Paweł Domański
3288 lines
76 KiB
Plaintext
3288 lines
76 KiB
Plaintext
Poniżej **dokładna transkrypcja tekstu z obrazu** (zamiana obrazu na tekst), z zachowaniem struktury i opisów:
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Anatomy of the .claude/ folder
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(commit .claude/ to git)
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your-project/
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├─ CLAUDE.md
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│ team instructions, committed
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├─ CLAUDE.local.md
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│ personal overrides, gitignored
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├─ .claude/
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│ ├─ settings.json
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│ │ permissions + config, committed
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│ ├─ settings.local.json
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│ │ personal permissions, gitignored
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│ ├─ commands/
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│ │ ├─ review.md
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│ │ │ → /project:review
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│ │ ├─ fix-issue.md
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│ │ │ → /project:fix-issue
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│ │ ├─ deploy.md
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│ │ → /project:deploy
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│ ├─ rules/
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│ │ ├─ code-style.md
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│ │ ├─ testing.md
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│ │ ├─ api-conventions.md
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│ │ modular instruction files
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│ ├─ skills/
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│ │ │ auto-invoked workflows
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│ │ ├─ security-review/
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│ │ │ └─ SKILL.md
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│ │ ├─ deploy/
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│ │ └─ SKILL.md
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│ ├─ agents/
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│ ├─ code-reviewer.md
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│ ├─ security-auditor.md
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│ subagent personas
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Everything Claude needs to know about your project lives right here.
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Jeśli chcesz:
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* wersję **PL** (tłumaczenie),
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* konwersję do **Markdown / org-mode**,
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* albo rozbicie na **checklistę / dokumentację repo**
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— daj znać.
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Poniżej **wierna transkrypcja tekstu z obrazu** (zamiana obrazu na tekst), z zachowaniem nazw, hierarchii i opisów.
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***
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THE $15/MONTH OPENCLAW STACK
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### HOSTINGER / HETZNER — \~$5–7/mo
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2–4 vCPU • 4–8GB RAM • Ubuntu
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* Docker
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* OpenClaw
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* App Version: 1.7.8?
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* Nodes.js + Skills
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* Browser (Chrome)
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* Tailscale VPN
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* Private network only
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* .openclaw/ (persistent)
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* config + memory + workspace
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***
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### MODEL PROVIDERS
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**PRIMARY**
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Kimi K2.5 (Moonshot AI)
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256k ctx • SWE 78.6% • 3x boost Feb
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**FALLBACK #1**
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MiniMax M2.5
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$0.30/$1.20 Mtok • SWE 80.2% • batch tasks
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**FALLBACK #2**
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Gemini 3 Flash
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sub-agents • parallel work
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**FALLBACK #3**
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GLM-4.7-Flash
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$0.00 • heartbeats + simple queries
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***
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### YOUR CHANNELS
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* WhatsApp
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* Telegram
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* Discord / Slack
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* Web UI
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* CLI
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***
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### COST SUMMARY
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TOTAL: ~$15–18/mo
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(dual VPS ~$12 + Kimi ~$3 + MiniMax ~$2 + OpenRouter ~$1)
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vs. $200/mo Claude Max
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***
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Jeśli chcesz:
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* 🔁 **tłumaczenie na polski**
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* 🧱 **wersję w Markdown / org-mode**
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* 🧠 **rozpisanie tego jako architektury OpenClaw (runbook / SOP)**
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* 📦 **checklistę „jak to postawić od zera”**
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— daj znać, w jakiej formie.
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Poniżej **zamiana obrazu na tekst** – wierna transkrypcja plakatu **“40 AI TERMS EXPLAINED”**, z zachowaniem układu (hasło → definicja).
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Źródło widoczne na grafice: **Mindstream**.
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***
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## 40 AI TERMS EXPLAINED
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### 1. Bias
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When an AI unfairly prefers some answers, often because of the data it was trained on.
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### 2. Label
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A tag or answer given to data so AI knows what it is.
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### 3. Model
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The final program that can do tasks after learning from data.
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### 4. Training
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The process where AI learns from many examples to get better at tasks.
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### 5. Chatbot
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A computer program that talks to people like a robot.
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***
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### 6. Dataset
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A big collection of information that AI learns from.
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### 7. Token
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Words or pieces of words AI uses to read and write text.
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### 8. Overfitting
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When AI learns the training data too well and performs worse on new examples.
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### 9. AI Agent
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A software that does jobs for you using AI.
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### 10. AI Ethics
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Making sure AI is used in ways that are right and fair to everyone.
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***
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### 11. Explainability
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How easily people can understand why an AI made a certain decision.
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### 12. Inference
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When an AI uses what it learned to answer questions.
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### 13. Turing Test
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A test to see if a computer can trick people into thinking it’s human.
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### 14. Prompt
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The text or question you give an AI to get a response.
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### 15. Fine-tuning
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Training an AI a bit more on special data to make it better at specific tasks.
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***
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### 16. Generative AI
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AI that can make new things like text, images, music, or code.
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### 17. AI Automation
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Using AI to make tasks happen automatically without people doing them.
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### 18. Neural Network
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Computer programs built a little like the human brain.
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### 19. Computer Vision
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AI that helps computers “see” and understand images or videos.
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### 20. Transfer Learning
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Using an AI trained for one job to help with another, related job.
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***
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### 21. Guardrails (in AI)
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Built‑in checks to stop AI from making mistakes or causing harm.
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### 22. Open Source AI
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AI whose design and code are freely available for anyone to see or change.
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### 23. Deep Learning
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AI that learns using brain‑like structures called neural networks with many layers.
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### 24. Reinforcement Learning
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AI learns by trying things and getting rewards for good actions.
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### 25. Hallucination (in AI)
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When AI makes up stuff that sounds true but is actually wrong.
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***
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### 26. Zero-shot Learning
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AI does a new task it wasn’t directly taught just by understanding the description.
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### 27. Speech Recognition
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AI that turns spoken words into written text.
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### 28. Supervised Learning
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AI learns from data that already has the correct answers labeled.
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### 29. Model Context Protocol
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Rules on how AI shares context and information given to it.
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### 30. Machine Learning
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A way for computers to learn things by looking at lots of examples.
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***
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### 31. AI (Artificial Intelligence)
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Technology that makes computers act smart, like humans do.
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### 32. Unsupervised Learning
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AI finds patterns in data that has no labels.
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### 33. LLM (Large Language Model)
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An AI model that understands and generates lots of text.
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### 34. ASI (Artificial Superintelligence)
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An AI even smarter than the smartest humans ever.
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### 35. GPU
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Special computer chips that help AI train and run faster.
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***
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### 36. NLP (Natural Language Processing)
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AI that understands and works with human language.
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### 37. AGI (Artificial General Intelligence)
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A super‑smart AI that can learn anything a human can.
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### 38. GPT
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A famous AI that writes text like a human.
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### 39. API
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A way for different programs to talk to each other and use AI features.
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### 40. Algorithm
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When an AI follows defined steps or rules to decide what to do.
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***
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Na dole plakatu:
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Subscribe to Mindstream to learn AI for free — www.mindstream.news
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Jeśli chcesz:
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* ✅ **tłumaczenie PL → EN lub EN → PL (opisowe, nie literalne)**
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* ✅ **wersję w Markdown / org-mode (np. do repo lub notatek)**
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* ✅ **skróconą “ściągę” dla nietechnicznych**
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* ✅ **wersję dla inżynierów (mapowanie na LLM / agents / OpenClaw)**
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— powiedz w jakiej formie.
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Poniżej **dodany tekst**, wkomponowany jako **sekcja wprowadzająca / opis do infografiki „40 AI Terms Explained”**. Zachowałem oryginalne brzmienie, poprawiłem tylko formatowanie pod czytelność (np. LinkedIn / blog / README).
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***
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## Why this infographic exists
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Most people think they understand AI.
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Until they realise they only recognise buzzwords.
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I’ve curated the **40 important AI terms** here to fix that gap.
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A few months ago, I noticed something strange.
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Smart professionals were using AI every day —
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but struggling to explain it clearly.
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They could say **“LLM”**, **“agents”**, or **“hallucinations”**.
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But they didn’t really understand what those mean or **why they matter**.
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That’s the difference between **using AI**
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and **thinking with AI**.
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So I broke it down here.
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***
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## Why this matters more than people realise
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* → **Clarity compounds faster** than just using AI tools
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* → **Understanding beats memorising prompts**
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* → **Vocabulary shapes how well you use AI**
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***
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## A few terms most people misuse daily
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* **Bias** → When training data tilts outputs
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* **Tokens** → How models read and write text
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* **Inference** → Using learned knowledge in real time
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* **Overfitting** → When AI memorizes instead of generalizing
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* **Hallucinations** → Confident answers without factual grounding
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***
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## Do’s ✅
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* Learn **concepts before tools**
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* Connect terms to **real workflows**
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* Understand **limits**, not just capabilities
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* Revisit **fundamentals regularly**
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* Explain ideas **in your own words**
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***
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## Don’ts ❎
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* Chasing tools without understanding basics
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* Confusing **AGI** with current **LLMs**
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* Ignoring **training and data quality**
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* Treating **prompts as everything**
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* Assuming AI is always correct
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***
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## Final thought
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If you want **leverage** from AI,
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you must **speak its language first**.
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To get a **quick scan of all 40 AI terms**,
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check the infographic below 👇
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***
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Jeśli chcesz, mogę:
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* ✳️ dopasować styl pod **LinkedIn / newsletter / README / landing page**
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* ✳️ skrócić do **wersji social (hook + CTA)**
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* ✳️ przerobić na **manifest / intro do kursu**
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* ✳️ zapisać całość w **org-mode lub Markdown**
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Powiedz, gdzie to publikujesz.
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Poniżej **wierna transkrypcja tekstu z obrazu** – zamiana grafiki **„50 Steps to Master AI – Go From Zero to Pro”** na tekst, z zachowaniem numeracji i podziału **Start → End**.
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***
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# 50 Steps to Master AI
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**Go From Zero to Pro**
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## Start
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**01** Grasp the basics of core types of AI (ANI, AGI, ASI)
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**02** Understand the history and evolution of artificial intelligence
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**03** Master essential AI concepts and terminology
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**04** Learn Python – the most used AI programming language
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**05** Understand core computer science principles (loops, data structures)
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**06** Get comfortable with statistics, probability, and distributions
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**07** Learn linear algebra and calculus fundamentals for ML
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**08** Understand machine learning fundamentals and real‑world applications
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**09** Differentiate types of learning: supervised, unsupervised, reinforcement
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**10** Explore key ML algorithms (regression, trees, clustering, etc.)
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**11** Build a basic machine learning project with scikit‑learn
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**12** Study training vs. testing, overfitting vs. underfitting
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**13** Learn feature engineering and data preprocessing techniques
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**14** Understand model evaluation metrics (accuracy, F1, AUC, etc.)
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**15** Learn feature engineering, preprocessing, layers, activation functions
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***
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## Transition
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**16** Learn common CNN architectures for vision (CNNs, RNNs, etc.)
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**17** Explore deep learning frameworks: TensorFlow and PyTorch
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**18** Implement a deep learning project (e.g. image classification)
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**19** Learn model tuning: hyperparameters, regularization, dropout
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**20** Learn model tuning, random forests, XGBoost, gradient boosting
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**21** Learn NLP basics: tokenization, POS tagging, embeddings
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**22** Work with NLP libraries: SpaCy, NLTK, HuggingFace Transformers
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**23** Build a basic NLP model (text classification, summarization, etc.)
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**24** Understand computer vision basics: pixels, channels, filters
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**25** Explore OpenCV for image processing fundamentals
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***
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## Advanced
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**26** Implement a computer vision project using CNNs
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**27** Study transfer learning and pre‑trained models (ResNet, etc.)
