| --- |
| 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. |
| models: |
| - openbmb/MiniCPM4-0.5B |
| tags: |
| - thousand-token-wood |
| - off-brand |
| - tiny-titan |
| - best-demo |
| - minicpm |
| - track:wood |
| - sponsor:openbmb |
| - sponsor:openai |
| - achievement:offgrid |
| - achievement:offbrand |
| - achievement:sharing |
| - achievement:fieldnotes |
| --- |
| |
| # MO§ES™ SigRank — the diagnostic x-ray of the token economy |
|
|
| 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](https://github.com/SunrisesIllNeverSee) · **Repo:** [SunrisesIllNeverSee/moses-sigrank](https://github.com/SunrisesIllNeverSee/moses-sigrank) · **Deck:** [mos2es.com/deck](https://mos2es.com/deck) · **Benchmarks:** [mos2es.com/benchmarks](https://mos2es.com/benchmarks) |
|
|
| --- |
|
|
| > **SigRank is Layer 2 of the MO§ES™ stack** (SIGRANK: human–AI operator intelligence). |
| > MO§ES™ is the substrate — compression, recursive execution, drift control, governed meaning at source. |
| > Built by [Deric J. McHenry](https://github.com/SunrisesIllNeverSee) · Ello Cello LLC. |
| > |
| > **IP:** 4 patent filings · IC 042 trademark · [Conservation Law of Commitment — Zenodo DOI 10.5281/zenodo.18792459](https://zenodo.org/records/20029607) |
|
|
| --- |
|
|
| ## Origin & Theory |
|
|
| 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 →](https://zenodo.org/records/20029607) |
|
|
| 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.** |
|
|
| ## What it does |
|
|
| 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 |
|
|
| ## How to measure yourself |
|
|
| 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](https://nodejs.org)): |
|
|
| ``` |
| 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 bare `ccusage --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 `*`. |
|
|
| ## The model (Tiny Titan / Best MiniCPM) |
|
|
| `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. |
|
|
| ## The full metric system |
|
|
| 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. |
|
|
| ### The cascade as a diagnostic |
|
|
| ``` |
| (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`. |
|
|
| ### Operator species |
|
|
| | 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 | |
|
|
| ### Species / quadrants |
|
|
| ``` |
| 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. |
|
|
| ### Full metric table |
|
|
| | 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** | |
|
|
| ## Benchmark convergence — two independent instruments |
|
|
| ### Instrument 1 — Artificial Analysis model benchmarks |
| 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](https://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](https://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. |
|
|
| ## IP & legal record |
|
|
| 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](https://zenodo.org/records/20029607) |
|
|
| Full legal ledger: [mos2es.com/legal](https://mos2es.com/legal) |
|
|
| ## The MO§ES™ stack |
|
|
| 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 support |
|
|
| 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:output` ratio — `est_input = output × (claude_input / claude_output)`. CLI builds this automatically (`./sigrank --codex` reads Claude first). |
|
|
| Every Codex-derived row is **flagged with `*`** and names the exact pathway used. |
| |
| ## Cost |
| |
| For Claude Code, ccusage supplies real cost → exact $/1M. For manual/wild rows, $/1M is a list-price estimate (shown with `~`). Either way: the cache-dominant operator at the top is also the cheapest per token, by an order of magnitude. |
| |
| ## Demo video |
| [Watch the demo on Loom →](https://www.loom.com/share/edc345e2e5164e20aed3acb6436a08c3) |
| |
| ## Social post |
| [View on X →](https://x.com/burnmydays/status/2066666214143758576?s=20) |
| |
| ## Notes |
| |
| - **Persistence boundary.** Pasted rows are scored live but not added to the persisted board — corpus is curated (owner-seeded). Backed by Supabase with hardcoded-seed fallback. |
| - **Cost provenance.** Board's `$/1M` is 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](https://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](https://github.com/SunrisesIllNeverSee)** · [github.com/SunrisesIllNeverSee](https://github.com/SunrisesIllNeverSee) · Ello Cello LLC · Codex + Claude + Devin |
|
|
| Patent pending 19/426,028 · IC 042 TM 99408355 · DOI [10.5281/zenodo.18792459](https://zenodo.org/records/20029607) |
|
|
