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

Tech.euEurope

Former Google DeepMind top researcher launches London “compute-efficient” AI company

10.20

A London-based AI company co-founded by a former Google DeepMind research lead and AlphaGo co-creator is launching today, pursuing a “compute-efficient” alternative path to Anthropic and OpenAI’s…

Chips & compute

Read at Tech.eu ↗

One letter a week, starting soon.

Latest

Former Google DeepMind top researcher launches London “compute-efficient” AI company

Related

SiliconNVIDIA BlueField-4 STX Server for CMX at OCP 2026 ServeTheHome

AsiaAnthropic in talks for $45b Nscale cloud computing deal Tech in Asia

AsiaAlibaba says XuanTie chip shipments top 6 billion Tech in Asia

AsiaAlibaba’s RISC-V IP powers over 6 billion shipped chips as tech giant targets AI, autos SCMP Tech

SiliconNVIDIA's Faster DLSS 5 Model Gets Extracted From NBA 2K27, Modders Report Up to 90% Gains TechPowerUp

ValleyAmazon introduces toolkit for running PyTorch workloads on AWS Trainium chips SiliconANGLE