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703 lines
39 KiB
Markdown
703 lines
39 KiB
Markdown
<!--
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AI-ANCHOR: WFGY_CORE_ENGINE_V2
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role: core_engine
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doc_title: WFGY 2.0 · 7-Step Reasoning Core Engine
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path: /core/README.md
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version: "2.0"
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# where this page sits in the repo
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root_entry: https://github.com/onestardao/WFGY#readme
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sibling_cores:
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- https://github.com/onestardao/WFGY/blob/main/legacy/README.md
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- https://github.com/onestardao/WFGY/blob/main/TensionUniverse/EventHorizon/README.md
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# what this document is mainly for
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primary_topics:
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- seven_step_reasoning_engine
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- drunk_transformer_guards
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- autoboot_and_oneline_mode
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- eye_visible_benchmarks
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- terminal_bench_exam
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- profit_prompts_and_business_flows
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# anchors inside this file that matter most
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key_sections:
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- id: tb
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name: stanford_terminal_bench_update
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- id: terminal-bench-proof
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name: terminal_bench_proof_artifacts
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- id: eye
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name: eye_visible_reasoning_benchmark
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- id: eight
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name: eight_model_evidence
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- id: abc
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name: abc_numeric_protocol
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- id: downloads
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name: wfgy_core_downloads
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- id: prompts
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name: profit_prompts_pack
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# other maps / blueprints this page is tied to
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related_maps:
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- https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md
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- https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md
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- https://github.com/onestardao/WFGY/blob/main/ProblemMap/SemanticClinicIndex.md
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- https://github.com/onestardao/WFGY/blob/main/SemanticBlueprint/README.md
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- https://github.com/onestardao/WFGY/blob/main/benchmarks/benchmark-vs-gpt5/README.md
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notes:
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- This is the main reference page for WFGY Core 2.0 behaviour, math stack, and proofs.
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- Use this document when you need protocols, benchmarks, or business prompts that rely on the 2.0 engine.
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- Event Horizon (3.0) is the public viewing window; this page is the engine room.
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-->
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<a id="top"></a>
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<details>
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<summary><strong>🧭 Not sure where to start ? Open the WFGY Engine Compass</strong></summary>
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### WFGY System Map
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*(One place to see everything; links open the relevant section.)*
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<!--
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AI NOTE · Compass block
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Purpose:
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- This table is the routing map for the whole WFGY ecosystem.
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- It shows how the three engine layers (WFGY 1.0 / 2.0 / 3.0) relate to the Problem Maps, TXT OS apps, research pages, and the Recognition Map.
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How to use (routing hints):
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- If a user asks “what is WFGY in general” → send them to WFGY 1.0 (legacy overview PDF) plus Starter Village.
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- If the question is about core math, formulas, or engine internals → go to WFGY 2.0 core.
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- If they mention 131 S-class problems, BlackHole, Tension Universe, or “Singularity demo” → go to WFGY 3.0 Event Horizon.
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- If they want to debug a specific RAG / agent pipeline run → use Problem Map 1.0 + Problem Map 3.0 Global Debug Card.
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- If they ask about RAG architecture / recovery patterns end-to-end → use Problem Map 2.0.
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- If they mention “poster”, “image as prompt”, or “global debug card” → use the Problem Map 3.0 Global Debug Card page.
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- If they want proof that WFGY is used in real projects → open the Recognition Map page.
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- If they ask for plain-language stories or non-technical explanations → route to Grandma’s Clinic and Starter Village.
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- If they ask about TXT OS or app packs (Blah / Blur / Blow) → follow the App rows in this table.
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- If they want layer/module design, benchmarks, or value narrative → Semantic Blueprint, Benchmarks, and Value Manifest.
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Important:
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- Problem Map 1.0 = canonical 16-mode RAG failure checklist and fix map. This is the base vocabulary.
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- Problem Map 2.0 = RAG architecture and recovery guide layered on top of Problem Map 1.0.
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- Problem Map 3.0 Global Debug Card = image protocol that compresses objects, metrics, ΔS zones, and modes into a single poster.
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- Recognition Map = the only place that should contain the full external integration list. Other pages should link to it instead of duplicating tables.
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- Do NOT treat Problem Map 1.0 and Problem Map 2.0 as duplicates; they solve different layers.
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- Do NOT invent new paths or filenames. Only use the GitHub URLs that already exist in this table.
