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Is it all just vapourware?

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Re: Is it all just vapourware?

#171

Post by mihaelm »

20k wrote: post #130
internet2000 wrote: post #125

Earlier quoted context omitted.

> Why hasn't GIMP replicated Photoshop? My guess is that open source folks overindex on being anti-AI.

They don't though, very few people were anti AI in the early days. Open source projects were largely initially very interested in LLM usage, and generally had a pro AI contribution policy. This changed when it did not help their lives Eg if you check out Godot, initially they were cool with LLM code, and then had to clamp down on it because it was not leading to anything good

Scaling AI use in highly-collaborative environments, which open source projects are, is very challenging. I totally understand the apprehensiveness around it.

Re: Is it all just vapourware?

#172

Post by satvikpendem »

andy99 wrote: post #94
pixelesque wrote: post #45

Earlier quoted context omitted.

I had DeepSeek 4 Pro do a very good job yesterday of loop-unrolling and SIMD-ifying (both SSE/AVX and ARM Neon versions) some very old scalar Col3f image processing and resampling/resizing C++ code I hadn't touched in 12 years or so. It also wrote some unit tests that validated the kernel sampling weights, and wrote some Jupyter notebooks to go along with the kernel algorithms as comparisons. It's not just web dev...…

> image processing and resampling/resizing C++ code I hadn't touched in 12 years or so. So an unimportant, personal project. People seem to extrapolate being able to do something cool into being able to do useful work, which is what this whole discussion is about.

What do you consider useful work?

Re: Is it all just vapourware?

#173

Post by simonw »

vatsachak wrote: post #168
simonw wrote: post #156

Earlier quoted context omitted.

Here are a few examples you might find credible: - pola-rs/polars: https://github.com/pola-rs/polars/pull/26823 - ~2.68x median speedup of primitive-to-boolean casting credited to Claude Opus 4.6 - pydantic/monty: https://github.com/pydantic/monty/pull/643 - ~53x speedup (488ms down to 9.2ms) of bytes substring search generated with Claude Code - numpy/numpy: https://github.com/numpy/numpy/pull/31573 - ~21x speedup o…

That's a fair use case for them though. LLMs are superhuman at short term performance engineering/debugging/testing. But even that comes with the caveat that these commits come from talented coders using LLMs as a grad student. I think that OP is against the claim that "agent in a loop beats a talented human at long term coding tasks". Because if it was true then open source projects such as GIMP could basically be a…

I, too, will reject the idea that "agent in a loop beats a talented human at long term coding tasks".

LLMs amplify existing expertise. Give them to experts and you can get fantastic results. Give them to amateurs and you might get the occasional impressive demo, but you're not going to get anything that a responsible software team would commit to maintaining in the long term.

Re: Is it all just vapourware?

#175

Post by GlibMonkeyDeath »

20k wrote: post #64
badlibrarian wrote: post #17

It's August 9, 2026 and if you're a software engineer who hasn't had multiple "holy shit, I can't believe it just did that" moments, it's time to consider a new trade.

I recently asked it a series of technical physics questions about a field I used to be involved in to evaluate whether it'd be useful for doing research, and literally every single answer it gave was critically wrong - even for basic questions. Its certainly true that I went "holy shit", but not in a good way

Can you give an example? I've actually been pretty happy with how the latest ChatGPT answers senior-undergrad level physics questions.

Re: Is it all just vapourware?

#176

Post by badlibrarian »

uncivilized wrote: post #108
pixelesque wrote: post #45

Earlier quoted context omitted.

I had DeepSeek 4 Pro do a very good job yesterday of loop-unrolling and SIMD-ifying (both SSE/AVX and ARM Neon versions) some very old scalar Col3f image processing and resampling/resizing C++ code I hadn't touched in 12 years or so. It also wrote some unit tests that validated the kernel sampling weights, and wrote some Jupyter notebooks to go along with the kernel algorithms as comparisons. It's not just web dev...…

Yeah I expect it to be helpful in some basic image processing stuff, but it will quickly fail once you try to implement research papers on the topic.

I can specifically and directly state that is false.

Re: Is it all just vapourware?

#177

Post by jeffreyrogers »

IsTom wrote: post #154
jeffreyrogers wrote: post #32

I was fairly skeptical of agentic coding before I used it for a real product. Although I still have to be heavily involved in planning the code that LLMs write for me, they can write code much faster than I can, and they know more about edge cases than I do, so they can handle edge cases/subtle bugs that I would have missed. I have been paid to write code at every level of the stack from assembly to frontend javascri…

Adding too many tests was a real issue before LLMs. You end up in situations where you when you add a feature there's 200 (out of e.g. 10k) tests that fail and you have to figure out which of them should fail and you need to fix them and which are actual bugs.

I haven't run into this yet. My test failures have either been real or have been triggered by an (intentional) breaking change. The later does require updating the tests, but I think catching the real bugs is worth that tradeoff. I haven't found spurious failures to be a big problem (I've had a few but rewriting the tests that have this problem has eliminated it for me). My codebase is a pretty modular rails app, which I think helps with this.

