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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 project

by 20k 2mo ago

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 generation, and there's no evidence of superprogrammers making superprojects in 1/10th of the time. With a 10x productivity gain, what used to take a year should take a month

mark242 2mo ago
Github's Octoverse report kind of gives you that evidence, no? Pull requests landed in 2025 were up 30% over 2024. This year it's going to be much higher.
- daveguy 2mo ago
  That's the point. Huge increases in pull requests. Virtually no increase in features or reliability.
- fatata123 2mo ago
  [dead]
anon7000 2mo ago
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.

by 20k 2mo ago

mark242 2mo ago

Github's Octoverse report kind of gives you that evidence, no? Pull requests landed in 2025 were up 30% over 2024. This year it's going to be much higher.

daveguy 2mo ago
That's the point. Huge increases in pull requests. Virtually no increase in features or reliability.
fatata123 2mo ago
[dead]

anon7000 2mo ago

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.