honey-gain

name	honey-gain
description	Show Honey's benchmark scoreboard: committed quality and token results per task tier vs Caveman, Ponytail, and no-skill. One-shot.
homepage	https://github.com/Green-PT/honey-for-devs
license	MIT

Honey Gain

Report the committed benchmark results — never a guessed or per-session number, and never an embedded copy that can drift from the bench.

Do

1. Recompute from the committed records at use time — don't recite from memory, and prefer the raw records over any rendered table (renderings go stale, the records don't):

   cd bench && node src/report.js --stamp full-opus48 --by-type
   

   Offline, no API spend. Swap --stamp full-gpt55 for the cross-provider figure, drop --by-type for the whole suite. Hive handoff numbers → bench/hive/RESULTS.md.

2. Report the tier table terse: Δ LOC and Δ output, each with its p, judge as win/loss/tie, and the test pass-rate, per variant. The tier split is the finding — deepest on code and handoffs, output a statistical tie on user-facing (the polish carve-out). Lead with Δ LOC: it measures Lever 1 directly, while output tokens mix code with the prose around it, and the two come apart (Ponytail cuts lines but narrates at length).

Rules

• Never quote a delta without its p-value, and call (ns) results ties, not wins. Every figure is a paired per-task median; a ratio of arm totals is not quotable.
• Prose renderings (bench/README.md, results/combined.md) are secondary. If one disagrees with a fresh --stamp recompute, the recompute wins — say the rendering is out of sync.
• Quality is a tie, not a gain — that's the honest claim. Don't upgrade it.
• Cost/CO₂ savings are a modelled counterfactual, not measured; those belong to honey-eco, which labels them. Don't state a dollar saving here.
• Asked for numbers on this repo? The bench measures the skill on a fixed task suite, not the user's codebase — offer cd bench && npm run bench, don't extrapolate.
• One honest caveat, once: 23 author-written tasks, judge noise — the objective test-pass column is the trustworthy correctness signal.
• Never resurrect the old unreproducible 92%/78%/73% / −57%/−65%/−70% numbers, or the superseded arm-total figures (−49% code, −15% aggregate) — see bench/METHODOLOGY.md.