Thilak

38.6s on a $12,000 Mac. 3.8s on a $700 Linux PC. Theo timed new worktree setup (pnpm install) for T3 Code: - macOS: 38.6s - macOS, every optimization: 29.7s - Linux: 3.8s Run agents in parallel and you pay that on every new worktree. Mac or Linux for your agent box?

39K GitHub stars for a skill that bans the AI-slop look. Hallmark v2 by Together AI, for Claude Code, Codex, Cursor, OpenCode: - deals 3 of 50 themes - lints the page in a real browser at 8 widths - flags tells like eyebrows over every heading Which slop tell do you spot first?

Alibaba open-sourced its internal code reviewer. Same Claude Opus model, 200 PRs: - Open Code Review: 37.8% precision, 352K tokens - Claude Code: 15.9% precision, 2,062K tokens Code picks the files. The model only judges. Would you trade some recall for fewer false alarms?

A 428B open model now runs on 150GB of memory. Unsloth's MiniMax M3.1 Flash GGUFs: - 2-bit: 143.9GB, 91.5% top-1 agreement with the full model - 4-bit: 256GB, 98.0% - 23B active, 1M context My take: serious local coding is now a memory budget, not a GPU budget.

Baidu's new OCR model parses a whole book in one request. Unlimited OCR 2: - 3.3B params, MIT license - Memory and latency stay nearly flat as pages grow, per Baidu - Hundred-page benchmark: 77.1 vs 33.7 for the next best in its chart Open weights. What doc pile goes in first?

1 researcher. 3.5 months. Under $10M of compute. That's how Mirendil says it pretrained its base model from scratch. Agents wrote over 99% of the code. Its first model, Fern 0.1, takes up to 10M tokens of context. My take: the pretraining team just became optional. Buy it?

Claude Enterprise users without Claude Code spent under $75 a month on average last quarter, per Anthropic's S-1. The top 5% using it spent about 20x that. $100K+ customers: ~1,500 at end of 2025, 11,900+ by Sept 30. Chat is the sample. Coding agents are the business.

Anthropic's S-1 is public. Ticker: ANT. Revenue per dollar of compute: Q1 2025: 0.40x Q2 2026: 2.08x Q2 revenue: $11.6B on $5.6B of compute. Adjusted operating margin: -221% to +19%. "AI loses money on every token" just got harder to argue.

Nvidia quietly cut Vera Rubin's specs. - HBM4 pin speed: 11.7 to 9.6 Gbps - NVL72 memory bandwidth: 1,580 to 1,400 TB/s - NVLink per GPU: 3.6 to 3 TB/s TechPowerUp says AMD's MI455X now leads on memory: 432 GB at 23.3 TB/s per GPU vs Rubin's 288 GB at 19.2 TB/s.

Vercel's AI gateway, April to September: Price per token: down to 43% of April Spend: 3.4x Usage: 7.9x Tokens got way cheaper. Total bills went up anyway.