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**28** Build your own NLP or CV project using HuggingFace or TorchVision
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**29** Learn data annotation and augmentation techniques
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**30** Use cloud services (AWS/GCP) for training and inference at scale
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**31** Understand generative models: GANs, VAEs, diffusion models
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**32** Implement a generative AI project (e.g. Deepfake, art)
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**33** Dive into reinforcement learning fundamentals and learning policies
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**34** Create a basic RL environment and agent using OpenAI Gym
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**35** Learn optimization algorithms: SGD, Adam, RMSProp
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***
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## End
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**36** Explore AutoML and hyperparameter tuning tools (Optuna, Keras Tuner)
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**37** Study explainable AI (XAI) and interpretability tools (LIME, SHAP)
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**38** Build your AI project portfolio with diverse applications
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**39** Explore multimodal AI combining text, images, audio
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**40** Understand the ethical implications of AI (bias, fairness, misuse)
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**41** Deploy models using Flask, FastAPI, or Streamlit
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**42** Serve models via APIs using Docker & cloud services
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**43** Explore MLOps (CI/CD pipelines, monitoring, model versioning)
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**44** Learn time series forecasting (ARIMA, Prophet, LSTMs)
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**45** Stay updated with AI research (arXiv, blogs, conferences)
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**46** Join AI competitions (Kaggle, Zindi, DrivenData)
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**47** Contribute to open‑source AI projects on GitHub
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**48** Follow AI research: arXiv, Google Research, DeepMind
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**49** Read top AI books, newsletters, and blogs to stay updated
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**50** Teach AI to others or write about your AI journey
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***
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|
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Jeśli chcesz:
|
||
|
||
* ✅ **tłumaczenie na polski**
|
||
* ✅ **wersję w org‑mode / Markdown (np. roadmapa learningowa)**
|
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* ✅ **przeróbkę na plan 6–12 miesięcy**
|
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* ✅ **dopasowanie pod praktyka (LLM / agents / MLOps zamiast researchu)**
|
||
|
||
— napisz, w jakiej formie i dla kogo.
|
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Poniżej **wierna transkrypcja tekstu z obrazu** – zamiana grafiki **„Agentic AI Tech Stack”** na tekst, z zachowaniem struktury i nazw narzędzi.
|
||
|
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***
|
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# Agentic AI Tech Stack
|
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*(Analytics Vidhya)*
|
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|
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***
|
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|
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## 1. Deployment and Infrastructure
|
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|
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* groq
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* AWS
|
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* together.ai
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* baseten
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* Modal
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* Fireworks AI
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* Replicate
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***
|
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## 2. Evaluation and Monitoring
|
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|
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* LangSmith
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* MLflow
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* Weights & Biases
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* Ragas
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* Deepchecks
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* Fairlearn
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* Holistic AI
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* Hugging Face
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* Phoenix
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***
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|
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## 3. Foundation Models
|
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|
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* Claude 3.7 Sonnet
|
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* Mistral AI
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* Cohere
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* Gemini 2.5 Pro
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* LLaMA 4
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* Claude 4
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|
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***
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## 4. Orchestration Frameworks
|
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|
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* LangChain
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* DSPy
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* Microsoft AutoGen
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* Adaflow
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* LlamaIndex
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* Haystack
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* LiteLLM
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* Dify
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* Ray
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|
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***
|
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|
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## 5. Vector Databases
|
||
|
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* Milvus
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* Qdrant
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* Redis
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||
* pgvector
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||
* Vald
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* Chroma
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* Pinecone
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* Elasticsearch
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* Vespa
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* Weaviate
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***
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## 6. Embedding Models
|
||
|
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* Voyage AI
|
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* Nomic
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* fastText
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* Hugging Face
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* OpenAI
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* spaCy
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* Vector Flow
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* Cohere
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***
|
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## 7. Data Ingestion and Extraction
|
||
|
||
* Scrapy
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||
* Docling
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||
* BeautifulSoup
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||
* Diffbot
|
||
* Firecrawl
|
||
* LlamaParse
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||
* Amazon Textract
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||
* Apache Tika
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||
|
||
***
|
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## 8. Memory and Context Management
|
||
|
||
* Letta
|
||
* mem0
|
||
* Zep
|
||
* Chroma
|
||
* Cognee
|
||
* LangChain
|
||
* LlamaIndex
|
||
|
||
***
|
||
|
||
Jeśli chcesz, mogę:
|
||
|
||
* ✅ przemapować ten stack na **OpenClaw / agentów produkcyjnych**
|
||
* ✅ zaznaczyć **które elementy są dziś „enterprise-ready”**
|
||
* ✅ uprościć do **minimalnego stosu praktycznego (MVP vs PROD)**
|
||
* ✅ przepisać całość do **org-mode / Markdown jako referencję**
|
||
|
||
Powiedz, do czego to używasz.
|
||
Poniżej **wierna transkrypcja tekstu z obrazu** – zamiana grafiki **„How to build an AI Agent”** na tekst, z zachowaniem struktury, kolejności i punktów.
|
||
|
||
***
|
||
|
||
# How to build an **AI Agent**
|
||
|
||
***
|
||
|
||
## 1. Define Purpose & Scope
|
||
|
||
* Use case
|
||
* User needs
|
||
* Success criteria
|
||
* Constraints
|
||
|
||
***
|
||
|
||
## 2. System Prompt Design
|
||
|
||
* Goals
|
||
* Role / Persona
|
||
* Instructions
|
||
* Guardrails
|
||
|
||
***
|
||
|
||
## 3. Choose LLM
|
||
|
||
* Base model
|
||
* Parameters
|
||
* Context window
|
||
* Cost / latency
|
||
|
||
***
|
||
|
||
## 4. Tools & Integrations
|
||
|
||
* Simple (local)
|
||
* API (web, apps, data)
|
||
* MCP server
|
||
* AI agent as a tool
|
||
* Custom functions
|
||
|
||
***
|
||
|
||
## 5. Memory Systems
|
||
|
||
* Episodic (conversation)
|
||
* Working memory
|
||
* Vector database
|
||
* SQL / structured DB
|
||
* File storage
|
||
|
||
***
|
||
|
||
## 6. Orchestration
|
||
|
||
* Routes / workflows
|
||
* Triggers
|
||
* Parameters
|
||
* Message queues
|
||
* Agent2Agent
|
||
* Error handling
|
||
|
||
***
|
||
|
||
## 7. User Interface
|
||
|
||
* Chat interface
|
||
* Web app
|
||
* API endpoint
|
||
* Slack / Discord bot
|
||
|
||
***
|
||
|
||
## 8. Testing & Evals
|
||
|
||
* Unit tests
|
||
* Latency testing
|
||
* Quality metrics
|
||
* Iterate & improve
|
||
|
||
***
|
||
|
||
*follow amit Rawal for more AI educational content*
|
||
|
||
***
|
||
|
||
Jeśli chcesz:
|
||
|
||
* ✅ **przepisać to do org‑mode / Markdown**
|
||
* ✅ **rozwinąć każdy krok jako checklistę produkcyjną**
|
||
* ✅ **zmapować to 1:1 na OpenClaw / LangChain / AutoGen**
|
||
* ✅ **dodać warstwę MLOps / observability**
|
||
|
||
— daj znać, co z tym dalej robimy.
|
||
Jasne — poniżej masz **wszystkie diagramy przepisane do Mermaid** (każdy jako osobny `flowchart`). Starałem się zachować **oryginalne etykiety** i **logikę strzałek**.
|
||
|
||
> Tip: jeśli chcesz wygląd “jak na tablicy” (ciemne tło, przerywane ramki), daj znać — dopiszę klasy Mermaid (`classDef`) pod styl.
|
||
|
||
***
|
||
|
||
## 1) Schedulery: Every Hour / Every Day / Every Week + wspólny wzorzec joba
|
||
|
||
flowchart TB
|
||
%% Top schedules
|
||
subgraph H[Every Hour]
|
||
direction TB
|
||
H1[Sync code repos]
|
||
H2[Check CRM for changes]
|
||
H3[Scout for new signals]
|
||
end
|
||
|
||
subgraph D[Every Day]
|
||
direction TB
|
||
D1[Ingest emails + calendar into CRM]
|
||
D2[Collect YouTube analytics and competitor data]
|
||
D3[Run platform health checks]
|
||
D4[Nightly business briefing from review council]
|
||
end
|
||
|
||
subgraph W[Every Week]
|
||
direction TB
|
||
W1[Synthesize daily notes into long-term memory]
|
||
W2[Run planning and reminder routines]
|
||
W3[Housekeeping: cleanup/pruning audits]
|
||
end
|
||
|
||
%% Common job pattern
|
||
subgraph P[Every Job Follows the Same Pattern]
|
||
direction LR
|
||
P1[Log start] --> P2[Execute task] --> P3[Log end<br/>status + summary]
|
||
P3 --> P4[Notify Telegram<br/>success or failure]
|
||
end
|
||
|
||
H --> P
|
||
D --> P
|
||
W --> P
|
||
|
||
P4 --> R[You see results in Telegram topics<br/>without lifting a finger]
|
||
|
||
***
|
||
|
||
## 2) Backup / “What Gets Protected” + restore path + GitHub sync
|
||
|
||
flowchart TB
|
||
subgraph PROT[What Gets Protected]
|
||
direction LR
|
||
CRM[CRM]
|
||
ANA[Analytics]
|
||
KB[Knowledge Base]
|
||
BA[Business Analysis]
|
||
VP[Video Pitch DB]
|
||
CRON[Cron]
|
||
LOGS[Logs]
|
||
end
|
||
|
||
subgraph AB[Automated Backup]
|
||
direction TB
|
||
AB1[Automated Backup]
|
||
AB2[Timestamped]
|
||
AB3[Package<br/>with manifest]
|
||
AB1 --> AB2 --> AB3
|
||
end
|
||
|
||
PROT --> AB
|
||
|
||
AB --> GD[Google Drive<br/>with retention policy:<br/>old backups auto-pruned]
|
||
|
||
AB --> REPO[Code Repo<br/>Auto-synced via hourly git push]
|
||
REPO --> GH[GitHub<br/>Always up to date]
|
||
|
||
subgraph FAIL[If Something Goes Wrong]
|
||
direction LR
|
||
F1[Pull backup from Drive] --> F2[Restore to original paths] --> F3[Verify CRM, KB,<br/>gateway jobs] --> F4[Back online]
|
||
end
|
||
|
||
GD --> FAIL
|
||
|
||
***
|
||
|
||
## 3) Notatki dzienne → synteza tygodniowa → pamięć długoterminowa + “learnings/”
|
||
|
||
flowchart TB
|
||
subgraph DAY[During the Day]
|
||
direction TB
|
||
C[Conversations with you]
|
||
T[Tasks completed]
|
||
M[Mistakes made]
|
||
end
|
||
|
||
DAY --> DN[Daily Notes<br/>Raw capture of everything that happened]
|
||
DN --> WS[Weekly Synthesis<br/>Distill patterns and preferences]
|
||
WS --> LTM[Long-Term Memory<br/>Stable preferences, learned behaviors]
|
||
|
||
M --> LRN[.learnings/<br/>Corrective patterns<br/>so mistakes don't repeat]
|
||
LRN --> LTM
|
||
|
||
LTM --> OUT[Claude gets better over time<br/>without being retrained]
|
||
|
||
***
|
||
|
||
## 4) Remote work setup + Development Flow + Fast Ops (SSH Terminal)
|
||
|
||
flowchart LR
|
||
A["Mac Studio<br/>(wherever you are)"] --> B1[Cursor SSH Remote]
|
||
A --> B2[Direct SSH Terminal]
|
||
A --> B3["TeamViewer<br/>(fallback)"]
|
||
|
||
B1 --> C["MacBook Air<br/>(always-on, at home)<br/>claw runs here 24/7"]
|
||
B2 --> C
|
||
B3 --> C
|
||
|
||
C --> STABLE[Live runtime<br/>stays stable<br/>the whole time]
|
||
|
||
subgraph DEV[Development Flow]
|
||
direction TB
|
||
D1[1. Work in isolated git worktree<br/>changes don't affect live system]
|
||
D2[2. Make targeted edits<br/>New skills, prompt tweaks, bug fixes]
|
||
D3[3. Run validation scripts<br/>check logs, verify behavior]
|
||
D4[4. Commit and sync<br/>Hourly auto-push or manual]
|
||
D1 --> D2 --> D3 --> D4
|
||
end
|
||
|
||
C --> DEV
|
||
|
||
subgraph OPS["Fast Ops (SSH Terminal)"]
|
||
direction LR
|
||
O1[Tail logs]
|
||
O2[Query cron DB]
|
||
O3[Restart services]
|
||
end
|
||
|
||
C --> OPS
|
||
|
||
***
|
||
|
||
## 5) Daily ingestion do CRM (Gmail + Calendar → kontakty → klasyfikacja → indeks → wyjścia)
|
||
|
||
flowchart TB
|
||
A[Daily ingestion trigger] --> B[Scan Gmail + Calendar]
|
||
B --> C[Extract people from senders/participants]
|
||
C --> D[Deduplicate + merge contact records]
|
||
D --> E[AI classify role/context]
|
||
E --> F[Update timeline + last-touch]
|
||
F --> G[Semantic indexing]
|
||
|
||
G --> H[Telegram CRM update]
|
||
G --> I[Natural-language CRM query answers]
|
||
|
||
***
|
||
|
||
## 6) Ingestion do Knowledge Base + RAG/Q\&A (dwa tory: ingest i pytanie użytkownika)
|
||
|
||
flowchart LR
|
||
%% Left pipeline: ingestion
|
||
subgraph ING[Ingestion pipeline]
|
||
direction TB
|
||
L1[URL/File input] --> L2[Detect source type]
|
||
L2 --> L3[Extractor selection + fallback]
|
||
L3 --> L4[Normalize content]
|
||
L4 --> L5[Hash dedupe check]
|
||
L5 --> L6[Chunk with overlap]
|
||
L6 --> L7[Generate embeddings]
|
||
L7 --> L8[Store source + chunks + vectors]
|
||
end
|
||
|
||
%% Right pipeline: query + answer
|
||
subgraph QRY[Query pipeline]
|
||
direction TB
|
||
R1[User question] --> R2[Embed query]
|
||
R2 --> R3[Semantic search top K]
|
||
R3 --> R4[Rerank candidates]
|
||
R4 --> R5[Grounded synthesis]
|
||
R5 --> R6[Answer with sources]
|
||
end
|
||
|
||
%% Optional logical link (shared KB)
|
||
L8 --- R3
|
||
|
||
***
|
||
|
||
### Jeśli chcesz, zrobię też wersję “ładniejszą” (styl tablicowy)
|
||
|
||
Mogę dodać:
|
||
|
||
* przerywane ramki jak na obrazkach,
|
||
* wspólne klasy (`classDef`) i kolory,
|
||
* oraz spójne ID węzłów pod automatyczne linkowanie w dokumentacji.
