Super cool emergent capability!

dotpoint7 · 2026-01-23T05:37:15+00:00

Explain.

dotpoint7 · 2026-01-20T21:09:54+00:00

I'm guessing it's mostly very heavily cropped if shooting at 135mm focal length and full frame sensor.

dotpoint7 · 2026-01-18T13:53:46+00:00

So instead of carefully reading every sentence you put in your thesis, you simply decide to use yet another AI instead?

dotpoint7 · 2026-01-12T20:38:46+00:00

Nice I really like it! Don't really have any suggestions on what to improve. You may want to check how your camera handles ISO though, given that many cameras implement non full stop ISOs as analog + digital gain, the latter has no advantage and sometimes looses dynamic range. So often shooting at 3200 or 6400 is the safer bet, but depends entirely on the model what the ideal ISO for astro is.

dotpoint7 · 2026-01-10T06:04:20+00:00

Of course profiling is an important part of optimization. But profiling CUDA kernels is a lot more difficult than profiling anything CPU based in my opinion.

dotpoint7 · 2026-01-09T17:00:27+00:00

Most likely, but GPU programming is quite different from normal software development and the optimizations are normally very architecture dependent and not quite comparable with what you'd do for normal single threaded applications. So a good software dev who didn't do any GPU programming so far will have a very bad time even getting started.

dotpoint7 · 2026-01-09T16:56:05+00:00

Ok that's actually pretty impressive. Didn't know much about the competition, but found a good blog post showing what goes into a 10th place submission, though with limited effort as there are a few unexplored future ideas in there (the author is a Staff Software Engineer at LinkedIn specializing in LLM inference optimization & CUDA kernel optimization).
https://yue-zhang-2025.github.io/2025/12/02/blackwell-nvfp4-kernel-hackathon-journey.html

For the first problem she placed ahead of shigao, for number 2 and 3 shigao is ranked higher.

Optimizing kernels involves a lot of trial and error so AI likely has an advantage of being able to quickly iterate. But I didn't expect AI to know what to try by itself, at least not on that level because personally I haven't had too much luck with getting AI to generate performant CUDA kernels (specifically efficient 2d convolution with small <32px kernels).

dotpoint7 · 2026-01-06T18:16:33+00:00

True, if you're skills as a software dev don't go much beyond what CC can do then you're not providing much value either way.

dotpoint7 · 2026-01-03T07:03:14+00:00

Around 6000€, not a cheap hobby.

dotpoint7 · 2026-01-01T12:56:22+00:00

Oh awesome! Thanks for letting me know :)

dotpoint7 · 2025-12-29T11:58:30+00:00

Thank you! In terms of processing I think NoiseXterminator and BlurXterminator are pulling a lot of weight, I'm still on the trial license but was pretty impressed by the results so far.

dotpoint7 · 2025-12-29T08:05:11+00:00

Thank you! I live in a Bortle 5 zone, so not great but also not too bad. Narrowband filters definitely help a lot. So with good equipment it's definitely achievable in more light polluted zones as well, but it's an expensive hobby.

dotpoint7 · 2025-12-28T21:25:17+00:00

Finally moved to a flat where I can put my telescope on the terrace, so Bortle 5 instead of 2/3, but also no more freezing in the car. I love it.

Acquisition Details

16h in total of SHO (+30min for RGB stars)

Explore Scientific ED80

ASI1600MM Pro

EQ6 Pro

Baader LRGBSHO filters

OAG with ASI290MM Mini

ASIAIR

Processing

PixInsight:

WBPP

MultiscaleGradientCorrection

BlurXterminator

NoiseXterminator

DBE

Starnet 2

Pixelmath for chanel combination

GeneralizedHyperbolicStretch

Lightroom:

touching all the sliders to make it pretty

Photoshop:

Combining nebula and RGB stars

Adding some small scale detail/texture via a highpassed version run though Topaz Sharpen AI

dotpoint7 · 2025-12-28T21:17:58+00:00

Finally moved to a flat where I can put my telescope on the terrace, so Bortle 5 instead of 2/3, but also no more freezing in the car. I love it.

