Simon Willison runs Claude Fable 5 through its paces, finding it slower and pricier than Opus 4.8 but far more knowledgeable thanks to its 1 million-token context. He tests it on real-world coding tasks—upgrading a MicroPython sandbox to full CPython in WASM and adding pause-resume hooks to Datasette Agent—showing it can build complex features end-to-end.

• Fable 5 costs twice as much as Opus 4.5–4.8 ($10/$50 per million tokens vs their pricing) and runs slower, but handles every complex prompt thrown at it.
• Its 1M-token context gives it dramatically deeper recall than Opus 4.8—listing a dozen detailed open-source projects with dates versus Opus's brief list of four.
• It successfully converted a MicroPython-wasm project to full CPython in WASM, ultimately producing a working 13.9 MB wheel runnable via uv, demonstrating real end-to-end coding capability.

OpenAI bought Ona to power persistent, secure agents in its Codex platform, while Anthropic lifted its hidden safeguards after researchers flagged degraded outputs. The issue also covers Xiaomi’s MiMo Code AI assistant beating Claude on long tasks and dives into tokenizers, vintage LLM builds, compute markets, data debugging, and PyTorch optimizations.

• OpenAI acquired Ona to bring secure cloud execution and persistent, cross-session agent orchestration to its Codex platform.
• Anthropic secretly rerouted certain Claude requests (training rival models, debugging AI code, tweaking neural nets) to a weaker model, then reversed the policy after researchers and users complained.
• Xiaomi open-sourced MiMo Code V0.1.0, a terminal-native coding assistant that beats Claude Code on tasks over 200 steps using a memory subagent to track context.
• A developer built a full transformer from scratch for about $80 on a home PC.

Stanford posted a 1h44 CS229 lecture that explains how to build large language models from scratch. Engineers with those skills can command over $750,000 a year at firms like Anthropic.

• Anthropic reportedly pays 750,000+ dollars a year to engineers who can build LLMs from scratch.
• Stanford's CS229 lecture (1h44m, free) teaches the actual mechanics of building LLMs—transformer math, training objectives, and code.
• The lecture covers attention mechanisms, training loop setup, techniques to prevent model collapse, and fine-tuning for tasks like summarization and QA.
• It's claimed to go deeper than the in-house training typically offered at major AI labs.

The article analyzes the unit economics of large language models (LLMs), focusing on the compute costs associated with training and inference. It discusses how companies like OpenAI and Anthropic manage their financial projections and cash flow, emphasizing the need for revenue growth or reduced training costs to achieve profitability.

• Inference costs are falling faster than training costs, so gross margins on deployed models improve over time even as frontier training runs get more expensive.
• OpenAI and Anthropic's path to profitability depends on either scaling revenue much faster than compute spend or finding ways to cut training costs, since current cash burn is dominated by training rather than serving models.
• Reported "profitability" claims from these labs often exclude massive R&D/training expenditures, making headline numbers misleading about true unit economics.