That was incredibly relieving to me to read “motivation was problem-solving, machine learning as a means to an end” in @lawrennd.bsky.social “Atomic human”. I always felt as an impostor among colleagues who want to solve intelligence, while I just enjoy working on cool stuff.

Many companies won't use Chinese *open* models for long-tail information and generated code security concerns - a major adjustment in how I see the open model ecosystem. Adoption is on the table for entrants amid DeepSeek and Qwen releasing some of the best models on paper. buff.ly/y6MMoBt

TIL about huggingface-cli delete-cache to clean the models or other resources (datasets, ...) you downloaded from huggingface albertcthomas.github.io/blog/removin...

« When software is open-source, it means it is open-source – that the source is open – nothing more. […] It does not mean open to contributions; It does not mean support is offered; It does not mean you’re entitled to feature requests; It does not mean the developer owes you their time; […] »

Really enjoying the series of posts by @beenwrekt.bsky.social on overfitting.

Just put on line a talk I gave summarizing what I have learned across the years as a maintainer of open source. It's _opinions_ (been there, done that), but I'm willing to defend them, having stewarded my share of successful open source projects. speakerdeck.com/gaelvaroquau...

Good, published, benchmarks of machine learning / data science is crucial. But so hard. Well-cited "SOTA" methods typically crash often. They tend to be very computational expensive. Both make a systematic study impossible. Finally, reviewers always ask for more methods, and more "SOTA".

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Here’s a little script I made which I use to get a server up and running automatically (after you answering a few questions, including “what’s your name”) in just a few minutes. You can even fully automate it with a few environment variables. github.com/AnswerDotAI/...

Totally agree. This is a great paper that everyone doing RL should read.