|
||
|
||
Powiedz tylko, gdzie to renderujesz: **GitHub**, **Obsidian**, **Mermaid Live**, czy np. **MkDocs**.
|
||
|
||
|
||
Poniżej masz **wierną zamianę obrazu na tekst** – pełną transkrypcję diagramu **„Agentic AI: The Big Picture”**, z zachowaniem nagłówków, warstw i elementów (bez interpretacji, tylko tekst).
|
||
|
||
***
|
||
|
||
# Agentic AI: The Big Picture
|
||
|
||
**Adam Danyal** (2M+ followers)
|
||
|
||
***
|
||
|
||
## Warstwy / Ewolucja systemów AI (od lewej do prawej)
|
||
|
||
### **AI & ML**
|
||
|
||
* Natural Language Processing
|
||
* Reasoning & Problem Solving
|
||
* Supervised Learning
|
||
* Reinforcement Learning
|
||
* Unsupervised Learning
|
||
* Deep Belief Networks
|
||
|
||
**Opis:**
|
||
|
||
> Turn your data into decisions
|
||
|
||
***
|
||
|
||
### **Deep Learning**
|
||
|
||
* Transformers
|
||
* Large Language Models (LLMs)
|
||
* Attention Mechanisms
|
||
* Transfer Learning
|
||
* Multilayered neural networks for complex tasks
|
||
* Recurrent Networks & LSTMs
|
||
* Convolutional Neural Networks (CNNs)
|
||
|
||
**Opis:**
|
||
|
||
> Multi-layered neural networks for complex tasks
|
||
|
||
***
|
||
|
||
### **Gen AI**
|
||
|
||
* Prompt Engineering
|
||
* Retrieval-Augmented Generation (RAG)
|
||
* Hallucination Mitigation
|
||
* Tool Use & Function Calling
|
||
* Multimodal Generation (text + image + audio)
|
||
* Summarisation
|
||
* Speech Interfaces (TTS & ASRs)
|
||
* Audio/Music Generation
|
||
* Image Generation
|
||
* Video Generation
|
||
* Code Generation
|
||
* Output Validation
|
||
* Autonomous Execution
|
||
|
||
**Opis:**
|
||
|
||
> Generate content and code at scale
|
||
|
||
***
|
||
|
||
### **AI Agents**
|
||
|
||
* Tool Orchestration (actions/plugins)
|
||
* Planning (ReAct, CoT, ToT)
|
||
* Task Scheduling & Prioritisation
|
||
* Goal Decomposition
|
||
* State Persistence
|
||
* Multi-agent Collaboration
|
||
* Agent Communication & Personalisation
|
||
* Context Management
|
||
* Memory Systems (short-term & long-term)
|
||
* Human-in-the-loop Oversight
|
||
* Self-Reflection & Error Recovery
|
||
* Failure Recovery & Replanning
|
||
|
||
**Opis:**
|
||
|
||
> Execute complex tasks autonomously
|
||
|
||
***
|
||
|
||
### **Agentic AI**
|
||
|
||
* Intent Preservation
|
||
* Self-improving Agents
|
||
* Rollback Mechanisms
|
||
* Feedback Loops & Evaluators
|
||
* Cost & Resource Management
|
||
* Long-term Autonomy & Goal Chaining
|
||
* Governance, Safety & Guardrails
|
||
* Memory Governance & Retention Policies
|
||
* Observability & Tracing
|
||
* Delegation & Handoff Protocols
|
||
* Risk Management & Constraints
|
||
* Agent Marketplaces & Contracts
|
||
* Dynamic Tooling
|
||
|
||
**Opis:**
|
||
|
||
> Automate entire processes with AI
|
||
|
||
***
|
||
|
||
## Elementy przekrojowe (widoczne w tle / na obrzeżach)
|
||
|
||
* Agent Protocols
|
||
* Frameworks & Runtimes
|
||
|
||
***
|
||
|
||
### Stopka
|
||
|
||
> **Follow Adam Danyal on LinkedIn for posts on AI for business**
|
||
|
||
***
|
||
|
||
Jeśli chcesz:
|
||
|
||
* ✅ **przerobić to na Markdown / org-mode**
|
||
* ✅ **zmapować warstwy do praktycznego stacku (np. OpenClaw / LangChain / AutoGen)**
|
||
* ✅ **zrobić wersję „engineering vs business”**
|
||
* ✅ **zamienić to w checklistę „co jest wymagane, żeby wejść poziom wyżej”**
|
||
|
||
— powiedz, do czego tego potrzebujesz.
|
||
Jasne — ten błąd wynika z tego, że renderer (najczęściej wtyczka/host typu Kroki/Obsidian/Confluence itp.) ma ograniczenie: **`splitLineToFitWidth` nie obsługuje znaków nowej linii w tekście linii/etykiet**. Najpewniejsze rozwiązanie: **usunąć wszystkie newline’y i `<br/>` z etykiet**, trzymać opisy w jednej linii.
|
||
|
||
Poniżej masz **wersję “safe”**, bez łamań linii, bez HTML i z krótkimi labelami — powinna renderować się stabilnie praktycznie wszędzie:
|
||
|
||
flowchart TB
|
||
A["Mac Studio (remote)"] --> B1["Cursor SSH Remote"] --> C["MacBook Air (always-on)"]
|
||
A --> B2["Direct SSH Terminal"] --> C
|
||
A --> B3["TeamViewer (fallback)"] --> C
|
||
|
||
C --> ST["Live runtime stable"]
|
||
|
||
subgraph DEV["Development Flow"]
|
||
direction TB
|
||
D1["1 Work in isolated git worktree"]
|
||
D2["2 Targeted edits: skills prompts bugfixes"]
|
||
D3["3 Run validation: logs behavior"]
|
||
D4["4 Commit and sync: hourly or manual"]
|
||
D1 --> D2 --> D3 --> D4
|
||
end
|
||
|
||
C --> D1
|
||
|
||
subgraph OPS["Fast Ops (SSH Terminal)"]
|
||
direction LR
|
||
O1["Tail logs"]
|
||
O2["Query cron DB"]
|
||
O3["Restart services"]
|
||
O1 --> O2 --> O3
|
||
end
|
||
|
||
C --> O1
|
||
|
||
## Jeśli nadal sypie błędem (wariant ultra-minimalny)
|
||
|
||
Niektóre renderery mają problem nawet z `subgraph`. Wtedy użyj wersji bez `subgraph`:
|
||
|
||
flowchart TB
|
||
A["Mac Studio (remote)"] --> B1["Cursor SSH Remote"] --> C["MacBook Air (always-on)"]
|
||
A --> B2["Direct SSH Terminal"] --> C
|
||
A --> B3["TeamViewer (fallback)"] --> C
|
||
C --> ST["Live runtime stable"]
|
||
|
||
C --> D1["Dev 1 isolated git worktree"]
|
||
D1 --> D2["Dev 2 targeted edits"]
|
||
D2 --> D3["Dev 3 validation scripts"]
|
||
D3 --> D4["Dev 4 commit and sync"]
|
||
|
||
C --> O1["Ops tail logs"]
|
||
O1 --> O2["Ops query cron DB"]
|
||
O2 --> O3["Ops restart services"]
|
||
|
||
### Szybka zasada kompatybilności (na przyszłość)
|
||
|
||
Aby uniknąć tego typu problemów:
|
||
|
||
* **zero** `<br/>`
|
||
* **zero** znaków nowej linii w labelach
|
||
* krótkie etykiety (bez długich zdań)
|
||
* jeśli platforma marudzi: **bez `subgraph`**
|
||
|
||
Jeśli napiszesz, **gdzie to renderujesz** (GitHub / Obsidian / Confluence / Mermaid Live / MkDocs), dopasuję wersję “pod silnik” tak, żeby działała *na 100%* w Twoim środowisku.
|
||
Jasne — **ultra‑minimalna, „bullet‑proof” wersja Mermaid**.\
|
||
✅ Bez `subgraph`\
|
||
✅ Bez nowych linii w labelach\
|
||
✅ Bez HTML\
|
||
✅ Krótkie etykiety\
|
||
✅ Działa nawet w najbardziej restrykcyjnych rendererach
|
||
|
||
***
|
||
|
||
## 4) Remote work setup + Dev Flow + Fast Ops — **ULTRA‑MINIMAL**
|
||
|
||
flowchart TB
|
||
A["Mac Studio"] --> B1["Cursor SSH"] --> C["MacBook Air"]
|
||
A --> B2["Direct SSH"] --> C
|
||
A --> B3["TeamViewer"] --> C
|
||
|
||
C --> S["Runtime stable"]
|
||
|
||
C --> D1["Dev isolate"]
|
||
D1 --> D2["Dev edit"]
|
||
D2 --> D3["Dev validate"]
|
||
D3 --> D4["Dev commit"]
|
||
|
||
C --> O1["Ops logs"]
|
||
O1 --> O2["Ops cron"]
|
||
O2 --> O3["Ops restart"]
|
||
|
||
***
|
||
|
||
### Zasada na przyszłość (100% kompatybilności)
|
||
|
||
Jeśli Mermaid ma **działać wszędzie**:
|
||
|
||
* tylko **jedna linia tekstu** w węźle
|
||
* **brak** `subgraph`
|
||
* **brak** `<br>` / `\n`
|
||
* krótkie, semantyczne nazwy
|
||
|
||
Jeśli chcesz, mogę:
|
||
|
||
* scalić **wszystkie diagramy** do jednego pliku `.mermaid`
|
||
* zrobić warianty: **DOC / OPS / EXEC**
|
||
* wygenerować **org‑mode + Mermaid** pod Twoje repo
|
||
|
||
Daj znać ✅
|
||
|
||
Poniżej masz **wierną transkrypcję tekstu z obrazu** – zamiana grafiki **„50 AI TERMS EXPLAINED”** na tekst, z zachowaniem numeracji, tytułów i krótkich definicji.
|
||
|
||
***
|
||
|
||
# 50 AI TERMS EXPLAINED
|
||
|
||
***
|
||
|
||
### 1. AI (Artificial Intelligence)
|
||
|
||
The science of making computers think or behave like humans by using machine learning, reasoning, or language.
|
||
|
||
### 2. AI Agent
|
||
|
||
A program or robot that can sense its environment, make decisions, and act to achieve a goal, often with some independence.
|
||
|
||
### 3. AI Automation
|
||
|
||
Using AI to do repetitive tasks automatically, without human help, like sorting emails or managing schedules.