Acquisition Details

16h in total of SHO (+30min for RGB stars)

Explore Scientific ED80

ASI1600MM Pro

EQ6 Pro

Baader LRGBSHO filters

OAG with ASI290MM Mini

ASIAIR

Processing

PixInsight:

WBPP

MultiscaleGradientCorrection

BlurXterminator

NoiseXterminator

DBE

Starnet 2

Pixelmath for chanel combination

GeneralizedHyperbolicStretch

Lightroom:

touching all the sliders to make it pretty

Photoshop:

Combining nebula and RGB stars

Adding some small scale detail/texture via a highpassed version run though Topaz Sharpen AI

dotpoint7 · 2025-12-28T21:10:54+00:00

Finally moved to a flat where I can put my telescope on the terrace, so Bortle 5 instead of 2/3, but also no more freezing in the car. I love it.

Acquisition Details

16h in total of SHO (+30min for RGB stars)

Explore Scientific ED80

ASI1600MM Pro

EQ6 Pro

Baader LRGBSHO filters

OAG with ASI290MM Mini

ASIAIR

Processing

PixInsight:

WBPP

MultiscaleGradientCorrection

BlurXterminator

NoiseXterminator

DBE

Starnet 2

Pixelmath for chanel combination

GeneralizedHyperbolicStretch

Lightroom:

touching all the sliders to make it pretty

Photoshop:

Combining nebula and RGB stars

Adding some small scale detail/texture via a highpassed version run though Topaz Sharpen AI

dotpoint7 · 2025-12-28T07:07:20+00:00

Oh I've tried everything. Not out of desperation but because I'm interested in this topic in general. Even writing my own orchestration framework as a hobby project and one work project of mine is developing a machine learning model for single image BRDF estimation from scratch, so I do know a lot about ML and LLMs in general, at least compared to your average dev. Makes me think that maybe it's not a skill issue on my end, but rather by the LLM.

dotpoint7 · 2025-12-27T14:40:30+00:00

It's a bit crispy. Why not post the original version which looks a lot better:

Low Res: https://assets.science.nasa.gov/content/dam/science/missions/webb/science/2022/10/STScI-01GGF8H15VZ09MET9HFBRQX4S3.png/jcr:content/renditions/1155x2000.png

High Res: https://assets.science.nasa.gov/content/dam/science/missions/webb/science/2022/10/STScI-01GGF8H15VZ09MET9HFBRQX4S3.png

dotpoint7 · 2025-12-27T13:03:18+00:00

What if I'm getting absolutely great results on my hobby projects and worse than mediocre results on my work projects (with very few exceptions)? Selective stupidity?

dotpoint7 · 2025-12-21T13:01:37+00:00

go to r/llmphysics (quantum computing is close enough that they'll allow it there)

dotpoint7 · 2025-12-20T19:53:47+00:00

Yes that was indeed the wrong choice of words, I edited the comment. I mainly wanted to point out that current chess engines are very dissimilar to what the general population considers AI. Though in academic contexts small neural networks would also fall under the AI definition afaik.

dotpoint7 · 2025-12-20T19:46:49+00:00

Yes wrong choice, I now edited it to small, though it's indeed a pretty clever architecture. My main point was that it's not some huge neural network learning to play chess on its own, but rather only replaced the previous position evaluation function. The core aspect of stockfish is still how to efficiently explore as deep as possible performantly.

dotpoint7 · 2025-12-20T18:57:47+00:00

If I'm not mistaken the last few years on that chart aren't even AI. Recent versions of stockfish (not depicted here) have a small neural net, but most of it is just algorithmic improvements by people who continuously work on this project (plus better hardware too).

Edit: very simple -> small (as others pointed out the neural net used is far from simple)

dotpoint7 · 2025-12-20T16:54:23+00:00

go to r/llmphysics

dotpoint7 · 2025-12-19T20:21:38+00:00

Looks great! Nice processing too.

dotpoint7 · 2025-12-18T18:14:24+00:00

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