🏥 WFGY Global Fix Map

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🚑 WFGY Emergency Room (for developers)

👨‍⚕️ Now online:
Dr. WFGY in ChatGPT Room

This is a share window already trained as an ER.
Just open it, drop your bug or screenshot, and talk directly with the doctor.
He will map it to the right Problem Map / Global Fix section, write a minimal prescription, and paste the exact reference link.
If something is unclear, you can even paste a screenshot of Problem Map content and ask — the doctor will guide you.

⚠️ Note: for the full reasoning and guardrail behavior you need to be logged in — the share view alone may fallback to a lighter model.

💡 Always free. If it helps, a ⭐ star keeps the ER running.
🌐 Multilingual — start in any language.

👵 Grandma’s AI Clinic (for everyone)

Visit Grandma Clinic →

• 16 common AI failure modes, each explained as a grandma story.
• Everyday metaphors: wrong cookbook, salt-for-sugar, burnt first pot.
• Shows both the life analogy and the minimal WFGY fix.
• Perfect entry point for beginners, or anyone who wants to “get it” in 30 seconds.

💡 Tip: Both tracks lead to the same Problem Map numbers.
Choose Emergency Room if you need a fix right now.
Choose Grandma’s Clinic if you want to understand the bug in plain words.

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most fixes today happen AFTER generation:

• the model outputs something wrong, then we patch it with retrieval, chains, or tools.
• the same failures reappear again and again.

WFGY inverts the sequence. BEFORE generation:

• it inspects the semantic field (tension, residue, drift signals).
• if the state is unstable, it loops, resets, or redirects the path.
• only a stable semantic state is allowed to generate output.

this is why each mapped failure family is typically stable again under the same semantic settings; if model/version/context shifts, we treat it as a new mapping and rerun the loop.
you’re not firefighting after the fact — you’re installing a reasoning firewall at the entry point.

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📊 Before vs After

	Traditional Fix (After Generation)	WFGY Semantic Firewall (Before Generation) 🏆✅
Flow	Output → detect bug → patch manually	Inspect semantic field → only stable state generates
Method	Add rerankers, regex, JSON repair, tool patches	ΔS, λ, coverage checked upfront; loop/reset if unstable
Cost	High — every bug = new patch, risk of conflicts	Low — once mapped and revalidated, recurrence is usually reduced for the same family
Ceiling	70–85% stability limit	90–95%+ observed on internal cohorts; setup-dependent and not a universal ceiling
Experience	Firefighting, “whack-a-mole” debugging	Structural firewall, “fix once under accepted targets, then monitor for drift”
Complexity	Growing patch jungle, fragile pipelines	Unified acceptance targets, one-page repair guide

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⚡ Performance impact

• Traditional patching: 70–85% stability ceiling. Each new patch adds complexity and potential regressions.
• WFGY firewall: around 90–95%+ stability in internal cohorts. A mapped fix is usually suppressed for that family under stable conditions, with drift requiring re-mapping. Debug time often drops by 60–80% under those conditions.
• Unified metrics: every fix is measured (ΔS ≤ 0.45, coverage ≥ 0.70, λ convergent). No guesswork.

🛑 Key notes

• This is not a plugin or SDK — it runs as plain text, zero infra changes.
• You must apply acceptance targets: don’t just eyeball; log ΔS and λ to confirm.
• Once acceptance holds, that path is treated as stable for the same setting. If drift recurs, it means a new failure mode needs mapping, not merely a re-fix of the old one.

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Summary:
Others patch symptoms AFTER output. WFGY blocks unstable states BEFORE output.
That is why it feels less like debugging, more like installing structural acceptance scaffolding with guardrails rather than universal guarantees.

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Goal: route your bug to the right fix in <60s. Pick your path:

1) Get oriented

• 🧭 What is this? → Global Fix Map (this page) — panoramic index of RAG / infra / reasoning fixes.
• 🧱 Problem Map 1.0 (16 reproducible failure modes) → open
• 🪪 Problem Map 2.0 — Global Debug Card (image-as-prompt debug protocol) → open
• 🌍 Problem Map 3.0 — AI Troubleshooting Atlas (expanded failure pattern map) → open

2) One-minute quick-start

• ⏳ TXT OS (plain-text OS) → copy–paste → ask “which Problem Map number am I hitting?” → open · txt
• 📄 WFGY 1.0 PDF (use as context file) → open
• 🧪 Minimal demos (no SDK lock-in) → open

3) Local LLaMA / on-device stacks

• 🖥️ LocalDeploy_Inference hub → open
  – llama.cpp → open · Ollama → open · textgen-webui → open · vLLM → open

4) Fast jumpers for RAG & retrieval

• 🗺️ Visual recovery map → RAG Architecture & Recovery
• 🔧 Retrieval Playbook → open · Traceability → open
• 🧮 Embeddings: Metric Mismatch → open · Hybrid Weights → open
• 🧱 Vector DB guardrails → open · Chunking checklist → open

Need triage?

• 🩺 Semantic Clinic (when unsure) → open
• 🧭 Diagnose by symptom → open · Beginner Guide → open

Contribute / ask / FAQ

• 🌟 Star unlocks & roadmap → open

Acceptance targets (for every fix):
ΔS(question, context) ≤ 0.45 · coverage ≥ 0.70 · λ convergent across 3 paraphrases.