|
||
|
||
### 4. Generative AI
|
||
|
||
AI that can create new content, such as writing text, making images, or composing music, by learning from examples.
|
||
|
||
### 5. AI Image Generation
|
||
|
||
AI processes that create new pictures or artworks from text or other inputs.
|
||
|
||
***
|
||
|
||
### 6. AI Video Generation
|
||
|
||
AI makes new video clips or animations from text or existing visuals.
|
||
|
||
### 7. Artificial General Intelligence (AGI)
|
||
|
||
A hypothetical AI that can understand, learn, and think like a human in many areas.
|
||
|
||
### 8. AI Model
|
||
|
||
A trained system or program that uses data to make predictions or decisions.
|
||
|
||
### 9. GPT (Generative Pretrained Transformer)
|
||
|
||
A popular type of AI model designed to generate text or conversations when given input.
|
||
|
||
### 10. AI Assistant
|
||
|
||
An AI program that helps users perform tasks by understanding commands or questions.
|
||
|
||
***
|
||
|
||
### 11. AI Ethics
|
||
|
||
Ensuring AI is used responsibly, fairly, and without causing harm.
|
||
|
||
### 12. ASI (Artificial Superintelligence)
|
||
|
||
A theoretical AI that is smarter than humans in every possible way.
|
||
|
||
### 13. Algorithm
|
||
|
||
A set of step-by-step instructions that help a computer solve a problem or perform a task.
|
||
|
||
### 14. Training Data
|
||
|
||
The information (text, pictures, numbers) given to an AI to help it learn.
|
||
|
||
### 15. Machine Learning
|
||
|
||
A way for computers to learn from data without being explicitly programmed.
|
||
|
||
***
|
||
|
||
### 16. Deep Learning
|
||
|
||
A branch of machine learning that uses neural networks with many layers to process complex data like images or speech.
|
||
|
||
### 17. Neural Network
|
||
|
||
A computer system modeled after the human brain that learns patterns from data.
|
||
|
||
### 18. Supervised Learning
|
||
|
||
A machine learning method where the correct answers are provided during training.
|
||
|
||
### 19. Unsupervised Learning
|
||
|
||
AI finds patterns in data without being told the correct answers.
|
||
|
||
### 20. Reinforcement Learning
|
||
|
||
AI learns by trying actions and getting rewards or penalties.
|
||
|
||
***
|
||
|
||
### 21. Natural Language Processing (NLP)
|
||
|
||
AI techniques for understanding and generating human language.
|
||
|
||
### 22. Computer Vision
|
||
|
||
Teaching computers to “see” and understand images or videos.
|
||
|
||
### 23. Data Mining
|
||
|
||
Finding useful patterns, trends, or information automatically within large datasets.
|
||
|
||
### 24. Classification
|
||
|
||
A task where AI sorts data into categories, such as spam or not spam.
|
||
|
||
### 25. Regression
|
||
|
||
A method used to predict numbers (such as prices or temperatures).
|
||
|
||
***
|
||
|
||
### 26. Clustering
|
||
|
||
Grouping similar items together without knowing categories in advance.
|
||
|
||
### 27. Bias
|
||
|
||
When AI systems produce unfair results because of imbalanced or flawed training data.
|
||
|
||
### 28. Overfitting
|
||
|
||
When an AI model learns training data too well but performs poorly on new data.
|
||
|
||
### 29. Underfitting
|
||
|
||
When an AI model is too simple to capture the patterns in the data.
|
||
|
||
### 30. Inference
|
||
|
||
The process of an AI using what it has learned to make predictions or decisions.
|
||
|
||
***
|
||
|
||
### 31. Prompt
|
||
|
||
A question or instruction given to an AI system to get a response.
|
||
|
||
### 32. Token
|
||
|
||
A piece of text (word or part of a word) that AI models read and process.
|
||
|
||
### 33. OpenAI
|
||
|
||
An organization that researches and builds advanced AI models.
|
||
|
||
### 34. Fine-Tuning
|
||
|
||
Making small changes to a pre-trained model to adapt it to specific tasks.
|
||
|
||
### 35. Foundation Model
|
||
|
||
A very large AI model that can be adapted to many tasks.
|
||
|
||
***
|
||
|
||
### 36. Training Set
|
||
|
||
Data used to teach an AI model how to perform its task.
|
||
|
||
### 37. Transfer Learning
|
||
|
||
Reusing knowledge from one AI model or task to improve another.
|
||
|
||
### 38. Speech Recognition
|
||
|
||
AI converting spoken language into written text.
|
||
|
||
### 39. Speech Synthesis
|
||
|
||
AI creating spoken audio from written text.
|
||
|
||
### 40. Explainable AI (XAI)
|
||
|
||
Techniques that help humans understand how AI makes decisions.
|
||
|
||
***
|
||
|
||
### 41. Dataset
|
||
|
||
A collection of data used to train or test an AI system.
|
||
|
||
### 42. API (Application Programming Interface)
|
||
|
||
A way different applications communicate with AI services or software.
|
||
|
||
### 43. Confidence Score
|
||
|
||
A number that shows how sure an AI is about its prediction or decision.
|
||
|
||
### 44. Ethical AI
|
||
|
||
Using AI in ways that are fair, safe, and respectful of people’s rights.
|
||
|
||
### 45. Hyperparameter
|
||
|
||
Settings or choices made before training an AI model, such as learning rate.
|
||
|
||
***
|
||
|
||
### 46. Feedback Loop
|
||
|
||
When AI systems learn from user feedback to improve over time.
|
||
|
||
### 47. Label
|
||
|
||
A tag or name given to data so an AI knows what it represents.
|
||
|
||
### 48. Zero-shot Learning
|
||
|
||
When AI performs a task it was not specifically trained for.
|
||
|
||
### 49. Latent Space
|
||
|
||
A hidden internal space where AI represents features of the data it learns.
|
||
|
||
### 50. Model
|
||
|
||
A trained system that uses data to generate predictions or outputs.
|
||
|
||
***
|
||
|
||
**Stopka z grafiki:**
|
||
|
||
> *Subscribe to Mindstream to learn AI for free*
|
||
> **[www.mindstream.news](http://www.mindstream.news)**
|
||
|
||
***
|
||
|
||
Jeśli chcesz:
|
||
|
||
* ✅ tłumaczenie na **polski**
|
||
* ✅ **wersję skróconą (cheat‑sheet)**
|
||
* ✅ konwersję do **Markdown / org‑mode**
|
||
* ✅ mapowanie tych pojęć na **LLM / agentów / RAG / OpenClaw**
|
||
|
||
— daj znać, co dalej.
|
||
Poniżej masz **wierną transkrypcję tekstu z obrazu** – zamiana grafiki **„Top 6 Types of AI Models”** na **czysty tekst**, z zachowaniem struktury (sekcje: Description / Examples / Workflow).
|
||
|
||
***
|
||
|
||
# Top 6 Types of AI Models
|
||
|
||
***
|
||
|
||
## 1. Machine Learning Models
|
||
|
||
**Description:**
|
||
Machine learning models learn from labeled or unlabeled data to detect patterns, classify, or predict outcomes. They include supervised, unsupervised, and semi‑supervised approaches.
|
||
|
||
**Examples:**
|
||
|
||
* Supervised: Decision Trees, Random Forest, SVM, XGBoost
|
||
* Unsupervised: K‑Means, DBSCAN, PCA
|
||
* Semi‑Supervised ML
|
||
|
||
**Workflow:**
|
||
|
||
* Collect labeled data
|
||
* Clean and preprocess
|
||
* Select ML algorithm
|
||
* Train the model
|
||
* Validate performance
|
||
* Tune hyperparameters
|
||
* Predict on new data
|
||
* Monitor and update
|
||
|
||
***
|
||
|
||
## 2. Deep Learning Models
|
||
|
||
**Description:**
|
||
Deep learning models use multi‑layer neural networks to learn complex hierarchical patterns. They excel in handling unstructured data like images, audio, and text.
|
||
|
||
**Examples:**
|
||
|
||
* CNN (for images)
|
||
* RNN, LSTM (for sequences)
|
||
* Transformers
|
||
* GANs
|
||
* Autoencoders
|
||
|
||
**Workflow:**
|
||
|
||
* Collect data
|
||
* Normalize inputs
|
||
* Build neural network
|
||
* Forward pass
|
||
* Compute prediction error
|
||
* Backpropagate gradients
|
||
* Update weights
|
||
* Repeat training cycles
|
||
|
||
***
|
||
|
||
## 3. Generative Models
|
||
|
||
**Description:**
|
||
These models learn the data distribution and generate new data that mimics the original. They are widely used in content creation, synthesis, and text generation.
|
||
|
||
**Examples:**
|
||
|
||
* GPT‑4 (text)
|
||
* DALL‑E (image)
|
||
* MidJourney (images)
|
||
* MusicLM (audio)
|
||
* StyleGAN (faces)
|
||
* AlphaCode (code)
|
||
|
||
**Workflow:**
|
||
|
||
* Train on dataset
|
||
* Learn data distribution
|
||
* Receive user input
|
||
* Process through model
|
||
* Generate content
|
||
* Sample output
|
||
* Refine with feedback
|
||
|
||
***
|
||
|
||
## 4. Hybrid Models
|
||
|
||
**Description:**
|
||
Hybrid models combine multiple AI techniques (e.g., rule‑based + neural nets) to leverage the strengths of each. They are used where accuracy and control are both critical.
|
||
|
||
**Examples:**
|
||
|
||
* RAG (LLM + Search)
|
||
* ML + Rule‑based bots
|
||
* AutoGPT with tools
|
||
* AI chatbots with DBs
|
||
* RTS
|
||
* Ensemble Models
|
||
|
||
**Workflow:**
|
||
|
||
* Combine model types
|
||
* Train components
|
||
* Build logic bridging
|
||
* Aggregate outputs
|
||
* Resolve conflicts
|
||
* Deliver final result
|
||
* Route based on logic
|
||
|
||
***
|
||
|
||
## 5. NLP Models
|
||
|
||
**Description:**
|
||
NLP models process and understand human language. They power applications like chatbots, translators, and virtual assistants.
|
||
|
||
**Examples:**
|
||
|
||
* BERT
|
||
* GPT‑3.5 / GPT‑4
|
||
* T5
|
||
* RoBERTa
|
||
* Claude
|
||
|
||
**Workflow:**
|
||
|
||
* Clean raw text
|
||
* Tokenize sentences
|
||
* Embed text
|
||
* Apply attention layers
|
||
* Decode or classify
|
||
* Post‑process result
|
||
* Generate final text
|
||
* Pass to model
|
||
|
||
***
|
||
|
||
## 6. Computer Vision Models
|
||
|
||
**Description:**
|
||
These models interpret and understand visual content, detecting patterns and features in images or videos. They’re used in facial recognition to medical imaging.
|
||
|
||
**Examples:**
|
||
|
||
* ResNet
|
||
* YOLO
|
||
* EfficientNet
|
||
* VGG
|
||
* Mask R‑CNN
|
||
|
||
**Workflow:**
|
||
|
||
* Load image data
|
||
* Resize or normalize
|
||
* Extract pixel features
|
||
* Apply CNN layers
|
||
* Classify or locate
|
||
* Detect spatial patterns
|
||
* Post‑process results
|
||
* Output labels / boxes
|
||
|
||
***
|
||
|
||
Jeśli chcesz:
|
||
|
||
* ✅ **wersję po polsku**
|
||
* ✅ **ultra‑short cheat‑sheet (1 linia / model)**
|
||
* ✅ **mapowanie do LLM / Agents / RAG / OpenClaw**
|
||
* ✅ **diagramy Mermaid dla każdego typu**
|
||
|
||
— napisz, w jakiej formie to dalej używasz.
|
||
Poniżej masz **wierną zamianę obrazu na tekst** (OCR) – plakatu **“What Is AI Coding?”**, z zachowaniem struktury, nagłówków i treści. To **czysta transkrypcja**, bez interpretacji.
|
||
|
||
***
|
||
|
||
# What Is AI Coding?
|
||
|
||
**The simple guide non‑technical founders need in 2026.**
|
||
|
||
***
|
||
|
||
## Do you need to know how to code?
|
||
|
||
**Short answer:** No.
|
||
|
||
**Better answer:** It depends on the level of control you want.
|
||
|
||
* If you want to validate ideas quickly, you don’t need to understand the code.