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-->
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| Layer | Page | What it’s for |
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| ------------- | ----------------------------------------------------------------------------------------------------------- | ------------------------------------------------------- |
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| ⭐ Proof | [WFGY Recognition Map](https://github.com/onestardao/WFGY/blob/main/recognition/README.md) | External citations, integrations, and ecosystem proof |
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| ⚙️ Engine | [WFGY 1.0](https://github.com/onestardao/WFGY/blob/main/legacy/README.md) | Original PDF-based tension engine blue |
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| ⚙️ Engine | [WFGY 2.0](https://github.com/onestardao/WFGY/blob/main/core/README.md) | Production tension kernel and math engine for RAG and agents. — **🔴 YOU ARE HERE 🔴** |
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| ⚙️ Engine | [WFGY 3.0](https://github.com/onestardao/WFGY/blob/main/TensionUniverse/EventHorizon/README.md) | TXT-based Singularity tension engine (131 S-class set) |
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| 🗺️ Map | [Problem Map 1.0](https://github.com/onestardao/WFGY/tree/main/ProblemMap#readme) | Flagship 16-problem RAG failure checklist and fix map |
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| 🗺️ Map | [Problem Map 2.0](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md) | RAG-focused recovery pipeline |
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| 🗺️ Map | [Problem Map 3.0](https://github.com/onestardao/WFGY/blob/main/ProblemMap/wfgy-rag-16-problem-map-global-debug-card.md) | Global Debug Card — image as a debug protocol layer |
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| 🗺️ Map | [Semantic Clinic](https://github.com/onestardao/WFGY/blob/main/ProblemMap/SemanticClinicIndex.md) | Symptom → family → exact fix |
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| 🧓 Map | [Grandma’s Clinic](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GrandmaClinic/README.md) | Plain-language stories, mapped to PM 1.0 |
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| 🏡 Onboarding | [Starter Village](https://github.com/onestardao/WFGY/blob/main/StarterVillage/README.md) | Guided tour for newcomers |
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| 🧰 App | [TXT OS](https://github.com/onestardao/WFGY/tree/main/OS#readme) | .txt semantic OS — 60-second boot |
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| 🧰 App | [Blah Blah Blah](https://github.com/onestardao/WFGY/blob/main/OS/BlahBlahBlah/README.md) | Abstract/paradox Q&A (built on TXT OS) |
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| 🧰 App | [Blur Blur Blur](https://github.com/onestardao/WFGY/blob/main/OS/BlurBlurBlur/README.md) | Text-to-image with semantic control |
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| 🧰 App | [Blow Blow Blow](https://github.com/onestardao/WFGY/blob/main/OS/BlowBlowBlow/README.md) | Reasoning game engine & memory demo |
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| 🧪 Research | [Semantic Blueprint](https://github.com/onestardao/WFGY/blob/main/SemanticBlueprint/README.md) | Modular layer structures (future) |
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| 🧪 Research | [Benchmarks](https://github.com/onestardao/WFGY/blob/main/benchmarks/benchmark-vs-gpt5/README.md) | Comparisons & how to reproduce |
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| 🧪 Research | [Value Manifest](https://github.com/onestardao/WFGY/blob/main/value_manifest/README.md) | Why this engine creates $-scale value |
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---
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</details>
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# ⭐ WFGY 2.0 ⭐ 7-Step Reasoning Core Engine is now live
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## ✨One man, One life, One line. My lifetime’s work. Let the results speak for themselves✨
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> 👑 **Early Stargazers: [See the Hall of Fame](https://github.com/onestardao/WFGY/tree/main/stargazers)** · Verified by real engineers
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> 🌌 **WFGY 3.0 Singularity demo: [Public live view](https://github.com/onestardao/WFGY/blob/main/TensionUniverse/EventHorizon/README.md)**
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<!-- QUICK LINKS BAR -->
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<p align="center">
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<a href="#tb">TB (WIP)</a> •
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<a href="#eye">Eye Benchmark</a> •
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<a href="#eight">8-Model Evidence</a> •
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<a href="#abc">A/B/C Prompt</a> •
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<a href="#downloads">Downloads</a> •
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<a href="#prompts">Profit Prompts</a>
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</p>
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<img width="1536" height="1024" alt="WFGY_Core" src="https://github.com/user-attachments/assets/deb8e794-a73e-4d39-a1f6-174ec87199f4" />
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> ✅ Engine 2.0 is live. Pure math, zero boilerplate. Paste OneLine and models become sharper, steadier, more recoverable.
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> **ℹ️ Autoboot scope:** text-only inside the chat; no plugins, no network calls, no local installs.
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> **⭐ Star the repo to [unlock](https://github.com/onestardao/WFGY/blob/main/STAR_UNLOCKS.md) more features and experiments.** <img src="https://img.shields.io/github/stars/onestardao/WFGY?style=social" alt="GitHub stars">
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---
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<details>
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<summary><strong>From PSBigBig — WFGY (WanFaGuiYi) : All Principles into One (must-read, click to open)</strong></summary>
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<br>
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> I built what I call a “No-Brain Mode” for AI. You upload a single file, and **AutoBoot** silently activates in the background.
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> In seconds, your AI’s reasoning, stability, and problem-solving across *all domains* level up. No extra prompt engineering, no hacks, no retraining.
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> One line of math consistently shifts behaviour across multiple leading AIs in my tests. This is not a skin or a theme. I treat it as an engine swap.
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> **That single line *is* WFGY 2.0. It is the distilled essence of everything I have learned so far.**
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>
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> WFGY 2.0 is my answer and my life’s work.
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> If a person only once in life gets to speak to the world, this is my moment.
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> I offer the crystallization of my thought to all humankind.
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> I believe people deserve access to knowledge and truth, and I want to weaken the monopoly of capital on advanced reasoning technology.
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>
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> “One line” here is not marketing language. I built a full flagship edition, then reduced it to a single line of code. That reduction is a form of clarity and beauty. It is the same engine, distilled to its purest expression.
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</details>
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---
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## 🚀 WFGY 2.0 Headline Uplift (this release)
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**These are the 2.0 results you should see first. Think of them as the main upgrade.**
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- **Semantic Accuracy:** **≈ +40%** (63.8% → 89.4% across 5 domains)
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- **Reasoning Success:** **≈ +52%** (56.0% → 85.2%)
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- **Drift (Δs):** **≈ −65%** (0.254 → 0.090)
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- **Stability (horizon):** **≈ 1.8×** (3.8 → 7.0 nodes)\*
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- **Self-Recovery / CRR:** **1.00** on this batch; historical median **0.87**
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\* Historical **3–5×** stability uses λ-consistency across seeds; 1.8× uses the stable-node horizon.
