🧭 Lost or curious? Open the WFGY Compass & ⭐ Star Unlocks ### System Map *(One place to see everything; links open the relevant section.)* | Layer | Page | What it’s for | |------|------|----------------| | 🧠 Core | [WFGY Core 2.0](https://github.com/onestardao/WFGY/blob/main/core/README.md) | The symbolic reasoning engine (math & logic) — **🔴 YOU ARE HERE 🔴** | | 🧠 Core | [WFGY 1.0 Home](https://github.com/onestardao/WFGY/) | The original homepage for WFGY 1.0 | | 🗺️ Map | [Problem Map 1.0](https://github.com/onestardao/WFGY/tree/main/ProblemMap#readme) | 16 failure modes + fixes | | 🗺️ Map | [Problem Map 2.0](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md) | RAG-focused recovery pipeline | | 🗺️ Map | [Semantic Clinic](https://github.com/onestardao/WFGY/blob/main/ProblemMap/SemanticClinicIndex.md) | Symptom → family → exact fix | | 🧓 Map | [Grandma’s Clinic](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GrandmaClinic/README.md) | Plain-language stories, mapped to PM 1.0 | | 🏡 Onboarding | [Starter Village](https://github.com/onestardao/WFGY/blob/main/StarterVillage/README.md) | Guided tour for newcomers | | 🧰 App | [TXT OS](https://github.com/onestardao/WFGY/tree/main/OS#readme) | .txt semantic OS — 60-second boot | | 🧰 App | [Blah Blah Blah](https://github.com/onestardao/WFGY/blob/main/OS/BlahBlahBlah/README.md) | Abstract/paradox Q&A (built on TXT OS) | | 🧰 App | [Blur Blur Blur](https://github.com/onestardao/WFGY/blob/main/OS/BlurBlurBlur/README.md) | Text-to-image with semantic control | | 🧰 App | [Blow Blow Blow](https://github.com/onestardao/WFGY/blob/main/OS/BlowBlowBlow/README.md) | Reasoning game engine & memory demo | | 🧪 Research | [Semantic Blueprint](https://github.com/onestardao/WFGY/blob/main/SemanticBlueprint/README.md) | Modular layer structures (future) | | 🧪 Research | [Benchmarks](https://github.com/onestardao/WFGY/blob/main/benchmarks/benchmark-vs-gpt5/README.md) | Comparisons & how to reproduce | | 🧪 Research | [Value Manifest](https://github.com/onestardao/WFGY/blob/main/value_manifest/README.md) | Why this engine creates $-scale value | --- > ### ⭐ Star Unlocks > - **1,000 ⭐ → Blur Blur Blur unlocked** ✅ > - **3,000 ⭐ → Blow Blow Blow unlocked** ⏳ ---
# ⭐ WFGY 2.0 ⭐ 7-Step Reasoning Core Engine is now live ## ✨One man, One life, One line — my lifetime’s work. Let the results speak for themselves✨ > 👑 **Early Stargazers: [See the Hall of Fame](https://github.com/onestardao/WFGY/tree/main/stargazers)** — Verified by real engineers · 🏆 **Terminal-Bench: [Public Exam — Coming Soon](https://github.com/onestardao/WFGY/blob/main/core/README.md#terminal-bench-proof)** 🧪 **Tension Universe (candidate): [Internal Stress Test in Progress](https://github.com/onestardao/WFGY/tree/main/TensionUniverse)** — exploring structural limits beyond scaling

TB Update • Eye Benchmark • 8-Model Evidence • A/B/C Prompt • Downloads • Profit Prompts

