Bitter Lesson Engineering

As AI gets better, Bitter Lesson Engineering becomes increasingly important.

Leans on Richard Sutton's 'The Bitter Lesson' to argue that prescriptive scaffolding around AI systems is a losing strategy in the limit: you should specify intent precisely and let the best available model figure out the path.

Supplies the underlying argument for Miessler's harness-engineering taxonomy. Useful anchor for the anti-prescriptive pole of the library.

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The conceptual base for Good and Bad Harness Engineering. The argument is a corollary of Sutton's "Bitter Lesson": methods that encode human prior knowledge get beaten in the long run by methods that scale general learning. Therefore: encode what you want (the construct, the outcome, the user intent) and let the model handle how. In practice this produces a design stance close to Tan's thin-harness, but arrived at from a different direction. Tan: "as models improve, scaffolding gets absorbed." Miessler-via-Sutton: "general methods beat prescriptive ones; prescriptive harness is prescriptive method." Disagreement preserved This entry deliberately scores low on repairability, observability, and institutional_ratification. That is the anti-prescriptive pole: less scaffolding means less to diagnose, less to inspect, and fewer institutional seams. Pair this entry with measurement-focused entries to see the tension.

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