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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.
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.
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.
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.