgrade from OWC). I installed Arch Linux on it with the i3 window manager.
2014-2018: Dell XPS 13 Developer Edition. I used the Ubuntu 14.04 that came with it with the i3 window manager.
2018-2024: Lenovo Thinkpad X1 Carbon Gen 6 with Intel Core i7 8640U, 16 GB of RAM, and 1 TB SSD. I installed Arch Linux on it with Sway.

1 Installing Asahi Linux

On the Asahi Linux website, there’s a one-liner which you can paste into the Terminal. It worked very well. The only complaint is that it seemed to take hours to copy root.img and boot.img over at 150 KB/s.

Since I intended to run it with the Sway Window Manager, and storage space is precious, I installed Fedora minimal.

2 Getting set up

I connected to wifi with

nmcli device wifi list
nmcli device wifi connect 'my_ssid' password 'mypassword'

and then I installed a bunch of packages I use, such as:

sudo dnf install @sway-desktop-environment fish alacritty rofi ruff rclone pavucontrol-qt i3status mako pass syncthing maim xdg-user-dirs firefox rustup openssl-devel ncdu fd-find neovim

Then, I cloned my personal dotfile git repo and ran setup.sh. Of course, my configs weren’t meant for the MacBook, so I had to make some changes (which I’ve pushed to the dotfiles).

3 Customizing for MacBook

By default, the whole row containing the notch is disabled, leading to a large-bezel look which I personally don’t like. There has got to be a way to use that screen real estate nicely!

I re-enabled that part of the screen with

grubby --args=apple_dcp.show_notch=1 --update-kernel=ALL

Then, I put the Sway bar on the top to make a seamless appearance where the left and right sides are used for useful information but the middle part is all black. By experimentation I found that the notch is 56px tall.

bar {
    position top
    status_command i3status
    modifier $mod
    tray_output primary
    # the height of the m2 macbook air's notch???
    height 56
    colors {
        background #000000
        statusline #cfcfd9
        separator #000000
        # border background text
        focused_workspace #0c0c0c #413459 #cfcfd9
        active_workspace #0c0c0c #413459 #cfcfd9
        inactive_workspace #0c0c0c #0c0c0c #cfcfd9
        urgent_workspace #2f343a #ff3300 #ffffff
    }
}

The full i3status shows a lot of information which might get occluded by the notch, and it doesn’t work with the MacBook battery levels by default, so I had to update the config:

general {
        colors = true
        interval = 5
}

order += "wireless _first_"
order += "ethernet _first_"
order += "battery 0"
order += "tztime local"

wireless _first_ {
        format_up = "W: (%quality at %essid) %ip"
        format_down = "W: down"
}

ethernet _first_ {
        format_up = "E: %ip (%speed)"
        format_down = "E: down"
}

battery 0 {
        format = "%status %percentage"
        hide_seconds = true
        path = /sys/class/power_supply/macsmc-battery/uevent
}

tztime local {
        format = "%Y-%m-%d %H:%M:%S"
}

I usually don’t like having the bar on the top (as with macOS), since you won’t be able to move your mouse cursor to the top edge to, say, click on tabs. Despite being mostly keyboard-driven, clicking on browser tabs with the mouse is something I still do often.

To fix that, I prevented the mouse cursor from entering the bar on the top, with

# use swaymsg -t get_inputs for the touchpad's identifier
input 1452:849:Apple_MTP_multi-touch map_to_region 0 56 2560 1608

3.1 Using Waybar instead

In around September 2025, I switched from the native Swaybar to Waybar. Somehow, I was running into some issues with swaymsg‘s handling of battery levels, and my computer ricing was due for a slight visual update anyway. It’s nice to save a tiny bit of screen real estate with icons instead of pure text, but of course, it is somewhat slower than Swaybar as it needs to render graphical stuff. The Waybar is still situated behind the notch.

The new waybar config and css are at waybar_config and waybar_style.

FIGURE 2 Screenshot with Waybar.

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4 Using it as a daily driver

I am very impressed with how smooth and problem-free Asahi Linux is. It is incredibly responsive and feels even smoother than my Arch Linux desktop with a 16 core AMD Ryzen 7945HX and 64GB of RAM.

The touchpad in particular is stunningly good and just as good as native macOS. The mouse cursor movement and two finger scroll with inertia just feel incredibly natural, much better than my old Thinkpad X1 Carbon.

One of the main reasons for getting the laptop was to use it for line scan photography. I was able to install the Alkeria SDK for ARM64 without any issues, even though it came as a deb file instead of an rpm. I didn’t manage to get alien to work properly (something about the architecture arm64 not matching Fedora’s convention of calling it aarch64?) so I just used bsdtar to extract the contents into the filesystem root, yolo!!! The M2 compiles my code super fast!

With high screen brightness and compiling lots of code, my battery went down from 100% to 60% after about 4.5 hours of use — not as good as the 15 hours of battery life on macOS but still pretty respectable.

That said, it isn’t perfect. Common issues are:

higher battery drainage during sleep, so I usually just shut it down entirely when not using it
no hardware acceleration for video decoding
some USB port quirks and external display quirks

Still, fairly decent overall.

FIGURE 3 Photo of me using my line scan camera with the Asahi Linux Macbook Air in London. Due to the Macbook’s tiny internal storage, I had to use an external 4 TB Crucial X8 SSD since the line scan camera racks up many gigabytes of data within seconds.

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Date: 2025-09-13 10:21:19

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How self-driving cars will not destroy cities

Mon, 11 Nov 2024 00:00:00 +0000

How self-driving cars will not destroy cities

2024-11-11

Recently, I watched this video below. For the record, I enjoy NotJustBikes, I am subscribed to and post on /r/fuckcars, and I fully embrace and love transit-oriented development and walkable cities.

That said, I don’t think this video is one of NotJustBike’s best videos. He ignores or downplays the legitimate benefits of the technology and instead makes a series of slippery slope fallacies as well as straw man arguments exaggerating some imagined downsides.

Don’t get me wrong, I largely agree with his overall message about improving urbanization. Self driving cars aren’t a panacea that will solve everything, and they do have lots of meaningful drawbacks, like the space efficiency just isn’t as good as a train.

But I believe that self driving cars are in fact a net public good, so the sheer amount of fallacies and leaps of logic in the NotJustBikes video was so annoying that I had to write this blog post.

FIGURE 1 I recently took a Waymo in San Francisco.

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Date: 2024-11-09 15:31:19

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1 Breaking down the claims

1.1 Moving fast and breaking people

The video opens with a lot of fearmongering about purported dangers of self driving cars, appealing to emotions rather than objective truth. But the numbers are clear: Waymo self driving cars are vastly safer than human drivers, as confirmed by insurance companies that have a vested interest in being objective about quantifying risk.

It is telling that in the many years and millions of miles driven, the video could only cling to two incidents — the Uber crash in Tempe (which happened almost a decade ago and Uber’s autonomous vehicle group got disbanded as a result), and the Cruise pedestrian dragging incident (which was caused by a human driver in the first place). Notably, the Waymo vehicles featured prominently in the video have not had any fatalities or at-fault injuries. In contrast, human drivers hit and kill pedestrians every day. Furthermore, not only do human drivers often negligently keep dragging the people they have run over, but they may even do it on purpose.

It is also strange that the video complains about rare instances of robotaxis blocking traffic — and then immediately encourages people to block traffic by putting cones on the vehicles. And not only is it strange, it is also cringy and not funny anymore. The “coning” trend died out a while ago. The city is very accepting of Waymo robotaxis now, in a great shift in attitudes from last year.

1.2 The real road safety issue

NotJustBikes points out the fact that US roads are poorly engineered and prioritize car convenience over pedestrian safety. This is a true and very real issue. However, it is an orthogonal topic from self driving cars, which can be implemented in either pedestrian-friendly urban environments, or in sprawling American suburbs.

The reason why cities have prioritized car convenience is due to political push from drivers. When most people drive instead of walking, they will vote for things that make driving more pleasant, especially as it is well known that people feel irrationally impatient and angry when driving.

I would wager that when the drivers are on their phones in robotaxis, they will stop paying attention to what slows a car down, and thus be less likely to care about traffic calming measures.

If anything, self driving cars thrive in pedestrian-friendly, dense cities more than in suburbs.

1.3 Promoting car centric cities

The video claims that American-trained self driving cars wouldn’t work in European cities and then ends with the bizarre assertion that tech companies won’t be able or willing to update their cars if the traffic patterns or rules change. First of all, this hasn’t even happened yet, so NotJustBikes is simply making up a fantasy scenario. Second, it is a lot easier for a company to update the software in their vehicles than to convince millions of human drivers to change their driving habits, so on the contrary it should be easier to make changes to cities.

The truth is that a lot of positive change away from car centric cities is blocked by human drivers, not by car company lobbyists or whatever. It is usually people — drivers, residents, and shopowners — who often vote and protest against things like bike lanes, such as the Fountain Avenue bike lanes in Los Angeles and bike path in Queens, New York.

In contrast, self driving cars will simply follow the rules of the road without complaining. Again, I find it quite plausible that it will be easier to implement changes like traffic calming, narrower streets, and bike lanes — when people are on their phones in robotaxis instead of at the wheel, they are much less likely to be frustrated by or to even notice infrastructure that slows cars down.

The video also makes the difficult-to-understand claim that once self driving cars have monopolized the transportation industry, they will stop prioritizing safety. But they are already safer than human drivers — does NotJustBikes think that somehow they will become more dangerous over time? Why would anyone do that? And how is a dangerous mode of transportation good business?

1.4 Cheaper taxis

Now, NotJustBikes makes a series of confused claims:

Self driving cars are just cheaper taxis and taxis do not solve any problems
Self driving cars are currently subsidized by tech companies and will become more expensive
People wouldn’t take them because they prefer their own cars, so they would buy self driving cars and take up lots of parking space
A criticism of self driving cars regarding people with disabilities, linking to an article from the Disability Visibility Project.

It seems he cannot make up his mind whether people will own them, or not, and whether it will be cheaper, or not. In any case, it’s just bad either way!

FIGURE 2 Even in Japan, where public transportation is as good as it gets, taxis are still useful.

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Regarding the point about Uber and Lyft subsidising the rides at the beginning and then hiking prices later, the fact is that taxis and rideshare are expensive because you have to pay a human driver. In fact, rideshare such as Uber and Lyft is by its very design meant to reinforce poverty: since each car can carry very few passengers, it is a mode of transportation that is very inefficient in terms of manpower needed. It is economically impossible to pay the drivers a reasonable living wage and affordable rideshares are only possible in places with extreme inequality. They are only feasible by exploiting a poorer underclass of workers, and back when Uber was offering very cheap rides, it had to be making a loss on every ride due to having to pay the driver. But this is unsustainable with human drivers as society advances so that the minimum standard of living rises. Whereas Uber and Lyft literally cannot afford to lower their prices, competing self driving cars have a lot of headroom for the prices to be lowered.

It is possible, of course, that self driving car companies will collude and price gouge everyone even when they are profitable, but healthy competition will likely result in lower prices. Furthermore, they would want to compete with the cost of driving private cars, which is around $0.67 per mile according to the 2024 IRS mileage rate — much lower than what Uber and Lyft are charging right now.

The notion that people would prefer to own private cars just out of a love of owning things is also a surprising insertion. This argument seems to weaken the final recommendation for public transport at the end of the video. I suppose that it is true for some people though.

Finally, regarding disabilities, the fact is that the last-mile problem of having to travel to the train station, or bus stop, is very difficult for people with disabilities. Contrary to the claims from the article from the Disability Visibility Project, self driving car companies have started offering wheelchair accessible rides, such as Waymo Wheelchair Accessible Rides in SF. And of course, as new designs like the Zoox robotaxi become a reality, the next generation of vehicles will be even more accessible.

1.5 Traffic congestion will be even worse

NotJustBikes asserts that the greater convenience of robotaxis will cause people to use them for frivolous trips and deliveries, and that people will send their kids to school in a robotaxi each, and that empty robotaxis will circle the block looking for passengers. The video mentions an anecdote where someone ordered a single tube of toothpaste. This contradicts the previous section where NotJustBikes claims that robotaxis will be just as expensive and inconvenient as taxis once companies stop subsidizing it.

