Wasn’t all that obvious to anyone who read the original post when they got to the section that explicitly explained what the author of this post is now summarizing?

Sure if you only read the title and brief at the top you’d walk away without the full picture though.

This is a better response imo: https://news.ycombinator.com/item?id=50344861

Wow that was fast

The original post explains what they did and why better than this post. This post taught me nothing that wasn’t in the original.

> The “just by looking at the raw weights” setting (raw floats in context, no tools, no running the network) only appears in experiments 1, 2 and 4, on networks of 17K–172K weights.

The author is being equally disingenuous.

That piece by itself is insightful. Meaning, LLMs are capable of deciphering what a model can do by looking at the weights alone.

The fact that we can’t squeeze enough of them through the context is a technical limitation. However, this does not diminish the importance of this finding/discovery. This is an unexpected emergent behaviour.

I'm still impressed by the "Weight-Space Programming" idea. Perhaps its because I didn't know that LLMs could understand or successfully modify weights at all previous to reading it. However, I think the examples provided in the original piece were eye-opening. Yes, humans can build tooling to modify weights, but the author here agrees that it's tough. The examples where Astra was interacting with weights are still novel, and Astra seemed to handle them with surprising ease.

I feel like a lot of this Astra editing Qwen stuff is regurgitation of existing relatively well known techniques, for example: https://arxiv.org/html/2406.11717v3 and conceptually similar to: https://arxiv.org/abs/2512.05648 (tho this is done in training iirc)

Either way, a lot of devs will be brute forcing explorations in this direction today.

A tangent, but I’m now realizing that the “recursive self improvement” thing that these LLM companies are talking about is (probably?) not about unsupervised Claude Code instances improving the training process, but rather LLMs modifying their own weights through tooling directly. It would certainly open up a whole new process for model optimization.

oh man someone should make this into a loop where the model can alter it weights after an inference. some kind of descent of the local gradient.

But can we use the idea inside the training loop, as an optimizer instead of (or next to) e.g. Adam?

something i found 15 minutes ago is that the car network test is "very very easy", as in Opus 5.5 in Claude Code did not even need to see the weights of the network to solve it correctly. i can give the transcript of the session. i'm pretty sure Opus didn't cheat

Welcome to publications involving ML in 2026... Glad to see someone else write this though because it's important to start having these discussions. Too much overselling has been happening for too long. There is so much great about ML. IMO it hurts the field long-term to continue overselling claims like this.

In my field (biology+ML) it is a genuine epidemic. You could make a career out of comparing high-profile ML models vs baselines from the 90s.

I think it's interesting how even inside tech, so many people seem to view neural network weights as "black magic" utterly beyond human comprehension. I guess that contributes to the sense of mystery around LLMs...

(And also the sharp difference in levels of sophistication you see inside neural nets and outside of it: Pipelines that feed text to LLMs so it can detect highly abstract and nuanced semantic differences in the text's contents - but the pipeline still uses regexes for sentence splitting...)

A better idea is to have Astra modify it's own weights and let it try if it can RSI, but you gonna need to closely monitor for actual leap in capabilities so you can keep a check on alignment.

The blog post exhibits the same flawed logic that many academic reviews do — looking for reasons to reject a work rather than accept it. It comes off cynical and contrarian. It's easy to find issues with an academic paper, but ACL has consistently pushed for a higher standard [1].

Being the author of spaCy, he should know better. I certainly enjoyed the straightforward simplicity of spaCy when I used it in my research (though I haven't touched it in years).

[1]: https://aclrollingreview.org/reviewerguidelines#-task-3-writ...

Some of original blog post really seems to follow Arditi et al. (2024) (https://arxiv.org/abs/2406.11717). Projecting out a direction can only remove behaviour and not add capability so the jump mostly comes from unblocking the looping.

So I do agree it is misleading and trying to present work as groundbreaking.

Just one word - MARKETING

Waiting for

The "The 'Astra can read and write weights to improve Qwen' post is misleading" post is misleading.

I for one think it's pretty cool how far too many people bend the truth or outright lie in tech these days.

There is going to be a reckoning for tech in the near future, because people will remember the lies and it's gonna be hard to beat the history.

It doesn't really matter how accurate the post is, what it shows is what one could assume how you'd end up with a system such as RSI, how you could prune harmful capabilities from models and build a more efficient training system with biases including and not limited to emergent behavior in astra that might have come from something they've never even trained the model on regarless if ML engineers can already do similar things.

None of the previous studies applies because Astra simply did not exist and this is worthwhile NEW knowledge that has been publicized and it is very interesting to see how it can be compared with other models as both openai and anthropic are keeping any and all RSI development completely private. The work is groundbreaking in a way that there will be a lot more experiments of this nature which will contribute to increasing the public knowledge of SOTA systems that was not normally discussed in model releases.

One could say is that we could now build a new benchmark which tests how many parameters a model can understand without any tooling other than eval or how complex of a system can be improved.

LOL. Tell me you want to be at the top of HN without telling me.

I don't get it. Because python was used, it's supposed to not be cool? IMHO, working with raw weights with Python is still working with raw weights.

Who watches the pruners?

Misleading how exactly when the original post states _exactly_ the same things as outlined in this objection?

I was blown away even by #1, and I don't feel after reading this that I was misled.