WhatA structured, empirically-tested reference that maps categories of work to whether LLMs handle them well end-to-end, need human scaffolding, or should be avoided — with an API that auto-routes tasks to the right workflow.
SignalSeveral commenters note they are still building intuition about what LLMs are fundamentally good at versus bad at, and see others repeatedly making the same mistakes — there is demand for a systematic framework rather than ad hoc tribal knowledge.
Why NowThe proliferation of models with different strengths (coding, vision, reasoning) makes task routing increasingly complex, and enterprises adopting AI at scale need systematic guidance rather than folklore.
MarketEnterprise AI teams and developer tool companies — adjacent to LLM observability platforms (Langsmith, Braintrust) worth $500M+; no one owns the task-routing taxonomy layer.
MoatEmpirical benchmark data on task-model fit becomes a proprietary dataset that improves with usage, creating a data network effect.
Using “underdrawings” for accurate text and numbersView discussion ↗ · Article ↗ · 369 pts · May 4, 2026
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