1.5.1 Frivolous deliveries

One of the main reasons for frivolous orders is low cost. As NotJustBikes mentioned, once companies stop subsidizing it, it will not be so cheap as to be able to make frivolous orders.

On the other hand, if the cost of getting the good delivered is actually so low that you can get goods delivered for such a low price without subsidies, then that’s actually amazingly convenient. For sure, the number of vehicles may increase. But, without human drivers, deliveries can be done by space-efficient, low-speed delivery robots rather than full-fledged cars. In China, delivery robots take up barely more room than a bicycle and can travel in the bike lanes.

FIGURE 3 Sidewalk or bike lane delivery robots, made possible by the investment in self-driving technology, could deliver your bubble tea with minimal impact to traffic.

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Of course, as density increases, metro systems still reign supreme. In China, actually, they have subway train cars dedicated to delivering goods, and autonomous delivery robots can drive into the station to drop off or pick up their goods from the trains.

1.5.2 Sending kids to school

For every suburban mom who sends each child in a separate robotaxi, there is a suburban mom who used to have to make a round trip just to fetch her child from school, who can now just let her child take a robotaxi home. Having robotaxis for one-way trips instead of having to drive someone to their destination (and having to drive the car back, and then repeating this later to pick them up) is likely a more common scenario that would reduce traffic. Letting children get to school by robotaxi would also allow them to get more sleep in the morning, which is very beneficial to their development.

Of course, it would be even better if everyone could live close enough to school that biking or walking is possible, but that is not feasible everywhere.

It’s also worth pointing out that currently Waymo’s terms of service prevent children from riding solo.

1.5.3 Empty robotaxis circling

While it’s true that robotaxis are much less space efficient than buses or trains, they can be more space efficient than existing cars, rideshare, and delivery services.

Currently, tons of Uber drivers are driving around looking for rides, and Doordash drivers are taking up parking spots all around restaurants in hopes for a meal delivery opportunity.

Much of this unnecessary driving would be gone if the cars are driverless. Delivery vehicles can be smaller, as mentioned. For robotaxis, centralized dispatch would be able to more efficiently position them rather than individual drivers all flocking to hot spots or randomly roaming, looking for a gig. It is wasteful and costly to have a car spend energy driving for no reason, and you can be sure that this waste will be kept to a minimum out of pure capitalist greed.

1.6 Eliminating pedestrians, speed limits, and traffic lights

The video makes a classic slippery slope argument about “AV companies will demand” this and that. It makes the hilarious claim that AV companies will put up fences around the streets, eliminate speed limits, and eliminate street crossings.

Self driving car customers are pedestrians, actually! The moment a person gets out of the self driving car, they turn into a pedestrian. Making the city unnecessarily hostile to pedestrians makes no sense from a business perspective. On the contrary, self driving cars thrive in dense urban places full of desirable destinations to enjoy.

Eliminating speed limits also makes little sense. Whereas individual drivers are needlessly impatient due to psychological factors, there is little benefit for a robotaxi to go faster. Currently, Waymos will prioritize routing in a safer way even if it takes longer, and it avoids highways. The Zoox robotaxi does not go on highways either and it physically can’t even go very fast (the maximum speed is 75 mph and it is currently limited to 45 mph in practice). Not only is going faster dangerous, it greatly increases energy usage in a quadratic fashion, not to mention wear and tear on the vehicle itself.

The video proceeds to bash pedestrian bridges, as though it had something to do with self driving cars.

Then the video makes a tangent about eliminating traffic lights and having vehicle-to-vehicle communication that lets vehicles zip through intersections without stopping. This is mere vaporware and is simply not something that self driving car companies are developing or even want.

1.7 Pollution

NotJustBikes points out that even electric cars will not eliminate pollution due to tyres, brakes, and wear on asphalt. This is true, although the video further exaggerates it by fallaciously assuming that the self driving cars will go very fast.

Compared to existing cars, there are some benefits in favour of robotaxis:

Robotaxis can have a much smaller battery than private cars because urban transportation is mostly about short trips, and robotaxi operators do not have an irrational “range anxiety” as car buyers do. As such, they can be much lighter than privately-owned electric cars. The weight of the electronics, sensors, and computers will be negligible.
Electric cars are not only much quieter and eliminate tailpipe emissions, but also greatly reduce brake wear thanks to regenerative braking.

Furthermore, I will also point out that, contrary to popular belief, robotaxis can be a lot more energy efficient than public transportation. For example, the Tesla Cybercab has an efficiency of 112 Wh/km and can carry two passengers. This is about 0.4 MJ/km, or 0.2 MJ/km assuming two passengers (or perhaps 0.5 MJ/km/passenger if we consider the time it spends driving around empty).

FIGURE 4 The aerodynamic design of the Tesla Cybercab allows it to be more efficient than buses.

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To compare against public transportation modes, let’s compare against figures from the Transportation Energy Data Book Edition 40, Table 2.12, although the table has the caveat that

Great care should be taken when comparing modal energy intensity data among modes. Because of the inherent differences among the transportation modes in the nature of services, routes available, and many additional factors, it is not possible to obtain truly comparable national energy intensities among modes. These values are averages, and there is a great deal of variability even within a mode

Mode	Energy (MJ/passenger/km)
Cars	1.8
Personal trucks	2.1
Motorcycles	1.5
Transit buses	3.0
Air	1.5
Intercity rail	1.0
Transit rail	0.6
Commuter rail	1.0
Tesla Cybercab	0.5

Table 1 Energy consumption by modality from the Transportation Energy Data Book.

The International Energy Agency provides somewhat different figures:

Mode	Energy (MJ/passenger/km)
Cars	1.8
Large cars	2.7
Two/three wheelers	0.5
Buses	0.7
Rail	0.2
Tesla Cybercab	0.5

Table 2 Energy consumption by modality from International Energy Agency.

In any case, the Tesla Cybercab is really good and at least competitive with public transit.

1.8 Utrecht vs London, Ontario

The video then makes a comparison between Utrecht and London, Ontario. Both cities started out with similar car-dependent infrastructure, but then Utrecht decided to embrace transit-oriented development. I fully agree that obviously transit-oriented development and walkable streets are amazing, and applaud Utrecht for doing so.

However, Utrecht is a lot richer than London, Ontario, and has four times the GDP, so it is comparatively easier for it to install expensive new infrastructure. Moreover, despite having good bike infrastructure and public transport, car ownership has actually risen in the Dutch population.

The video also makes a funny statement that Utrecht managed to achieve this “without spending billions” on developing self driving technology — as though you have to spend billions for each city, rather than it being a one-time cost that can scale up to many different cities at once.

1.9 What should we do about it?

This is the section that I largely agree with. After going on a whole tirade of nonsensical fearmongering, NotJustBikes has finally arrived at — well, the exact same conclusion that every other one of his videos shares. The same urbanist vision that I agree with.

I agree that we should limit access to cars, both autonomous and otherwise. I am a big fan of walkable streets and mixed use neighborhoods. I also love public transportation. Road usage tax is probably also not a bad idea.

I suppose we differ in our attitudes on how to achieve this urban utopia though. I believe that robotaxis are helpful in reducing car dependence and will solve many problems, but NotJustBikes seems to think that they are a deleterious force to be kept in check.

2 The case for self driving cars

2.1 People will give up their cars

Some people will actually give up private car ownership for self driving cars, but not for other forms of public transit.

Many people who enjoy travelling by car value the following things:

Comfort
Perceived safety
Convenience

Self driving cars provide these, but in some American cities, other forms of public transit may be lacking in certain respects. For example, a common complaint is that public transportation is perceived as less safe due to rampant drug use and property crime, even though violent crime is in fact quite rare. Thanks to the availability of self driving cars, I personally know people living in San Francisco who are not planning to buy a car because, in addition to public transportation, Waymo provides the necessary flexibility for special trips.

Once fewer people buy cars, there will in fact be less parking needed, and then walkable development becomes even more feasible.

2.2 Biking becomes safer

The main reason why most people don’t bike in the US is because it is dangerous with all the cars around. Self driving cars simply don’t hit bicycles, while distracted, negligent, and sometimes belligerent human drivers often do.

If all the cars were replaced by self driving cars, I would buy a bicycle the next day. This sentiment is shared by many on reddit.

Of course, dedicated bicycling infrastructure, like in Utrecht, would be better. But literally rebuilding all the existing stroads and building that much bike infrastructure takes a lot of capital and political willpower and the probability of that happening, say, in San Jose where I live, is practically zero.

There is a benign reinforcement loop where safer self driving cars allow more people to bike, and more people biking results in more money being spent on bike infrastructure, and then everyone wins in the end.

2.3 Compatibility with public transport

Self driving cars are fundamentally compatible with public transportation in a way that private automobiles are not.

The sad fact is that currently, large swaths of the US are composed of boring suburbs that are simply too spread out. The only way to get to public transportation is, ironically, to drive. And once you start driving, well, why not drive all the way?

As such, “park-and-ride” schemes are a horrible failure. Parking lots take up a huge amount of space around transit stations, making them useless to anyone else but drivers, and the drivers themselves find it neither cost-effective nor time-efficient to park their cars in order to take transit.

Imagine getting from South San Jose to San Francisco. The Caltrain should be superior in every way to driving, in theory. Especially with its recent upgrades, it is efficient and comfortable and competitive with driving in terms of speed. The problem of course is that my house is too far away from the Caltrain station and the bus that goes from my house to there only comes once every 30 minutes and needs 20 minutes of walking.

FIGURE 5 I would surely take this more if I could get to the station by robotaxi.

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Having robotaxis would solve this problem, and then parking spots around “park-and-ride” stations can be eliminated and turned into housing and shops.

2.4 Maybe some public transport just isn't that good?

Some public transit systems in the US are just so underutilized that they are just not that good. San Mateo buses cost $4.30 per passenger mile which is more than a robotaxi.

For less densely populated areas, I think that robotaxis should replace these infrequent and ineffective public transit services, while arterial trains and buses should get more funding and improvements.

Consider the topic of getting to the Caltrain station in the previous section. The bus comes once every 30 minutes and you’d have to walk 20 minutes to get there. Nobody wants that. It’s essentially a useless service that costs the city a lot of money. Anecdotally, the buses are always nearly empty.

Replacing those empty buses with robotaxis should actually reduce space used and encourage more ridership of actually good public transport.

2.5 Weather

NotJustBikes points out that driving in Phoenix, Arizona is “easy mode”. Apart from planning foolishness, another good reason for it to be so car-dependent is its ridiculously hot climate. It is simply unpleasant to exist outdoors, not to mention walk anywhere when the temperatures exceed 310 K.

Anyway, a strength of self driving cars is that they can function in any weather. In contrast, bicycling is unpleasant in the rain, snow, and extreme heat, and public transportation is unpleasant when you have to walk a long distance exposed to the elements to get to the bus or train station. However, taking the train becomes a lot more attractive if you have the option to either walk, or take a robotaxi when it’s pouring.

2.6 Carpooling

Just as Uber has implemented Uber Pool, it is quite conceivable that robotaxis will implement carpooling to further lower costs and improve efficiency.

As an aside, some people wonder why the Tesla Cybercab is a two seater. The reason is that the vast majority of car trips only have one or two people, so the aerodynamic two seater is way more efficient. It actually uses less than half the energy of some other electric cars. And don’t forget that the Model Y still exists and can function as a robotaxi if you need more seats.

3 What about the drivers?

A common criticism against self driving cars is that the efficiency gains will only serve to profit the big tech companies, leaving existing drivers penniless. However, history shows us that automation is almost always a net positive in the long run.

It is important to remember that humanity used to be mostly farmers:

pre-1800s: 70%–90%
1800s: 50%–80% in industrializing regions
1900s: ~40% at the beginning of the century, ~5% by the mid-century in developed regions
2000s: 26% globally, <2% in developed countries

Thanks to improved agricultural tools and mechanization (and recently automation), nowadays in developed countries less than 2% of the population can feed the remaining 98%, and now we have enough food to feed everyone.