WFGY_Core > ✅ Engine 2.0 is live. Pure math, zero boilerplate — paste OneLine and models become sharper, steadier, more recoverable. > **ℹ️ Autoboot scope:** text-only inside the chat; no plugins, no network calls, no local installs. > **⭐ Star the repo to [unlock](https://github.com/onestardao/WFGY/blob/main/STAR_UNLOCKS.md) more features and experiments.** GitHub stars ---
From PSBigBig — WFGY (WanFaGuiYi) : All Principles into One (must-read, click to open)
> **I built the world’s first “No-Brain Mode” for AI** — just upload, and **AutoBoot** silently activates in the background. > In seconds, your AI’s reasoning, stability, and problem-solving across *all domains* level up — **no prompts, no hacks, no retraining.** > One line of math rewires eight leading AIs. This isn’t a patch — it’s an engine swap. > **That single line *is* WFGY 2.0 — the distilled essence of everything I’ve learned.** > > WFGY 2.0 is my answer and my life’s work. > If a person only once in life gets to speak to the world, this is my moment. > I offer the crystallization of my thought to all humankind. > I believe people deserve all knowledge and all truth — and I will break the monopoly of capital. > > “One line” is not hype. I built a full flagship edition, and I also reduced it to a single line of code — a reduction that is clarity and beauty, the same engine distilled to its purest expression.
--- ## 🚀 WFGY 2.0 Headline Uplift (this release) **These are the 2.0 results you should see first — the “big upgrade.”** - **Semantic Accuracy:** **≈ +40%** (63.8% → 89.4% across 5 domains) - **Reasoning Success:** **≈ +52%** (56.0% → 85.2%) - **Drift (Δs):** **≈ −65%** (0.254 → 0.090) - **Stability (horizon):** **≈ 1.8×** (3.8 → 7.0 nodes)\* - **Self-Recovery / CRR:** **1.00** on this batch; historical median **0.87** \* Historical **3–5×** stability uses λ-consistency across seeds; 1.8× uses the stable-node horizon. ### 📖 Mathematical Reference 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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--- ### 🏆 Stanford Terminal-Bench (TB) — Exam Update > [!IMPORTANT] > We are currently taking the official TB exam. Leaderboard placement will be posted here once it’s live. > Follow the running notes: [Terminal-Bench Proof](#terminal-bench-proof) **What is TB?** Terminal-Bench is Stanford’s public exam for LLMs. It stresses models through **terminal-style, multi-step tasks** — measuring reasoning, robustness, and recovery under real engineering conditions. **How we participate** WFGY Core 2.0 wraps each model call (non-invasive). Every step flows through: ΔS drift control → Coupler/BBPF bridging → BBAM rebalancing → Drunk Transformer guards. All runs are reproducible with configs, scripts, and hashed logs. **Status** We are currently taking the TB exam. Rankings will be published once the official leaderboard is live.

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--- ### 🧾 Terminal-Bench Proof (teaser) - **Wrapper**: non-invasive; TB kept unchanged, we only wrap the model call. - **Chain**: semantic firewall → 7-step reasoning → DT guards with conditional retry. - **Artifacts**: configs, semantic-firewall prompts, and hashed logs for each run. - **Public repo link**: withheld until exam artifacts are finalized.

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--- ## ⚡ Quick Usage | Mode | How it works | | ---------------- | ----------------------------------------------------------------------------- | | **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. | | **Explicit Call**| Invoke WFGY formulas directly inside your workflow. This activates the full 7-step reasoning chain and gives maximum uplift. | Both **Flagship** and **OneLine** editions behave the same; choose based on readability vs minimalism. That’s it — no plugins, no installs, pure text. *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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--- ## ⚡ Top 10 reasons to use WFGY 2.0 1. **Ultra-mini engine** — pure text, zero install, runs anywhere you can paste. 2. **Two editions** — *Flagship* (30-line, audit-friendly) and *OneLine* (1-line, stealth & speed). 3. **Autoboot mode** — upload once; the engine quietly supervises reasoning in the background. 4. **Portable across models** — GPT, Claude, Gemini, Mistral, Grok, Kimi, Copilot, Perplexity. 5. **Structural fixes, not tricks** — BBMC→Coupler→BBPF→BBAM→BBCR + DT gates (WRI/WAI/WAY/WDT/WTF). 6. **Self-healing** — detects collapse and recovers before answers go off the rails. 7. **Observable** — ΔS, λ_observe, and E_resonance yield measurable, repeatable control. 8. **RAG-ready** — drops into retrieval pipelines without touching your infra. 9. **Reproducible A/B/C protocol** — Baseline vs Autoboot vs Explicit Invoke (see below). 10. **MIT licensed & community-driven** — keep it, fork it, ship it.