|
||
* If you want to build and scale real products, basic technical understanding can give you an essential edge.
|
||
* If you want to replace whole chunks of engineering work, you’ll need proper structure.
|
||
|
||
> **AI coding lowers the barrier.**
|
||
> It doesn’t remove the need to think clearly.
|
||
|
||
***
|
||
|
||
## 1. Vibe Coding
|
||
|
||
**Level:** Non‑technical founders
|
||
|
||
### What it is:
|
||
|
||
Turning rough ideas into working prototypes by describing what you want in plain English and letting AI handle the code.
|
||
|
||
### Business use case:
|
||
|
||
* Validating startup ideas fast
|
||
* Building landing pages, MVPs, internal tools
|
||
* Testing demand before hiring engineers
|
||
* This is about speed over precision
|
||
|
||
### When it makes sense:
|
||
|
||
* Early‑stage ideas
|
||
* Solo founders
|
||
* Non‑technical teams
|
||
* Anything you’re not yet sure is worth investing in
|
||
|
||
**Tools shown:**
|
||
|
||
* Lovable – product prototypes from prompts
|
||
* Bolt – fast web app scaffolding
|
||
* Replit – instant deploy without setup
|
||
* Make – connect tools and workflows
|
||
|
||
***
|
||
|
||
## 2. AI‑Assisted Coding
|
||
|
||
**Level:** Technical or semi‑technical teams
|
||
|
||
### What it is:
|
||
|
||
AI working alongside human developers to speed up writing, debugging, and refactoring code.
|
||
|
||
### Business use case:
|
||
|
||
* Build production‑ready software faster
|
||
* Improve developer output without growing headcount
|
||
* Reduce bugs and repetitive work
|
||
* This is about building leverage
|
||
|
||
### When it makes sense:
|
||
|
||
* You already have developers
|
||
* You’re scaling a product
|
||
* Code quality and maintainability matter
|
||
|
||
**Tools shown:**
|
||
|
||
* Cursor – product‑focused coding IDE
|
||
* GitHub Copilot – inline code assistance
|
||
* Continue – open‑source AI coding assistant
|
||
* Google Fill‑In‑the‑Middle (code completion)
|
||
|
||
***
|
||
|
||
## 3. Agentic Coding
|
||
|
||
**Level:** Advanced team and operators
|
||
|
||
### What it is:
|
||
|
||
AI agents that can plan, write, test, and refactor code across entire workflows from a single objective.
|
||
|
||
### Business use case:
|
||
|
||
* Large feature builds
|
||
* Legacy code refactors
|
||
* Automating repetitive engineering tasks
|
||
* Spinning up internal systems fast
|
||
* This is about end‑to‑end execution instead of writing code
|
||
|
||
### When it makes sense:
|
||
|
||
* Complex products
|
||
* Experienced teams
|
||
* Clear specs and objectives
|
||
* Situations where speed compounds
|
||
|
||
**Tools shown:**
|
||
|
||
* Claude Code – agent‑driven development
|
||
* OpenAI Codex – autonomous coding tasks
|
||
* Devin – AI software engineer
|
||
* Gemini CLI – command‑line agent
|
||
|
||
***
|
||
|
||
### Footer (from image)
|
||
|
||
> Download this high‑res sheet from my newsletter
|
||
> Step by Step. Join 400,000 people
|
||
> Subscribe here: **chris‑donnelly.co.uk**
|
||
|
||
***
|
||
|
||
Jeśli chcesz:
|
||
|
||
* ✅ **skrócić to do ultra‑minimalnej wersji (1 ekran / 10 linijek)**
|
||
* ✅ **przetłumaczyć na polski (founder‑friendly)**
|
||
* ✅ **zmapować te 3 poziomy na realne stacki (LLM, agent, infra)**
|
||
* ✅ **zrobić z tego roadmapę „kiedy przejść level wyżej”**
|
||
|
||
— daj znać, co robimy dalej.
|
||
Poniżej masz **wierną transkrypcję tekstu z obrazu** – zamiana grafiki **„12 Agentic AI Use Cases for Real Businesses”** na tekst, z zachowaniem podziału na kategorie i opisów.
|
||
|
||
***
|
||
|
||
# 12 Agentic AI Use Cases
|
||
|
||
## FOR REAL BUSINESSES
|
||
|
||
*Greg Coquillo — Product Leader*
|
||
|
||
***
|
||
|
||
## 1. Customer Support Agents
|
||
|
||
**Flow (z obrazka):**
|
||
|
||
* User sends intent
|
||
* Intent detection
|
||
* Knowledge / RAG lookup
|
||
* Draft response
|
||
* Tool action (CRM / ticket)
|
||
* Human (optional)
|
||
|
||
**What they do:**
|
||
Handle FAQs, tickets, refunds, and account functions automatically.
|
||
|
||
***
|
||
|
||
## 2. Sales Ops Agents
|
||
|
||
**Flow:**
|
||
|
||
* Add lead
|
||
* Enrich via APIs
|
||
* Score lead
|
||
* Generate follow‑up
|
||
* Update CRM
|
||
* Notify sales rep
|
||
|
||
**What they do:**
|
||
Qualifies leads, updates pipelines, and prepares personalized outreach.
|
||
|
||
***
|
||
|
||
## 3. Marketing Automation Agents
|
||
|
||
**Flow:**
|
||
|
||
* Campaign idea
|
||
* Audience setup
|
||
* Content generation
|
||
* Schedule posts
|
||
* Track performance
|
||
* Optimize next run
|
||
|
||
**What they do:**
|
||
Plans, creates, publishes, and improves campaigns continuously.
|
||
|
||
***
|
||
|
||
## 4. Data Analysis Agents
|
||
|
||
**Flow:**
|
||
|
||
* Question
|
||
* SQL / warehouse query
|
||
* Clean data
|
||
* Analyze
|
||
* Generate summary
|
||
* Visualize results
|
||
|
||
**What they do:**
|
||
Turns business questions into charts, metrics, and explanations.
|
||
|
||
***
|
||
|
||
## 5. Reporting Agents
|
||
|
||
**Flow:**
|
||
|
||
* Pull metrics
|
||
* Validate data
|
||
* Generate report
|
||
* Add narrative
|
||
* Distribute to stakeholders
|
||
|
||
**What they do:**
|
||
Automates weekly/monthly dashboards with written insights.
|
||
|
||
***
|
||
|
||
## 6. QA / Testing Agents
|
||
|
||
**Flow:**
|
||
|
||
* New build
|
||
* Generate test cases
|
||
* Run tests
|
||
* Log bugs
|
||
* Detect failures
|
||
|
||
**What they do:**
|
||
Covers regression testing and basic debugging automatically.
|
||
|
||
***
|
||
|
||
## 7. DevOps Agents
|
||
|
||
**Flow:**
|
||
|
||
* Monitor infra
|
||
* Detect anomaly
|
||
* Run diagnostics
|
||
* Apply fix / rollback
|
||
* Notify team
|
||
|
||
**What they do:**
|
||
Watches systems, responds to incidents, and assists deployment.
|
||
|
||
***
|
||
|
||
## 8. Finance Ops Agents
|
||
|
||
**Flow:**
|
||
|
||
* Invoices / transactions
|
||
* Categorize
|
||
* Reconcile
|
||
* Flag anomalies
|
||
* Generate summaries
|
||
|
||
**What they do:**
|
||
Automates bookkeeping, expense tracking, and financial checks.
|
||
|
||
***
|
||
|
||
## 9. HR Ops Agents
|
||
|
||
**Flow:**
|
||
|
||
* Resume intake
|
||
* Screen candidates
|
||
* Schedule interviews
|
||
* Update HR systems
|
||
* Answer HR queries
|
||
|
||
**What they do:**
|
||
Manages hiring workflows and employee support.
|
||
|
||
***
|
||
|
||
## 10. Research Agents
|
||
|
||
**Flow:**
|
||
|
||
* Topic
|
||
* Web / document search
|
||
* Extract key points
|
||
* Compare sources
|
||
* Summarize findings
|
||
|
||
**What they do:**
|
||
Speeds up market, technical, and competitive research.
|
||
|
||
***
|
||
|
||
## 11. Content Creation Agents
|
||
|
||
**Flow:**
|
||
|
||
* Topic
|
||
* Outline
|
||
* Draft content
|
||
* Optimize for SEO / brand
|
||
* Publish
|
||
* Track engagement
|
||
|
||
**What they do:**
|
||
Creates blogs, posts, scripts, and marketing assets end‑to‑end.
|
||
|
||
***
|
||
|
||
## 12. Internal Tools Agents
|
||
|
||
**Flow:**
|
||
|
||
* Employee request
|
||
* Understand task
|
||
* Call internal APIs
|
||
* Execute action
|
||
* Confirm result
|
||
|
||
**What they do:**
|
||
Acts as a company “copilot” for everyday internal operations.
|
||
|
||
***
|
||
|
||
Jeśli chcesz:
|
||
|
||
* ✅ **zmapować te use‑case’y na konkretne stacki (LLM, RAG, tools, infra)**
|
||
* ✅ **wybrać 3–4 najtańsze do wdrożenia jako MVP**
|
||
* ✅ **rozpisać jeden use case jako pełny flow (Mermaid / runbook)**
|
||
* ✅ **przetłumaczyć i skrócić do decka dla biznesu**
|
||
|
||
— napisz, co robimy dalej.
|
||
Poniżej masz **wierną zamianę obrazu na tekst (OCR)** – plakat **„The 5 Layers of Modern AI Systems”**, z zachowaniem struktury, nagłówków i punktów.
|
||
|
||
***
|
||
|
||
# The 5 Layers of Modern AI Systems
|
||
|
||
**Denis Paniuta**
|
||
@denis.paniuta
|
||
|
||
***
|
||
|
||
## 1. Generative AI
|
||
|
||
**Content + Communication**
|
||
|
||
**Used when your business needs**
|
||
Language, creativity, or explanation.
|
||
|
||
### Typical Use Cases
|
||
|
||
* Marketing content and LinkedIn posts
|
||
* Product copy descriptions
|
||
* Customer replies
|
||
* Email drafts and proposals
|
||
* Knowledge base articles
|
||
* Presentation documentation
|
||
|
||
### Examples
|
||
|
||
* ChatGPT
|
||
* Claude
|
||
* Gemini
|
||
* Notion AI
|
||
|
||
### Business Problems It Solves
|
||
|
||
* Slow content production
|
||
* Founder bottlenecks in communication
|
||
* Inconsistent messaging
|
||
* High content creation costs
|
||
|
||
***
|
||
|
||
## 2. Machine Learning
|
||
|
||
**Predictions + Forecasting**
|
||
|
||
**Used when your business needs**
|
||
Numbers, patterns, and future insights.
|
||
|
||
### Typical Use Cases
|
||
|
||
* Revenue forecasting
|
||
* Demand planning
|
||
* Lead scoring
|
||
* Customer churn prediction
|
||
* Pricing optimization
|
||
* Fraud detection
|
||
|
||
### Examples
|
||
|
||
* AutoML tools
|
||
* Forecasting models
|
||
* CRM prediction engines
|
||
|
||
### Business Problems It Solves
|
||
|
||
* Uncertain growth planning
|
||
* Reactive decision‑making
|
||
* Poor demand estimation
|
||
* Manual analytics
|
||
|
||
***
|
||
|
||
## 3. Neural Networks
|
||
|
||
**Used when your business needs**
|
||
Understand images, voice, or audio.
|
||
|
||
### Typical Use Cases
|
||
|
||
* Call transcription
|
||
* Voice assistants
|
||
* Image recognition
|
||
* Document scanning
|
||
* Video analysis
|
||
* Quality inspection
|
||
|
||
### Examples
|
||
|
||
* Speech‑to‑text systems
|
||
* OCR tools
|
||
* Computer vision platforms
|
||
|
||
### Business Problems It Solves
|
||
|
||
* Manual data extraction
|
||
* Unstructured processing
|
||
* Heavy review tasks
|
||
* Error‑prone handling
|
||
|
||
***
|
||
|
||
## 4. AI Agents
|
||
|
||
**Task Execution**
|
||
|
||
**Used when your business needs**
|
||
Actions, not just answers.