We note that, even with enough food to feed everyone, some people are still going hungry, even though this amount is much less than before. This is due to inequalities in society that we have yet to solve. But, to me, it is clear in retrospect that the improvements in agriculture are a net good for humanity. Although some individual farmers had a bad time with the gradual change, and some evil corporations like the United Fruit Company got rich as a result, humanity as a whole is vastly better off than before: innovations and industries (healthcare, education, technology) that benefit society wouldn’t have developed if most of the population were still focused on subsistence farming.

I personally wish for other industries that provide basic necessities for life, such as transportation, to undergo the same shift where very few people are needed to provide this necessity, freeing up the rest to work in more productive avenues. Hopefully the bulk of the work will be done by automation, just like in farming, rather than exploiting the labor of a large underclass.

Honestly, the fact that a whole human being with thoughts and dreams is relegated to the mindless task of driving an Uber or truck or whatever reminds me of the robot whose purpose in life is to pass butter. It’s a terrible waste of a human mind, just like countless human lives were wasted on subsistence farming in antiquity, and we should put an end to it as soon as possible.

In the case of agriculture, surplus labor from agriculture often took up employment in new, emerging sectors—particularly services like education, healthcare, and transportation, which became more prominent as societies became wealthier and more urbanized. As Uber drivers eventually get outcompeted by more efficient means of transportation, I have no doubt that the surplus labor will look for employment in other emerging sectors. Human beings are smart, resilient, resourceful, and adaptable.

4 Conclusion

I think self driving cars are a positive change that will improve safety, encourage more public transportation use, and improve density and urbanization, while not causing the problems of congestion and pollution described in the video. Robotaxis are especially suited to bringing these improvements to American cities that are currently entrenched in car-dependent infrastructure, and provide a useful route towards reducing car dependency.

We should be reducing car dependency in conjunction with embracing self driving cars. Self driving cars should not be singled out to be targeted by regulatory lawfare. Zoning laws should be relaxed and parking minimums should be abolished so that it is possible to build housing and shops in places where there used to be barren parking lots. Places like, say, Chinatown in San Francisco should be converted to a pedestrian-only boulevard without cars.

Dogs

Mon, 07 Oct 2024 00:00:00 +0000

Dogs

2024-10-07

Here are some dogs that I’ve had the pleasure of meeting.

1 Nono the Pomeranian

Nono is a female Pomeranian born in 2023-12.

nonothepompom on Instagram

FIGURE 1 Nono

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FIGURE 2 Nono

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FIGURE 3 Nono

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FIGURE 4 Nono

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FIGURE 5 Nono

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2 Kailewey the Samoyed

Kailewey is a female Samoyed born on 2023-05-31.

kailewey on Instagram

FIGURE 6 Lewey

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FIGURE 7 Lewey

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FIGURE 8 Lewey

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3 Emily the Samoyed

Emily is a female Samoyed born in 2023-02.

FIGURE 9 Emily

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FIGURE 10 Emily

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FIGURE 11 Emily

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Date: 2023-07-04 16:05:27

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FIGURE 12 Emily

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Date: 2023-09-24 14:42:31

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FIGURE 13 Emily

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Date: 2023-09-24 15:01:19

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4 Syl the Samoyed

Sylphrena is a female Samoyed born on 2021-11-26.

sylphrena.samoyed on Instagram

FIGURE 14 Syl

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FIGURE 15 Syl

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FIGURE 16 Syl

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FIGURE 17 Syl

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5 Yumi the Shiba Inu

Yumi is a four year old female cream Shiba Inu.

yumicreamshiba on Instagram

FIGURE 18 Yumi

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FIGURE 19 Yumi

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FIGURE 20 Yumi

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FIGURE 21 Yumi

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FIGURE 22 Yumi

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Date: 2024-04-08 19:23:40

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6 Meepo the Corgi

Meepo is a male corgi.

FIGURE 23 Meepo

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FIGURE 24 Meepo

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FIGURE 25 Meepo

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Date: 2022-07-04 15:27:51

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Comparison of two train systems

Wed, 25 Sep 2024 00:00:00 +0000

Comparison of two train systems

2024-09-25

Here I will compare Caltrain and the Guangzhou-Zhuhai Intercity rail. It’s an apples-to-oranges comparison, as the two systems are built in very different places, with very different needs and considerations. So this comparison is just for fun!

FIGURE 1 Caltrain Stadler KISS EMU

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FIGURE 2 CRH-6A, such as on the Zhuhai-Guangzhou intercity line

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Train	Caltrain	Guangzhou-Zhuhai
Type of construction	Modernization, grade separation, electrification	New construction from scratch
Rolling stock	Stadler KISS EMU caltrainwiki	CRH-6A EMU gzwiki
Length (km)	77 km (San Jose to San Francisco) caltrainwiki	116 km gzwiki
Operating speed	127 km/h (79 mph) caltrainwiki	200 km/h (120 mph) gzwiki
Cost	USD 2.72B caltraincost	RMB 18.19B (USD 2.59B) gzdoc
Grade separated	$\times$ (71 at grade crossings left crossings)	$\checkmark$ (92% viaducts gzwiki)
Commenced	2017-07 calmodwiki	2005-12 gzwiki
Opened	2024-09 calmodwiki	2011-01 gzwiki
Time to build	7 years	5 years
Daily ridership	24583 caltrainwiki	342000 gzwiki
Goes along a bay area	$\checkmark$ yes	$\checkmark$ yes

Table 1 Comparison of two systems.

At first, the fact that the Caltrain modernization program cost more than it took to build the whole Guangzhou-Zhuhai intercity line from scratch may seem like a shock. But this is easily explained by the following facts:

The cost of labor in the San Francisco Bay Area is many times higher than in the Guangdong Bay Area.
The US does not have the economies of scale for, say, viaduct manufacturing that comes with a rapidly expanding nationwide high speed rail network as in China.
The Caltrain program was much later, so inflation plays a role.

The other observation is that Caltrain is a slower line. First of all, it doesn’t make too much sense to invest hugely to speed up a line that has a ridership less than a tenth of the Guangzhou-Zhuhai intercity, so the current speed, which roughly matches driving, is acceptable. Secondly, there are some technical reasons. In fact, the Stadler KISS trains are capable of up to 200 km/h, but the speed is limited in practice for two reasons: a large number of crossings, and the turns and track quality of the old line. Currently, there’s a big effort to eliminate grade crossings crossings, so at least the first reason will be gone hopefully!

Also, the fact that Caltrain has bilevel coaches comes with tradeoffs. It is a cost-effective way to provide nearly twice the capacity given the same frequency and platform length. However, it reduces the speed of boarding.

Finally, some technical quirks further reduce Caltrain’s performance. For example, the trains take an extra 8-10 seconds to begin opening the doors when stopping at stations, resulting in a high dwell time. Hopefully these quirks will be ironed out soon!

1 References

caltrainwiki Caltrain on Wikipedia https://en.wikipedia.org/wiki/Caltrain
calmodwiki Caltrain modernization program on Wikipedia https://en.wikipedia.org/wiki/Caltrain_Modernization_Program
crossings Caltrain Corridor Crossings Strategy https://www.caltrain.com/projects/ccs
caltraincost San Francisco County Transportation Authority https://www.sfcta.org/projects/caltrain-modernization
gzwiki Guangzhou-Zhuhai intercity railway on Wikipedia https://en.wikipedia.org/wiki/Guangzhou%E2%80%93Zhuhai_intercity_railway
gzdoc 新建铁路广州至珠海城际快速轨道交通工程调整补充环境影响报告书 https://jz.docin.com/p-706353308.html

Trip to Japan and Hong Kong

Tue, 24 Sep 2024 00:00:00 +0000

Trip to Japan and Hong Kong

2024-09-24

Joanne and I went to Japan and China again! The itinerary is: three nights in Tokyo, three in Hong Kong, about a week in Zhuhai (with a one night trip to Guangzhou in between), and a night in Macao.

1 Sunday 2024-09-08: Arrival in Japan

We took Japan Airlines flight JL1 from SFO to HND.

FIGURE 1 Airplane food.

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Date: 2024-09-07 13:52:52

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FIGURE 2 Airplane food.

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We stayed at All Day Place Shibuya, a hotel catered to Westerners.

FIGURE 3 Our room at All Day Place Shibuya.

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FIGURE 4 View in Shibuya.

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Date: 2024-09-08 01:35:32

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1.1 Ikebukuro

My friend Andrew and his wife made a special one hour trip to grab dinner with us in Ikebukuro. We had pork skewers (yakiton), along with two of my other friends.

FIGURE 5 View in Ikebukuro.

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FIGURE 6 Pork skewers.

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I then grabbed Joanne and one of my friends to see Uonami Fish Bar in Toshima city. Apparently, this shop is a meme photo spot on Reddit and other social media sites. Little did I know, a festival was going on with a huge amount of people! Also, we had trouble navigating Ikebukuro station (we didn’t have cellular data since I left my phone with free data roaming at home in California), so going to the fish bar was a terrible ordeal that took an hour for what we thought was a mere one mile trip. We were exhausted from the flight, and extremely grumpy as a result!

FIGURE 7 Uonami fish bar.

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Date: 2024-09-08 03:46:02

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2 Monday 2024-09-09: Akihabara and tempura

2.1 Breakfast in Shibuya: Shinpachi Shokudo

We went to Shinpachi Shokudo for a quick and cheap breakfast. I got a grilled mackerel teishoku. It was quite affordable:

Set (teishoku): ￥440
Raw egg addon: ￥30
Negitoro (minced tuna with scallions) addon: I forgot but it was very cheap

FIGURE 8 Grilled mackerel teishoku.

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Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 1/27 s
ISO: ISO 1250
Software: darktable 4.8.1
Date: 2024-09-08 17:17:09

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2.2 Lunch in Akihabara: Gyukatsu

Four of my friends and I went to Akihabara for sightseeing.

FIGURE 9 A small camera bazaar on the pavement.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/4.0
Shutter: 1/34 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.12
Date: 2024-09-09 12:00:10

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For lunch, we had Gyu Katsu Motomura. It’s lightly fried steak which you then grill to your desired doneness. It was very delicious — the beef was very tender and marbled, and the batter was very light.

FIGURE 10 Gyukatsu set.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/42 s
ISO: ISO 1250
Software: darktable 4.8.1
Date: 2024-09-09 12:48:34

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FIGURE 11 You grill the meat to your desired level.

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Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/4.0
Shutter: 1/42 s
ISO: ISO 500
Software: darktable 4.8.1
Date: 2024-09-09 13:01:25

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FIGURE 12 Sofmap, known for its cool escalator tubes.

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Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 1/180 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-09 12:22:47

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FIGURE 13 Obligatory street view of Akihabara.

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Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/480 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-09 13:29:58

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We got ice cream at Azabu Sabo and then went shopping for camera lenses! We went to Yodobashi Camera as well as Lemon and 2nd Base by Sanpou Camera. However, I didn’t find any good lenses to buy.

2.3 Back to Shibuya

FIGURE 14 Took the Ginza line back to Shibuya.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/42 s
ISO: ISO 2500
Software: darktable 4.8.1
Date: 2024-09-09 16:03:08

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FIGURE 15 Shibuya Crossing seen from the metro station.

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Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/1.7
Shutter: 1/550 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-09 16:36:23

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I spotted a really cool Porsche 930 slantnose! There are lots of cool cars in Shibuya. I am not sure if this was an original slantnose — only 948 units were built in total. Slantnoses were each built by hand by remodeling the front fenders and cost up to 60% more than the standard price at the time.

FIGURE 16 Porsche 930 slantnose.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 1/400 s
ISO: ISO 800
Software: darktable 4.8.1
Date: 2024-09-09 16:50:23

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FIGURE 17 Porsche 930 slantnose.

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Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 1/340 s
ISO: ISO 800
Software: darktable 4.8.1
Date: 2024-09-09 16:50:24

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I took a shower and rested a bit at our hotel.

FIGURE 18 Our hotel.

