The name is the button.

An agent writes the code now, and a human thumb hovers over Accept all a hundred times a day. The honest question of this decade is what, exactly, you just accepted. So I read the diff.

Eight years as a backend engineer in Stockholm, back in Tokyo since 2023, and I still ship something small most nights, usually badly. My one rule: the only real review of a model is the thing you built with it. That is why there is no leaderboard on this site.

Around a hundred and forty sources come through — the labs' own posts, the papers, the tools, the runtimes and languages nobody is watching yet, the chip under the demo, and the newsletters people actually forward. What is moving gets counted so the week reads at a glance, and every few days I explain one mechanism in plain words.

A story here is a headline, a short excerpt, one image and the link to whoever wrote it. We never reproduce the post; the images belong to their authors. A post written in Japanese appears in Japanese, exactly as it was published, and the link goes to the original.

— Yuki Halvorsen, Tokyo

← Accept All Archive

Nature Machine IntelligenceResearch

Towards foundation-style models for energy-frontier heterogeneous neutrino detectors via self-supervised pretraining

September 30

Nature Machine Intelligence, Published online: 30 September 2026; doi:10.1038/s42256-026-01309-6 Alonso-Monsalve et al.

Read at Nature Machine Intelligence ↗

One letter a week, in your inbox.

Latest

Towards foundation-style models for energy-frontier heterogeneous neutrino detectors via self-supervised pretraining

More from Nature Machine Intelligence on Accept All

ResearchRegional climate risk assessment from climate models using probabilistic machine learning Nature Machine Intelligence

ResearchTask-structured modularity emerges in artificial networks and aligns with brain architecture Nature Machine Intelligence

ResearchMinute-scale training for microrobot navigation Nature Machine Intelligence

ResearchGuiding large language models to predict edit sequences for molecular synthesizability optimization Nature Machine Intelligence

ResearchA collaborative agent with two lightweight synergistic models for autonomous crystal materials research Nature Machine Intelligence