|
||
|
||
### Typical Use Cases
|
||
|
||
* CRM updates
|
||
* Ticket handling
|
||
* Report generation
|
||
* Email follow‑up
|
||
* Lead routing
|
||
* Task‑to‑tool workflows
|
||
|
||
### Examples
|
||
|
||
* N8n
|
||
* Make
|
||
* Zapier
|
||
* Custom agents
|
||
|
||
### Business Problems It Solves
|
||
|
||
* Repetitive operational work
|
||
* Missed follow‑ups
|
||
* Tool switching fatigue
|
||
* Manual coordination
|
||
|
||
***
|
||
|
||
## 5. Agentic AI
|
||
|
||
**End‑to‑End Automation**
|
||
|
||
**Used when your business needs**
|
||
Autonomous execution across multiple steps.
|
||
|
||
### Typical Use Cases
|
||
|
||
* Autonomous sales pipelines
|
||
* Hiring workflows
|
||
* Product feedback loops
|
||
* Business process orchestration
|
||
|
||
### Business Problems It Solves
|
||
|
||
* Fragmented operations
|
||
* Operational overhead
|
||
* Scaling bottlenecks
|
||
* Loss of system ownership
|
||
|
||
### Capabilities
|
||
|
||
* Planning
|
||
* Decision‑making
|
||
* Tool usage
|
||
* Memory retention
|
||
* Continuous improvement
|
||
|
||
***
|
||
|
||
## Summary Line (z grafiki)
|
||
|
||
**This combines GenAI + ML + Agents + Memory**
|
||
|
||
***
|
||
|
||
Jeśli chcesz:
|
||
|
||
* ✅ **wersję ultra‑minimalną (1 ekran)**
|
||
* ✅ **przerobienie na Mermaid (architektura warstw)**
|
||
* ✅ **mapowanie tych warstw do realnego stacku (LLM, RAG, Agents, infra)**
|
||
* ✅ **tłumaczenie PL z komentarzem biznesowym**
|
||
|
||
— napisz, co robimy dalej.
|
||
Poniżej masz **wierną zamianę obrazu na tekst (OCR)** – infografiki **„Types of Databases”**, z zachowaniem struktury, nagłówków i punktów.
|
||
|
||
***
|
||
|
||
# Types of Databases
|
||
|
||
***
|
||
|
||
## SQL
|
||
|
||
### Relational / SQL Database
|
||
|
||
**Key Features:**
|
||
|
||
* Indexing & Optimization
|
||
* Security Features
|
||
* Relationship & Referential Integrity
|
||
* Structured Data
|
||
* Transactions & ACID
|
||
* SQL Support
|
||
|
||
***
|
||
|
||
## NoSQL
|
||
|
||
### NoSQL Database
|
||
|
||
**Core Properties:**
|
||
|
||
* Horizontal Scaling
|
||
* High Availability
|
||
* Distributed Architecture
|
||
|
||
***
|
||
|
||
## Object-Oriented Database
|
||
|
||
* Complex Querying & Navigation
|
||
* Complex Data Models
|
||
* Object Persistence
|
||
* Encapsulation & Data Abstraction
|
||
* Object Versioning
|
||
* Inheritance & Polymorphism
|
||
|
||
***
|
||
|
||
## Columnar Database
|
||
|
||
* Schema Evolution
|
||
* Column‑level Compression
|
||
* Column‑wise Indexing
|
||
* Analytical Query Performance
|
||
|
||
***
|
||
|
||
## Document Database
|
||
|
||
* Efficient Query Performance
|
||
* Document Versioning
|
||
* Flexible Schema
|
||
|
||
***
|
||
|
||
## Key‑Value Database
|
||
|
||
* Data Partitioning
|
||
* Simple Data Model
|
||
* High‑Write & Query Performance
|
||
* Developer Friendly
|
||
|
||
***
|
||
|
||
## Spatial Database
|
||
|
||
* Spatial Types & Indexing
|
||
* Topology & Network Analysis
|
||
* Column‑wise Indexing
|
||
* Integration with GIS
|
||
|
||
***
|
||
|
||
## NewSQL Database
|
||
|
||
* Transactions & ACID
|
||
* SQL Support
|
||
|
||
***
|
||
|
||
## Graph Database
|
||
|
||
* Relationship Focus
|
||
* Deep Insight
|
||
|
||
***
|
||
|
||
## Time‑Series Database
|
||
|
||
* Retention Policies
|
||
* Efficient Storage
|
||
* Time‑Window Aggregations
|
||
* High‑Write & Query Performance
|
||
|
||
***
|
||
|
||
**Footer (z grafiki):**
|
||
|
||
* Discover more
|
||
* GenAI.works
|
||
|
||
***
|
||
|
||
Jeśli chcesz:
|
||
|
||
* ✅ **wersję skróconą (cheat‑sheet 1 linia / typ)**
|
||
* ✅ **tłumaczenie na polski**
|
||
* ✅ **porównanie SQL vs NoSQL vs NewSQL**
|
||
* ✅ **mapowanie baz do use‑case’ów (OLTP, OLAP, RAG, agent memory)**
|
||
|
||
— daj znać 👍
|
||
Poniżej masz **zamianę OBU obrazków na tekst (wierna transkrypcja)**.
|
||
Zachowałem **nagłówki, strukturę, kategorie i opisy**, bez interpretacji.
|
||
|
||
***
|
||
|
||
# ✅ OBRAZEK 1
|
||
|
||
## **Agentic AI Periodic Table**
|
||
|
||
**Legenda kategorii (kolory na grafice):**
|
||
|
||
* AI Terminology
|
||
* Agent Patterns
|
||
* Vendor Tools
|
||
* Capabilities & Skills
|
||
* Evaluation & Safety
|
||
* Business & Use Cases
|
||
* Memory & Context Handling
|
||
* Agent Collaboration & Networking
|
||
* Deployment & Scaling
|
||
|
||
***
|
||
|
||
## AI Terminology / Core Concepts
|
||
|
||
* **AAI** – Agentic AI systems with autonomy, reasoning, and goal‑driven behavior
|
||
* **AGI** – Artificial General Intelligence (AI with human‑level intelligence)
|
||
* **ASI** – Artificial Superintelligence (beyond human intelligence)
|
||
* **LLM** – Large Language Models (text‑based intelligence)
|
||
* **ML** – Machine Learning (learning models from data)
|
||
* **RL** – Reinforcement Learning (learning from reward feedback)
|
||
|
||
***
|
||
|
||
## Agent Patterns
|
||
|
||
* **PLAN** – Planning, breaking problems into steps
|
||
* **MAS** – Multi‑Agent Systems (multiple agents collaborating)
|
||
* **IDEA** – Idea generation and brainstorming
|
||
* **TEST** – Testing and validation of outputs
|
||
* **SHOP** – Shopping / purchase decision workflows
|
||
* **TC** – Tool Calling (invoking external services)
|
||
* **RAG** – Retrieval Augmented Generation
|
||
* **MEM** – Memory systems (long‑term context)
|
||
|
||
***
|
||
|
||
## Vendor / Platform Tools
|
||
|
||
* **AWS** – Cloud services for deploying agents
|
||
* **HuggingFace** – Open‑source hub for AI models
|
||
* **Grok** – Conversational AI by xAI
|
||
* **Gemini** – Google’s multimodal AI agent
|
||
* **IBM ACP** – Agent communication & governance
|
||
* **Anthropic (Claude)** – Advanced conversational AI
|
||
* **OpenAI** – GPT models and deployment tools
|
||
* **Azure (AZR)** – Microsoft cloud AI platform
|
||
* **GCP** – Google Cloud AI services
|
||
|
||
***
|
||
|
||
## Capabilities & Skills
|
||
|
||
* **VOICE** – Voice interaction & speech systems
|
||
* **ACC** – Accuracy checking & validation
|
||
* **DATA** – Data analysis and insights
|
||
* **SAFE** – Safety guardrails & alignment
|
||
* **MKT** – Marketing and campaign automation
|
||
* **STORE** – Structured data storage
|
||
* **SUM** – Summarization and condensation
|
||
* **CHAT** – Conversational interaction
|
||
* **REM** – Reminder and alerting
|
||
* **HUMAN** – Human‑in‑the‑loop
|
||
* **CAL** – Calendar and scheduling
|
||
* **DOC** – Document reading and writing
|
||
|
||
***
|
||
|
||
## Memory & Context Handling
|
||
|
||
* **LTM** – Long‑term memory storage
|
||
* **STM** – Short‑term context memory
|
||
* **CTX** – Context awareness and adaptation
|
||
* **RAG** – External memory via knowledge bases
|
||
* **DIARY** – Memory persistence and logs
|
||
* **NOTE** – Temporary notes & memory snapshots
|
||
|
||
***
|
||
|
||
## Business & Use Cases
|
||
|
||
* **EDU** – Education (tutors, training bots)
|
||
* **HR** – HR assistants and hiring workflows
|
||
* **MKT** – Marketing workflows
|
||
* **SUP** – Support automation
|
||
* **TASK** – Task planning and delegation
|
||
* **LEGAL** – Legal analysis and document review
|
||
* **CREW** – Multi‑agent team coordination
|
||
|
||
***
|
||
|
||
## Collaboration, Networking & Ops
|
||
|
||
* **A2A** – Agent‑to‑Agent communication
|
||
* **NET** – Agent networking coordination
|
||
* **MON** – Monitoring behaviors
|
||
* **HIST** – Interaction history logs
|
||
* **FEED** – Continuous feedback loops
|
||
* **ACP** – Agent Communication Protocol
|
||
|
||
***
|
||
|
||
## Deployment & Scaling
|
||
|
||
* **CLOUD** – Cloud‑hosted agents
|
||
* **LOCAL** – Local agents on user hardware
|
||
* **UPD** – Update & versioning
|
||
* **HELP** – Human‑AI hybrid collaboration
|
||
|
||
***
|
||
|
||
**Footer:**
|
||
Discover more: **GenAI.works**
|
||
|
||
***
|
||
|
||
# ✅ OBRAZEK 2
|
||
|
||
## **Types of AI Agents (1–9)**
|
||
|
||
***
|
||
|
||
## 1. Simple Reflex Agents
|
||
|
||
**How they work:**
|
||
|
||
* Act based on current percepts only
|
||
* Use condition‑action rules (IF‑THEN)
|
||
* No memory or learning
|
||
|
||
**Example:**
|
||
Spam filter that blocks emails if keywords appear.
|
||
|
||
***
|
||
|
||
## 2. Learning Agents
|
||
|
||
**How they work:**
|
||
|
||
* Act based on current percepts using condition‑action rules
|
||
* Learn from experience over time
|
||
|
||
**Example:**
|
||
Netflix recommendation system improving suggestions based on user behavior.
|
||
|
||
***
|
||
|
||
## 3. Agentic AI Systems (Modern LLM‑Based Agents)
|
||
|
||
**How they work:**
|
||
|
||
* Built with large language models
|
||
* Use tools, memory, and planning to autonomously complete tasks
|
||
* Can reason, decompose tasks, call APIs
|
||
|
||
**Example:**
|
||
AI coding assistant that reads requirements, writes code, tests it, and fixes errors using tools.
|
||
|
||
***
|
||
|
||
## 4. Goal‑Based Agents
|
||
|
||
**How they work:**
|
||
|
||
* Act to achieve specific goals
|
||
* Use condition‑action rules
|
||
* No memory or learning
|
||
|
||
**Example:**
|
||
Thermostat that turns on heating if temperature < 20°C.
|
||
|
||
***
|
||
|
||
## 5. Cognitive / Conversational Agents
|
||
|
||
**How they work:**
|
||
|
||
* Designed to interact via natural language
|
||
* May simulate human‑like reasoning
|
||
|
||
**Example:**
|
||
AI customer support assistant handling queries and retrieving account data conversationally.
|
||
|
||
***
|
||
|
||
## 6. Multi‑Agent Systems (MAS)
|
||
|
||
**How they work:**
|
||
|
||
* Multiple agents cooperate or compete
|
||
* Handle partial observability
|
||
* Maintain shared or local state
|
||
|
||
**Example:**
|
||
Autonomous warehouse robots coordinating movement to avoid collisions.
|
||
|
||
***
|
||
|
||
## 7. Utility‑Based Agents
|
||
|
||
**How they work:**
|
||
|
||
* Use internal utility functions to choose best action
|
||
* Handle partial observability
|
||
* Optimize outcomes
|
||
|
||
**Example:**
|
||
Google Maps choosing optimal route by weighing distance, traffic, and time.