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Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 1/180 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-09 16:55:42

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2.4 Dinner in Roppongi: Tempura Yokota

For dinner, we booked Tempura Yokota. It’s situated right next to the similarly-named Ten Yokota, which is Michelin starred. Tempura Yokota is supposed to be the (slightly) inferior of the two. It was run by the dad of the guy who runs Ten Yokota, until 2021, when he was replaced by another one of his apprentices. Just like Ten Yokota, this apprentice is also apparently much better than his master.

To save space, I’ll just post eight out of the 21 courses.

FIGURE 19 Shrimp.

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Lens: 125
Aperture: f/NaN
Shutter: 1/52 s
ISO: ISO 3200
Software: darktable 4.8.1
Date: 2024-09-09 18:30:58

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FIGURE 20 Green pepper.

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Lens: 125
Aperture: f/NaN
Shutter: 1/40 s
ISO: ISO 3200
Software: darktable 4.8.1
Date: 2024-09-09 18:36:48

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FIGURE 21 Ayu.

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Lens: 125
Aperture: f/NaN
Shutter: 1/56 s
ISO: ISO 3200
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Date: 2024-09-09 18:38:49

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FIGURE 22 Sashimi course.

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Lens: 125
Aperture: f/NaN
Shutter: 1/58 s
ISO: ISO 3200
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Date: 2024-09-09 18:40:44

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FIGURE 23 Cooked fish, and a snail.

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Lens: 125
Aperture: f/NaN
Shutter: 1/50 s
ISO: ISO 3200
Software: darktable 4.8.1
Date: 2024-09-09 18:46:47

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FIGURE 24 Maitake mushroom.

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Lens: 125
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Shutter: 1/52 s
ISO: ISO 3200
Software: darktable 4.8.1
Date: 2024-09-09 18:47:41

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FIGURE 25 Scallop with uni on top.

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Lens: 125
Aperture: f/NaN
Shutter: 1/45 s
ISO: ISO 3200
Software: darktable 4.8.1
Date: 2024-09-09 18:52:40

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FIGURE 26 Shrimp with caviar.

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Lens: 125
Aperture: f/NaN
Shutter: 1/45 s
ISO: ISO 3200
Software: darktable 4.8.1
Date: 2024-09-09 19:02:21

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FIGURE 27 Conger eel.

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Camera: FUJIFILM GFX100S
Lens: 125
Aperture: f/NaN
Shutter: 1/34 s
ISO: ISO 3200
Software: darktable 4.8.1
Date: 2024-09-09 19:15:00

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2.5 Post-dinner in Shibuya

We went to SOAK at the top floor of Sequence Hotel on Miyashita Park.

FIGURE 28 Some kind of cocktail. In the background is my friend Anting.

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Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/2.0
Shutter: 1/40 s
ISO: ISO 1600
Software: Digital Camera GFX100S Ver2.12
Date: 2024-09-09 21:28:23

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FIGURE 29 View of the Shibuya Sky Tree and Miyashita Park.

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Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/1.7
Shutter: 1/9.0 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-09 21:47:04

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FIGURE 30 View of Tower Records in Shibuya.

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Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/1.7
Shutter: 1/170 s
ISO: ISO 3200
Software: darktable 4.8.1
Date: 2024-09-09 21:54:36

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3 Tuesday 2024-09-10: Shibuya, Shinjuku

For breakfast, we had western food in our hotel, that is, Good cheese good pizza. It’s actually really good.

FIGURE 31 Tartine with smoked salmon and avocado.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/50 s
ISO: ISO 400
Software: darktable 4.8.1
Date: 2024-09-10 07:13:34

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Then we went to Google Japan for second breakfast!

FIGURE 32 Google Japan Shibuya building.

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Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 1/140 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-10 15:21:54

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FIGURE 33 View south.

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Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/8.0
Shutter: 1/200 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-10 10:03:31

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FIGURE 34 View east.

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Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 1/450 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-10 10:05:49

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3.1 Lunch: Unaju

We went to Unagi Toku. It’s not as good as Unatetsu which we went to in December 2023, but still vastly better than almost any unagi in the US. The unagi was very charred and smoky which is great.

FIGURE 35 Unagi Toku.

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Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/5.0
Shutter: 1/27 s
ISO: ISO 1000
Software: darktable 4.8.1
Date: 2024-09-10 11:46:53

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In the afternoon, I went to Map Camera Shinjuku to hunt for lenses. However, I didn’t find any that I wanted. I was looking for a fast 50mm or 35mm lens that would work reasonably on both my line scan camera and my Fuji GFX camera, but the Voigtländer Nokton 50mm f/1.2 VM is discontinued and impossible to find, and the 50mm f/1.5 is out of stock until November. Oh well.

Anyway, I met up with an online friend, Jake, and we chatted about cameras and then went to take photos of trains!

FIGURE 36 Voigtländer lenses are cheap at Map Camera in Japan.

EXIF data

Camera: FUJIFILM GFX100S
Lens: 125
Aperture: f/NaN
Shutter: 1/180 s
ISO: ISO 2500
Software: darktable 4.8.1
Date: 2024-09-10 13:47:48

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3.2 Dinner: Izakaya

We went to Shirubee for dinner. It’s a very local Japanese restaurant where everyone there was Japanese, and the menu was handwritten in Japanese. Fortunately, we can read a bit of Japanese. We ordered a ton of food including the grilled mackerel and pacific saury.

FIGURE 37 Grilled mackerel. Nothing A015 with , 4.7 mm, F1.9, 1/100 s, ISO 454.

EXIF data

Camera: Nothing A015
Aperture: f/1.8
Shutter: 1/100 s
ISO: ISO 454
Software: MediaTek Camera Application
Date: 2024-09-10 17:23:55

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FIGURE 38 Grilled pacific saury (sanma no shioyaki). It was circled in a big red circle on the handwritten menu so we knew it had to be good. Nothing A015 with , 4.7 mm, F1.9, 1/100 s, ISO 428.

EXIF data

Camera: Nothing A015
Aperture: f/1.8
Shutter: 1/100 s
ISO: ISO 428
Software: MediaTek Camera Application
Date: 2024-09-10 17:53:58

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FIGURE 39 Sashimi moriawase. Nothing A015 with , 4.7 mm, F1.9, 1/100 s, ISO 418.

EXIF data

Camera: Nothing A015
Aperture: f/1.8
Shutter: 1/100 s
ISO: ISO 418
Software: MediaTek Camera Application
Date: 2024-09-10 17:51:58

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4 Wednesday 2024-09-11: Flight to Hong Kong

4.1 Breakfast in Shibuya: Ichiran

Upon waking up, we had Ichiran Ramen for breakfast.

FIGURE 40 Ichiran ramen.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/2.8
Shutter: 1/42 s
ISO: ISO 640
Software: darktable 4.8.1
Date: 2024-09-11 07:52:51

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Then we went to Blue Bottle Coffee for second breakfast! They had a cheesecake brulee which is a Shibuya-exclusive item.

FIGURE 41 Blue Bottle Cheesecake.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/42 s
ISO: ISO 160
Software: darktable 4.8.1
Date: 2024-09-11 08:36:55

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4.2 Morning stroll in Yoyogi Park

FIGURE 42 Performance auditorium thing.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/250 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-11 09:14:16

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FIGURE 43 Yamanote Line.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/1400 s
ISO: ISO 400
Software: darktable 4.8.1
Date: 2024-09-11 09:31:41

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4.3 Lunch: Ochazuke

We went to Komeraku which was right next to Unagi Toku from yesterday’s lunch. We were too lazy to search for food based on reviews and despite its rather average rating on Tabelog (which we looked up after the fact), it was still delicious.

FIGURE 44 Ochazuke

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/6.4
Shutter: 1/27 s
ISO: ISO 1250
Software: darktable 4.8.1
Date: 2024-09-11 11:39:20

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Then we took the train to the airport!

FIGURE 45 Keisei Skyliner to the airport.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/4.0
Shutter: 1/640 s
ISO: ISO 400
Software: darktable 4.8.1
Date: 2024-09-11 13:04:10

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4.4 Second lunch: Sushi

I got some takeout sushi at the airport just before boarding the flight to Hong Kong.

FIGURE 46 Takeout sushi.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 1/27 s
ISO: ISO 250
Software: darktable 4.8.1
Date: 2024-09-11 14:41:33

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4.5 Dinner: Barbecue rice

At the Hong Kong airport we got a late dinner. I got roast goose and soya sauce chicken on rice.

FIGURE 47 Roast goose and soya sauce chicken on rice.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/4.0
Shutter: 1/75 s
ISO: ISO 3200
Software: darktable 4.8.1
Date: 2024-09-11 20:20:04

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5 Thursday 2024-09-12: Palace Museum

5.1 Breakfast: Cha chaan teng

First we took the tram to get breakfast.

FIGURE 48 View from the tram.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 1/40 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-12 06:59:17

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FIGURE 49 A random taxi.

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Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/7.1
Shutter: 1/20 s
ISO: ISO 200
Software: darktable 4.8.1
Date: 2024-09-12 07:02:47

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We got breakfast at the Milk Cafe.

FIGURE 50 Milk cafe.

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Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/4.0
Shutter: 1/240 s
ISO: ISO 800
Software: darktable 4.8.1
Date: 2024-09-12 07:09:31

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FIGURE 51 Luncheon meat and egg sandwich.

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Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/4.0
Shutter: 1/75 s
ISO: ISO 800
Software: darktable 4.8.1
Date: 2024-09-12 07:18:48

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5.2 Extreme lens shopping

After breakfast, we went to Sim City in Mong Kok to hunt for camera lenses. This was a terrible mistake since most shops only open at noon! However, I did manage to find a Voigtländer Ultron 35mm f/1.7 which works surprisingly well on the Fuji GFX camera. It cost me HKD 3500. This lens would be very useful for taking line scan photos of trams.

FIGURE 52 Shelf full of Voigtländer lenses.

EXIF data

Camera: FUJIFILM GFX100S
Lens: 60
Aperture: f/NaN
Shutter: 1/9.0 s
ISO: ISO 3200
Software: darktable 4.8.1
Date: 2024-09-12 11:26:36

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FIGURE 53 The lens I bought, mounted on the line scan camera.

EXIF data

Camera: FUJIFILM GFX100S
Lens: 125
Aperture: f/NaN
Shutter: 12.0 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-12 18:31:00

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5.3 Lunch: Rùn

For lunch we went to the Rùn restaurant with two Michelin stars.

FIGURE 54 Har gow.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/6.4
Shutter: 1/42 s
ISO: ISO 125
Software: darktable 4.8.1
Date: 2024-09-12 12:30:25

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FIGURE 55 Char siu.

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Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/4.0
Shutter: 1/52 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-12 12:34:34

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FIGURE 56 Fried balls.

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Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/4.0
Shutter: 1/60 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-12 12:51:24

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FIGURE 57 Most delicious fried rice we have ever had.

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Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/4.0
Shutter: 1/60 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-12 12:53:25

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FIGURE 58 Guava dessert.

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Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/4.0
Shutter: 1/42 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-12 13:14:05

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Overall, it was extremely delicious and highly recommended. The service was also outstanding.

5.4 Palace Museum

After lunch, we took a ferry to Kowloon.

FIGURE 59 Watching the ferry depart as we just missed it upon arriving at the ferry terminal.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/600 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-12 14:01:36

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FIGURE 60 A cool ship seen from the ferry.

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Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/5.6
Shutter: 1/750 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-12 14:17:27

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We went to the Hong Kong Palace Museum, which was recently built in 2022. It’s very spacious and the exhibits are great. The architecture of the museum is also quite nice. Like many emperors of the late Qing Dynasty, I enjoyed looking at the very elaborate clocks.

FIGURE 61 The Hong Kong Palace Museum.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 1/500 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-12 14:44:02

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FIGURE 62 A cool clock.

EXIF data

Camera: FUJIFILM GFX100S
Lens: 35
Aperture: f/NaN
Shutter: 1/52 s
ISO: ISO 2000
Software: darktable 4.8.1
Date: 2024-09-12 16:14:20

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FIGURE 63 Another cool clock.

EXIF data

Camera: FUJIFILM GFX100S
Lens: 35
Aperture: f/NaN
Shutter: 1/18 s
ISO: ISO 3200
Software: darktable 4.8.1
Date: 2024-09-12 16:22:49

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FIGURE 64 Breakwater.

