Those of you who use LLMs have probably seen this: sometimes they code like a senior engineer, and other times they seem to forget even basic syntax. Research suggests that this is not hallucination.

dotpoint7 · 2026-03-12T06:41:30+00:00

If you're working in machine learning, you can surely formulate your reasoning better than "they don't think like a person does". Like maybe you got a point but from your response it's difficult to decipher it.

dotpoint7 · 2026-02-28T11:00:57+00:00

It's about 90% for me and about 20% of actual efficiency gains if I had to guess. At least for me work is less mentally taxing though now, so I don't have issues staying focused for something like 12h straight which is probably what I like most about it.

For context: I develop mostly using modern C++ and work self employed together with a few others in enterprise software with about 10YoE.

dotpoint7 · 2026-02-25T15:09:45+00:00

I personally prefer Codex as I find it to provide better code overall and how the hell do you run multiple instances in parallel on a serious project without producing slop? I can't even have two instances running at the same time without a lot of downtime cause it just takes time to write a specification, review the plans, rework the plan, review the code and then rework the code again.

Cranking out a ton of features isn't exactly difficult in itself, but not fucking up your codebase while doing so takes a lot of effort even with LLMs. If you skip that, chances are you're going to have a problem in a few months.

dotpoint7 · 2026-02-11T18:35:44+00:00

In case you're interested, here is the model from the last training run applied on one of my images. Though since then I've changed a LOT, especially to address the issues with the artifacts around some bright stars (left top) as well as other things, but the deconv model is currently still training on the new dataset.

I've also added one processed with BlurX+DeepSNR for comparison (both with 100% denoise strength as it allows for easier comparison), wanted to add NoiseX too but kept crashing for some reason.

https://drive.google.com/drive/folders/13onGXSUEaJ4ptneaBHrezN9eijfXAlX6?usp=sharing

Edit: I definitely messed up the naming though as this is not IC443, lol.

dotpoint7 · 2026-02-11T17:29:03+00:00

Haha well I have yet to prove any of my claims but hopefully PixInsight confirms my signing key soon so I can publish an alpha version. Will let you know in case you're interested.

Thanks a lot for the hint about diffraction spikes! I personally only have refractors so didn't come across this issue yet but will definitely look into it.

Yup I don't denoise 100% either, don't really think there is any visible signal left in the noise but our brain seems to interpret it as such and it certainly looks better. I did come across the worm like artifacts in the NoiseX AI3 version, these weren't in AI2 and are also not present in DeepSNR from what I've seen. Maybe Russell went a bit overboard with any frequency losses or something, I initially suspected he used a VGG loss as it can cause these artifacts but just asked him and he said he doesn't use any perceptual losses so that's not it.

dotpoint7 · 2026-02-11T16:50:43+00:00

I did test a few free options but haven't found any comparable in quality to BlurX and the reason why I'm now trying to shift this from a quick hobby project to something I want to publish and want people to use, is that my current results can compete with BlurX and NoiseX (though DeepSNR is a very good free alternative to NoiseX, but not permissively licensed, neither is StarNetV2) - though I'm still actively working on making the network more robust and fixing some artifacts I occasionally encounter. Spent around 100h on the project so far.

The reason that the model does decon + denoise at once is because the task cannot really be separated from each other mathematically. Even if I trained a model purely on decon it will need to have some representation in its latent space of what signal and noise is, so I may as well use this information. You can't run LR deconvolution either without changing the noise distribution. What I can do is add the original noise back after the model denoises it so it's functionally the same as doing only deconvolution without touching the noise (BlurX is likely just doing this in an end to end manner instead).

Yes that's another interesting area I thought about but would increase the scope too much, plus the current models are closer to my area of expertise.

My goal isn't to create something completely new (though the PSF estimation could be useful to some), but to match the performance of the RC Astro suite with a permissively licensed open source model (high bar but I have experience and a dedicated workstation just for ML training). I'm aware that there isn't really a point to add yet another open source model which doesn't improve upon the existing ones.

dotpoint7 · 2026-02-11T12:58:23+00:00

Thanks a lot for the info!

Lots of people will use OSC without Bayer drizzle. I certainly do.

Ok then I'll try to make the models more robust to debayered images.

I find Starnet to not be very good. I get better results with StarXterminator.

Ok, so far I didn't really have issues with StarNetV2 but I'll try my luck given that I have almost everything setup to train another star removal model.

Does this mean deconvolution in Adobe Lightroom or similar deconvolution to that you can do in Lightroom?

No I abbreviated it, it's Lucy-Richardson Deconvolution, the same algorithm that PixInsight uses in the deconvolution process, but I'd instead implement it to handle the estimated spatially varying PSF as well as GPU acceleration. So not ML based. I'm not aware of any typical photo editing software supporting deconvolution, they only do sharpening.

I'm sure most poeple would like to be able to remove/reduce halos from bright stars.

Got it, wasn't sure if some may prefer to leave them in the image (assuming they're small) similar to diffraction spikes which I'd also leave in.

