3 ms·

Sure, it just seems a little odd that no LLMgineer ever contributes their incredible more performant scheduler back though right? After all if you can do it wit

by 20k 2mo ago

Sure, it just seems a little odd that no LLMgineer ever contributes their incredible more performant scheduler back though right? After all if you can do it with claude, anyone can, all it'd take is to ask claude to rewrite it. Linux accepts LLM generated PRs, all the code has to do is meet the review bar and one of the most critical pieces of software engineering on the planet gets better for everyone

vatsachak 2mo ago
I would submit it but the pr was too long to fit on GitHub
- deleted 2mo ago
  [deleted]
- weakfish 2mo ago
  … doesn’t Linux work through email, not GitHub?
  - cyclopeanutopia 2mo ago
    It's a reference to Fermat's note.
Lerc 2mo ago
Why does it seem odd when people who do attempt to contribute back are attacked? AI lowers the bar to making submissions which permits flawed subissions to be thoughtlessly submitted by people who don't really understand what they are doing, technically or culturally. It does, however, enable people who put in the effort to work on something significant. Those people are fewer in number, but exist. They do get to see the animosity that unwitting novices receive. I don't think it would be surprising for those people to opt out of engaging with a toxic environment.
- acdha 2mo ago
  People are attacked for low quality. There are counter examples but it is definitely striking how infrequently you see a PR which doesn’t stand out for being turgid or flawed, and whose author clearly expects a cookie for their “contribution” of a couple minutes of prompting.
  - Lerc 2mo ago
    Are you alleging that there are far more things that takes no effort than things that takes quite a lot of effort, You might have something there. It probably holds true for a lot of things. Either that or most gutairs sold are defective.
- 20k 2mo ago
  You'd think one 10x engineer or one core developer already part of the project would have completely eclipsed all the other engineers, and worked around the difficulties there Or, failing that, you'd think any of these engineers - given the truly vast amount of programming power now available at their fingertips with their 10x productivity - would have simply replaced these projects. It'd only take a few 10xers who wanted to show that everyone else was wrong. That's how open source often works, someone just gets a been in their bonnet and then we get linux Instead: I can't find any of it. There's no evidence of this productivity boost in the wild. There aren't new high quality 10x open source projects springing up that are replacing everything. There aren't high quality LLM contributions, feature development isn't going faster. There's almost no evidence of high quality AI code generation at all in the open source space, existing projects or otherwise Where's all the code? I want the receipts if people are claiming a 10x productivity. Because at the moment, the much more likely explanation seems to be that its simply not true
  - Lerc 2mo ago
    I can't point you at any because I don't want to send a mob their way. For any instances where people have made things and revealed them to the world have been declared to have been grossly flawed by being held to a standard of scrutiny that no person usually gets. It seems everyone is Cardinal Richelieu now.
simonw 2mo ago
Here are a few examples you might find credible: - pola-rs/polars: https://github.com/pola-rs/polars/pull/26823 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 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 https://github.com/numpy/numpy/pull/31573 - ~21x speedup of Python datetime → datetime64 conversion, with Claude Code used for profiling and implementing the performance improvements - apache/datafusion: https://github.com/apache/datafusion/pull/21182 https://github.com/apache/datafusion/pull/21182 - up to 49x speedup of LIMIT queries by eliminating unnecessary sorts and pushing limits into file scans, generated with Claude Code - apache/datafusion: https://github.com/apache/datafusion/pull/21651 https://github.com/apache/datafusion/pull/21651 - ~4.39x speedup of ClickBench Q6 by resolving MIN/MAX directly from Parquet metadata instead of scanning columns, generated with Claude Code - pola-rs/polars: https://github.com/pola-rs/polars/pull/27958 https://github.com/pola-rs/polars/pull/27958 - ~3.2x speedup of Int8 Series sum, developed with assistance from Claude Fable and Claude Opus 4.8 - numpy/numpy: https://github.com/numpy/numpy/pull/31274 https://github.com/numpy/numpy/pull/31274 - up to ~1.44x speedup of common small NumPy reductions, with the fast path written by Claude Code and manually refined
- vatsachak 2mo ago
  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 as feature heavy as Photoshop overnight.
  - simonw 2mo ago
    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.
simonw 2mo ago
If you want to see contributions to Linus that used AI you can search for commits with tags that look like this in their commit message: Assisted-by: Codex:gpt-5.5 Assisted-by: Claude:claude-opus-4.8 This search on GitHub seems to find about 2,000 of those: repo:torvalds/linux assisted-by (claude OR codex) https://github.com/search?q=repo%3Atorvalds%2Flinux+assisted-by+%28claude+OR+codex%29&type=commits https://github.com/search?q=repo%3Atorvalds%2Flinux+assisted... ... but if you clone the git repo you can get an exact count of 824 commits (not sure why the search over-counted). Here's a good candidate for a material performance improvement: https://github.com/torvalds/linux/commit/e1bf79628453e6afac81ffa57f4f40f28e5512ff https://github.com/torvalds/linux/commit/e1bf79628453e6afac8... Single-stream throughput (MB/s): Before After Change seq-write/dontcache 298 897 +201% rand-write/dontcache 131 236 +80%

by 20k 2mo ago

vatsachak 2mo ago

I would submit it but the pr was too long to fit on GitHub

deleted 2mo ago
[deleted]
weakfish 2mo ago
… doesn’t Linux work through email, not GitHub?
- cyclopeanutopia 2mo ago
  It's a reference to Fermat's note.