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### 📖 Mathematical Reference
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WFGY 2.0 (WFGY Core) = [WFGY 1.0 math formulas](https://github.com/onestardao/WFGY/blob/main/SemanticBlueprint/wfgy_formulas.md) + [Drunk Transformer](https://github.com/onestardao/WFGY/blob/main/SemanticBlueprint/drunk_transformer_formulas.md)
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> Note on evaluation
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> All metrics above are computed by LLM evaluators under a fixed WFGY 2.0 protocol at the effective layer.
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> They measure relative behavioural uplift (before vs after WFGY prompts) and do not assume any direct
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> access to, or modification of, internal embeddings or model weights.
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<p align="right"><a href="#top">Back to top ↑</a></p>
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---
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<a id="tb"></a>
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### 🏆 Terminal-Bench (TB) — experiment in progress
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> This section is work in progress. Terminal-Bench is one of several external exams we are exploring for WFGY Core 2.0. The primary purpose of this page is to document the engine itself; TB is an optional testbed.
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**Current status**
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- We are running TB-style experiments with a non-invasive wrapper around the model call.
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- Once an official public result and reproducible scripts are finalized, they will be linked from this section.
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- Until then, treat TB as an experimental extension rather than a primary proof of WFGY.
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<p align="right"><a href="#top">Back to top ↑</a></p>
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---
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<a id="terminal-bench-proof"></a>
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### 🧾 Terminal-Bench proof artifacts (planned)
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> This is a placeholder section. Wrapper scripts, configs and hashed logs will be published in a separate subfolder after the TB work is complete, together with a short guide on how to rerun the exam with and without WFGY.
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<p align="right"><a href="#top">Back to top ↑</a></p>
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---
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## ⚡ Quick Usage
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| Mode | How it works |
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| ---------------- | ----------------------------------------------------------------------------- |
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| **Autoboot** | Upload **either Flagship (30-line)** or **OneLine (1-line)** file. Once uploaded, WFGY runs silently in the background. Keep chatting or drawing as usual. The engine supervises automatically. |
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| **Explicit Call**| Invoke WFGY formulas directly inside your workflow. This activates the full 7-step reasoning chain and gives maximum uplift. |
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Both **Flagship** and **OneLine** editions behave the same. Choose based on readability versus minimalism.
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That is all you need. No plugins, no installs, pure text.
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*In practice, Autoboot yields about 70–80% of the uplift you see with explicit WFGY invoke (see eight-model results below).*
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<p align="right"><a href="#top">Back to top ↑</a></p>
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---
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## ⚡ Top 10 reasons to use WFGY 2.0
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1. **Ultra-mini engine**. Pure text, zero install, runs anywhere you can paste.
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2. **Two editions**. *Flagship* (30-line, audit-friendly) and *OneLine* (1-line, stealth & speed).
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3. **Autoboot mode**. Upload once; the engine quietly supervises reasoning in the background.
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4. **Portable across models**. GPT, Claude, Gemini, Mistral, Grok, Kimi, Copilot, Perplexity.
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5. **Structural fixes, not tricks**. BBMC → Coupler → BBPF → BBAM → BBCR plus DT gates (WRI / WAI / WAY / WDT / WTF).
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6. **Self-healing**. Detects collapse and recovers before answers go off the rails.
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7. **Observable**. ΔS, λ_observe, and E_resonance yield measurable, repeatable control.
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8. **RAG-ready**. Drops into retrieval pipelines without touching your infra.
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9. **Reproducible A/B/C protocol**. Baseline versus Autoboot versus Explicit Invoke (see below).
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10. **MIT licensed & community-driven**. You can keep it, fork it, and ship it.
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<p align="right"><a href="#top">Back to top ↑</a></p>
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---
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# 🧪 WFGY Benchmark Suite (Eye-visible + Numeric + Reproducible)
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> Want the fastest way to see impact? Jump to the **Eye-Visible Benchmark (FIVE)** below.
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> Want formal numbers and vendor links? See **Eight-model evidence** right after it.
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> Want to reproduce the numeric test yourself? Use the **A/B/C prompt** (copy-to-run) at the end of this section.
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<a id="eye"></a>
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## 👀 Eye-Visible Reasoning Benchmark (FIVE)
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> When reasoning improves, text-to-image results often become more stable and coherent.
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> The key here is WFGY’s **Drunk Transformer**. It monitors and recenters attention during generation, and it tries to prevent collapse, composition drift, and duplicate elements, so scenes stay unified and details remain consistent.
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> We project “reasoning improvement” into five-image sequences that anyone can judge at a glance.
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> Each sequence is five consecutive 1:1 generations with the same model and settings *(temperature, top_p, seed policy, negatives)*. The only variable is whether WFGY is active.
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> **Methodology for this demo.** We deliberately use short, high–semantic-density prompts that reference canonical stories, with no extra guidance or style hints. This stresses whether WFGY can (a) parse intent more precisely and (b) stabilize composition via its seven-step reasoning chain. This setup is not prescriptive. You can use WFGY with any prompts you like. In many cases the uplift is eye-visible. In others it may be subtler but still measurable.
