Latest

On the calendar

予定 · Conferences and launch events, this week's stories from each corner of the industry, and what its press is covering — in English

On view

This week, from that corner the newest stories from the corner of the industry you picked

The wire their own posts, translated

How code moves in 2026 from the industry's own reports

Sources

Fetched every three hours, linked to the original

About

Accept All is a magazine, a radar and a diary about artificial intelligence and the craft of code — read from Tokyo, for people who build.

The name is the button. Code is written by agents now and reviewed by a human thumb that hovers over Accept all a hundred times a day; the whole question of this decade is what, exactly, you just accepted. So we read the diff. A magazine, because every week one model, one tool or one idea is worth reading about slowly. A radar, because we count what the labs, the Valley, GitHub and the chip makers are talking about — releases, agents, open weights, compute, funding, rules — and show it as it moves. A diary, because a seal is set each day in that day's colour, and the days add up.

We read the research labs' own posts, the American and European tech press, the Japanese and Chinese developer sites and the newsletters people actually forward, and bring it together in English and Japanese. Everything here links back to the people who wrote it; the images belong to their authors.

Accept All will keep changing. New sources, new signals, new ways of looking — a little better each week, never finished. If something here made you open a terminal, that is enough.

Thank you for reading. ありがとうございます。

← Accept All Archive

Hacker News ShowGitHub

Show HN: Compute polynomials twice as fast

September 9Work

A few years ago my coauthor and I was wondering if we could reduce the number of multiplications used for hashing algorithms. We had a construction and a 100 page proof, but we were not 100% sure it was correct. Now we h

Chips & compute

Read at Hacker News Show ↗

SiliconMeta Superintelligence – Leadership Compute, Talent, and Data SemiAnalysis

SiliconIntel 18A Details & Cost, Future of DRAM 4F2 vs 3D, Backside Power Adoption (or Not), China’s FlipFET, Digital Twins from Atoms to Fabs, and More SemiAnalysis

One letter a week, in your inbox.

Latest

Show HN: Compute polynomials twice as fast

Related

SiliconMeta Superintelligence – Leadership Compute, Talent, and Data SemiAnalysis

SiliconIntel 18A Details & Cost, Future of DRAM 4F2 vs 3D, Backside Power Adoption (or Not), China’s FlipFET, Digital Twins from Atoms to Fabs, and More SemiAnalysis

SiliconScaling the Memory Wall: The Rise and Roadmap of HBM SemiAnalysis

SiliconH100 vs GB200 NVL72 Training Benchmarks – Power, TCO, and Reliability Analysis, Software Improvement Over Time SemiAnalysis

SiliconHuawei Ascend Production Ramp: Die Banks, TSMC Continued Production, HBM is The Bottleneck SemiAnalysis

SiliconAnother Giant Leap: The Rubin CPX Specialized Accelerator & Rack SemiAnalysis