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Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/8.0
Shutter: 1/400 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-12 16:51:46

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5.5 Dinner: Wonton noodle

For dinner we took the bus to Shek Kee Wan Ton. The owner looks very mean but is actually very nice once you start talking to her. The restaurant is very old school and has many photos of celebrities on the wall.

FIGURE 65 Interior of the restaurant.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/4.0
Shutter: 1/15 s
ISO: ISO 125
Software: darktable 4.8.1
Date: 2024-09-12 17:17:27

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FIGURE 66 Combination soup.

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Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/4.0
Shutter: 1/27 s
ISO: ISO 320
Software: Digital Camera GFX100S Ver2.12
Date: 2024-09-12 17:23:32

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FIGURE 67 Shek Kee Wan Ton on the right.

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Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/4.0
Shutter: 1/15 s
ISO: ISO 250
Software: darktable 4.8.1
Date: 2024-09-12 17:44:02

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After wontons we went back to the hotel.

FIGURE 68 Chungking Mansions.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/7.1
Shutter: 1/15 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-12 17:53:12

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FIGURE 69 A stairwell in Tin Hau station.

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Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 1/15 s
ISO: ISO 500
Software: darktable 4.8.1
Date: 2024-09-12 18:12:45

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FIGURE 70 Extreme bamboo scaffolding on the Pak Lee Mansion!

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 1/15 s
ISO: ISO 200
Software: darktable 4.8.1
Date: 2024-09-12 18:16:49

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6 Friday 2024-09-13: Kennedy Town and the Peak

6.1 Breakfast: Porridge

For breakfast we went to Chung Kee Congee Shop. Whereas Joanne got the porridge, I got the cheong fun. It was decent but not too amazing compared to the thin, wrinkly ones we later got in Zhuhai. I guess they are more known for their porridge.

FIGURE 71 Cheong fun.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/4.0
Shutter: 1/27 s
ISO: ISO 200
Software: darktable 4.8.1
Date: 2024-09-13 09:24:19

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6.2 Kennedy Town

In Kennedy Town, there is a well known meme photo spot at a basketball court. You’re supposed to take a photo through a hole that someone dug in a chain link fence. So we went there — here’s the resulting photo.

FIGURE 72 View of Kennedy Town from a basketball court.

EXIF data

Camera: FUJIFILM GFX100S
Lens: 125
Aperture: f/NaN
Shutter: 1/180 s
ISO: ISO 160
Software: darktable 4.8.1
Date: 2024-09-13 10:26:52

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I accidentally messed it up since I had leaves in the frame on the right side. If you time it right, you can see a large variety of modes of transportation, including ships, trams, buses, cars, and so on.

FIGURE 73 Some high rise buildings.

EXIF data

Camera: FUJIFILM GFX100S
Lens: 125
Aperture: f/NaN
Shutter: 1/400 s
ISO: ISO 800
Software: darktable 4.8.1
Date: 2024-09-13 10:34:25

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FIGURE 74 Stonecutter’s bridge.

EXIF data

Camera: FUJIFILM GFX100S
Lens: 125
Aperture: f/NaN
Shutter: 1/300 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-13 10:49:36

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Then we got coffee with a whippet!!! The whippet is a four month old female puppy belonging to a guy who is trying to set up an ecommerce business. The puppy was imported from Australia and is very cute. She can run very fast!

FIGURE 75 Whippet.

EXIF data

Camera: FUJIFILM GFX100S
Lens: 125
Aperture: f/NaN
Shutter: 1/180 s
ISO: ISO 200
Software: darktable 4.8.1
Date: 2024-09-13 11:21:42

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FIGURE 76 Whippet.

EXIF data

Camera: FUJIFILM GFX100S
Lens: 125
Aperture: f/NaN
Shutter: 1/180 s
ISO: ISO 250
Software: darktable 4.8.1
Date: 2024-09-13 11:27:48

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6.3 Lunch: Famous Hong Kong food

We went to Sheung Hei for some classic Hong Kong dishes.

FIGURE 77 Roast pork and roast goose leg rice.

EXIF data

Camera: FUJIFILM GFX100S
Lens: 125
Aperture: f/NaN
Shutter: 1/90 s
ISO: ISO 3200
Software: Digital Camera GFX100S Ver2.12
Date: 2024-09-13 12:08:02

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FIGURE 78 Pork patty claypot rice.

EXIF data

Camera: FUJIFILM GFX100S
Lens: 125
Aperture: f/NaN
Shutter: 1/70 s
ISO: ISO 3200
Software: Digital Camera GFX100S Ver2.12
Date: 2024-09-13 12:33:36

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6.4 Monster Building

In the afternoon it was too hot so Joanne stayed in the hotel while I went exploring.

FIGURE 79 A pink tram.

EXIF data

Camera: FUJIFILM GFX100S
Lens: 125
Aperture: f/NaN
Shutter: 1/180 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-13 13:31:06

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My friend spaqin took me to see the Yick Cheong building, affectionately nicknamed the Monster Building. He used to live there! It has two atria, the first one which is more popular since it is colourful and enclosed by another building, and the second one which is a bit more drab. But I actually prefer the second one.

FIGURE 80 Monster building atrium 1.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/8.0
Shutter: 1/240 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-13 13:58:27

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FIGURE 81 Monster building atrium 2.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/8.0
Shutter: 1/140 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-13 14:04:15

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FIGURE 82 Fire Services Headquarter Building.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 1/60 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-13 14:42:26

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FIGURE 83 Kut Cheong Mansion.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 1/280 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-13 14:44:37

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FIGURE 84 North Point Mansion.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 1/550 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-13 14:45:22

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6.5 Line scan photos of trams

After the Monster Building, I spent some time taking line scan photos of trams.

FIGURE 85 Tram.

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FIGURE 86 Tram.

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FIGURE 87 Tram.

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FIGURE 88 Tram.

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6.6 The Peak

At night we ascended the Victoria Peak.

FIGURE 89 Murray Building.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 1/200 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-13 17:21:37

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FIGURE 90 View from the Peak.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/4.0
Shutter: 1/3.1 s
ISO: ISO 400
Software: darktable 4.8.1
Date: 2024-09-13 18:31:57

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FIGURE 91 Handheld panorama stitched from five frames.

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FIGURE 92 Crowds on the Peak.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/1.7
Shutter: 1/15 s
ISO: ISO 800
Software: Digital Camera GFX100S Ver2.12
Date: 2024-09-13 18:44:17

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On the way down we couldn’t afford the taxi so we took the bus. Like I literally ran out of money in my Octopus card and we had no cash lol.

FIGURE 93 Here’s a taxi that we did not take due to lack of money.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/2.8
Shutter: 1/42 s
ISO: ISO 1250
Software: Digital Camera GFX100S Ver2.12
Date: 2024-09-13 19:10:38

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7 Saturday 2024-09-14: Travelling to Zhuhai

FIGURE 94 Morning stroll.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/8.0
Shutter: 1/42 s
ISO: ISO 200
Software: darktable 4.8.1
Date: 2024-09-14 08:05:03

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For lunch we ate Hainanese chicken rice at a restaurant called “Cock N Rice”.

FIGURE 95 Chicken rice.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/7.1
Shutter: 1/27 s
ISO: ISO 800
Software: darktable 4.8.1
Date: 2024-09-14 11:51:01

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Then we took a bus to the bus terminal. Joanne figured out how to pay for stuff so crisis was averted.

FIGURE 96 View from the bus.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF20-35mmF4 R WR
Aperture: f/5.6
Shutter: 1/400 s
ISO: ISO 1000
Software: darktable 4.8.1
Date: 2024-09-14 12:57:45

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Then we took another bus to Zhuhai across the long bridge!

FIGURE 97 Bridge.

EXIF data

Camera: FUJIFILM GFX100S
Lens: GF55mmF1.7 R WR
Aperture: f/4.0
Shutter: 1/640 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-14 14:20:27

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The next leg of the trip will be covered in a future blog post. Stay tuned!

Customizing my line scan camera

Sat, 31 Aug 2024 00:00:00 +0000

Customizing my line scan camera

2024-08-31

I have an Alkeria Necta N4K2-7C, which I use to take pictures of stuff like trains.

FIGURE 1 Caltrain

EXIF data

Software: GIMP 2.10.38

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1 Sony E mount

Today I adapted my line scan camera to Sony E mount. It originally came with a Nikon F mount. There are lots of industrial lenses for Nikon F mount, such as the Zeiss Interlock series, but I use the camera recreationally for taking line scan photos of trains. There are way more high quality manual focus lenses for Sony E mount, or for mounts that can be adapted to Sony E mount but not Nikon F mount (due to the much shorter flange focal distance). For example, Leica M mount lenses can be used on an E mount camera but not an F mount one. Moreover, E mount allows the camera and lens to be way more compact. There are also some legendary and yet relatively affordable lenses like the Voigtländer Apo Lanthar 65mm f/2 which I consider to be superior to almost any manual lens available for Nikon F mount. So that’s why I did it.

FIGURE 2 The finished lens mount with the Voigtländer Ultron 35mm f/1.7 VM attached via the Voigtländer VM-E close focus adapter.

EXIF data

Camera: FUJIFILM GFX100S
Lens: 125
Aperture: f/NaN
Shutter: 12.0 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-09-12 18:31:00

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FIGURE 3 The finished lens mount with the Voigtländer Apo Lanthar 90mm f/3.5 SL in M42 mount attached via an M42 to E mount adapter. Original F mount adapter on the right.

EXIF data

Camera: FUJIFILM GFX100S
Lens: SIGMA 70mm F2.8 DG MACRO A018
Aperture: f/5.6
Shutter: 1.1 s
ISO: ISO 100
Software: Digital Camera GFX100S Ver2.12
Date: 2024-08-31 22:12:49

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FIGURE 4 Diagram of the camera (from Alkeria).

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The camera has four M4 screw holes.

To make the Sony E mount adapter, first, I bought a Neewer extension tube. The extension tube has two sets of male and female E mounts, so it’s great — even if I screw up one of them, I still have another one to fall back on. There are cheaper extension tubes without electronic contacts but the Neewer one, along with similar products from Meike and others, seems sturdier with four screws on instead of three on the lens mount.

Then, I measured everything carefully with callipers and made a model to attach it to the four screw holes on the Alkeria camera. It’s my first time using Onshape.

I found that, compared to the schematic above, the vertical distance between the four M4 screw holes is only 35 mm rather than 36 mm. Not sure if this was a typo or due to physical changes in later versions of the camera (I bought mine in 2017).

FIGURE 5 Screenshot of the CAD model.

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Download STL file

Then I printed it out with my Ankermake M5 3D printer. It took me three tries due to fiddliness with the lens release button and the confusion between 35 mm rather than 36 mm between screw holes. For the final print I used 100% infill and 0.16 mm layer heights. It seems pretty sturdy — even when I exerted some force on an earlier version with only 20% infill to try to pry off the lens, I couldn’t.

FIGURE 6 Final lens mount.

EXIF data

Camera: FUJIFILM GFX100S
Lens: SIGMA 70mm F2.8 DG MACRO A018
Aperture: f/18.0
Shutter: 9.0 s
ISO: ISO 100
Software: darktable 4.8.1
Date: 2024-08-31 22:07:22

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There’s only one caveat, which is that due to various mechanical reasons the lens is mounted upside-down. That is to say, when the line is in the horizontal position, the top of the lens, with the distance markers, faces downwards. I could flip it 180 degrees but the lens release button would occlude the L-bracket. However, I would usually use it in vertical position, where the distance marker would be to the side.

Also, due to the potential occlusion with the four M4 screws, it is rotated very slightly — but it is not a big deal as most full frame lenses cover a 36 × 24 mm area whereas the line is only 28 mm long.

2 L-bracket (2018-2020)

To securely attach my line scan camera to my tripods equipped with Arca-Swiss dovetail quick-release clamps, I designed an L-bracket in 2018. The L-bracket allows me to attach the camera both vertically (for scanning stuff like trains) and horizontally (for example, to capture moving cars from above).

Due to difficulties in getting Rhinoceros 3D to work on Linux via Wine, I drew it by hand.