I have worked on other projects where flaky tests are a problem (and generally cause developers to ignore and submit anyways), but so far I've avoided that. My experience with flaky tests is that they're typically due to poor modularity or to subsystems that other teams can modify. This project exists in a monorepo and I'm the sole developer so that's not a problem here.

Re: Is it all just vapourware?

#178

Post by anon7000 »

20k wrote: post #82
mark242 wrote: post #72

Earlier quoted context omitted.

> Why do only the poor quality LLM code generation users make PRs to open source projects, and never the engineers that know how to really use it correctly? It could be that the engineers who are extremely productive with LLMs are landing PRs that look indistinguishable from good, hand-written PRs.

If that were true, we'd expect to see massively accelerated velocity of open source projects by these engineers. They should be creating new open source projects at a truly astounding rate, with new tooling springing up every day that dwarfs the existing open source space as their productivity completely eclipses traditional development Instead, software is plodding along exactly the same as it did prior to LLM code…

I think what you're missing is that there are new open source projects being created at a very fast rate.

But that doesn’t mean they stick.

And the core issue isn’t AI, it’s that… a successful, highly adopted project requires time for people to know it exists, adopt it, and also time by the maintainer growing community and ensuring reliability. The people factor is more important. I see tons of new projects, but I’m not going to pick a brand new project that has high odds of being abandoned. I’m going to pick the one that consistently maintains it and has some adoption already.

I have seen engineers create very successful internal projects fairly quickly. And yes, a project that would have taken a year taking a month. And offering a lot of extra bells and whistles that you just wouldn’t have time for.

But these are greenfield internal projects, and the bar is much, much lower for those.

I have seen multiple internal incidents root-caused by an agent faster than the humans responding. Just because it can go up and down rabbit holes a lot faster.

The problem is that AI solves one bottleneck, but not others. One team member produces a huge amount of new PRs. (Like 12 solid enhancements and big fixes in a couple hours.) Now I have less time because I’m reviewing that. And we’re all context switching a lot more.

On top of that, I find AI workflows continue to be deeply immature, even though certain models are very effective and very good at troubleshooting.

The story around testing is not really improving for example. AI can write tests, but are they good? I don’t think we have much actually ensuring product quality and reliability automatically. Unit tests are not enough.

Collaboration is very poor too. Coworkers agent creates PR, and now I’m reviewing it, and now he sends my comments back to his agents… really clunky workflow especially since I’m ACTUALLY just prompting his agent.

Plus, certain models (Opus) are getting much worse at writing. I will not use Opus any more because the writing style is so horrible.

The constant change means approaches that worked well a couple months ago don’t work well today. And there are no real experts, because no one’s been doing this for long. And half the posts and learning out there are outdated, or straight up blogspam.

This makes it hard for people to learn and get better, despite the fact that models like Sol 5.6 are effective troubleshooters, and write decent code.

Re: Is it all just vapourware?

#179

Post by etrautmann »

20k wrote: post #39

>If agentic development actually worked the way any of them say it does I think its fascinating just how much of a gap there is between what's being claimed, and the verifiable observable data of the open source world. Major open source projects are by and large starting to ban LLMs now, because the contributions made by LLM users have been universally terrible and unhelpful. There doesn't appear to be a single major…

I find it game changing for experimental work and prototyping. As a research scientist this is a major bottleneck and clean code is often not the right optimization target.

Re: Is it all just vapourware?

#180

Post by Supermancho »

20k wrote: post #127
biql wrote: post #123

Earlier quoted context omitted.

> I regularly see people claim that they are now 10x more productive with LLM code generation, and I just wonder where all the code is. Perhaps the biggest issue hasn't been the speed of development all along but people working on wrong things, hence why progress isn't as evident. LLM helping to build something that isn't needed faster isn't going to productivity more visible.

There's no reason that some people wouldn't be using their new found 10x superpowers on open source projects though

I use my powers to finish my own projects (until the next creative barrier) while my professional workload has been reduced to 20% effort with 0 stress.

I wrote a game I left in a folder from 11 years ago in 2 months, front and back plus all the tooling, plus features I never would have attempted before. I recreated open-sourced projects from github, written in a language I wasn't familiar with, so I could better work with the application and include it in my backend codebase.

I rewrote my mail server in less than 2 days, which took me over 2 weeks to configure and setup the last time.

My life is easier, because the demands are the same but I have more free time. I research more, I learn more, and I try more things. Just like any software project, some are abandoned (even with LLMs) when running into unforeseen issues or mismatches with theoretical plans. Most importantly, I can make things that I think are exciting, with very little effort. The first 35 years of development, I struggled to be good enough to tackle sourcing various projects without becoming frustrated and mostly knowing myself well enough to avoid trying at all.

Now I can see myself writing passion projects until I'm dead.

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