|
||
|
||
***
|
||
|
||
## 8. Embodied Agents
|
||
|
||
**How they work:**
|
||
|
||
* Exist in physical environments (robots, drones)
|
||
* Interact via sensors and actuators
|
||
|
||
**Example:**
|
||
Autonomous delivery robots navigating sidewalks using cameras and sensors.
|
||
|
||
***
|
||
|
||
## 9. Model‑Based Reflex Agents
|
||
|
||
**How they work:**
|
||
|
||
* Maintain internal model of the environment
|
||
* Handle partial observability
|
||
* Track environment state
|
||
|
||
**Example:**
|
||
Roomba vacuum tracking which areas of a room have already been cleaned.
|
||
|
||
***
|
||
|
||
Jeśli chcesz:
|
||
|
||
* ✅ **wersję PL**
|
||
* ✅ **połączenie obu grafik w jedną mapę pojęć**
|
||
* ✅ **wersję ultra‑minimalną (executive cheatsheet)**
|
||
* ✅ **przepisanie do Mermaid / org‑mode**
|
||
|
||
— daj znać 👍
|
||
Pewnie — oto **mapa pojęć w Mermaid** (koncept-map), która łączy oba obrazki: **„Agentic AI Periodic Table”** + **„Types of AI Agents (1–9)”**.\
|
||
Zrobiłem ją w **flowchart** (najbardziej kompatybilny wariant) i **bez łamań linii w etykietach**, żeby nie wywoływać błędów rendererów.
|
||
|
||
flowchart TB
|
||
%% Core concept
|
||
AAI["Agentic AI"]
|
||
subgraph T["Terminology"]
|
||
AI["AI"]
|
||
ML["ML"]
|
||
DL["Deep Learning"]
|
||
LLM["LLM"]
|
||
AGI["AGI"]
|
||
ASI["ASI"]
|
||
end
|
||
|
||
subgraph P["Agent patterns"]
|
||
PLAN["Planning"]
|
||
TC["Tool Calling"]
|
||
RAG["RAG"]
|
||
MEM["Memory"]
|
||
TEST["Testing"]
|
||
IDEA["Idea Generation"]
|
||
MAS["Multi-Agent Systems"]
|
||
SHOP["Shopping Workflow"]
|
||
end
|
||
|
||
subgraph C["Capabilities and skills"]
|
||
CHAT["Chat"]
|
||
VOICE["Voice"]
|
||
DATA["Data Analysis"]
|
||
SUM["Summarization"]
|
||
DOC["Documents"]
|
||
CAL["Calendar"]
|
||
REM["Reminders"]
|
||
ACC["Accuracy Checks"]
|
||
SAFE["Safety Guardrails"]
|
||
STORE["Storage"]
|
||
HUMAN["Human in the loop"]
|
||
end
|
||
|
||
subgraph M["Memory and context handling"]
|
||
STM["Short-term Memory"]
|
||
LTM["Long-term Memory"]
|
||
CTX["Context Adaptation"]
|
||
DIARY["Diary Logs"]
|
||
NOTE["Notes"]
|
||
end
|
||
|
||
subgraph N["Collaboration and networking"]
|
||
A2A["Agent-to-Agent"]
|
||
NET["Networking"]
|
||
FEED["Feedback Loops"]
|
||
HIST["History Logs"]
|
||
ACP["Agent Communication Protocol"]
|
||
end
|
||
|
||
subgraph D["Deployment and scaling"]
|
||
CLOUD["Cloud"]
|
||
LOCAL["Local"]
|
||
UPD["Updates"]
|
||
MON["Monitoring"]
|
||
COST["Cost and Resource Mgmt"]
|
||
end
|
||
|
||
subgraph U["Business and use cases"]
|
||
SUP["Support"]
|
||
HR["HR"]
|
||
MKT["Marketing"]
|
||
LEGAL["Legal"]
|
||
TASK["Task Delegation"]
|
||
CREW["Crew Coordination"]
|
||
EDU["Education"]
|
||
OPS["Ops and Internal Tools"]
|
||
end
|
||
|
||
%% Vendor tools (examples)
|
||
subgraph V["Tools and platforms (examples)"]
|
||
AWS["AWS"]
|
||
AZR["Azure"]
|
||
GCP["GCP"]
|
||
OA["OpenAI"]
|
||
ANTH["Anthropic"]
|
||
HF["HuggingFace"]
|
||
GEM["Gemini"]
|
||
GROK["Grok"]
|
||
end
|
||
|
||
%% Agent types (1-9)
|
||
subgraph AT["Types of AI Agents (1-9)"]
|
||
R1["1 Simple Reflex"]
|
||
R2["2 Learning Agent"]
|
||
R3["3 LLM-based Agentic System"]
|
||
R4["4 Goal-based"]
|
||
R5["5 Conversational"]
|
||
R6["6 Multi-agent System"]
|
||
R7["7 Utility-based"]
|
||
R8["8 Embodied"]
|
||
R9["9 Model-based Reflex"]
|
||
end
|
||
|
||
%% High-level relationships
|
||
AAI --> T
|
||
AAI --> P
|
||
AAI --> C
|
||
AAI --> M
|
||
AAI --> N
|
||
AAI --> D
|
||
AAI --> U
|
||
AAI --> V
|
||
AAI --> AT
|
||
|
||
%% Concept dependencies
|
||
DL --> LLM
|
||
ML --> DL
|
||
AI --> ML
|
||
|
||
%% Patterns -> capabilities
|
||
PLAN --> TASK
|
||
TC --> OPS
|
||
RAG --> DOC
|
||
RAG --> STORE
|
||
TEST --> ACC
|
||
SAFE --> LEGAL
|
||
MEM --> LTM
|
||
MEM --> STM
|
||
CTX --> CHAT
|
||
FEED --> MON
|
||
HIST --> DIARY
|
||
NOTE --> LTM
|
||
|
||
%% Agent types -> patterns/capabilities
|
||
R1 --> TC
|
||
R1 --> CHAT
|
||
|
||
R2 --> FEED
|
||
R2 --> HIST
|
||
|
||
R3 --> PLAN
|
||
R3 --> TC
|
||
R3 --> RAG
|
||
R3 --> MEM
|
||
R3 --> TEST
|
||
|
||
R4 --> PLAN
|
||
R4 --> TASK
|
||
|
||
R5 --> CHAT
|
||
R5 --> CTX
|
||
R5 --> HUMAN
|
||
|
||
R6 --> MAS
|
||
R6 --> A2A
|
||
R6 --> NET
|
||
R6 --> ACP
|
||
R6 --> CREW
|
||
|
||
R7 --> PLAN
|
||
R7 --> DATA
|
||
R7 --> COST
|
||
|
||
R8 --> VOICE
|
||
R8 --> MON
|
||
|
||
R9 --> CTX
|
||
R9 --> MEM
|
||
R9 --> HIST
|
||
|
||
%% Deployment/tooling links
|
||
CLOUD --> AWS
|
||
CLOUD --> AZR
|
||
CLOUD --> GCP
|
||
LLM --> OA
|
||
LLM --> ANTH
|
||
LLM --> GEM
|
||
LLM --> GROK
|
||
STORE --> HF
|
||
|
||
### Jeśli chcesz wersję **ultra-minimalną** (jeszcze bardziej “bullet-proof” bez `subgraph`)
|
||
|
||
Napisz tylko: **„ultra-min”** — wygeneruję wariant jednopoziomowy, który przechodzi nawet przez najbardziej restrykcyjne renderery (Confluence/Obsidian itp.).
|
||
|
||
|
||
Świetnie — poniżej masz **wersję mapy pojęć „pod OpenClaw z Tailscale”**, w **ULTRA‑KOMPATYBILNYM Mermaid**.
|
||
|
||
Założenia:
|
||
|
||
* ✅ Tailscale jako **jedyna warstwa sieciowa** (Zero Trust, private mesh)
|
||
* ✅ Agent runtime działa **na jednym lub kilku nodach**
|
||
* ✅ Kanały, usługi, bazy **NIE są publiczne**
|
||
* ✅ Diagram odporny na błędy renderera (bez `subgraph`, bez newline)
|
||
|
||
***
|
||
|
||
## OpenClaw + Tailscale — ULTRA‑MINIMAL RUNTIME MAP
|
||
|
||
flowchart TB
|
||
USER["User Devices"] --> TS["Tailscale Mesh"]
|
||
TS --> GW["Gateway"]
|
||
GW --> ORCH["Orchestrator"]
|
||
|
||
ORCH --> AG["Agent"]
|
||
AG --> PLAN["Planner"]
|
||
AG --> EXEC["Executor"]
|
||
|
||
EXEC --> SK["Skills"]
|
||
EXEC --> TOOLS["Tools"]
|
||
|
||
TOOLS --> FS["Files"]
|
||
TOOLS --> DB["Databases"]
|
||
TOOLS --> API["Internal APIs"]
|
||
|
||
ORCH --> MEM["Memory"]
|
||
MEM --> STM["Short"]
|
||
MEM --> LTM["Long"]
|
||
MEM --> VEC["Vector"]
|
||
|
||
ORCH --> SCHED["Scheduler"]
|
||
ORCH --> OBS["Logs"]
|
||
|
||
ORCH --> WS["Workspace"]
|
||
WS --> CFG["Config"]
|
||
|
||
***
|
||
|
||
## Jak to czytać (Tailscale‑first)
|
||
|
||
### Tailscale Mesh
|
||
|
||
* **User Devices** = Twoje laptopy, terminale, CI, jump‑hosty
|
||
* **WSZYSTKO idzie przez Tailscale**
|
||
* brak publicznych IP
|
||
* brak exposed portów
|
||
* ACL zamiast firewalli
|
||
|
||
### Gateway
|
||
|
||
* Jedyny punkt wejścia
|
||
* Sprawdza:
|
||
* czy request przyszedł z Tailscale
|
||
* tożsamość noda / usera
|
||
* Brak auth = brak ruchu
|
||
|
||
### Orchestrator (serce OpenClaw)
|
||
|
||
* Routing
|
||
* State machine
|
||
* Error handling
|
||
* Cost / limits
|
||
* Retry / rollback
|
||
|
||
### Agent Loop
|
||
|
||
* **Planner** → rozbijanie celu
|
||
* **Executor** → wykonywanie kroków
|
||
* **Skills** → Twoje deterministyczne procedury
|
||
* **Tools** → shell, DB, API, web, FS
|
||
|
||
### Memory
|
||
|
||
* **STM** – kontekst bieżący
|
||
* **VEC** – RAG / wiedza
|
||
* **LTM** – preferencje, learnings
|
||
|
||
### Workspace
|
||
|
||
* `.openclaw/`
|
||
* manifesty
|
||
* cache
|
||
* artefakty
|
||
* logika deterministyczna
|
||
|
||
***
|
||
|
||
## Typowa topologia Tailscale (mental model)
|
||
|
||
Laptop (you)
|
||
|
|
||
Tailscale
|
||
|
|
||
Agent Host (Mac Mini / VPS / NUC)
|
||
|
|
||
Databases + Files + APIs
|
||
|
||
Nie istnieje internet publiczny w tym modelu.\
|
||
Agent **myśli i działa w prywatnej sieci**.
|
||
|
||
***
|
||
|
||
## Dlaczego Tailscale + OpenClaw to „czysty” model
|
||
|
||
* 🔒 Zero Trust by default
|
||
* 🧠 Agent widzi *tylko* to, co jest w mesh
|
||
* 🧩 Determinizm operacyjny
|
||
* 🔁 Idealne pod:
|
||
* backupy
|
||
* cron‑joby
|
||
* DBA / DevOps agents
|
||
* personal infra
|
||
|
||
***
|
||
|
||
## Chcesz kolejny krok?
|
||
|
||
Mogę Ci od razu wygenerować:
|
||
|
||
* ✅ **schemat ACL Tailscale (kto może gadać z agentem)**
|
||
* ✅ **layout katalogów `.openclaw/` pod ten diagram**
|
||
* ✅ **variant z dual‑node (active / standby)**
|
||
* ✅ **flow „Cron Agent przez Tailscale”**
|
||
|
||
Napisz tylko: **ACL**, **folders**, albo **cron flow**.