FIGURE 7 Hand-drawn schematic that I drew on 2018-08-26.

EXIF data

Camera: samsung SM-G950U1
Aperture: f/1.7
Shutter: 1/12 s
ISO: ISO 200
Software: G950U1UES5CRF5
Date: 2018-08-26 15:17:59

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Just kidding, I actually did make it in CAD using Rhinoceros later (more than a year later due to extreme procrastination) but I seem to have lost the file.

FIGURE 8 Screenshot of the CAD model of the L-bracket.

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Since this part needs to be really strong to support the weight of the camera, whose cantilevered position causes huge amounts of stress, I decided to get it made by CNC at 3D Hubs (now part of Protolabs). It cost me nearly $300.

FIGURE 9 Receipt to get it made.

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I also accidentally made it too narrow so it is loose on certain clamps (it is very secure on my Peak Design tripod, though). Later I added some felt pads to it for extra grip.

3 Scope (2023)

It’s really hard to see what the camera is pointing at sometimes, since it only captures a single column of pixels. So I made a janky “scope” to aim the camera.

FIGURE 10 Front view with scope.

EXIF data

Camera: FUJIFILM GFX100S
Lens: 180
Aperture: f/NaN
Shutter: 1/2.0 s
ISO: ISO 400
Software: Digital Camera GFX100S Ver2.10
Date: 2023-12-17 17:02:27

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FIGURE 11 Rear view with scope.

EXIF data

Camera: FUJIFILM GFX100S
Lens: 180
Aperture: f/NaN
Shutter: 1/2.0 s
ISO: ISO 400
Software: Digital Camera GFX100S Ver2.10
Date: 2023-12-17 17:02:58

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FIGURE 12 View through the scope.

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Download STL file

4 Future ideas

4.1 Rotating line camera

I can make it into a rotating line camera and capture seamless 360 degree panoramas. I’m thinking of getting a Syrp Genie Mini II or similar.

4.2 Custom compute with display

Currently, the line scan camera plugs into my laptop (a Thinkpad X1 Carbon gen 6 from 2018). The main problem is that the line scan camera functions best in bright conditions, such as a sunny day, since the exposure time of each column must be very short, but the brightness of a laptop screen is quite poor. Even though the Thinkpad has one of the brightest screens at 400 nits, it is sometimes hard to see the screen during the day. Moreover, having to carry a whole laptop in one hand can be quite unergonomic.

Getting a small embedded computer running my capture software with a small camera monitor suitable for outdoor use would be great. There are some that are 1200 nits and above.

For the choice of computer, there are some choices like an MSI MS-C918S.

I would need to make some changes to my capture software to make it work more nicely with touchscreens and run it automatically upon boot.

Orange Skies Day

Wed, 19 Jun 2024 00:00:00 +0000

Orange Skies Day

2024-06-19

On 2020-09-09 in San Francisco, a rare meteorological phenomenon known as Orange Skies Day occurred. Here are the pics!

As always, the full resolution pictures are licensed CC-By-SA 4.0.

FIGURE 1 Coca cola sign.

EXIF data

Camera: FUJIFILM GFX 50R
Lens: GF50mmF3.5 R LM WR
Aperture: f/16.0
Shutter: 3.0 s
ISO: ISO 100
Software: darktable 4.6.1
Date: 2020-09-09 10:39:45

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FIGURE 2 Market St.

EXIF data

Camera: FUJIFILM GFX 50R
Lens: GF50mmF3.5 R LM WR
Aperture: f/11.0
Shutter: 4.3 s
ISO: ISO 100
Software: darktable 4.6.1
Date: 2020-09-09 11:14:06

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FIGURE 3 Transamerica Pyramid.

EXIF data

Camera: FUJIFILM GFX 50R
Lens: GF50mmF3.5 R LM WR
Aperture: f/3.5
Shutter: 1/45 s
ISO: ISO 1600
Software: darktable 4.6.1
Date: 2020-09-09 11:45:02

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FIGURE 4 Columbus Tower.

EXIF data

Camera: FUJIFILM GFX 50R
Lens: GF50mmF3.5 R LM WR
Aperture: f/8.0
Shutter: 1.0 s
ISO: ISO 100
Software: darktable 4.6.1
Date: 2020-09-09 11:58:55

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FIGURE 5 City Lights bookstore.

EXIF data

Camera: FUJIFILM GFX 50R
Lens: GF50mmF3.5 R LM WR
Aperture: f/5.6
Shutter: 1/3.1 s
ISO: ISO 100
Software: darktable 4.6.1
Date: 2020-09-09 12:02:27

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FIGURE 6 Transamerica Pyramid.

EXIF data

Camera: FUJIFILM GFX 50R
Lens: GF50mmF3.5 R LM WR
Aperture: f/5.6
Shutter: 1/2.1 s
ISO: ISO 100
Software: darktable 4.6.1
Date: 2020-09-09 12:03:14

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FIGURE 7 Phelan Building.

EXIF data

Camera: FUJIFILM GFX 50R
Lens: GF50mmF3.5 R LM WR
Aperture: f/3.5
Shutter: 1/85 s
ISO: ISO 3200
Software: darktable 4.6.1
Date: 2020-09-09 12:33:28

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Trips to Brisbane, Seattle, Madrid, and Shanghai (2018)

Fri, 14 Jun 2024 00:00:00 +0000

Trips to Brisbane, Seattle, Madrid, and Shanghai (2018)

2024-06-14

In 2018 I went to Brisbane, Seattle, Madrid, and Shanghai.

1 2018-05: Brisbane, Australia

I went to Brisbane for the IEEE International Conference on Robotics and Automation (ICRA). I was very impressed with how clean and modern the city was, and the fact that housing is very affordable compared to San Francisco thanks to the construction of numerous high rise buildings.

FIGURE 1 A covered small plaza of some sort.

EXIF data

Camera: SONY ILCE-7RM2
Lens: E 20mm F2
Aperture: f/8.0
Shutter: 1/20 s
ISO: ISO 100
Software: darktable 4.6.1
Date: 2018-05-22 21:23:34

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FIGURE 2 Rainbow lorikeet at the Lone Pine Koala Sanctuary.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/4.5
Shutter: 1/320 s
ISO: ISO 200
Software: darktable 4.6.1
Date: 2018-05-25 03:38:27

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FIGURE 3 Skyline of Brisbane at sunset.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/5.6
Shutter: 1/5 s
ISO: ISO 100
Software: darktable 4.6.1
Date: 2018-05-25 07:00:01

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FIGURE 4 Skyline of Brisbane at blue hour.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/5.6
Shutter: 334.0 s
ISO: ISO 100
Software: darktable 4.6.1
Date: 2018-05-25 07:13:01

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FIGURE 5 Brisbane City Hall.

EXIF data

Camera: SONY ILCE-7RM2
Lens: E 20mm F2
Aperture: f/8.0
Shutter: 1/500 s
ISO: ISO 100
Software: darktable 4.6.1
Date: 2018-05-26 02:25:55

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FIGURE 6 Albert Street Uniting Church nestled betwixt the modern buildings.

EXIF data

Camera: SONY ILCE-7RM2
Lens: E 20mm F2
Aperture: f/8.0
Shutter: 1/100 s
ISO: ISO 100
Software: darktable 4.6.1
Date: 2018-05-26 05:18:08

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1.1 Day trip to the Gold Coast

I didn’t have time to go scuba diving. The Great Barrier Reef will have to wait until next time! Hopefully it will still exist by then…

FIGURE 7

EXIF data

Camera: SONY ILCE-7RM2
Lens: E 20mm F2
Aperture: f/8.0
Shutter: 1/200 s
ISO: ISO 100
Software: darktable 4.6.1
Date: 2018-05-27 04:54:21

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FIGURE 8

EXIF data

Camera: SONY ILCE-7RM2
Lens: E 20mm F2
Aperture: f/8.0
Shutter: 1/200 s
ISO: ISO 100
Software: darktable 4.6.1
Date: 2018-05-27 04:56:39

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2 2018-09: Seattle, WA, USA

I went to Seattle to visit my friend.

FIGURE 9 Space Needle viewed from the Dimension condominium rooftop.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/5.6
Shutter: 1.6 s
ISO: ISO 100
Software: darktable 4.6.1
Date: 2018-09-15 09:01:35

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FIGURE 10 The Ballard Bridge, a bascule bridge that spans the Lake Washington Ship Canal.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/5.6
Shutter: 1/400 s
ISO: ISO 100
Software: darktable 4.6.1
Date: 2018-09-15 23:01:49

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FIGURE 11 Skyline of Seattle from Dr. Jose Rizal Bridge.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/4.0
Shutter: 13.0 s
ISO: ISO 100
Software: darktable 4.6.1
Date: 2018-09-17 03:12:39

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3 2018-09 to 2018-10: Madrid, Spain

I went to Madrid for the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

FIGURE 12 The Palacio de Cristal del Retiro at dawn.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/5.6
Shutter: 10.0 s
ISO: ISO 100
Software: darktable 4.6.1
Date: 2018-10-01 05:36:15

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FIGURE 13 Sol.

EXIF data

Camera: SONY ILCE-7RM2
Lens: SAMYANG AF 35mm F2.8
Aperture: f/7.1
Shutter: 1/500 s
ISO: ISO 100
Software: darktable 4.6.1
Date: 2018-10-05 10:02:45

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FIGURE 14 Cathedral of the Royal Palace.

EXIF data

Camera: SONY ILCE-7RM2
Lens: SAMYANG AF 35mm F2.8
Aperture: f/8.0
Shutter: 1/500 s
ISO: ISO 100
Software: darktable 4.6.1
Date: 2018-10-05 14:22:46

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FIGURE 15 View from the rooftop of the Circulo de Bellas Artes. I got yelled at by a security guard when taking the photo since I had climbed over a railing.

EXIF data

Camera: SONY ILCE-7RM2
Lens: ZEISS Batis 2.8/18
Aperture: f/3.5
Shutter: 1/25 s
ISO: ISO 400
Software: darktable 4.6.1
Date: 2018-10-05 18:05:58

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FIGURE 16 Some kind of delicious food.

EXIF data

Camera: SONY ILCE-7RM2
Lens: SAMYANG AF 35mm F2.8
Aperture: f/4.5
Shutter: 1/40 s
ISO: ISO 100
Software: ILCE-7RM2 v3.30
Date: 2018-10-06 18:12:51

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4 2018-10: Shanghai, China

I went to Shanghai to visit my parents! My dad was working there at the time.

FIGURE 17 Pudong viewed from the Bund.

EXIF data

Camera: SONY ILCE-7RM2
Lens: SAMYANG AF 35mm F2.8
Aperture: f/8.0
Shutter: 1.0 s
ISO: ISO 100
Software: darktable 4.6.1
Date: 2018-10-19 10:28:20

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FIGURE 18 Beautiful bridge in Zuibaichi.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/5.6
Shutter: 20.0 s
ISO: ISO 100
Software: darktable 3.0.0rc2+101~g12a3e26fc
Date: 2018-10-20 03:52:24

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FIGURE 19 Porsche 356C at the Auto Museum.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/8.0
Shutter: 1/1.2 s
ISO: ISO 100
Software: ILCE-7RM2 v3.30
Date: 2018-10-19 07:32:31

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FIGURE 20 The Bund viewed from the Financial Center.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/2.8
Shutter: 1/50 s
ISO: ISO 400
Software: darktable 4.6.1
Date: 2018-10-21 09:14:40

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FIGURE 21 The Nanpu Bridge.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/5.6
Shutter: 1/1.6 s
ISO: ISO 100
Software: darktable 4.6.1
Date: 2018-10-21 10:18:24

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FIGURE 22 A big flower thing.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/4.0
Shutter: 1/100 s
ISO: ISO 200
Software: darktable 2.5.0+729~g5e0a293e6
Date: 2018-10-23 08:47:55

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FIGURE 23 The Bund at dusk.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/4.0
Shutter: 1/125 s
ISO: ISO 100
Software: darktable 2.7.0+1123~g8555f5feb
Date: 2018-10-27 08:57:39

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FIGURE 24 The Transrapid to the airport.

