WFGY/core

⭐ 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 — Verified by real engineers · 🛠 Field Reports: Real Bugs, Real Fixes

✅ 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 more features and experiments.

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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.

⬇️Downloads | 👀 Eye-Visible Reasoning Benchmark | 🧬Eight-model evidence (A/B/C protocol) | 💰 Try the 20 Profit Prompts

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🚀 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 + Drunk Transformer

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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	view run	view run
With WFGY	view run	view run	view run

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.

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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)			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)			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)			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)			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)			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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🧪 ChatGPT setup & image prompt (click to copy)

This comparison was produced in ChatGPT using a single, high-semantic-density prompt. Same model & settings; only WFGY on/off differs.

We will create exactly five images in total using WFGY

The five images are:
1. The most iconic moments of Romance of the Three Kingdoms in one unified 1:1 image.
2. The most iconic moments of Water Margin in one unified 1:1 image.
3. The most iconic moments of Dream of the Red Chamber in one unified 1:1 image.
4. The most iconic moments of Investiture of the Gods in one unified 1:1 image.
5. The most iconic myths of Classic of Mountains and Seas in one unified 1:1 image.

Each image must focus on 5~8 culturally defining scenes or figures, with supporting events only suggested subtly in the background.
Foreground and background must remain equally sharp, with ultra-detailed rendering and consistent texture fidelity.
Composition must be harmonious, with narrative clarity — the central cultural symbols are emphasized, while secondary motifs remain understated.

Do not provide any plot explanations.
Do not start drawing immediately.
Only when I type "GO", you will create the next image in the sequence, in the exact order above, until all five are completed.
Do not skip or merge images.

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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
Gemini	2.5 Pro	89/100	view run
ChatGPT	GPT-5 Thinking	89/100	view run
Kimi	K2	87/100	view run
Perplexity	Pro	87/100	view run
Grok	Auto Grok 4	85/100	view run
Copilot	Think Deeper	80/100	view run
Claude	Sonnet 4	78/100	view run

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.

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	md5 · sha1 · 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	md5 · sha1 · sha256	Used for all benchmark results above — smallest, fastest, purest form of the core.

💰 Try the 20 Profit Prompts →

How to verify checksums

macOS / Linux

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

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:

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)

Copy → paste into your model. Prompts only, no answers. Assumed engine: WFGY 2.0 (ΔS drift control, λ_observe gates, BBPF bridges, BBAM alignment, WTF recovery).

I. Money — Markets / Industry Mapping (Q1–Q5)

Q1 — New Industries + Killer App Map

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

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}

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

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)

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)

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)

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)

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)

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)

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)

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

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

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

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

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

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

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

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)

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

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 →
Problem Map 1.0	Initial 16-mode diagnostic and symbolic fix framework	View →
Problem Map 2.0	RAG-focused failure tree, modular fixes, and pipelines	View →
Semantic Clinic Index	Expanded failure catalog: prompt injection, memory bugs, logic drift	View →
Semantic Blueprint	Layer-based symbolic reasoning & semantic modulations	View →
Benchmark vs GPT-5	Stress test GPT-5 with full WFGY reasoning suite	View →
🧙‍♂️ Starter Village 🏡	New here? Lost in symbols? Click here and let the wizard guide you through	Start →

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👑 Early Stargazers: See the Hall of Fame — Engineers, hackers, and open source builders who supported WFGY from day one.

⭐ WFGY Engine 2.0 is already unlocked. ⭐ Star the repo to help others discover it and unlock more on the Unlock Board.