🌙 3AM: a dev collapsed mid-debug… 🩺 WFGY Triage Center — Emergency Room & Grandma’s AI Clinic
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👨⚕️ 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.
💡 Always free. If it helps, a ⭐ star keeps the ER running.
🌐 Multilingual — start in any language.
- 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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⏱️ 60 seconds: WFGY as a Semantic Firewall — Before vs After
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 every failure mode, once mapped, stays fixed.
you’re not firefighting after the fact — you’re installing a reasoning firewall at the entry point.
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, bug sealed permanently Ceiling 70–85% stability limit 90–95%+ achievable, structural guarantee Experience Firefighting, “whack-a-mole” debugging Structural firewall, “fix once, stays fixed” Complexity Growing patch jungle, fragile pipelines Unified acceptance targets, one-page repair guide
- Traditional patching: 70–85% stability ceiling. Each new patch adds complexity and potential regressions.
- WFGY firewall: 90–95%+ achievable. Fix once → the same bug never resurfaces. Debug time cut by 60–80%.
- Unified metrics: every fix is measured (ΔS ≤ 0.45, coverage ≥ 0.70, λ convergent). No guesswork.
- 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 sealed. If drift recurs, it means a new failure mode needs mapping, not a re-fix of the old one.
Summary:
Others patch symptoms AFTER output. WFGY blocks unstable states BEFORE output.
That is why it feels less like debugging, more like installing a structural guarantee.
⚡ Quick Links — first-time here? click to open
Goal: route your bug to the right fix in <60s. Pick your path:
- 🧭 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
- ⏳ 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
- 🖥️ LocalDeploy_Inference hub → open
–llama.cpp→ open ·Ollama→ open ·textgen-webui→ open ·vLLM→ open
- 🗺️ 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
- 🌟 Star unlocks & roadmap → open
Acceptance targets (for every fix):
ΔS(question, context) ≤ 0.45 · coverage ≥ 0.70 · λ convergent across 3 paraphrases.
What is the Global Fix Map?
A vendor-neutral panoramic index that consolidates 300+ topics, frameworks, and reproducible failure modes (RAG, embeddings, chunking, OCR/language, reasoning/memory, agents, serverless, eval/governance).
Purpose: convert repeatable bugs into verifiable structural repairs — fix once, stays fixed.
Principles
- Zero-install: boot with TXT OS / WFGY PDF as context.
- Measurable: acceptance targets for every fix → ΔS(question, context) ≤ 0.45, coverage ≥ 0.70, λ convergent across 3 paraphrases.
- Store-agnostic: same rails work with OpenAI/Claude/Gemini, llama.cpp/Ollama/vLLM, FAISS/pgvector/Redis, Chroma/Weaviate/Milvus, etc.
- Routable: organized into Providers & Agents / Data & Retrieval / Input & Parsing / Reasoning & Memory / Automation & Ops / Eval & Governance.
Who it’s for
- Local or cloud LLM users; RAG & agents teams; platform/data engineers; SRE/Ops.
Use in 60 seconds
- Pick the relevant section.
- Open the adapter page and apply the minimal repair.
- Verify the targets above.
- Gate merges with the provided CI/CD templates.
Related maps
- Problem Map 1.0 — 16 reproducible failure modes with fixes → open
- Problem Map 2.0 — RAG Architecture & Recovery → open
- WFGY Core (2.0) — engine & math → open
A one-stop index to route real-world bugs to the right repair page.