EXIF data

Camera: SONY ILCE-7RM2
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/5.6
Shutter: 1/2000 s
ISO: ISO 200
Software: darktable 2.7.0+1123~g8555f5feb
Date: 2018-10-28 02:01:05

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Trip to Japan (2017)

Mon, 10 Jun 2024 00:00:00 +0000

Trip to Japan (2017)

2024-06-10

In August 2017, my friends and I went to Japan!

FIGURE 1 View of Tokyo Tower from the Mori building.

EXIF data

Camera: SONY ILCE-7R
Lens: Voigtlander MACRO APO-LANTHAR 65mm F2 Aspherical
Aperture: f/8.0
Shutter: 1/200 s
ISO: ISO 100
Software: darktable 2.3.0+893~g0b45f4abb
Date: 2017-08-25 06:33:07

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As always, these pictures are available in full resolution and are licensed under the Creative Commons Attribution Share Alike 4.0 license.

1 2017-08-11: arrival in Tokyo

FIGURE 2 Arrival at Haneda airport.

EXIF data

Camera: SONY ILCE-7R
Lens: E 20mm F2
Aperture: f/5.0
Shutter: 1/60 s
ISO: ISO 500
Software: darktable 2.3.0+893~g0b45f4abb
Date: 2017-08-11 19:58:49

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Upon arriving, we grabbed a quick meal at Yoshinoya. It was delicious! We stayed in an AirBnB on Odaiba.

FIGURE 3 Statue of Liberty on Odaiba.

EXIF data

Camera: SONY ILCE-7R
Lens: E 20mm F2
Aperture: f/11.0
Shutter: 1/125 s
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Date: 2017-08-11 23:24:54

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FIGURE 4 Taking the Yurikamome.

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Date: 2017-08-12 01:59:25

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2 2017-08-12: Tokyo sightseeing

2.1 Asakusa

FIGURE 5 Asakusa visitor’s center.

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Date: 2017-08-12 02:43:00

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FIGURE 6 Senso-ji in Asakusa.

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Date: 2017-08-12 02:52:16

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2.2 Tokyo University

Then we went to Tokyo University. One of my friends was interning there.

FIGURE 7 Yasuda Auditorium in Tokyo University.

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Date: 2017-08-12 06:35:26

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FIGURE 8 Another building in Tokyo University.

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Date: 2017-08-12 04:54:19

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2.3 Shinjuku

We walked around Kabukicho.

FIGURE 9 The ubiquitous (at the time) Toyota Crown Comfort cabs.

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FIGURE 10 Amazing colorful lights of Shinjuku.

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Date: 2017-08-12 08:57:01

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FIGURE 11 More cool buildings in Shinjuku.

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Date: 2017-08-12 09:18:24

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FIGURE 12 A nice alley.

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Date: 2017-08-12 09:21:35

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2.4 Dinner: Izakaya

For dinner we went to Kaikaya.

FIGURE 13 Exterior of the restaurant.

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Date: 2017-08-12 09:28:26

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FIGURE 14 Sashimi Moriawase.

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FIGURE 15 Toro nigiri.

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Date: 2017-08-12 10:26:35

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3 2017-08-13: More Tokyo

Honestly I just followed my friends around and have no idea where we went. My only suggestion was to check out the soon-to-be demolished Nakagin Capsule Tower.

FIGURE 16 Detail of the Nakagin Capsule Tower

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Date: 2017-08-13 03:47:46

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I bought the legendary Voigtlander Apo Lanthar 65mm f/2 at Yodobashi Camera. It was just released at the time. This lens is truly amazing and remained my favourite lens for many years until I had to sell it upon switching to the Fujifilm GFX system (which the lens is not physically compatible with). This lens is notable for its wonderful sharpness across the frame and complete absence of chromatic aberrations. As it is a 0.5x macro lens, it is also suitable for food photography.

FIGURE 17 A random photographer guy.

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Date: 2017-08-13 07:16:47

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FIGURE 18 Somewhere near Akihabara.

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Date: 2017-08-13 07:40:48

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FIGURE 19 Rainbow bridge.

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Date: 2017-08-13 08:33:08

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3.1 Dinner: Loach

We went to Komakata dojou to eat loach. I had never eaten loach before.

FIGURE 20 Loach.

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Date: 2017-08-13 10:32:13

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FIGURE 21 Sardine sashimi.

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Date: 2017-08-13 10:50:35

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FIGURE 22 Rainbow bridge again at night. Look at the beautiful sunstars from the Apo Lanthar.

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Date: 2017-08-13 13:07:27

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4 2017-08-14: Sapporo

We flew to Sapporo!

FIGURE 23 Some cute cars.

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Date: 2017-08-14 06:05:47

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For lunch we had a delicious ramen at a random spot that we didn’t plan on visiting. Unfortunately I forgot what it was called.

FIGURE 24 Intersection viewed from the Sapporo TV Tower.

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Date: 2017-08-14 08:34:25

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FIGURE 25 Vintage Type 250 tram.

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Date: 2017-08-14 09:42:59

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4.1 Dinner: Traditional

We went to Oga traditional, which makes traditional Hokkaido food.

FIGURE 26 Hokkaido fried chicken.

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Date: 2017-08-14 10:40:56

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FIGURE 27 Koi sashimi.

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Date: 2017-08-14 10:42:34

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FIGURE 28 Fried fish.

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FIGURE 29 Saba oshizushi. Somehow we got this even though it is a specialty of the Kansai region instead.

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Date: 2017-08-14 11:51:04

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5 2017-08-15: Otaru

FIGURE 30 Sapporo Shiryokan viewed from across the Sunken Garden.

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Date: 2017-08-15 02:05:41

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We took the Hakodate main line to Otaru.

FIGURE 31 Nagasakiya mall in Otaru.

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Otaru is known for its seafood.

FIGURE 32 Arctic surf clams. I would later have the joy of eating one in 2024.

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Date: 2017-08-15 03:36:04

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FIGURE 33 Uni for sale at the triangle market.

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Date: 2017-08-15 03:36:50

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FIGURE 34 Otaru canal.

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Date: 2017-08-15 04:16:16

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5.1 Lunch: Kaisen don

We went to Kitano donburiya takinami shokudou in the Triangle market.

FIGURE 35 Uni ikura don.

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Date: 2017-08-15 05:02:36

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FIGURE 36 Kaisen don.

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Date: 2017-08-15 05:02:43

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5.2 Otaru sightseeing

FIGURE 37 A famous clock tower in Otaru.

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Date: 2017-08-15 06:20:45

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FIGURE 38 The Otaru orugoru-do, a music box store. Notice the steam clock, one of only a few made by Raymond Saunders, and very similar to the more famous one in Vancouver.

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Date: 2017-08-15 06:31:59

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FIGURE 39 Charming streets of Otaru.

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Date: 2017-08-15 06:32:42

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5.3 Back to Sapporo

FIGURE 40 Waiting for the train back to Sapporo.

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FIGURE 41 Sapporo station.

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Date: 2017-08-15 08:48:00

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FIGURE 42 Chevy Bel Air.

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Date: 2017-08-15 08:55:53

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FIGURE 43 Detail of the Sapporo TV Tower.

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Date: 2017-08-15 09:30:11

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FIGURE 44 A performance for the Makoto Shinkai exhibition.

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Date: 2017-08-15 10:01:23

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5.4 Dinner: Genghis Khan

Genghis Khan, or jingisukan, is a genre of Japanese yakiniku. This one probably was at Daruma.

FIGURE 45 Meats expectations.

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Date: 2017-08-15 12:48:40

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6 2017-08-16: Sapporo

FIGURE 46 Hello kitty road dividers just before leaving Sapporo.

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Date: 2017-08-16 02:08:22

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6.1 Lunch: Soup curry

Sapporo soup curry is a famous dish of the region and said to cure colds.

FIGURE 47 Sapporo soup curry.

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Date: 2017-08-16 03:31:41

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6.2 More Sapporo sightseeing

FIGURE 48 Sapporo Station again.

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Date: 2017-08-16 05:48:45

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FIGURE 49 A little bridge.

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Date: 2017-08-16 06:02:41

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FIGURE 50 Odori park from the TV Tower.

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Date: 2017-08-16 06:33:51

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FIGURE 51 Random views from the TV Tower.

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Date: 2017-08-16 06:38:46

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6.3 Dinner: Crab

We went to the Michelin-starred Hanasaki for extreme crab eating!

We had an immense king crab. Arthropod meat degrades within minutes so it is imperative to eat raw crab quickly. The chef cut off the legs of the crab and we alternated between taking a bite of a segment and the chef dipping the remaining crab leg in hot water to cook it further. So the first bite was completely raw but the leg became progressively cooked with each bite. It was very delicious and I have never had anything like that before.

FIGURE 52 My friend holding the giant crab. He (the friend) is like 195 cm tall.

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Date: 2017-08-16 10:27:30

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FIGURE 53 Yubari King melon.

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Date: 2017-08-16 11:43:38

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FIGURE 54 Nighttime Sapporo.

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Date: 2017-08-16 12:38:03

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7 2017-08-17: Osaka

We grabbed a miso ramen and then flew to Osaka.

FIGURE 55 Miso ramen.

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Date: 2017-08-17 01:16:05

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FIGURE 56 Line scan photo of the Nankai 6000 series.

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Software: GIMP 2.10.30

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FIGURE 57 Nankai line in Osaka.

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Date: 2017-08-17 05:54:56

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7.1 Umeda Sky Building

I went to take pictures from the Umeda Sky Building!

FIGURE 58 Amazing bridges.

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Date: 2017-08-17 08:10:36

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FIGURE 59 Hazy sunset.

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Date: 2017-08-17 09:18:13

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FIGURE 60 Blue hour view of a highway that goes through a building.

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Date: 2017-08-17 09:50:10

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FIGURE 61 Bridges again but it’s blue hour.

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Date: 2017-08-17 09:56:14

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FIGURE 62 More blue hour.

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Date: 2017-08-17 09:57:20

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7.2 Dinner: Tasting menu

We went to the Michelin-starred Kamoshiya Kusumoto for dinner.

FIGURE 63 Uni ice cream.

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Date: 2017-08-17 10:53:19

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FIGURE 64 Some tasty sashimi.

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Date: 2017-08-17 11:04:05

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FIGURE 65 Grilled Ayu.

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Date: 2017-08-17 11:29:26

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FIGURE 66 Some more tasty sashimi.

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Date: 2017-08-17 11:42:09

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FIGURE 67 Foam.

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Date: 2017-08-17 11:59:54

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FIGURE 68 Noodle.

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Date: 2017-08-17 12:42:46

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8 2017-08-18: Kyoto

Prior to going to Kyoto for a day trip, we had a quick bite of takoyaki.

FIGURE 69 Takoyaki.

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Date: 2017-08-18 01:33:55

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8.1 Fushimi Inari

FIGURE 70 Torii.

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FIGURE 71 A lot of torii.

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FIGURE 72 Small torii models.

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Date: 2017-08-18 04:44:07

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FIGURE 73 Serene mosquito breeding ground.

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FIGURE 74 Smaller torii models.

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Date: 2017-08-18 05:54:05

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FIGURE 75 I had to wait a while to get this shot without tourists in it.

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Date: 2017-08-18 06:01:00

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8.2 Various Kyoto sightseeing

FIGURE 76 Matcha roll cake.

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Date: 2017-08-18 08:01:31

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FIGURE 77 A surface train station very reminiscent of Kyoto Animation works.

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Date: 2017-08-18 06:41:39

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FIGURE 78 Yasaka Shrine.

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Date: 2017-08-18 08:22:30

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FIGURE 79 A random street.

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FIGURE 80 More shrine.

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FIGURE 81 Shrine.

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FIGURE 82 Sanmon Gate.

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Date: 2017-08-18 09:13:23

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8.3 Dinner: Kaiseki

We went to Soujiki Nakahigashi. It is known for traditional Kyoto cuisine with a focus on vegetables but it also has some meat/fish dishes.

FIGURE 83 The menu.

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Date: 2017-08-18 10:43:45

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FIGURE 84 Some veggie jellies and stuff.

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FIGURE 85 Whatever it is, it is very delicious and you can find out what it is by reading the handwritten menu above.