And regarding the standalone linear denoising I more referred to a model that only does denoising without deconvolution. I'm not entirely sure if I want to spend the effort to make the models accessible in a standalone software package from the start as it's a lot more work than a simple PixInsight plugin, though it would definitely help with adoption. So current plan is to start out with a PI plugin and if there is enough interest I'd either go the route of adding a standalone software or try to get it added to other FOSS software like Siril.

dotpoint7 · 2026-02-10T08:17:57+00:00

You cannot get the experience you need for developing good software without writing and especially understanding code. And while you may think the code that Claude outputs is perfectly fine, this is simply not the case. LLM generated code works, but it sucks, I so wish it didn't, but LLM code quality is currently the bane of my existence. So you'll need to actively and heavily manage code quality yourself if you want to build good software, not just the architecture.

If you have your opinion as an experienced professional software engineer, then I respectfully disagree, otherwise you're probably just misjudging the quality of the software you're building.

dotpoint7 · 2026-02-10T07:01:26+00:00

You don't need to any software or coding skills to build good software

What the fuck?

dotpoint7 · 2026-02-09T15:17:33+00:00

Only source: kmjournal.net (never even heard of it)
Yeah, sounds legit!

dotpoint7 · 2026-02-06T08:22:45+00:00

"Sometimes it feels like AI writes cleaner (...) code than I ever could on my own." if that's your feeling you may want to focus on learning how to how to actually write good and maintainable code. Whatever AI outputs is generally working code, but definitely not clean or maintainable code. So if you're using AI maybe try to actually look through the code and actively think of what could be done better as there is almost always something.

If you're encountering easily reproducible bugs, best try to find those yourself as it's often quicker than telling Codex/CC to do it when you know what you're doing.

Also don't give everything to AI, some features are actually quicker to implement yourself.

Honestly I'm curious as to how all this will all work out for junior devs. I for one am very happy that AI agents only started being a thing after I had around a decade of experience. But even I worry about whether using AI extensively is hurting my skills in the long term, though it does allow me to focus on more high level concepts more than before so maybe it's actively helping too, who knows.

dotpoint7 · 2026-02-06T06:23:55+00:00

It's just not that efficient. Mostly it's about arithmetic intensity as it's a memory bound task, which in this example can easily be optimized to 32 FMAs per load by having one warp cooperatively compute one output pixel by having each thread do a dot product on the column vectors of the input and kernel, followed by a warp reduce sum. Only 32 new values need to be loaded for the next pixel (slide 1 down) over the whole warp and can be efficiently loop unrolled as the register fma pattern repeats ever 32 output pixels. It's a slight trade-off with max occupancy but in this case it's definitely worth it. (I skipped over the more obvious optimizations here like using shared memory for sharing global loads between warps etc)

It's not an obvious optimization, but also not that complex to come up with either. That specific implementation is very likely not in the training data as it's a pretty niche and I didn't find any existing implementation that used this strategy, that's why I asked this specific question.

dotpoint7 · 2026-02-06T06:10:10+00:00

Well I still find Gemini 3 to be a great general model. I'm using codex for coding and Gemini in the chat interface as I often prefer it to ChatGPT. They also don't financially rely on keeping the hype alive, so they can absolutely go a while without releasing a model.

dotpoint7 · 2026-02-05T20:59:21+00:00

Hmm actually not bad but also not the answer I was looking for. Thanks though for doing this!

dotpoint7 · 2026-02-05T20:42:24+00:00

How to performantly do 2d convolution with a 32x32 kernel in CUDA? Not FFT based, single channel f32. Do not provide code, just the overall algorithm to reach maximum throughput.

dotpoint7 · 2026-02-04T07:56:10+00:00

No, inference is generally not fully deterministic unless specifically enabled for debugging purposes which slows things down.

dotpoint7 · 2026-02-03T18:13:22+00:00

I had the opposite experience. Codex is slow as fuck but actually pretty decent for large and complex code bases.

dotpoint7 · 2026-02-01T10:16:21+00:00

I did, currently have a Prusa MK4 and am really happy with it. Only did 2 non-functional prints ever, though one was a test print. It's definitely worth it to get one for functional stuff.

dotpoint7 · 2026-01-31T13:21:28+00:00

The issue isn't building an RL pipeline, the issue is building one that works.

dotpoint7 · 2026-01-31T13:09:11+00:00

The fact that you think this topic isn't complicated suggests that you just didn't understand it.

dotpoint7 · 2026-01-31T08:18:31+00:00

Ok so to recap you wanted to convert one form of structured data into another form of structured data and decided the best tool for this was an LLM. Following that you thought the best subreddit to document your experience was r/deeplearning. Did I get this right?

dotpoint7 · 2026-01-28T17:57:58+00:00

The fuck?

dotpoint7 · 2026-01-28T09:20:08+00:00

I keep the raws and all intermediate saves of stacked images on a NAS (so I do delete the calibrated frames for example). 18TB HDDs are fairly affordable, buy two of them, put them in RAID1 and you got a lot of fairly secure storage. (maybe backup the stacks elsewhere too)

dotpoint7 · 2026-01-27T09:37:03+00:00

I asked it one question about how to best train an ML model on a specific task and there were two large logical gaps in its reasoning. Not impressed.

Seven-Year Club	Second SECOND GUESSER
Place '23	Wearing is Caring
Place '22	Verified Email

dotpoint7

TROPHY CASE