Pick your stack, open the adapter, apply the structural fix, then verify:
- ΔS(question, context) ≤ 0.45
- coverage ≥ 0.70
- λ remains convergent across 3 paraphrases
| Family | What it covers | Open |
|---|---|---|
| LLM Providers | provider-specific quirks, schema drift, API limits | LLM_Providers |
| Agents & Orchestration | role drift, tool fences, recovery bridges, cold boot order | Agents_Orchestration |
| Chatbots / CX | bot frameworks, CX stacks, handoff gaps | Chatbots_CX |
| Automation | Zapier / Make / n8n, idempotency, warmups, fences | Automation |
| Cloud Serverless | cold start, concurrency, secrets, routing, DR, compliance | Cloud_Serverless |
| DevTools & Code AI | IDE/assist rails, prompts in editors, local workflows | DevTools_CodeAI |
| Family | What it covers | Open |
|---|---|---|
| RAG (end-to-end) | visual routes, acceptance targets, failure trees | RAG |
| RAG + VectorDB | store-agnostic knobs, contracts, routing | RAG_VectorDB |
| Retrieval | playbook, traceability, rerankers, query split | Retrieval |
| Embeddings | metric mismatch, normalization, dimension checks | Embeddings |
| VectorDBs & Stores | FAISS/Redis/Weaviate/Milvus/pgvector guardrails | VectorDBs_and_Stores |
| Chunking | chunk/section discipline, IDs, layouts, reindex policy | Chunking |
| Family | What it covers | Open |
|---|---|---|
| Document AI / OCR | document AI stacks, pipeline interfaces | DocumentAI_OCR |
| OCR + Parsing | pre-embedding text integrity, parser drift checks | OCR_Parsing |
| Language | multilingual routing, cross-script stability | Language |
| Language & Locale | tokenizer mismatch, normalization, locale drift | LanguageLocale |
| Family | What it covers | Open |
|---|---|---|
| Reasoning | entropy overload, loops, logic collapse, proofs | Reasoning |
| Memory & Long Context | long-window guardrails, state fork, coherence | MemoryLongContext |
| Multimodal Long Context | cross-modal alignment, joins, anchors | Multimodal_LongContext |
| Safety / Prompt Integrity | prompt injection, role confusion, JSON/tools | Safety_PromptIntegrity |
| Prompt Assembly | contracts, templates, eval kits for prompts | PromptAssembly |
| Family | What it covers | Open |
|---|---|---|
| Eval | SDK-free evals, acceptance targets, failure guards | Eval |
| Eval Observability | drift alarms, coverage tracking, ΔS thresholds | Eval_Observability |
| OpsDeploy | prod safety rails, rollbacks, backpressure, canary | OpsDeploy |
| Enterprise Knowledge & Gov | data residency, expiry, sensitivity, compliance | Enterprise_Knowledge_Gov |
| Governance | policies, change control, org-level workflows | Governance |
| Local Deploy & Inference | ollama, vLLM, tgi, llama.cpp, textgen-webui, exllama, koboldcpp, gpt4all, jan, AutoGPTQ/AWQ/bitsandbytes | LocalDeploy_Inference |
- Identify your stack (provider/agents, data & retrieval, input/parsing, reasoning, ops/eval).
- Open the folder page and follow the minimal repair steps.
- Verify your acceptance targets: ΔS ≤ 0.45, coverage ≥ 0.70, λ convergent on 3 paraphrases.
- Gate merges with CI/CD templates so fixes stick.
- Visual recovery map: RAG Architecture & Recovery
- Retrieval knobs: Retrieval Playbook
- Why-this-snippet tables: Retrieval Traceability
- Snippet / citation schema: Data Contracts
| Tool | Link | 3-Step Setup |
|---|---|---|
| WFGY 1.0 PDF | Engine Paper | 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + <your question>” |
| TXT OS (plain-text OS) | TXTOS.txt | 1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly |
| Layer | Page | What it’s for |
|---|---|---|
| ⭐ Proof | WFGY Recognition Map | External citations, integrations, and ecosystem proof |
| ⚙️ Engine | WFGY 1.0 | Original PDF tension engine and early logic sketch (legacy reference) |
| ⚙️ Engine | WFGY 2.0 | Production tension kernel for RAG and agent systems |
| ⚙️ Engine | WFGY 3.0 | TXT based Singularity tension engine (131 S class set) |
| 🗺️ Map | Problem Map 1.0 | Flagship 16 problem RAG failure taxonomy and fix map |
| 🗺️ Map | Problem Map 2.0 | Global Debug Card for RAG and agent pipeline diagnosis |
| 🗺️ Map | Problem Map 3.0 | Global AI troubleshooting atlas and failure pattern map |
| 🧰 App | TXT OS | .txt semantic OS with fast bootstrap |
| 🧰 App | Blah Blah Blah | Abstract and paradox Q&A built on TXT OS |
| 🧰 App | Blur Blur Blur | Text to image generation with semantic control |
| 🏡 Onboarding | Starter Village | Guided entry point for new users |
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