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Date: 2017-08-18 10:28:09

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FIGURE 86 Grilled ayu.

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Date: 2017-08-18 10:40:24

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FIGURE 87 Vegetable goo.

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Date: 2017-08-18 10:50:14

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FIGURE 88 Some fish roe bricks or something.

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Date: 2017-08-18 11:08:00

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FIGURE 89 Vegetables.

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Date: 2017-08-18 11:14:28

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FIGURE 90 Mushrooms or something.

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Date: 2017-08-18 11:25:12

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FIGURE 91 Noodle.

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Date: 2017-08-18 11:32:32

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FIGURE 92 Small fish.

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FIGURE 93 Dessert.

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Date: 2017-08-18 12:06:45

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8.4 Back to Osaka

FIGURE 94 Nighttime strolling in Kyoto before heading to the station.

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Date: 2017-08-18 13:31:55

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FIGURE 95 Train ride back.

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Date: 2017-08-18 14:13:02

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9 2017-08-19: Osaka

Kushikatsu, or fried skewers, are a specialty of Osaka (which is generally known for its fried foods in general).

FIGURE 96 Kushikatsu.

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Date: 2017-08-19 03:25:21

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FIGURE 97 Dotonbori river.

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Date: 2017-08-19 03:59:00

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FIGURE 98 Glico man.

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Date: 2017-08-19 04:11:14

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FIGURE 99 Osaka skyscrapers.

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Date: 2017-08-19 07:49:00

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FIGURE 100 Shin Osaka station.

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Date: 2017-08-19 08:07:07

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FIGURE 101 A random mall.

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Date: 2017-08-19 09:31:24

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9.1 Dinner: Wagyu

We went to Wagyu Matsushita for wagyu!

FIGURE 102 Niku sushi.

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Date: 2017-08-19 11:45:01

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FIGURE 103 MEAT!!!!

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Date: 2017-08-19 11:48:57

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FIGURE 104 Dotonbori neon gate.

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Date: 2017-08-19 14:17:50

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10 2017-08-20: Himeji, Hiroshima, Fukuoka

10.1 Himeji

We took the Shinkansen to Fukuoka, with stops in Himeji and Hiroshima.

FIGURE 105 Shinkansen N700 series, area scan photo.

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Date: 2017-08-20 00:48:42

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At Himeji Station, I took a line scan photo of an N700A series.

FIGURE 106 Shinkansen N700 Series, line scan photo.

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FIGURE 107 Himeji castle.

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Date: 2017-08-20 02:25:40

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10.2 Riding the train

FIGURE 108 Asahi river, Okayama, seen from the Shinkansen.

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Date: 2017-08-20 05:39:23

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FIGURE 109 A train depot.

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Date: 2017-08-20 05:44:07

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FIGURE 110 A waste incineration plant.

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Date: 2017-08-20 05:50:53

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10.3 Hiroshima

FIGURE 111 Atomic bomb dome.

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Date: 2017-08-20 06:52:05

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FIGURE 112 Hiroshima Peace Memorial Museum.

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Date: 2017-08-20 06:54:45

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10.4 Fukuoka

For dinner we had basashi, or raw horse meat! I am not a huge fan — it was chewy and much worse than wagyu beef in every way.

FIGURE 113 Horse sashimi.

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Date: 2017-08-20 12:13:51

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FIGURE 114 This one was beef I think.

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Date: 2017-08-20 12:16:43

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FIGURE 115 The Naka river.

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Date: 2017-08-20 13:25:16

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11 2017-08-21: Fukuoka

11.1 Lunch: Tonkotsu ramen

FIGURE 116 Cool how they drape the meat on the sides.

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Date: 2017-08-21 03:41:38

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11.2 Dinner: Izakaya

We went to an izakaya called Kiharu no Gomasabaya.

FIGURE 117 Slightly grilled fish.

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Date: 2017-08-21 10:17:50

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FIGURE 118 Sardine sashimi or something.

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Date: 2017-08-21 10:33:26

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FIGURE 119 Arancini.

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Date: 2017-08-21 10:39:56

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FIGURE 120 Tamagoyaki.

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Date: 2017-08-21 10:40:13

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FIGURE 121 Grilled fish.

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Date: 2017-08-21 11:09:42

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12 2017-08-22: Ibusuki

FIGURE 122 A Ki-Ha diesel multiple unit.

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Date: 2017-08-22 00:46:16

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FIGURE 123 View from Ibusuki station.

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Date: 2017-08-22 02:29:43

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For lunch we had Tousenkyou Soumen Nagashi but I didn’t take any photos since I was too busy catching noodles.

We enjoyed a great sand bath. However, sand is coarse and rough and irritating and gets everywhere so I didn’t risk bringing my camera.

12.1 Dinner: Ryokan

We stayed at the amazing Shusuien.

FIGURE 124 Very small fish sashimi.

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Date: 2017-08-22 10:12:17

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FIGURE 125 Braised pork.

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Date: 2017-08-22 10:56:03

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FIGURE 126 Steamed egg.

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Date: 2017-08-22 11:18:36

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FIGURE 127 Lobster.

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Date: 2017-08-22 11:26:53

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13 2017-08-23: Kagoshima

FIGURE 128 Line scan photo of the Ibusuki no Tamatebako. The train is half black and half white and this is the white side.

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FIGURE 129 A canal.

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Shutter: 1/2000 s
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Date: 2017-08-23 02:08:15

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13.1 Lunch: Katsu

FIGURE 130 The place where we originally wanted to get lunch wasn’t available so we got katsu instead. Still delicious.

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Date: 2017-08-23 04:58:30

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13.2 Kagoshima sightseeing

FIGURE 131 A random mall.

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Date: 2017-08-23 05:28:18

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FIGURE 132 Arcade of said random mall.

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Date: 2017-08-23 05:29:22

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FIGURE 133 Sakurajima volcano, one of the most active volcanoes in Japan and a symbol of Kagoshima.

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Date: 2017-08-23 08:01:50

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FIGURE 134 Kagoshima skyline.

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Date: 2017-08-23 08:02:39

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FIGURE 135 A nice gate.

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Date: 2017-08-23 08:09:05

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FIGURE 136 Close up of said gate.

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Date: 2017-08-23 09:02:26

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FIGURE 137 Amazingly shaped plant.

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Shutter: 1/200 s
ISO: ISO 200
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Date: 2017-08-23 09:06:10

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14 2017-08-24: Back to Tokyo

FIGURE 138 Fuji mountain from the airplane.

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Shutter: 1/500 s
ISO: ISO 100
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Date: 2017-08-24 08:26:06

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14.1 Dinner: Yakitori

We went to Torie for some really delicious and fancy yakitori.

FIGURE 139 Yakitori.

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Date: 2017-08-24 12:22:35

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FIGURE 140 Yakitori.

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Date: 2017-08-24 12:40:26

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FIGURE 141 Chicken balls.

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Date: 2017-08-24 13:31:59

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15 2017-08-25: Tsukiji and Ginza

We went to the Tsukiji fish market.

FIGURE 142 Building near the fish market.

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Date: 2017-08-24 23:58:44

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FIGURE 143 Tamagoyaki.

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ISO: ISO 200
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Date: 2017-08-25 00:08:32

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FIGURE 144 Kinmedai.

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Date: 2017-08-25 00:20:55

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FIGURE 145 Wasabi.

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Date: 2017-08-25 00:30:45

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15.1 Breakfast: fish

FIGURE 146 Fish.

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Date: 2017-08-25 01:00:02

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After breakfast the fish market is relatively empty since it is busiest in the early morning. Still, there was a lot going on.

FIGURE 147 Tsukiji.

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Aperture: f/8.0
Shutter: 1/125 s
ISO: ISO 800
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Date: 2017-08-25 01:23:39

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FIGURE 148 Tsukiji.

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Date: 2017-08-25 01:41:40

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15.2 Second breakfast: tuna

FIGURE 149 Tuna sushi.

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Date: 2017-08-25 02:14:01

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15.3 Ginza

FIGURE 150 Ginza.

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Date: 2017-08-25 04:14:02

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FIGURE 151 Ginza.

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Shutter: 1/800 s
ISO: ISO 100
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Date: 2017-08-25 04:15:45

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15.4 Lunch: Tempura

We went to the acclaimed and fancy Tenpura Kondo. Unlike lesser tempura places, this one gets its fish fresh from Tsukiji and lightly fries it so that the fish taste is preserved and the inside is exquisitely tender.

FIGURE 152 Shrimp heads.

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Date: 2017-08-25 04:40:27

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FIGURE 153 Eggplant.

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Date: 2017-08-25 05:01:28

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FIGURE 154 Fish.

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Date: 2017-08-25 05:13:32

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FIGURE 155 Fish.

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Date: 2017-08-25 05:27:34

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15.5 Roppongi

We went up Mori tower.

FIGURE 156 Tokyo Tower.

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Date: 2017-08-25 06:33:07

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FIGURE 157 Skyline of Tokyo.

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Date: 2017-08-25 06:54:09

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Then we somehow went back to Shinjuku.

FIGURE 158 Shinjuku.

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Date: 2017-08-25 09:12:23

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15.6 Dinner: Teppanyaki

For dinner we went back to Ginza, at the amazing Ukai Tei.

FIGURE 159 Sashimi.

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Date: 2017-08-25 11:13:59

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FIGURE 160 Lobster.

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Date: 2017-08-25 11:39:30

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FIGURE 161 Lobster.

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Date: 2017-08-25 11:44:08

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FIGURE 162 Eggplant.

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Date: 2017-08-25 11:59:07

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FIGURE 163 Wagyu.

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Date: 2017-08-25 12:04:29

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16 2017-08-26: Tokyo

We hung out in Akihabara.

FIGURE 164 Sega escalators.

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Date: 2017-08-26 03:27:26

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16.1 Lunch: Katsudon

For lunch I got a katsudon.

FIGURE 165 Katsudon.

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Date: 2017-08-26 02:57:34

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For dessert we got this bread thing.

FIGURE 166 Extreme stuffed toast.

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Aperture: f/6.3
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ISO: ISO 400
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Date: 2017-08-26 05:26:05

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16.2 Dinner: Tsuta

We went to the Michelin-starred Tsuta, probably the most famous ramen restaurant in the world. It was pretty good I thought but my friends were less impressed.

FIGURE 167 Michelin starred ramen! What a time to be alive!

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Date: 2017-08-26 09:32:39

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FIGURE 168 Nighttime scene.

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ISO: ISO 800
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Date: 2017-08-26 11:01:27

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17 2017-08-27: Chiba

17.1 Lunch: Omurice

FIGURE 169 Beef omurice with demi glace. The chef handed me this plate but I was struggling to put away my camera and he got really impatient and slammed it in front of me. I felt bad. Still tasty.

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Date: 2017-08-27 04:28:56

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FIGURE 170 Seafood omurice.

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ISO: ISO 1600
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Date: 2017-08-27 04:29:14

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17.2 Chiba sightseeing

FIGURE 171 Club Trump.

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Date: 2017-08-27 05:00:32

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FIGURE 172 Chiba monorail.

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Date: 2017-08-27 05:20:18

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FIGURE 173 Chiba monorail.

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Shutter: 1/800 s
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Date: 2017-08-27 05:13:20

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FIGURE 174 Some kind of a dome.

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Shutter: 1/800 s
ISO: ISO 200
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Date: 2017-08-27 05:23:51

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17.3 The flight back

Unfortunately, despite showing up hours ahead of time, they wouldn’t let us check in to our flight. Earlier, we also tried online check in but their website had a problem. As a consequence we had to take the next flight which necessitated a transfer in Hawaii. In fact, when we got to the gate, we found that our original flight hadn’t even begun boarding yet, but oh well, more time for eating food!!!

We got a really delicious tekka don at the airport.

FIGURE 175 Tuna.

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Date: 2017-08-27 08:15:17

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We were shocked that the Honolulu airport didn’t have air conditioning everywhere, and the food there was very bad.

FIGURE 176 Honolulu airport.

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Shutter: 1/800 s
ISO: ISO 100
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Date: 2017-08-27 20:19:22

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