the largest tech companies (at the top 1%) aren't using fewer tokens, they're just being cost conscious and moving to cheaper, still very capable open models running vs SOTA model usage
Podcast. Cal Newport’s new book is a great distillation of what I’ve come to believe is the right, sustainable, healthy approach to social media, and technology more broadly. Browse less, make the best of it, and live more.
I think the skill nowadays is less about prompt engineering and more about figuring out how do you give Claude a hard task that seems a little bit too hard. And then how do you make it possible for Claude to verify its work along the way? And the verification I think is probably the single most important thing that people do not get right
I think evals, they outlive the harness a little bit, but not by that much. Like an eval might live for maybe one, two, three model generations, but nowadays the you know, we're on the exponential. The model is improving so quickly, very often we just saturate the eval, and then we have to throw it away, and we have to come up with a new eval.
But in terms of like features, like a website like you can just throw features in there nowadays that nobody really cares about and you can you can do it so quickly. Like a new feature every single day, but do people actually care about that? Is that making it better? It could be making things worse.
This is a super exciting release - Claude Fable 5 is the same underlying model as Mythos but with added safeguards. The benchmarks are great and it's SOTA on everything by a margin but I'll add that *qualitatively* also, this is a major-version-bump-deserving step change forward
And the question nowadays is less about like how to build because if you can describe the problem in the solution you want, usually AI can do it. Maybe not today, but maybe like two or three years from now on they can do a lot of those. The question is like what to build because we talk about like yes, if something exists, right? AI can replicate it.
These models don't really have this distillation phase um of taking what happened, analyzing it, obsessively thinking through it, um basically doing some kind of a synthetic data generation process and distilling it back back into the weights
Distillation is standard practice in industry. Whether or not, if you're at a closed lab where you care about terms of service and IP closely, you distill from your own models.
I'm excited about what this means for the open-source community and research, with the gap between closed-source and open-weight models closing and SOTA-level conversational models being more easily accessible.
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