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New Roadster is beyond movies, beyond imagination, beyond human, yet still made by humans on Earth

interpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai's first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works already-clean.goodfire.com/

everything that can be will be converted into `state = memo { f(log) }` all other state management is just too complex at the limit

@grok is this true?

What an coincidental release we just did (v0.2.11). Both you and Ante can now setup its own inference stack programmatically

if "just press the magic button" is what the industry wants, then i'm out. not because my programmer feefees are hurt. i've written enough code in my life to be very tired of it. but i couldn't imagine a more boring waste of my time than just pressing the magic button.

super refreshing take! i think the "software engineering is solved" perspective forgets that one of the most core parts of the job is turning smaller unstructured messiness/goals into a candidate (but likely imperfect and iteratively improved) artifact that helps more complex goals get solved. writing code used to be part of this recipe, but the supervision of the artifact is definitely still the same with agents code generation throughout the years has always been about putting bigger and bigger "holes" in your spec and and having an automated solver try and fill it in (it's a feature not a bug!), and while we should definitely strive for Good Code™, what makes code "good" has always been contextual: not just whether an implementation works, but whether its tradeoffs make sense for the messy, evolving goals of the system it's meant to serve. benchmarking this will be a super fun thing to do :) more interestingly, the frontier of software engineering research might increasingly be about understanding how much ambiguity we can safely delegate. making the holes in specs bigger is only useful as long as we can have reasonible feedback loops that tell us whether the solver filled them correctly, and the hardest part is often that we don't even know what correctness means until we've built something and seen how it behaves.

Good artists copy, great artists steal

Good definition of slop. It's not an attitude or an apriori argument. It actually picks out specific pain points, which are ones I have observed as well fwiw.

I just signed an executive order permanently declaring Artificial Intelligence “Artificial Intelligence” in California. You’re welcome, America.