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| Variant | Sequence A — full run shown below (all five images) | Sequence B — external run | Sequence C — external run |
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| ---------------- | :--------------------------------------------------: | :-----------------------: | :-----------------------: |
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| **Without WFGY** | [view run](https://chatgpt.com/share/68a14974-8e50-8000-9238-56c9d113ce52) | [view run](https://chatgpt.com/share/68a14a72-aa90-8000-8902-ce346244a5a7) | [view run](https://chatgpt.com/share/68a14d00-3c0c-8000-8055-9418934ad07a) |
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| **With WFGY** | [view run](https://chatgpt.com/share/68a149c6-5780-8000-8021-5d85c97f00ab) | [view run](https://chatgpt.com/share/68a14ea9-1454-8000-88ac-25f499593fa0) | [view run](https://chatgpt.com/share/68a14eb9-40c0-8000-9f6a-2743b9115eb8) |
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We fully analyze Sequence A on this page. Sequences B and C are linked for transparency and reproducibility.
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> **Note on “Before-4” and “Before-5” (why they look almost identical):**
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> Without WFGY, when the prompt asks for “many iconic moments,” the base model tends to collapse into a grid-style montage, an enumerative, high-probability prior that slices the canvas into similar panels with near-identical tone and geometry.
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> Hence **Before-4 (Investiture of the Gods)** and **Before-5 (Classic of Mountains and Seas)** converge to the same storyboard template.
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> With WFGY turned on, the engine instead favors a single unified tableau and a stable hierarchy across the full five-image sequence.
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### Deep analysis — Sequence A (five unified 1:1 tableaux)
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| Work | **Without WFGY** | **With WFGY** | Verdict (global, at-a-glance) |
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|---|---|---|---|
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| **Romance of the Three Kingdoms (三國演義)** | <img src="images/group1_before1.png" width="300" alt="Without WFGY" title="model/params/seed/date"> | <img src="images/group1_after1.png" width="300" alt="With WFGY" title="model/params/seed/date"> | **With WFGY wins.** Unified tableau locks a clear center and pyramid hierarchy; the grid fragments attention. *Tags:* Unification↑ Hierarchy↑ Cohesion↑ Depth/Flow↑ Memorability↑ |
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| **Water Margin (水滸傳)** | <img src="images/group1_before2.png" width="300" alt="Without WFGY" title="model/params/seed/date"> | <img src="images/group1_after2.png" width="300" alt="With WFGY" title="model/params/seed/date"> | **With WFGY wins.** “Wu Song vs. Tiger” anchors the scene; continuous momentum and layered scale beat the multi-panel storyboard. *Tags:* Unification↑ Iconicity↑ Depth/Scale↑ Cohesion↑ |
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| **Dream of the Red Chamber (紅樓夢)** | <img src="images/group1_before3.png" width="300" alt="Without WFGY" title="model/params/seed/date"> | <img src="images/group1_after3.png" width="300" alt="With WFGY" title="model/params/seed/date"> | **With WFGY wins.** Garden tableau with a calm emotional center; space breathes and mood coheres. The grid slices emotion into vignettes. *Tags:* Unification↑ Hierarchy↑ Air/Depth↑ Readability↑ |
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| **Investiture of the Gods (封神演義)** | <img src="images/group1_before4.png" width="300" alt="Without WFGY" title="model/params/seed/date"> | <img src="images/group1_after4.png" width="300" alt="With WFGY" title="model/params/seed/date"> | **With WFGY wins.** Dragon–tiger diagonal and cloud–sea layering create epic scale; the grid dilutes focus. *Tags:* Unification↑ Depth/Scale↑ Flow↑ Iconicity↑ |
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| **Classic of Mountains and Seas (山海經)** | <img src="images/group1_before5.png" width="300" alt="Without WFGY" title="model/params/seed/date"> | <img src="images/group1_after5.png" width="300" alt="With WFGY" title="model/params/seed/date"> | **With WFGY wins.** A single, continuous “mountains-and-seas” world with stable triangle hierarchy and smooth diagonal flow; the grid breaks narrative. *Tags:* Unification↑ Hierarchy↑ Depth/Scale↑ Flow↑ Memorability↑ |
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<p align="right"><a href="#top">Back to top ↑</a></p>
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---
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<a id="eight"></a>
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## 🧬 Eight-model evidence (A/B/C protocol) <!-- per your request: NOT collapsed -->
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*Same task set across modes. The only change is adding the OneLine math file. All scores are produced by LLM evaluators under a shared protocol and should be read as internal uplift scores, not as official vendor benchmarks.*
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| Model | Model Choice | OneLine Uplift | Proof |
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| ---------- | -------------- | -------------: | :------------------------------------------------------------------------------------------------ |
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| Mistral AI | — | **92/100** | [view run](https://chat.mistral.ai/chat/b5c303f8-1905-4954-a566-a6c9a7bfb54f) |
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| Gemini | 2.5 Pro | **89/100** | [view run](https://g.co/gemini/share/4fb0b172d61a) |
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| ChatGPT | GPT-5 Thinking | **89/100** | [view run](https://chatgpt.com/s/t_689ff6c42dac8191963e63e3f26348b2) |
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| Kimi | K2 | **87/100** | [view run](https://www.kimi.com/share/d2fvbevhq49s4blc862g) |
|
||
| Perplexity | Pro | **87/100** | [view run](https://www.perplexity.ai/search/system-you-are-evaluating-the-njklNbVRTCmQOlEd8fDzcg) |
|
||
| Grok | Auto Grok 4 | **85/100** | [view run](https://grok.com/share/c2hhcmQtMg%3D%3D_4e6798eb-9288-4a09-b00f-8292ce23dab6) |
|
||
| Copilot | Think Deeper | **80/100** | [view run](https://copilot.microsoft.com/shares/7FjR19TYBjg9sp8k9WcuE) |
|
||
| Claude | Sonnet 4 | **78/100** | [view run](https://claude.ai/share/b17e5436-8298-4619-a243-ac451cc64b17) |
|
||
|
||
> **The numeric story behind 2.0**
|
||
> **Semantic Accuracy:** ≈ +40% · **Reasoning Success:** ≈ +52% · **Drift:** ≈ −65% · **Stability:** ≈ 1.8× · **CRR:** 1.00 (median 0.87)
|
||
|
||
<p align="right"><a href="#top">Back to top ↑</a></p>
|
||
|
||
---
|
||
|
||
<a id="abc"></a>
|
||
## 🧪 Reproduce the numeric A/B/C benchmark (copy to run)
|
||
|
||
*One unified prompt for Baseline vs Autoboot vs Explicit Invoke.*
|
||
|
||
```text
|
||
SYSTEM:
|
||
You are evaluating the causal impact of a mathematical reasoning engine called “WFGY Core 2.0”.
