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The early rapid expansion of AI capabilities that focused on frontier models was largely ushered into the world by a few powerful, US-based AI labs. Open-weight models released from labs in China, early on from DeepSeek, and later from Moonshot, Z.ai, and others, have in part disrupted that…
This is the third post in a four-part series about post-training. If you missed them, read part 1 and part 2. The final post, on implementing your own pipeline, will be coming October 7.
When considering the history of AI, Richard Sutton observed that brute force and compute scale has always trumped human expertise, and when you look for it, you can see this “bitter lesson” play out throughout tech history.
The following article originally appeared on Addy Osmani’s blog and is being republished here with the author’s permission. A software factory harnesses loops at scale. You can run the loop with humans in it (light factory), trading judgment and concentration against speed and breakage.
The way we talk about data is changing faster than the way we build it. Every quarter a vendor ships a new approach, coins a new term for it, or quietly adopts a term someone else has been using and redefines it to fit the shape of their product. None of this is malicious. Every […]
Every knowledge program seems to begin with the same request. A senior engineer is leaving in six weeks, and someone asks her to document the process she’s carried for years. She returns a clean flowchart of the happy path. The drawing is accurate and may even be elegant.
My guest on this past week’s Live with Tim O’Reilly was Emmanuel Ameisen, a researcher on Anthropic’s AI interpretability team. I’d heard him give a short talk at Foo Camp on Anthropic’s research into what is going on inside an LLM while it is processing, and I wanted him to reprise the talk and…
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