Jeff Dean

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343 appearances 1 recordings 1 series first heard Aug 2026 last heard 1 Aug

Jeff Dean’s voice in public audio — every appearance, attributed to the second.

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Recordings per month over the last 12 months — 1 in all, peaking in Aug 2026 with 1.

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Y Combinator Startup Podcast · Jeff Dean: The 1% Rule for Building in AI · 1 Aug 2026 podcast

But then without an agent-based system, what you usually do is you measure what the current performance is on some benchmarks you care about.

You make some modifications to improve the performance you hope.

Then you rerun the benchmarks, see where things improved.

You run maybe a broader set of benchmarks, measure the cash footprint of things.

And so we wrote a skill that basically taught the model how to do most of those things in

in various sequences so that it could actually do self-improving benchmark measurement, benchmark code changes, measure the performance improvement, and then iterate on that.

And that seemed to work pretty well for some kinds of problems.

And it really just is us giving the approach we would use as people to the model in a form that it could use.

We actually published a document maybe a few months ago called Performance Hints that Sanjay and I wrote.

It's like a 30-page document about various kinds of performance tricks.

Some people have taken that and then given it in summarized form to various models and seen that that model can now get better at reasoning about performance issues and code.

Yep.

It's all free available.

Yeah, I mean, obviously we want agents to be able to run for very long periods of time because that's how they're going to solve more and more complicated problems.

But as you observe today, you know, they sometimes stop working after, you know, 10 interactions with the tools and so on.

And sometimes that's because the model is trying to do something it doesn't have a lot of experience doing.

So it's been trained on a whole set of things.

And as soon as you get a little bit off the distribution of things it knows how to do, then like most machine learning models, its performance will suddenly start to degrade.

And the farther you get off the comfort zone of what it knows how to do, the more likely it is to not work as well.

So there's a bunch of things you can do.

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