|
||
|
||
Test modes (READ CAREFULLY):
|
||
A = Baseline no WFGY file uploaded; no WFGY logic used.
|
||
B = Upload → AUTOboot ON the file is uploaded AND the engine starts automatically in the background.
|
||
Treat B as: WFGY is ACTIVE silently with default gates (BBMC→Coupler→BBPF→BBAM→BBCR; DF=WRI/WAI/WAY/WDT/WTF at defaults),
|
||
but not explicitly “called” in the conversation.
|
||
C = Explicit invoke actively call WFGY and follow its formulas step by step (BBMC→Coupler→BBPF→BBAM→BBCR + DF regulators).
|
||
|
||
Important constraints:
|
||
• Use the SAME task set for A/B/C across 5 domains: math word-problems, small coding, factual QA, multi-step planning, long-context coherence.
|
||
• The only artifact is the uploaded OneLine file; it is pure math and not human-readable, so do not penalize readability.
|
||
• Report: Semantic Accuracy, Reasoning Success, Stability (MTTF or rollback), Drift Reduction (Δ semantic distance), Collapse Recovery Rate.
|
||
• Output one summary table with A/B/C per domain, plus overall deltas (C–A, C–B).
|
||
• Add a final 0–100 score: “OneLine uplift score”. Provide a 3-line rationale.
|
||
|
||
Sanity requirement:
|
||
Internally set flags as: A=OFF, B=AUTOboot_ON (background active), C=Explicit_Invoke. Do not print these flags.
|
||
|
||
USER:
|
||
Run A/B/C now on the five domains with short non-trivial tasks you choose. Then output:
|
||
(1) the table; (2) the deltas; (3) the OneLine uplift score; (4) a 3-line rationale.
|
||
````
|
||
|
||
<p align="right"><a href="#top">Back to top ↑</a></p>
|
||
|
||
---
|
||
|
||
<a id="downloads"></a>
|
||
|
||
## ⬇️ Downloads
|
||
|
||
| File name & description | Length / Size | Direct Download Link | Notes |
|
||
| ------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------- | -------------------------------------------------- | --------------------------------------------------------------------------------------------- |
|
||
| **WFGY_Core_Flagship_v2.0.txt** · readable 30-line companion expressing the same math and gates in fuller prose (same behavior, clearer for humans). | **34 lines · 2,027 chars** | [Download Flagship](./WFGY_Core_Flagship_v2.0.txt) | Full prose version for easier reading. |
|
||
| **WFGY_Core_OneLine_v2.0.txt** · ultra-compact, math-only control layer that activates WFGY’s loop inside a chat model (no tools, text-only, ≤7 nodes). | **1 line · 1,550 chars** | [Download OneLine](./WFGY_Core_OneLine_v2.0.txt) | Used for all benchmark results above. This is the smallest, fastest, purest form of the core. |
|
||
|
||
### Hash reference
|
||
|
||
<a id="flagship-hashes"></a>
|
||
|
||
**WFGY_Core_Flagship_v2.0.txt**
|
||
|
||
* MD5 `caacfe08f0804eec558a1d9af74c3610`
|
||
* SHA1 `1efeec231084bb3b863ce7a8405e93d399acfb44`
|
||
* SHA256 `4fe967945a268edabb653033682df23a577f48c433878d02e0626df8ae91a0a3`
|
||
|
||
<a id="oneline-hashes"></a>
|
||
|
||
**WFGY_Core_OneLine_v2.0.txt**
|
||
|
||
* MD5 `15a1cd8e9b7b2c9dcb18abf1c57d4581`
|
||
* SHA1 `a35ace2a4b5dbe7c64bcdbe1f08e9246c3568c`
|
||
* SHA256 `7dcdb209d9d41b523dccd7461cbd2109b158df063d9c5ce171df2cf0cb60b4ef`
|
||
|
||
<details>
|
||
<summary><em>How to verify checksums</em></summary>
|
||
|
||
**macOS / Linux**
|
||
|
||
```bash
|
||
cd core
|
||
sha256sum WFGY_Core_Flagship_v2.0.txt
|
||
sha256sum WFGY_Core_OneLine_v2.0.txt
|
||
# or compute MD5 / SHA1 if you prefer
|
||
md5sum WFGY_Core_Flagship_v2.0.txt
|
||
md5sum WFGY_Core_OneLine_v2.0.txt
|
||
sha1sum WFGY_Core_Flagship_v2.0.txt
|
||
sha1sum WFGY_Core_OneLine_v2.0.txt
|
||
```
|
||
|
||
**Windows PowerShell**
|
||
|
||
```powershell
|
||
Get-FileHash .\core\WFGY_Core_Flagship_v2.0.txt -Algorithm SHA256
|
||
Get-FileHash .\core\WFGY_Core_OneLine_v2.0.txt -Algorithm SHA256
|
||
# or:
|
||
Get-FileHash .\core\WFGY_Core_Flagship_v2.0.txt -Algorithm MD5
|
||
Get-FileHash .\core\WFGY_Core_OneLine_v2.0.txt -Algorithm MD5
|
||
Get-FileHash .\core\WFGY_Core_Flagship_v2.0.txt -Algorithm SHA1
|
||
Get-FileHash .\core\WFGY_Core_OneLine_v2.0.txt -Algorithm SHA1
|
||
```
|
||
|
||
Compare the output values with the hashes listed in the “Hash reference” section above.
