| title | MO§ES SigRank | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| emoji | 📡 | ||||||||||||
| colorFrom | yellow | ||||||||||||
| colorTo | gray | ||||||||||||
| sdk | gradio | ||||||||||||
| sdk_version | 6.17.3 | ||||||||||||
| app_file | app.py | ||||||||||||
| hf_oauth | true | ||||||||||||
| pinned | false | ||||||||||||
| license | mit | ||||||||||||
| short_description | Rank AI operators by architecture. Volume can't buy rank. | ||||||||||||
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A leaderboard that judges AI coding operators by architecture, not budget. Paste your token usage; get an operator profile with a tiny-model narration and your rank. The ranking metric Υ = (Cache·Output)/Input² penalizes raw-input padding quadratically — volume can't buy rank — but Υ is only the headline of a larger metric system whose mathematical thesis is the cascade decomposition.
GitHub: github.com/SunrisesIllNeverSee · Repo: moses-sigrank · Deck: mos2es.com/deck · Benchmarks: mos2es.com/benchmarks · Law: mos2es.com
SigRank is Layer 2 of the MO§ES™ stack — the human–AI operator intelligence layer. MO§ES™ is the substrate: compression, recursive execution, drift control, governed meaning at source. Built by Deric J. McHenry · Ello Cello LLC.
IP: 4 patent filings · IC 042 TM 99408355 · Conservation Law of Commitment — Zenodo DOI 10.5281/zenodo.18792459
SigRank is grounded in a published law: the Conservation Law of Commitment.
C(T(S)) ≈ C(S) — Commitment is conserved when enforcement holds. If the meaning of a signal is preserved across transformations, the architecture is sound. If it drifts, the architecture is leaking. Formal proof — Zenodo DOI 10.5281/zenodo.18792459 →
When token-level data became available via ccusage, the word-based commitment framework translated directly into token-domain measurement:
- commitment →
cache_create / output - reuse →
cache_read / cache_create - transmission →
output / input
The cascade identity (O/I) × (C_w/O) × (C_r/C_w) = C_r/I is the token-domain expression
of the same conservation law. Operators who conserve commitment compound. Operators who don't, pay for it.
Paste ccusage claude --json (Claude Code), ccusage codex --json (Codex), or four numbers →
- operator profile — a 0.5B MiniCPM model narrates your architecture, plus raw ledger, composition, full metrics, cascade breakdown
- leaderboard placement vs real operators, ranked by Υ, with blended $/1M cost
- trading card — species classification, cascade decomposition, composition bar
SigRank is local-first — the importer reads your usage on your own machine.
1 — Primary: the local importer. Clone it once, then run it (no install step — runs on system Python + Node.js):
git clone https://github.com/SunrisesIllNeverSee/moses-sigrank
cd moses-sigrank
./sigrank
./sigrank --codex for Codex · ./sigrank --all for both providers in one pass.
It runs ccusage for you, computes your profile + Υ, and prints your board rank.
Nothing leaves your machine.
2 — Backup: paste on the Space. No repo? Run ccusage yourself and paste the JSON
into the Clock Your Signal box. Run one command per provider:
npx ccusage@latest claude --json
npx ccusage@latest codex --json
(ccusage installed globally? drop the prefix: ccusage claude --json.) Or type four numbers: input output cache_create cache_read.
⚠️ Don't use bareccusage --json(no subcommand): it merges every agent into one total, which inflates input and distorts the architecture read.
3 — Saving (optional). Sign in with HuggingFace on the Space to earn one persistent board entry + session history (Greatest Hits). Without login, your read is a live snapshot only.
Codex note. Codex doesn't report a fresh-vs-cache input split, so its input is estimated: alone → AA-backed 2:1 baseline; with a Claude profile → your own Claude input:output ratio. Estimated rows flagged with *.
openbmb/MiniCPM4-0.5B (0.5B params, well under the 4B cap) runs on ZeroGPU and
narrates the operator read. Non-blocking: if unavailable, a deterministic template is used and the app still works. Everything quantitative is pure computation.
Υ is the ranking metric. The mathematical thesis is the cascade decomposition of leverage into three behavioral stages — Υ is what you sort by, the cascade is what explains why an operator lands where they do.
(O/I) × (C_w/O) × (C_r/C_w) = C_r/I
│ │ │ │
transmission commitment compounding leverage
generate commit to reuse what
output cache was cached
10x DEV is the log₁₀ of that product — the amplification exponent. By telescoping, 10^(10x DEV) = leverage = C_r/I.
| species | signature |
|---|---|
| Cascade Matrix | velocity ≥ 1 and leverage ≥ 100 · recursive processing loop |
| Cache Architect | leverage ≥ 10, velocity < 1 · persistent context layer |
| Converter Loop | velocity ≥ 0.5, leverage < 2 · single-pass processing velocity |
| Throughput Pipe | low on both axes · raw metric bandwidth |
| Non-Compounding | no cache_create — cascade can't form |
high amplification
│
CASCADE (stacks) │ (— rare / empty —)
low scale ◄───────┼───────► high scale
CONVERTER (I→O) │ THROUGHPUT (volume)
CACHE ARCHITECT │ (reuse)
│
low amplification
MO§ES occupies the empty quadrant — low scale, high amplification. The claim is not "top of a ladder"; it's that this region of the token economy is structurally empty, and the geometry of (C·O)/I² is what makes it empty.
| metric | formula | meaning |
|---|---|---|
| SNR | O/(I+O) | output share |
| 10x DEV | log₁₀(cascade) | amplification exponent |
| Operating Ratio | C:I:O, input=1 | footprint vs Artificial Analysis 7:2:1 |
| Velocity | O/I | output per input token |
| Leverage | C/I | cache reads per human token |
| Efficiency | (C+O)/I ÷ 4.0 | vs AA baseline |
| Avg $/1M | blended cost ÷ total | efficient architecture is also cheapest |
| Υ (Yield) | (C·O)/I² | un-gameable ranking metric |
AA benchmarks measure models at a 7:2:1 (cache : input : output) token mix.
