davidatbu

So would you take these claims seriously if they came from OpenAI (since Codex is a pretty lean CLI app)?

If so, I think it would be in the spirit of HN to discuss the subject matter of the blogpost (increasingly autonomous coding towards the end goal of RSI) as if the blog post was indeed from OpenAI. OpenAI is, by all accounts, going through a very similar process anyways.

By frontend, do you mean wasm in the browser? I'd have expected multithreading there, especially in wasm, to be extremely unergonomic.

Maybe you mean to refer to concurrency?

Note that something that helped the misinformation was that, on Twitter, there were Kimi employees expressing their surprise that the base model was Kimi K2.5, and their indignation that Cursor didn't credit Kimi. They later deleted their tweets (what I infer from that is that some employees were not aware of some pre-existing agreement or understanding between Cursor and Kimi until the drama happened).

I can spare a minute :). This isn't exhaustive because this is just stuff I know of, obviously.

- At Stanford, Led research on the first (to my knowledge) crop of joint image/text models. Super widely cited work.

- At Tesla, led their whole self driving effort for a while, came up with critical techniques that allowed them to make progress (e.g., the concept of "auto labelling": using a much larger NN to generate training data with which to train smaller models that could fit in the on-device compute. IIRC, Elon said they would not have been able to make progress without this insight).

I'm not sure his educative efforts for the mold of what you're looking for, but if so, the course he designed at Stanford (and availed online):for neural networks, as well as his blog posts, (most famous of which, to my knowledge, is "the unreasonable effectiveness of LSTMs"), made a huge impact on educating a generation of tinkerers and researchers.

Thank you!

Gotcha. I'm genuinely curious: by "impressive", are you referring to coverage? I'd be grateful if you could say a few words about it could be more impressive (e.g, if you indeed meant to talk about coverage, say what functionality/edge cases aren't covered as of now)

Do you have some examples?

Not OP. For this particular use case, I think performance is a primary concern.

But if you mean in general, I also totally feel that languages that let you represent more invariants statically are better fit for LLMs. I'd love to see experimentation with LLMs with dependent types and managed effects.

Fwiw, that's not the stated motivation for the rewrite experiment. In fact, the Rust rewrite is slower to compile than the zig code when compiled with their internal fork of zig (tho it is faster when OG zig is used).

I don't want to infringe upon your right to speculate. I just want to point out that your statement is at best a speculation.

I'm pretty sure that they have decided that backwards-compat is not the best path for Mojo. Matter of fact, the following is the _last_ item on the roadmap on the home page:

> Supporting more of Python's dynamic features like classes, inheritance, and untyped variables to maximize compatibility with Python code.

What's more, note how it says "to maximize compatibility" not "to achieve full compatibility."

Well, of the top of my head, both chatgpt.com and Gemini have text on their home page to the effect of "AI can make mistakes". I'll bet a few bucks such copy can be found in other places, including the terms of service.

Do you do rolling deploys?

I'd love to see a link to these emails, if you have one handy!