I think the latest round of models is about to do to CAD the same thing that has been happening to development. I don't know if CAD designers have picked up on this. What happened to dev is probably going to happen to CAD designers soon. I've been having Opus 5.5 design parametric openscad models, and it can do some rather amazing work.

Was Astra able to disclose its methodology for analyzing the data structures?

Too much inequality in society is a mortal danger to 1%.

That’s the beauty of the democratic capitalism system. If a government isn’t totalitarian, it naturally seeks to preserve stability thus having to elevate common people, lest they would cease to form a civilized group or simply vote away the politicians.

99% rule the 1% through elections.

There is no better system of checks and balances where opposite engines of greed vector sum to faster and faster abundance for everyone.

This seems emergent to me. LLMs are not trained directly on how to read weights, because humans do not understand raw weights, so there is no training data. They're trained on how to use the tools we have like writing python, examining training output and iterating, not writing whole weights from scratch.

I tried it in the Claude web version with Opus 5.5 Medium and it also identified Breakout, even though in one of its explanations it seems to have mixed up some indexes, but the overall reading of the weights was right.

That's also matching what I see when I work with Opus 5.5 to optimize models. It seems to understand what individual layers are for, and can thus sometimes suggest optimizations, where I as a layman would be completely clueless.

The latest models are performing amazingly. Because of this, I disagree with popular comments saying that eg. DeepSeek is good enough, AI-assisted development was where the real value was at, etc.

We see huge progress at the frontier, while the open competition is around GPT-5.5's level, and that model was released in April. Let's see how things develop in the future.

> It seems natural that the models would be explicitly trained to excel at all sorts of ML related tasks as a result. Is this one of those tasks, or is this emergent?

Maybe there's some general capability, call it g, that can explain all the underlying capabilities and improvements, including on ML tasks? Huh, I am getting a bit of déjà vu here.

Could it be already baked into these models because they have already been trained on this task this way?

Something like mirror neurons.

Some structures in Astra get activated by the statistics of the Qwen weights, and these structures are associated with "breakout"

My guess: There is a ton of data online of just plain weights laying around. No human would ever actually read it. LLM's read it all during training.

Based on our current understanding of LLM's, it shouldn't be.

This sort of throws a wrench in the whole "stochastic parrot" idea...

I am not surprised, though I expect many to be.

See my other comment in this thread