Ships or Slips

Verdict: adopt context discipline, caching, and routing before scaling any agent. Token cost compounds quietly because multi-turn agents re-send a growing history each step, and self-attention scales quadratically with sequence length. Trim context to the working set, cache stable prefixes, and route easy turns to cheaper models. Naive full-context prompting slips.

Use it — but only for the syntax. Grammar-constrained decoding makes malformed JSON structurally impossible, which kills retry loops and regex scraping in production. It does not make the output correct: models still fabricate required fields and emit valid-but-wrong values. Treat schema-validity as necessary, never sufficient, and keep a semantic gate.

Verdict: use RAG, but retrieval — not the model — decides whether it ships. A generator conditions on whatever passages the retriever returns, so a bigger model just writes fluent wrong answers over bad context. Fix chunking, embeddings, and index freshness, and measure retrieval recall before you touch the model.

Ideogram 4.5 is unproven as a precise-edit model: the company's claims of cleaner multi-turn edits, native 2K output and pricing from 0.8 cents per image rest on its own page and demos, with no published benchmark. Treat it as a candidate to test on your own edit-drift suite, not a verified upgrade.

FlyTrex shows the human-on-the-loop model working in practice, but only as an anecdote. According to MIT Technology Review, one remote pilot may oversee up to 10 drones at peak while unmanned aircraft systems traffic management shares positions to avoid collisions. No intervention rate or incident log is public, so the 10-to-1 load is a claim, not a benchmark.

ML Intern produced narrow small models whose model cards support modest claims only. Citrus Doctor lifted strict accuracy from 14.9% to 52.8% on 335 test photos but scores 0.0% on healthy leaves. Agate Preview 002's 4-step student scored 0.536 GenEval against the teacher's 0.563, with overlapping 95% intervals, so the quality loss is unproven.

Coding agents take away the puzzle that made programming satisfying, but Jeremy Scheff's account does not show lost work: his business is having a strong year. Simon Willison reads the same shift as a gift for builders. It is a first-person essay, not data, so treat it as a morale signal.