Lerc 2mo ago

Why does it seem odd when people who do attempt to contribute back are attacked? AI lowers the bar to making submissions which permits flawed subissions to be thoughtlessly submitted by people who don't really understand what they are doing, technically or culturally. It does, however, enable people who put in the effort to work on something significant. Those people are fewer in number, but exist. They do get to see the animosity that unwitting novices receive. I don't think it would be surprising for those people to opt out of engaging with a toxic environment.

acdha 2mo ago
People are attacked for low quality. There are counter examples but it is definitely striking how infrequently you see a PR which doesn’t stand out for being turgid or flawed, and whose author clearly expects a cookie for their “contribution” of a couple minutes of prompting.
- Lerc 2mo ago
  Are you alleging that there are far more things that takes no effort than things that takes quite a lot of effort, You might have something there. It probably holds true for a lot of things. Either that or most gutairs sold are defective.
20k 2mo ago
You'd think one 10x engineer or one core developer already part of the project would have completely eclipsed all the other engineers, and worked around the difficulties there Or, failing that, you'd think any of these engineers - given the truly vast amount of programming power now available at their fingertips with their 10x productivity - would have simply replaced these projects. It'd only take a few 10xers who wanted to show that everyone else was wrong. That's how open source often works, someone just gets a been in their bonnet and then we get linux Instead: I can't find any of it. There's no evidence of this productivity boost in the wild. There aren't new high quality 10x open source projects springing up that are replacing everything. There aren't high quality LLM contributions, feature development isn't going faster. There's almost no evidence of high quality AI code generation at all in the open source space, existing projects or otherwise Where's all the code? I want the receipts if people are claiming a 10x productivity. Because at the moment, the much more likely explanation seems to be that its simply not true
- Lerc 2mo ago
  I can't point you at any because I don't want to send a mob their way. For any instances where people have made things and revealed them to the world have been declared to have been grossly flawed by being held to a standard of scrutiny that no person usually gets. It seems everyone is Cardinal Richelieu now.

simonw 2mo ago

Here are a few examples you might find credible: - pola-rs/polars: https://github.com/pola-rs/polars/pull/26823 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 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 https://github.com/numpy/numpy/pull/31573 - ~21x speedup of Python datetime → datetime64 conversion, with Claude Code used for profiling and implementing the performance improvements - apache/datafusion: https://github.com/apache/datafusion/pull/21182 https://github.com/apache/datafusion/pull/21182 - up to 49x speedup of LIMIT queries by eliminating unnecessary sorts and pushing limits into file scans, generated with Claude Code - apache/datafusion: https://github.com/apache/datafusion/pull/21651 https://github.com/apache/datafusion/pull/21651 - ~4.39x speedup of ClickBench Q6 by resolving MIN/MAX directly from Parquet metadata instead of scanning columns, generated with Claude Code - pola-rs/polars: https://github.com/pola-rs/polars/pull/27958 https://github.com/pola-rs/polars/pull/27958 - ~3.2x speedup of Int8 Series sum, developed with assistance from Claude Fable and Claude Opus 4.8 - numpy/numpy: https://github.com/numpy/numpy/pull/31274 https://github.com/numpy/numpy/pull/31274 - up to ~1.44x speedup of common small NumPy reductions, with the fast path written by Claude Code and manually refined

vatsachak 2mo ago
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 as feature heavy as Photoshop overnight.
- simonw 2mo ago
  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.

simonw 2mo ago

If you want to see contributions to Linus that used AI you can search for commits with tags that look like this in their commit message: Assisted-by: Codex:gpt-5.5 Assisted-by: Claude:claude-opus-4.8 This search on GitHub seems to find about 2,000 of those: repo:torvalds/linux assisted-by (claude OR codex) https://github.com/search?q=repo%3Atorvalds%2Flinux+assisted-by+%28claude+OR+codex%29&type=commits https://github.com/search?q=repo%3Atorvalds%2Flinux+assisted... ... but if you clone the git repo you can get an exact count of 824 commits (not sure why the search over-counted). Here's a good candidate for a material performance improvement: https://github.com/torvalds/linux/commit/e1bf79628453e6afac81ffa57f4f40f28e5512ff https://github.com/torvalds/linux/commit/e1bf79628453e6afac8... Single-stream throughput (MB/s): Before After Change seq-write/dontcache 298 897 +201% rand-write/dontcache 131 236 +80%