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Banning general-purpose computation in favor of specialized anal AI agents' success rate on end-to-end, economically valuable fre Slowing or banning the open-source AI model ecosystem is an effe Frontier AI agents' reliability on long-horizon, multi-step, mul

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Mechanism◆Article

Newer Anthropic models have been specifically trained, presumably via reinforcement learning, to better use the edit tools baked into Claude Code, and this makes them more likely to misuse the differently-shaped custom edit tools of other coding harnesses like Pi.

Armin's theory for the regression: Anthropic has likely RL-trained recent models heavily on Claude Code's own edit tool, which inadvertently degrades their ability to correctly use differently-designed edit tools in third-party harnesses like Pi.

Armin Ronacher · Simon Willison's WeblogExtends · 2 Examples · 1 Supports · 2

Claim◆Article

Fable, and to a certain extent Opus, work best when allowed to use their own judgement about how to do a task rather than being given dictated, rigid instructions.

A tip from the Claude Code team: let Fable and Opus decide how to approach a task themselves instead of prescribing exact rules.

Cat Wu and Thariq Shihipar · Simon Willison's WeblogExamples · 3

Claim◆Article

To avoid burning through valuable Fable tokens before the price increase, tell Fable to hand off smaller tasks to other models, letting it use its own judgement about which model to use.

Jesse Vincent's tip: have Fable judge when a task is small enough to route to a cheaper model, saving tokens ahead of an upcoming price rise.

Jesse Vincent · Simon Willison's Weblog

MechanismArticle

Implementation work rarely needs the top-tier model, so coding tasks should be spawned as subagents with a model override (Sonnet for substantive implementation, Haiku for trivial/mechanical edits), while judgment, review, and synthesis stay with the main loop.

The saved memory file spells out the rationale and rule: hand off routine coding to cheaper subagent models, but keep judgment-heavy work (design, review, synthesis) in the main model.

Claude · Simon Willison's Weblog

ExampleArticle

Rather than giving Fable an explicit rule like 'only use automated testing for larger features, don't update and run tests for small copy or design changes,' it's better to tell Fable to use its own judgement about when to write tests.

Testing was the concrete example: instead of hard-coding a rule about when to test, just tell Fable to judge for itself.

Cat Wu and Thariq Shihipar · Simon Willison's Weblog