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On 2026-05-03 @pvncher argues there is substantial "low hanging fruit" in…
Brief
@pvncher (2026-05-03) claims optimizing tool responses and context use—improving token density and ergonomic request-for-help patterns—is low‑hanging fruit. Citing Ryan Lopopolo (@_lopopolo), they argue models learn from massive real‑world use ("billions of failed invocations"), so external harnesses that don't match a model's lab harness will be outcompeted; Codex shell JSON/nested‑quote quirks are given as an example.
Why it matters
On 2026-05-03 @pvncher argues there is substantial "low hanging fruit" in optimizing tool responses for context usage, urging work on token density and ergonomic ways for the model to ask for help.
Key details
- Harnesses control what context a model sees; @pvncher, citing Ryan Lopopolo (@_lopopolo), warns that alternative external harnesses will be "bitter lesson'd away" because models adapt to the harnesses they encounter during post‑training.
- Lopopolo claims "billions of failed invocations" have trained models to current harness behaviors — example: Codex shell tool's nested‑quote/JSON quirks and ioctl‑like tolerances that models now handle despite API flaws.