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Comment by bdauvergne | Hacker News Reader

Seems people read the blog but not the code, I looked at the stated rewrite of Numpy in Rust:

> As an introductory project, I rewrote Numpy in Rust. It was great fun.

That's not a rewrite at all it's just a wrapping of an existing linear algebra Rust library (faer, blas, etc..) with a more Numpy like API. It seems to me that every AI project I look at is just a mashup/wrapper over existing things. Where are the real bootstrapped new things with AI ? Is there any big OSS project (Linux kernel, postgresql, Django, whatever) with serious bugfixes or new features implemented by AI that we could look at ?

Are so much people in programming implementing middleware / wrapping existing API all day that it gives them a feeling of liberation to be able to delegate those tasks ?

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>Are so much people in programming implementing middleware / wrapping existing API all day that it gives them a feeling of liberation to be able to delegate those tasks ?

Yes. A lot of jobs are providing the glue between other pieces like this and not inventing new algorithms and such.

Perhaps this is why there is such a divide in sincere opinions about AI.

Which does indicate that even if AI becomes good at coding, we will still need humans to glue all the AI stuff together.

yeah, I don't think the jenga tower is changing but the levels of abstraction will.

https://github.com/timescale/pg_textsearch

BM25 index for postgres, mostly written by a single (very smart) guy and Claude Code.

Written by a nobody of course:

> T.J. Green is a Senior Staff Engineer at Tiger Data, creators of TimescaleDB, where he is the implementor of pg_textsearch, a new Postgres extension for high-performance BM25 ranked text search. He brings deep expertise in database systems internals and indexing methods to this project. At Tiger Data, he has also contributed to pgvectorscale, the company's vector search extension for Postgres.

The guy could have written it eyes closed.

Yes TJ is very experienced and smart. But no he would not have been able to build this in a few months by himself without AI. In fact he uses Claude so much that he goes over the max subscription and spends thousands of dollars each month on tokens to continue delivering this fast.

AI is a big multiplier for experienced folks like him.

Incredible.

If I get production ready bm25 and incremental view maintenance in postgres, it really will do everything I care about.

Especially with the browser/client synch maintenance stuff people are working on.

I feel like we should pin this or something as a canonical counter to the now excruciatingly tired claim:

“Generative software code doesn’t work”

> Anecdotally, we use Opus 4.5 constantly on Zed's code base, which is almost a million lines of Rust code and has over 150K active users, and we use it for basically every task you can think of - new features, bug fixes, refactors, prototypes, you name it. The code base is a complex native GUI with no Web tech anywhere in it.

https://news.ycombinator.com/item?id=46522437

Zed is open source so you can look at their PRs and commit history. They even rolled their own Rust GUI framework for the project so it’s a decent case study of working on code that is barely in the training data (if at all).

Zed on Linux is buggy as hell. On macos it's somehow more stable. Maybe Zed is indeed a "good" example of Ai-coded products.

This is a thing you can credibly say only if you've never used a popular new non-mainline editor. Sublime Code was "buggy as hell" too; all new editors are. Editors are incredibly difficult to do well. And Zed is doing it on hard mode, cross-platform.

No true Scotsman would ever do the kind of programming that 95% of the programming in the world is right?

Anyway https://www.reddit.com/r/osdev/comments/1opsicd/just_how_far...

There's a good thread in there highlighting the fact that a novice wouldn't be nearly as successful as the OP who has the experience to guide the LLM.

Blas and faer are used only for small corners of the API (linalg and fft) which is exactly what numpy does. I encourage you to follow your own advice and look more closely at the interaction of ufuncs, strides, and dtypes.

> Are so much people in programming implementing middleware / wrapping existing API all day that it gives them a feeling of liberation to be able to delegate those tasks ?

Yes

The overwhelming majority of production code are simply wrappers and APIs calling existing frameworks and libraries

The type of projects you’re talking about are so unbelievably rare that almost nobody starts them or makes progress on them because predominantly they are social organizations that happen to export code

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