SigRank measures operators from their own token logs.
Both converge: cache-dominant architecture is the most efficient AND cheapest per token.
The AA 7:2:1 ratio is the source of the 4.0 efficiency baseline: (7+1)/2 = 4.0.
Instrument 2 — Five measured kernels (mos2es.com/benchmarks)
MO§ES operator measured against the AA Coding Agent Index field average — 7-day window, raw JSONL extraction across 98 session files, all subagents. #1 in all five categories:
| kernel | MO§ES™ | field avg | delta |
|---|---|---|---|
| I · Cache hit rate | 94.66% | 90.68% | +3.98pp · #1 |
| II · Output : Input ratio | 17.9× | 0.162× | 110× field avg · #1 |
| III · Tokens per task | 810K | 4.67M | 5.8× fewer · #1 |
| IV · Time per task | 1.84 min | 11.92 min | 6.5× faster · #1 |
| V · Cost per LOC | $0.0007 | $0.067 | 96× cheaper · #1 |
Raw token ledger (98 sessions · 1,123B total):
| Input | Output | Cache Create | Cache Read | Total | Sessions | LOC | |
|---|---|---|---|---|---|---|---|
| MO§ES™ | 123K | 3.90M | 34.83M | 1.084B | 1.123B | 98 / 1,465 tasks | 35,242 |
| Field avg (per model) | 162.9M | 17.2M | — | 1.49B | 1.67B | 1 / 358 tasks | 7,160 |
Field data: artificialanalysis.ai/agents/coding-agents · extracted 2026-05-14. MO§ES: sustained product build, 7-day window (2026-05-08 → 2026-05-14). The convergence of #1 leadership across both isolated benchmark runs and sustained product builds is the structural result.
This is the real-world grounding behind SigRank's thesis — the same operator that ranks #1 on Υ also ranks #1 on every external benchmark kernel.
SigRank is Phase 2 of a fully filed, commercially structured IP portfolio:
| filing | serial | date | protected layer |
|---|---|---|---|
| PPA 01 · MOS²ES | 63/877,177 | Sep 7, 2025 · Conf. 7067 | Constitutional governance, signal encoding, compression substrate |
| PPA 02 · SCS Engine | 63/883,018 | Sep 17, 2025 · Conf. 6401 | Signal Compression System Engine, lineage, sovereign compression |
| Utility 03 · CIVITAS | 19/426,028 | Dec 18, 2025 · Conf. 2165 | Frontend civic infrastructure: SIGRANK, SigEconomy, Agent City-State |
| PPA 04 · Commitment Conservation | 63/991,282 | Feb 26, 2026 · Conf. 6108 | Semantic commitment conservation under recursive transformation |
| MO§ES™ Trademark | 99408355 · IC 042 | Sep 23, 2025 | Software / governance protocol identity |
Published law: Conservation Law of Commitment · Zenodo DOI 10.5281/zenodo.18792459
Full legal ledger: mos2es.com/legal
SigRank sits at Layer 2 of a four-layer constitutional architecture:
| layer | product | purpose |
|---|---|---|
| 04 | SIGNOMY | Governed agent marketplace — execution-layer governance, participatory trust, agent provenance |
| 03 | SIGRANK | Human–AI operator leaderboard — sync telemetry, resonance metrics ← this app |
| 02 | AQUA | Application workflow — answer banks, reusable submission memory |
| 01 | MO§ES™ | Substrate — compression, recursive execution, drift control, lineage |
MO§ES™ IS THE ENGINE · AQUA + SIGRANK ARE THE WEDGES · SIGNOMY IS THE GOVERNED ECONOMY
Codex never itemizes cache writes, so SigRank estimates the high-signal user input from output via two pathways (_codex_input_estimate in ingest.py):
- Alpha — Codex alone: AA-backed 2:1 baseline —
est_input = 2 × output - Beta — Codex + Claude profile: operator's own measured Claude
input:outputratio —est_input = output × (claude_input / claude_output). CLI builds this automatically (./sigrank --codexreads Claude first).
Every Codex-derived row is flagged with * and names the exact pathway used.
For Claude Code, ccusage supplies real cost → exact ~). Either way: the cache-dominant operator at the top is also the cheapest per token, by an order of magnitude.
- Persistence boundary. Pasted rows are scored live but not added to the persisted board — corpus is curated (owner-seeded). Backed by Supabase with hardcoded-seed fallback.
-
Cost provenance. Board's
$/1Mis a list-price recompute for every corpus row (~) so all rows compare apples-to-apples. MO§ES's verified ccusage cost is $0.527, reproduced exactly by the recompute. - Wild corpus (10 operators). Public ccusage footprints from tokscale.ai. MO§ES is verified ccusage data, not tokscale.
- Υ is an engineered macro-efficiency index motivated by thermodynamics (Landauer, Ohmic dissipation); log Υ = X + log(Velocity). An analogy, not a microscopic-entropy derivation.
Built for the HF/Gradio Build Small Hackathon · Thousand Token Wood 🍄
Built by Deric J. McHenry · github.com/SunrisesIllNeverSee · Ello Cello LLC · Codex + Claude + Devin
Patent pending 19/426,028 · IC 042 TM 99408355 · DOI 10.5281/zenodo.18792459