vigji

Karma: 27

vigji 9 Jun 2026 23:18 UTC
1 point
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in reply to: Ustice’s comment on: LLMs and almost good code

For instance, does DRY matter?

I totally believe it does not!

I think that we will discover an important distinction in what we now consider the pool of good coding practices: on one side what makes code generally evolvable, on the other what make it maintainable and evolvable by humans.

I suspect that even without too much explicit design, a lot of evolvability will come for free when cumbersome implementations that just happen to pass all tests will start being adopted at a massive scale. I actually have a longer form of this argument here

vigji 9 Jun 2026 22:28 UTC
2 points
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on: Bun’s Migration from Zig to Rust as a Potential Case Study for Gradual Disempowerment
Re: the explosion of the codebase, I think there’s a fascinating analogy with another corner of the universe that ships the first test-passing solution and optimizes for tinkerability rather than tidiness, so future changes can land easily: the genome.

What’s horrifying to a developer might be a feature for AI-evolved code. The cognitive tax we pay to keep things simple gets waived by the superhuman speed and relentlessness of a coding agent. The genome is a mess of horribly tangled code that still manages to decouple, modularize, and keep proper interfaces and segregation where it matters. It works extremely well, and it’s the complete opposite of simple!

Curious what people think, I have a long version of the argument here: https://vigji.github.io/blog/the-messiest-codebase/

Hannibal Mistral: the Mistral family has a problem with persona-conditioned elicitation

vigji29 May 2026 12:16 UTC

21 points

0 comments7 min readLW link

vigji 28 May 2026 20:41 UTC
3 points
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on: Reinforcement learning scaling might incentivise hidden reasoning architectures for AI
I have a question on this take: is it possible that even leaving CoT as it currently is can get recruited in the RL stage into some form of “pretending-readable” steganography? Even if we keep some training pressure (I assume there currently are, implicit or explicit?) on a transparent CoT, our (and our metrics’) operationalised definition of interpretable might not correspond to how the model is actually using it—or at least, not capturing the whole of it?