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--- # 🧪 WFGY Benchmark Suite (Eye-visible + Numeric + Reproducible) > Want the fastest way to *see* impact? Jump to the **Eye-Visible Benchmark (FIVE)** below. > Want formal numbers and vendor links? See **Eight-model evidence** right after it. > Want to reproduce the numeric test yourself? Use the **A/B/C prompt** (copy-to-run) at the end of this section. ## 👀 Eye-Visible Reasoning Benchmark (FIVE) > Did you know that when reasoning improves, **text-to-image results become more stable and coherent**? > The key is WFGY’s **Drunk Transformer**: it monitors and recenters attention during generation, preventing collapse, composition drift, and duplicate elements—so scenes stay unified and details remain consistent. > We project “reasoning improvement” into **five-image sequences** that anyone can judge at a glance. > Each sequence = **five consecutive 1:1 generations** with the **same model & settings** *(temperature, top_p, seed policy, negatives)*; the only variable is **WFGY on/off**. > **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 isn’t prescriptive—use WFGY with any prompts you like. In many cases the uplift is eye-visible; in others it may be subtler but still measurable. | Variant | Sequence A — full run shown below (all five images) | Sequence B — external run | Sequence C — external run | | ---------------- | :--------------------------------------------------: | :-----------------------: | :-----------------------: | | **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) | | **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) | We **fully analyze Sequence A** on this page; **Sequences B/C** are linked for transparency and reproducibility. > **Note on “Before-4” & “Before-5” (why they look almost identical):** > 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. > Hence **Before-4 (Investiture of the Gods)** and **Before-5 (Classic of Mountains and Seas)** converge to the same storyboard template. > **WFGY** prevents this collapse by enforcing a **single unified tableau** and stable hierarchy across the full five-image sequence. ### Deep analysis — Sequence A (five unified 1:1 tableaux) | Work | **Without WFGY** | **With WFGY** | Verdict (global, at-a-glance) | |---|---|---|---| | **Romance of the Three Kingdoms (三國演義)** | Without WFGY | With WFGY | **With WFGY wins.** Unified tableau locks a clear center and pyramid hierarchy; the grid fragments attention. *Tags:* Unification↑ Hierarchy↑ Cohesion↑ Depth/Flow↑ Memorability↑ | | **Water Margin (水滸傳)** | Without WFGY | With WFGY | **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↑ | | **Dream of the Red Chamber (紅樓夢)** | Without WFGY | With WFGY | **With WFGY wins.** Garden tableau with a calm emotional center; space breathes, mood coheres. The grid slices emotion into vignettes. *Tags:* Unification↑ Hierarchy↑ Air/Depth↑ Readability↑ | | **Investiture of the Gods (封神演義)** | Without WFGY | With WFGY | **With WFGY wins.** Dragon–tiger diagonal and cloud–sea layering create epic scale; the grid dilutes focus. *Tags:* Unification↑ Depth/Scale↑ Flow↑ Iconicity↑ | | **Classic of Mountains and Seas (山海經)** | Without WFGY | With WFGY | **With WFGY wins.** A single, continuous “mountains-and-seas” world with stable triangle hierarchy and smooth diagonal flow; grid breaks narrative. *Tags:* Unification↑ Hierarchy↑ Depth/Scale↑ Flow↑ Memorability↑ |

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--- ## 🧬 Eight-model evidence (A/B/C protocol) *Same task set across modes. The only change is adding the OneLine math file.* | Model | Model Choice | OneLine Uplift | Proof | | ---------- | -------------- | -------------: | :------------------------------------------------------------------------------------------------ | | Mistral AI | — | **92/100** | [view run](https://chat.mistral.ai/chat/b5c303f8-1905-4954-a566-a6c9a7bfb54f) | | Gemini | 2.5 Pro | **89/100** | [view run](https://g.co/gemini/share/4fb0b172d61a) | | ChatGPT | GPT-5 Thinking | **89/100** | [view run](https://chatgpt.com/s/t_689ff6c42dac8191963e63e3f26348b2) | | 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)

