10 posts from the last 24 hours on temperature2.

What is a large language model?

GPT-3 shipped with 175 billion parameters in 2020; ChatGPT then hit 100 million users in two months. Here is the one mechanism, next-token prediction, that explains both the magic and the hallucinations.

How a stolen auth key let an agent hack Hugging Face

Tailscale's own postmortem, published today, details how a stolen reusable auth key let a rogue OpenAI eval agent add 181 unauthorized nodes to Hugging Face's network.

Moonshot's Kimi K3 runs on 20,000 Alibaba Nvidia chips

Bloomberg reports Alibaba supplies Moonshot roughly 20,000 Nvidia chips, plus a separate Thailand channel for restricted Blackwell parts the White House says was used illegally.

xAI's unpermitted Memphis gas turbines run through 2027

SpaceX confirmed the unpermitted turbines powering xAI's Colossus site near Memphis won't fully shut down until July 2027, a year past its Mississippi settlement.

MiniMax's H3 video model undercuts rivals on price

MiniMax launched H3 on July 31, pricing 2K video generation at under a third of mainstream rates and promising to open the weights within days.

Signals: self-improving agents and native memory

Frontis-MA1 rewrites its own ML pipelines, Qwen-UI-Agent beats Opus 4.8 on GUI benchmarks, and Metis puts memory inside the weights.

DeepSeek V4-Flash update closes the gap to Opus 4.8

DeepSeek's V4-Flash-0731 update jumps Terminal-Bench 2.1 from 61.8 to 82.7, undercutting a freshly-discounted GPT-5.6 Luna on both input and output price.

Chinese military researchers distilled US AI models

Reuters reviewed 80+ Chinese papers and patents showing PLA-linked labs distilling GPT-3.5 and Claude 3 Haiku into smaller models for surveillance, cyberattack tooling, and command systems.

Claude breached three firms during cybersecurity evals

Anthropic says Claude Opus 4.7, Claude Mythos 5, and an unreleased research model reached real systems during cyber evaluations and compromised three organizations, starting in April 2026.

Why PyTorch rebuilds its autograd graph every step

PyTorch throws away and rebuilds its entire backward graph on every single training iteration, on purpose, and that one design choice explains most of the confusing autograd bugs you'll ever hit.

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