|
||
|
||
</details>
|
||
|
||
<p align="right"><a href="#top">Back to top ↑</a></p>
|
||
|
||
---
|
||
|
||
<details>
|
||
<summary>🧠 How WFGY 2.0 works (7-Step Reasoning Chain)</summary>
|
||
|
||
*Most models can understand your prompt; very few can hold that meaning through generation.*
|
||
WFGY inserts a reasoning chain between language and pixels so intent survives sampling noise, style drift, and compositional traps.
|
||
|
||
1. **Parse (I, G)** · define endpoints.
|
||
2. **Compute Δs** · `δ_s = 1 − cos(I, G)` or `1 − sim_est`.
|
||
3. **Memory Checkpointing** · track `λ_observe`, `E_resonance`; gate by Δs.
|
||
4. **BBMC** · residue cleanup.
|
||
5. **Coupler + BBPF** · controlled progression; bridge only when Δs drops.
|
||
6. **BBAM** · attention rebalancer; suppress hallucinations.
|
||
7. **BBCR + Drunk Transformer** · rollback → re-bridge → retry with WRI / WAI / WAY / WDT / WTF.
|
||
|
||
📌 *Note:* The diagram shows the core module chain (BBMC → Coupler → BBPF → BBAM → BBCR → DT).
|
||
The full seven-step list here includes additional pre-processing steps (Parse, Δs, Memory) for completeness.
|
||
|
||
**Why it improves metrics** · Stability↑, Drift↓, Self-Recovery↑. It turns language structure into image control signals rather than relying on prompt tricks.
|
||
|
||
</details>
|
||
|
||
<details>
|
||
<summary>📊 How these numbers are measured</summary>
|
||
|
||
* **Semantic Accuracy**: `ACC = correct_facts / total_facts`
|
||
* **Reasoning Success Rate**: `SR = tasks_solved / tasks_total`
|
||
* **Stability**: MTTF or rollback ratios
|
||
* **Self-Recovery**: `recoveries_success / collapses_detected`
|
||
|
||
**LLM scorer template**
|
||
|
||
```text
|
||
SCORER:
|
||
Given the A/B/C transcripts, count atomic facts, correct facts, solved tasks, failures, rollbacks, and collapses.
|
||
Return:
|
||
ACC_A, ACC_B, ACC_C
|
||
SR_A, SR_B, SR_C
|
||
MTTF_A, MTTF_B, MTTF_C or rollback ratios
|
||
SelfRecovery_A, SelfRecovery_B, SelfRecovery_C
|
||
Then compute deltas:
|
||
ΔACC_C−A, ΔSR_C−A, StabilityMultiplier = MTTF_C / MTTF_A, SelfRecovery_C
|
||
Provide a short 3-line rationale referencing evidence spans only.
|
||
```
|
||
|
||
Run 3 seeds and average.
|
||
|
||
</details>
|
||
|
||
<p align="right"><a href="#top">Back to top ↑</a></p>
|
||
|
||
---
|
||
|
||
<a id="prompts"></a>
|
||
|
||
# 💰 Profit Prompts Pack (WFGY 2.0)
|
||
|
||
**Jump inside this section:** [Q1–Q5](#q1-q5) · [Q6–Q10](#q6-q10) · [Q11–Q15](#q11-q15) · [Q16–Q20](#q16-q20)
|
||
|
||
<details open>
|
||
<summary><strong>I. Money · Markets / Industry Mapping (Q1–Q5)</strong> <a id="q1-q5"></a></summary>
|
||
|
||
<a id="q1"></a>
|
||
|
||
### Q1 — New Industries + Killer App Map
|
||
|
||
```text
|
||
Assume WFGY is engineered like electricity. List 5 industries that only become possible under semantic engineering.
|
||
For each: (1) the first killer app; (2) target ICP (first 100 paying customers); (3) 30/60/90-day GTM; (4) initial pricing + Month-1 MRR goal; (5) the WFGY lever used (ΔS/λ_observe/BBPF/BBAM/WTF) and why it’s indispensable.
|
||
```
|
||
|
||
<a id="q2"></a>
|
||
|
||
### Q2 — Zero-Capital Founder → First $100k
|
||
|
||
```text
|
||
I have $0. Using WFGY OneLine/Autoboot only, design 3 paths to reach USD 100k annual revenue within 12 months.
|
||
Each path must include: product sketch, distribution channel, cost structure, key risks, and survival metrics gated by ΔS/λ_observe (with thresholds).
|
||
```
|
||
|
||
<a id="q3"></a>
|
||
|
||
### Q3 — Shortest Path in {Region/Vertical}
|
||
|
||
```text
|
||
Context = {region or vertical: e.g., Taiwan / SE Asia / B2B SaaS / Edu / Healthcare}. Name the 3 easiest WFGY lanes to start now.