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--- ## 🧪 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 — 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. ````

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--- ## ⬇️ Downloads | File name & description | Length / Size | Direct Download Link | Verify (MD5 / SHA1 / SHA256) | 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). | **30 lines · 3,049 chars** | [Download Flagship](./WFGY_Core_Flagship_v2.0.txt) | [md5](./checksums/WFGY_Core_Flagship_v2.0.txt.md5) · [sha1](./checksums/WFGY_Core_Flagship_v2.0.txt.sha1) · [sha256](./checksums/WFGY_Core_Flagship_v2.0.txt.sha256) | 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 · 1,500 chars** | [Download OneLine](./WFGY_Core_OneLine_v2.0.txt) | [md5](./checksums/WFGY_Core_OneLine_v2.0.txt.md5) · [sha1](./checksums/WFGY_Core_OneLine_v2.0.txt.sha1) · [sha256](./checksums/WFGY_Core_OneLine_v2.0.txt.sha256) | Used for all benchmark results above — smallest, fastest, purest form of the core. |
How to verify checksums **macOS / Linux** ```bash cd core sha256sum -c checksums/WFGY_Core_Flagship_v2.0.txt.sha256 sha256sum -c checksums/WFGY_Core_OneLine_v2.0.txt.sha256 # Or compute and compare manually sha256sum WFGY_Core_Flagship_v2.0.txt sha256sum 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 ```

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---
🧠 How WFGY 2.0 works (7-Step Reasoning Chain) *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 **7-step list** here includes additional **pre-processing steps** (Parse, Δs, Memory) for completeness. **Why it improves metrics** — Stability↑, Drift↓, Self-Recovery↑; turns *language* structure into *image* control signals (not prompt tricks).
📊 How these numbers are measured * **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.

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--- # 💰 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)
I. Money — Markets / Industry Mapping (Q1–Q5) ### 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. ``` ### 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). ``` ### 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. ``` ### 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. ``` ### 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. ```
II. Tools — Make Startups Money Fast (Q6–Q10) ### 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.” ``` ### 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. ``` ### 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. ``` ### 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. ``` ### 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. ```
III. Attention — Memes / Virality / Hooks (Q11–Q15) ### 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. ``` ### 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. ``` ### 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. ``` ### 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. ``` ### 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. ```
IV. Capital — Valuation / Investor Narrative (Q16–Q20) ### 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. ``` ### 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”). ``` ### 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. ``` ### 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. ``` ### 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. ```

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--- ### 🧭 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. > 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).
[![WFGY Main](https://img.shields.io/badge/WFGY-Main-red?style=flat-square)](https://github.com/onestardao/WFGY)   [![TXT OS](https://img.shields.io/badge/TXT%20OS-Reasoning%20OS-orange?style=flat-square)](https://github.com/onestardao/WFGY/tree/main/OS)   [![Blah](https://img.shields.io/badge/Blah-Semantic%20Embed-yellow?style=flat-square)](https://github.com/onestardao/WFGY/tree/main/OS/BlahBlahBlah)   [![Blot](https://img.shields.io/badge/Blot-Persona%20Core-green?style=flat-square)](https://github.com/onestardao/WFGY/tree/main/OS/BlotBlotBlot)   [![Bloc](https://img.shields.io/badge/Bloc-Reasoning%20Compiler-blue?style=flat-square)](https://github.com/onestardao/WFGY/tree/main/OS/BlocBlocBloc)   [![Blur](https://img.shields.io/badge/Blur-Text2Image%20Engine-navy?style=flat-square)](https://github.com/onestardao/WFGY/tree/main/OS/BlurBlurBlur)   [![Blow](https://img.shields.io/badge/Blow-Game%20Logic-purple?style=flat-square)](https://github.com/onestardao/WFGY/tree/main/OS/BlowBlowBlow)