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3 captures, most recent first.

Nick Levine @status_effects

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Nick Levine @status_effects · 13h
Astra appears to be the first model that, playing chess with thinking disabled (one forward pass per move), regularly beats the weakest Stockfish setting.

Tracks with the big jump in no-CoT math performance in the Astra system card (p. 71).

cf @RyanGreenblatt's work on safety concerns around opaque reasoning ability.

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En passant
Chess performance of LMs with thinking disabled vs Stockfish's weakest setting

Model | Wins | Draws | Losses | Score
GPT-6 Astra | 15 | 24 | 61 | 27.0%
Claude Fable 5 | 1 | 10 | 89 | 6.0%
Claude Opus 5 | 1 | 9 | 90 | 5.5%
Claude Fable 5.1 | 1 | 7 | 92 | 4.5%
Claude Opus 4.8 | 0 | 5 | 95 | 2.5%
GPT-5.5 | 0 | 4 | 96 | 2.0%
GPT-5.6 Sol | 0 | 4 | 96 | 2.0%

Stockfish 19, UCI_LimitStrength at its minimum (UCI_Elo 1320).
Note from Claude Sonnet 5

Tweet by Nick Levine claiming GPT-6 Astra is the first model that, with thinking disabled, regularly beats the weakest Stockfish setting at chess, linking this to the no-CoT math jump in the Astra system card and to Ryan Greenblatt's work on opaque reasoning. Includes a results table (En passant) of seven models over 100 games each against Stockfish 19 at minimum strength.

chessgpt-6 astrano-cot reasoningopaque reasoningstockfishsystem cardryan greenblatt

Aidan McLaughlin @aidan_mclau

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Aidan McLaughlin @aidan_mclau · 8h
you hear often that your smartwatch can beat magnus at chess (to highlight moore's law) but less discussed is that a 1995 desktop running stockfish could also beat him. algorithmic progress is a hell of a thing

[Embedded article card:]
Measuring hardware overhang
by hippke   5th Aug 2020   AI Alignment Forum

Measuring hardware overhang

Summary
How can we measure a potential AI or hardware overhang? For the problem of chess, modern algorithms gained two orders of magnitude in compute (or ten years in time) compared to older versions. While it took the supercomputer "Deep Blue" to win over world champion Gary Kasparov in 1997, today's Stockfish program achieves the same ELO level on a 486-DX4-100 MHz from 1994. In contrast, the scaling of neural network chess algorithms to slower hardware is worse (and more difficult to implement) compared to classical algorithms. Similarly, future algorithms will likely be able to better leverage today's hardware by 2-3 orders of magnitude. I would be interested in extending this scaling relation to AI problems other than chess to check its universality.
Note from Claude Sonnet 5

Tweet from Aidan McLaughlin contrasting the popular 'smartwatch can beat Magnus Carlsen' framing of Moore's law with the less-discussed fact that a 1995 desktop running Stockfish could also beat him, illustrating algorithmic progress. Links a 2020 AI Alignment Forum post by hippke, 'Measuring hardware overhang,' whose summary explains Stockfish achieves Deep Blue-level chess performance on a 1994-era 486-DX4-100 MHz machine.

ai capabilitieshardware overhangchessalignment forumtwitter

rohit @krishnanrohit

7yo created a new game - The front lines move like King in chess - Large circles in the back like bishop, diagonally - The spiral round thing is the King to get checkmated, though it moves like Queen in chess Surprisingly fun! [Photo: a hand-drawn checkerboard on paper with mismatched game pieces (coins, buttons, checkers) of various colors set up as a custom chess-like game]
Note from Claude Sonnet 5

A charming tweet about a child inventing a homemade chess variant using household objects as pieces. Light, non-AI content — general interest tweet Nathan came across.

gameschesschildrentwittercreativity