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  1. The Myth of unsafe Open Source AI Researchers, often working at closed labs, argue that open models are inherently unsafe because you cannot control them after their release, and because they can be fine-tuned for any malicious use case.

  2. The vibes in China’s AI labs # For the past ~10 days, I got the opportunity to visit Chinese AI labs together with the incredible folks at SAIL.

  3. Quo vadis, LLM benchmarks?

    Quo vadis, LLM benchmarks?

  4. Local models are (not) cope

    Local models are (not) cope The imminent release of the shiny and aptly named DGX Spark led to some discussions about what you actually do with this thing.

  5. You're not using LLMs enough A consistent theme I have observed lately by working and talking with a lot of different people is that a lot of them are underestimating the capabilities of LLMs or they don't know that things can be easily automated these da…

  6. Living in the Agentic Era

    Living in the Agentic Era As I write this, several instances of amp are running in the background, chugging along.

  7. Using OpenAI's Deep Research to save Time and Money OpenAI's Deep Research (ODR), which was released a bit over a month ago, has become a valuable tool for me.

  8. A Guide to LLMs for Programmers Although LLMs are all the rage these days, I found that a surprising amount of developers is still dismissing them and their potential.

  9. Sane Python dependency management with uv Astral, the company behind the fast and beloved python linter ruff has been working on uv for some time now, with the ambitious goal to bring the philosophy of cargo, rusts package manager, to the python ecosystem.

  10. How FastHTML sparked my joy in web development I've been wanting to create a personal site for a while now.