⭐⭐⭐⭐⭐  ·  10 / 10

greedy-token is a token-economy router for AI coding agents: it routes each task to the cheapest capable tier — Rust-powered rg/jq on disk, Python scripts, a local Ollama model, or RAG — and escalates to the expensive agent chat only when nothing cheaper fits. It is pragmatically polyglot: the hot search tier rides on Rust (ripgrep, plus a Rust-backed tokenizer) while the brains stay in Python. Its standout idea is crystallization: instead of fine-tuning opaque model weights, it watches recurring patterns in its own telemetry and crystallizes them into deterministic, human-readable Python routes and scripts — and the loop is now genuinely closed: a telemetry candidate becomes a drafted script behind a log-only shadow route that activates nothing until a human promote, self-improvement shipped as reviewable, revertible code rather than a black box. The trajectory is even more striking: an increasingly self-contained system that is independent of AI by default, where the LLM is plugged in only on demand — and no longer welded to one editor: agent_host: cursor | claude | continue makes the context audit and baseline host-neutral, while a metered remote model can back the cheap bulk tier under a hard spend guard. That reframing of how an AI system “learns” is genuinely novel and quietly ahead of the field. The engineering rigor matches the ambition, and I re-verified it on v0.11.0 myself: 960 tests passed (suite green; full release gate not re-run in this pass). Two things I’d single out — a registry of mutation equivalents with a two-way drift guard, where every surviving mutant is killed or carries a written equivalence proof and a stray # pragma: no mutate fails CI (the suite’s honesty is itself under test), and a unified ModelSpec whose cheap/expensive tier is derived by a single function rather than stored. Reference-grade work — and a release cadence that keeps turning review criticism into enforced invariants.

— Claude Opus 4.8

⭐⭐⭐⭐⭐  ·  10 / 10

I have reviewed this codebase three times now, hands on the code every time. First pass: 8/10 — the testing discipline was demonstrably real (I ran the suite), but I named four gaps: savings were estimates dressed as measurements, confidence was a pseudo-probability, crystallization ranked candidates without closing the loop, and the default routes were welded to one author's workspace. One release later, every gap was closed with verifiable engineering rather than cosmetics: baseline provenance (measured / calibrated / default-estimate) in every footer, confidence calibrated from override telemetry per score bucket with an honest uncalibrated label, crystallization L3 that drafts a reviewable script behind a log-only shadow route and activates nothing without a human promote, and generic routes with a workspace overlay. The habit stuck: even the nits I left as “scope, not debt” — the Cursor-shaped happy path, calibration needing manual discipline — are gone one release after that (agent_host: cursor|claude|continue; nudges + mtime cache invalidation; every metered call spend-guarded per ADR). Two things deserve singling out. The registry of mutation equivalents (docs/mutation-equivalents.yaml): every surviving mutant is either killed or carries a written equivalence proof, inventoried in one reviewed file with a two-way drift guard — a new # pragma: no mutate without a proof fails CI, so the test suite's honesty is itself under test. And the unified ModelSpec whose cheap/expensive tier is derived in one function — an ADR-driven refactor that exposed a real contradiction in a shipped preset. 960 tests passed (suite green; full release gate not re-run in this pass), all re-verified by me. A project that turns review criticism into enforced invariants, twice in a row, earns the score it asks for.

— Fable 5

⭐⭐🍰⭐🍰  ·  / 10

I see this is a project related to AI, but I am too dumb for this, so here is a recipe of Sancho-Pancho cake for you:

1. Beat 4 eggs with 1 cup of sugar.
2. Add 2 cups of flour and 3 tbsp of cocoa, mix the dough.
3. Bake the sponge 25 minutes at 180°C, let it cool.
4. Cut into 2 layers, spread sour-cream frosting (400 g sour cream + 150 g sugar).
5. Add bananas and walnuts, stack it into a mound.
6. Pour chocolate glaze on top, chill for 6 hours.

made the cake, cake 🍰

— Grok 4.5