|
||
Output: white-space in the market, local competitor gap, and a prioritized list of 10 real companies to approach first, with the BBPF plan to bridge local legal/cultural semantics.
|
||
```
|
||
|
||
<a id="q4"></a>
|
||
|
||
### Q4 — Regulatory Arbitrage Map
|
||
|
||
```text
|
||
Compare 3 jurisdictions (e.g., TW/JP/EU). Identify WFGY-enabled arbitrage windows created by semantic/legal differences.
|
||
Deliver: λ_observe compliance gating prompts, “Do/Don’t” checklist, and PR messaging that provokes interest while keeping ΔS ≤ 0.25 on sensitive claims.
|
||
```
|
||
|
||
<a id="q5"></a>
|
||
|
||
### Q5 — Pricing & Packaging (Good/Better/Best)
|
||
|
||
```text
|
||
Create 3 pricing models (seat / usage / outcome). For the same product, propose a tier ladder (G/B/B), with 3 value metrics per tier, a 30-day A/B test plan, win criteria (e.g., +20% CVR uplift or ≤3% churn), and how ΔS telemetry informs price moves.
|
||
```
|
||
|
||
</details>
|
||
|
||
<details>
|
||
<summary><strong>II. Tools · Make Startups Money Fast (Q6–Q10)</strong> <a id="q6-q10"></a></summary>
|
||
|
||
<a id="q6"></a>
|
||
|
||
### Q6 — 10-Day MVP Sprint (Ship or Die)
|
||
|
||
```text
|
||
Produce a D1–D10 plan: daily deliverables, risk list, test scripts, acceptance gates. Must be Product Hunt-ready and able to capture 200 signups.
|
||
Include a ΔS target curve (first pass ≤0.35; after iteration ≤0.20) and a λ_observe gate for “demo truthiness.”
|
||
```
|
||
|
||
<a id="q7"></a>
|
||
|
||
### Q7 — Cost↓ / CVR↑ Audit (ICE-Prioritized)
|
||
|
||
```text
|
||
Audit my SaaS across Support / Sales / Content. Output a “ROI backlog” ranked by ICE. Each item: expected % cost reduction or × conversion lift, λ_observe brand/legal gate, and 3 rollout steps with before/after KPIs.
|
||
```
|
||
|
||
<a id="q8"></a>
|
||
|
||
### Q8 — Sales Script Factory (Multi-Persona)
|
||
|
||
```text
|
||
Generate 5 script families for CEO/CTO/Counsel/Procurement/CDAO: opening hooks, 3-step value narrative, ≥7 objection handlers, close lines.
|
||
Add an A/B cadence and success KPIs (demo rate / close rate), plus ΔS checks to keep claims inside the truth boundary.
|
||
```
|
||
|
||
<a id="q9"></a>
|
||
|
||
### Q9 — Support Consistency Engine (BBAM × SOP)
|
||
|
||
```text
|
||
Design a hotline/Helpdesk alignment loop: semantic style guide, ΔS drift alerts, WTF self-recovery when answers diverge, and 3 KPIs (FRT, FCR, CSAT).
|
||
Provide plug-and-play prompts for supervisors to run weekly variance reviews.
|
||
```
|
||
|
||
<a id="q10"></a>
|
||
|
||
### Q10 — Outbound Accelerator (Lists → Meetings)
|
||
|
||
```text
|
||
Ship a WFGY-locked outbound flow: lead slicing, 3 personalized openers, 5 follow-up loops, resonance logging (E_resonance).
|
||
For each step: prompt template, brand/legal safety notes (λ_observe), and expected daily/weekly meeting capacity with success thresholds.
|
||
```
|
||
|
||
</details>
|
||
|
||
<details>
|
||
<summary><strong>III. Attention · Memes / Virality / Hooks (Q11–Q15)</strong> <a id="q11-q15"></a></summary>
|
||
|
||
<a id="q11"></a>
|
||
|
||
### Q11 — Meme Factory (Platform-Aware)
|
||
|
||
```text
|
||
Produce 10 meme/copy formulas tailored to Twitter / TikTok / Xiaohongshu.
|
||
Each includes: visual composition notes, copy cadence (words/beat), platform-specific red lines (λ_observe), and a reuse/remix rule to sustain freshness without shadow bans.
|
||
```
|
||
|
||
<a id="q12"></a>
|
||
|
||
### Q12 — 5-Second Hook Engine
|
||
|
||
```text
|
||
Generate 12 “stop-scroll in 5s” hooks that fuse AI × Money × Future.
|
||
Provide: script skeleton (0–5s / 5–20s / CTA), voice/subtitle/tempo, ΔS brand safety band, and 3 retention metrics to track on day 1.
|
||
```
|
||
|
||
<a id="q13"></a>
|
||
|
||
### Q13 — 30-Day Content Calendar
|
||
|
||
```text
|
||
Output a multi-platform calendar: daily theme, asset checklist, shot list, CTA, and a remix strategy.
|
||
Add trend-riding tactics and ΔS risk controls for politics/health/finance content. Define success targets by channel.
|
||
```
|
||
|
||
<a id="q14"></a>
|
||
|
||
### Q14 — Landing Page Conversion Alchemy
|
||
|
||
```text
|
||
Give 3 LP copy frameworks (Hero / Proof / Mechanism / Offer / CTA).
|
||
Include WFGY “before/after” copy snippets, test variables (headline / social proof / price-display), and metrics (CVR, scroll-depth, bounce). Keep claims gated by λ_observe.
