Topic
Retrieval And Tool Use
3 episodes
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A field guide to Claude Fable 5: Finding your unknowns | Claude | Claude by Anthropic
Thariq Shihipar from Anthropic's Claude Code team argues that with Fable 5, the bottleneck has shifted from model capability to the human's ability to clarify unknowns before, during, and after implementation. He frames this as the difference between the map (your prompt, skills, context) and the territory (the actual codebase and constraints). The core insight: working with a more capable model requires systematic discovery of what you don't know — known unknowns, unknown knowns, and unknown unknowns — using concrete techniques like blind spot passes, brainstorming, interviews, implementation notes, and post-ship quizzes.
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Overview: Tokenization
We slow down and explain tokenization from the ground up: how raw text becomes numbered pieces a model can process, why those pieces are usually subwords, and why the tokenizer quietly affects cost, context, language handling, and product behavior.
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Snowflake CEO finds GLM 5.2 competitive with Opus 4.7 at a fraction of the cost
Cooper and Miles dig into Snowflake's claim that GLM-5.2 can hang with Claude Opus 4.7 on a real coding benchmark for much less money, and why the interesting part is not 'GLM wins' but 'cheap models are getting close enough that harness quality and retry policy start to matter more than leaderboard prestige.'