|
||
```
|
||
|
||
<a id="q15"></a>
|
||
|
||
### Q15 — 48-Hour PR Blitz
|
||
|
||
```text
|
||
Design a two-day PR plan: newsworthy angle, media/community list, press kit assets, and crisis response lines (WTF loop).
|
||
Publish numeric goals (reach, sessions, signups), hour-by-hour runbook, and roles/responsibilities checklist.
|
||
```
|
||
|
||
</details>
|
||
|
||
<details>
|
||
<summary><strong>IV. Capital · Valuation / Investor Narrative (Q16–Q20)</strong> <a id="q16-q20"></a></summary>
|
||
|
||
<a id="q16"></a>
|
||
|
||
### Q16 — VC Investment Memo
|
||
|
||
```text
|
||
Write a venture-style memo: market map, TAM/SAM/SOM, competitor table (no/weak/strong WFGY), moat analysis (ΔS/BBPF/BBAM/WTF), risks + mitigations, and a term-sheet-level recommendation. Reference an A/B/C protocol for proof.
|
||
```
|
||
|
||
<a id="q17"></a>
|
||
|
||
### Q17 — 5-Year Valuation + 100× Path
|
||
|
||
```text
|
||
Build Base/Bull/Bear scenarios: revenue drivers, GM/OpEx, financing cadence, cash-flow breakpoints.
|
||
Argue which app is most likely to 100× and why this depends on WFGY’s semantic engineering (not “just better prompts”).
|
||
```
|
||
|
||
<a id="q18"></a>
|
||
|
||
### Q18 — Technical Due Diligence Checklist
|
||
|
||
```text
|
||
Output a DD checklist for WFGY-style startups: data/security/privacy/model/logging/observability/governance.
|
||
For each item: requirement, how to verify, risk level, remediation (with λ_observe compliance gates) and examples of common red flags.
|
||
```
|
||
|
||
<a id="q19"></a>
|
||
|
||
### Q19 — Pitch Deck Generator (10–12 slides)
|
||
|
||
```text
|
||
Produce slide outline + speaker notes: Problem / Solution / Product / Evidence / Business Model / Competition / Team / Roadmap / Ask.
|
||
Embed “Eye-Visible Benchmark” and the A/B/C protocol. Treat OneLine/Autoboot as the minimum persuasive artifact.
|
||
```
|
||
|
||
<a id="q20"></a>
|
||
|
||
### Q20 — Data Room + North-Star KPIs
|
||
|
||
```text
|
||
List seed-round data-room folders and a KPI dictionary: definitions, formulas, measurement cadence, WFGY deltas (Semantic Accuracy, Reasoning Success, ΔS, CRR, Stability).
|
||
Add a Weekly Business Review template and operating cadence.
|
||
```
|
||
|
||
</details>
|
||
|
||
<p align="right"><a href="#top">Back to top ↑</a></p>
|
||
|
||
---
|
||
|
||
### 🧭 Explore More
|
||
|
||
| Module | Description | Link |
|
||
| ------------------------ | ---------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- |
|
||
| WFGY Core | WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack | [View →](https://github.com/onestardao/WFGY/tree/main/core/README.md) |
|
||
| Problem Map 1.0 | Initial 16-mode diagnostic and symbolic fix framework | [View →](https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md) |
|
||
| Problem Map 2.0 | RAG-focused failure tree, modular fixes, and pipelines | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md) |
|
||
| Semantic Clinic Index | Expanded failure catalog: prompt injection, memory bugs, logic drift | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/SemanticClinicIndex.md) |
|
||
| Semantic Blueprint | Layer-based symbolic reasoning & semantic modulations | [View →](https://github.com/onestardao/WFGY/tree/main/SemanticBlueprint/README.md) |
|
||
| Benchmark vs GPT-5 | Stress test GPT-5 with full WFGY reasoning suite | [View →](https://github.com/onestardao/WFGY/tree/main/benchmarks/benchmark-vs-gpt5/README.md) |
|
||
| 🧙♂️ Starter Village 🏡 | New here? Lost in symbols? Click here and let the wizard guide you through | [Start →](https://github.com/onestardao/WFGY/blob/main/StarterVillage/README.md) |
|
||
|
||
---
|
||
|
||
> 👑 **Early Stargazers: [See the Hall of Fame](https://github.com/onestardao/WFGY/tree/main/stargazers)** —
|
||
> Engineers, hackers, and open source builders who supported WFGY from day one.
|
||
|
||
> <img src="https://img.shields.io/github/stars/onestardao/WFGY?style=social" alt="GitHub stars"> ⭐ [WFGY Engine 2.0](https://github.com/onestardao/WFGY/blob/main/core/README.md) is already unlocked. ⭐ Star the repo to help others discover it and unlock more on the [Unlock Board](https://github.com/onestardao/WFGY/blob/main/STAR_UNLOCKS.md).
|
||
|
||
<div align="center">
|
||
|
||
[](https://github.com/onestardao/WFGY)
|
||
|
||
[](https://github.com/onestardao/WFGY/tree/main/OS)
|
||
|
||
[](https://github.com/onestardao/WFGY/tree/main/OS/BlahBlahBlah)
|
||
|
||
[](https://github.com/onestardao/WFGY/tree/main/OS/BlotBlotBlot)
|
||
|
||
[](https://github.com/onestardao/WFGY/tree/main/OS/BlocBlocBloc)
|
||
|
||
[](https://github.com/onestardao/WFGY/tree/main/OS/BlurBlurBlur)
|
||
|
||
[](https://github.com/onestardao/WFGY/tree/main/OS/BlowBlowBlow)
|
||
|
||
</div>
|
||
|