|
||
Pewnie — zrobię wariant mapy pojęć **pod OpenClaw / agent runtime** (czyli: runtime + orkiestracja + skills + narzędzia + pamięć + kanały + obserwowalność + deploy). Poniżej masz **dwa poziomy**:
|
||
|
||
1. **Ultra‑minimal** (bullet‑proof, 1 ekran)
|
||
2. **Minimal‑PROD** (nadal kompatybilny, ale pokazuje realne klocki runtime)
|
||
|
||
> Oba warianty: ✅ bez `subgraph`, ✅ bez nowych linii w labelach, ✅ bez HTML, ✅ krótkie etykiety.
|
||
|
||
***
|
||
|
||
## 1) OpenClaw / Agent Runtime — ULTRA‑MINIMAL
|
||
|
||
flowchart TB
|
||
CH["Channels"] --> GW["Gateway"]
|
||
GW --> ORCH["Orchestrator"]
|
||
ORCH --> PLAN["Planner"]
|
||
ORCH --> EXEC["Executor"]
|
||
EXEC --> SK["Skills"]
|
||
EXEC --> TOOLS["Tools"]
|
||
ORCH --> MEM["Memory"]
|
||
MEM --> STM["Short"]
|
||
MEM --> LTM["Long"]
|
||
ORCH --> OBS["Observability"]
|
||
ORCH --> SCHED["Scheduler"]
|
||
ORCH --> STORE["Workspace"]
|
||
STORE --> CFG["Config"]
|
||
|
||
### Co tu jest “OpenClaw‑specific”
|
||
|
||
* **Gateway** = wejście z kanałów (CLI/Web/Telegram/Slack) + normalizacja wiadomości
|
||
* **Orchestrator** = router (kto/co/po co), kontrola stanu i błędów
|
||
* **Skills** = Twoje workflow “auto‑invoked” / wywoływane jawnie
|
||
* **Workspace + Config** = `.openclaw/` (persistent) + ustawienia runtime
|
||
|
||
***
|
||
|
||
## 2) OpenClaw / Agent Runtime — MINIMAL‑PROD (bardziej “realny”)
|
||
|
||
flowchart TB
|
||
CH["Channels"] --> GW["Gateway"]
|
||
GW --> AUTH["Auth"]
|
||
AUTH --> ORCH["Orchestrator"]
|
||
|
||
ORCH --> POL["Policy"]
|
||
ORCH --> ROUTE["Router"]
|
||
ROUTE --> AG["Agent"]
|
||
AG --> PLAN["Plan"]
|
||
AG --> ACT["Act"]
|
||
|
||
ACT --> SK["Skills"]
|
||
ACT --> TOOL["ToolRunner"]
|
||
|
||
TOOL --> EXT["APIs"]
|
||
TOOL --> FS["Files"]
|
||
TOOL --> DB["DB"]
|
||
TOOL --> WEB["Web"]
|
||
|
||
ORCH --> MEM["Memory"]
|
||
MEM --> STM["STM"]
|
||
MEM --> VEC["Vector"]
|
||
MEM --> SQL["SQL"]
|
||
MEM --> LTM["LTM"]
|
||
|
||
ORCH --> EVAL["Evals"]
|
||
ORCH --> OBS["LogsTraces"]
|
||
ORCH --> RETRY["RetryRollback"]
|
||
|
||
ORCH --> SCHED["Cron"]
|
||
SCHED --> JOB["Jobs"]
|
||
|
||
ORCH --> STATE["State"]
|
||
STATE --> WS["Workspace"]
|
||
WS --> CFG[".openclaw config"]
|
||
|
||
### Jak czytać ten diagram (w 30 sekund)
|
||
|
||
* **CH → GW → AUTH**: kanały wejściowe + uwierzytelnienie (VPN/Tailscale, tokeny)
|
||
* **ORCH**: serce runtime — trzyma stan, robi routing, zbiera telemetrię
|
||
* **POL**: guardrails (co wolno agentowi, limity, allowlist narzędzi)
|
||
* **ROUTE → AG**: wybór agenta/persony (np. code-reviewer, ops-runner)
|
||
* **PLAN/ACT**: pętla agentowa (plan → akcje → obserwacja → korekta)
|
||
* **SK + ToolRunner**: Skills to Twoje procedury; ToolRunner to “wykonawca” narzędzi
|
||
* **MEM**: pamięć robocza + wektory (RAG) + SQL (fakty/metryki) + LTM (preferencje)
|
||
* **EVAL + OBS + RETRY**: jakość, obserwowalność, recovery/rollback
|
||
* **SCHED → JOB**: automaty (heartbeats, daily digests, housekeeping)
|
||
* **STATE → WS → CFG**: persistent workspace i konfiguracja `.openclaw/`
|
||
|
||
***
|
||
|
||
## Słownik mapowania “Twoje diagramy → OpenClaw klocki”
|
||
|
||
Żebyś mógł konsekwentnie przepisywać kolejne grafiki na runtime:
|
||
|
||
* **Skills** = procedury (`/project:*`, “auto-invoked workflows”, SOP-y)
|
||
* **Agents** = persony/subagenci (np. “security-auditor”, “code-reviewer”)
|
||
* **Tools** = integracje (API, shell, DB, web, pliki)
|
||
* **Memory** = STM + LTM + Vector + SQL (w zależności od danych)
|
||
* **Orchestrator** = routing + stan + error handling + cost/limits
|
||
* **Scheduler** = cron/beat (hourly/daily/weekly)
|
||
* **Observability** = logi, trace, metryki, alerty
|
||
* **Workspace** = katalog persistent (`.openclaw/`), manifesty, cache, artefakty
|
||
|
||
***
|
||
|
||
## Jeśli chcesz, dopasuję to do Twojego realnego setupu (bez “potwierdzania kroków”)
|
||
|
||
Powiedz tylko 3 rzeczy (jednym zdaniem):
|
||
|
||
1. kanały: **Telegram/CLI/WebUI/Slack?**
|
||
2. pamięć: **Vector (Qdrant/Chroma?) + SQL (Postgres/MSSQL?)?**
|
||
3. gdzie runtime: **VPS/Docker/Tailscale?**
|
||
|
||
A ja wtedy zrobię Ci:
|
||
|
||
* wersję Mermaid **1:1 pod Twój runtime**
|
||
* oraz **schemat katalogów `.openclaw/` + przepływ jobów** (też w Mermaid).
|
||
|
||
Pewnie — zrobię wariant mapy pojęć **pod OpenClaw / agent runtime** (czyli: runtime + orkiestracja + skills + narzędzia + pamięć + kanały + obserwowalność + deploy). Poniżej masz **dwa poziomy**:
|
||
|
||
1. **Ultra‑minimal** (bullet‑proof, 1 ekran)
|
||
2. **Minimal‑PROD** (nadal kompatybilny, ale pokazuje realne klocki runtime)
|
||
|
||
> Oba warianty: ✅ bez `subgraph`, ✅ bez nowych linii w labelach, ✅ bez HTML, ✅ krótkie etykiety.
|
||
|
||
***
|
||
|
||
## 1) OpenClaw / Agent Runtime — ULTRA‑MINIMAL
|
||
|
||
|
||
|
||
### Co tu jest “OpenClaw‑specific”
|
||
|
||
* **Gateway** = wejście z kanałów (CLI/Web/Telegram/Slack) + normalizacja wiadomości
|
||
* **Orchestrator** = router (kto/co/po co), kontrola stanu i błędów
|
||
* **Skills** = Twoje workflow “auto‑invoked” / wywoływane jawnie
|
||
* **Workspace + Config** = `.openclaw/` (persistent) + ustawienia runtime
|
||
|
||
***
|
||
|
||
## 2) OpenClaw / Agent Runtime — MINIMAL‑PROD (bardziej “realny”)
|
||
|
||
flowchart TB
|
||
%% Core concept
|
||
AAI["Agentic AI"]
|
||
subgraph T["Terminology"]
|
||
AI["AI"]
|
||
ML["ML"]
|
||
DL["Deep Learning"]
|
||
LLM["LLM"]
|
||
AGI["AGI"]
|
||
ASI["ASI"]
|
||
end
|
||
|
||
subgraph P["Agent patterns"]
|
||
PLAN["Planning"]
|
||
TC["Tool Calling"]
|
||
RAG["RAG"]
|
||
MEM["Memory"]
|
||
TEST["Testing"]
|
||
IDEA["Idea Generation"]
|
||
MAS["Multi-Agent Systems"]
|
||
SHOP["Shopping Workflow"]
|
||
end
|
||
|
||
subgraph C["Capabilities and skills"]
|
||
CHAT["Chat"]
|
||
VOICE["Voice"]
|
||
DATA["Data Analysis"]
|
||
SUM["Summarization"]
|
||
DOC["Documents"]
|
||
CAL["Calendar"]
|
||
REM["Reminders"]
|
||
ACC["Accuracy Checks"]
|
||
SAFE["Safety Guardrails"]
|
||
STORE["Storage"]
|
||
HUMAN["Human in the loop"]
|
||
end
|
||
|
||
subgraph M["Memory and context handling"]
|
||
STM["Short-term Memory"]
|
||
LTM["Long-term Memory"]
|
||
CTX["Context Adaptation"]
|
||
DIARY["Diary Logs"]
|
||
NOTE["Notes"]
|
||
end
|
||
|
||
subgraph N["Collaboration and networking"]
|
||
A2A["Agent-to-Agent"]
|
||
NET["Networking"]
|
||
FEED["Feedback Loops"]
|
||
HIST["History Logs"]
|
||
ACP["Agent Communication Protocol"]
|
||
end
|
||
|
||
subgraph D["Deployment and scaling"]
|
||
CLOUD["Cloud"]
|
||
LOCAL["Local"]
|
||
UPD["Updates"]
|
||
MON["Monitoring"]
|
||
COST["Cost and Resource Mgmt"]
|
||
end
|
||
|
||
subgraph U["Business and use cases"]
|
||
SUP["Support"]
|
||
HR["HR"]
|
||
MKT["Marketing"]
|
||
LEGAL["Legal"]
|
||
TASK["Task Delegation"]
|
||
CREW["Crew Coordination"]
|
||
EDU["Education"]
|
||
OPS["Ops and Internal Tools"]
|
||
end
|
||
|
||
%% Vendor tools (examples)
|
||
subgraph V["Tools and platforms (examples)"]
|
||
AWS["AWS"]
|
||
AZR["Azure"]
|
||
GCP["GCP"]
|
||
OA["OpenAI"]
|
||
ANTH["Anthropic"]
|
||
HF["HuggingFace"]
|
||
GEM["Gemini"]
|
||
GROK["Grok"]
|
||
end
|
||
|
||
%% Agent types (1-9)
|
||
subgraph AT["Types of AI Agents (1-9)"]
|
||
R1["1 Simple Reflex"]
|
||
R2["2 Learning Agent"]
|
||
R3["3 LLM-based Agentic System"]
|
||
R4["4 Goal-based"]
|
||
R5["5 Conversational"]
|
||
R6["6 Multi-agent System"]
|
||
R7["7 Utility-based"]
|
||
R8["8 Embodied"]
|
||
R9["9 Model-based Reflex"]
|
||
end
|
||
|
||
%% High-level relationships
|
||
AAI --> T
|
||
AAI --> P
|
||
AAI --> C
|
||
AAI --> M
|
||
AAI --> N
|
||
AAI --> D
|
||
AAI --> U
|
||
AAI --> V
|
||
AAI --> AT
|
||
|
||
%% Concept dependencies
|
||
DL --> LLM
|
||
ML --> DL
|
||
AI --> ML
|
||
|
||
%% Patterns -> capabilities
|
||
PLAN --> TASK
|
||
TC --> OPS
|
||
RAG --> DOC
|
||
RAG --> STORE
|
||
TEST --> ACC
|
||
SAFE --> LEGAL
|
||
MEM --> LTM
|
||
MEM --> STM
|
||
CTX --> CHAT
|
||
FEED --> MON
|
||
HIST --> DIARY
|
||
NOTE --> LTM
|
||
|
||
%% Agent types -> patterns/capabilities
|
||
R1 --> TC
|
||
R1 --> CHAT
|
||
|
||
R2 --> FEED
|
||
R2 --> HIST
|
||
|
||
R3 --> PLAN
|
||
R3 --> TC
|
||
R3 --> RAG
|
||
R3 --> MEM
|
||
R3 --> TEST
|
||
|
||
R4 --> PLAN
|
||
R4 --> TASK
|
||
|
||
R5 --> CHAT
|
||
R5 --> CTX
|
||
R5 --> HUMAN
|
||
|
||
R6 --> MAS
|
||
R6 --> A2A
|
||
R6 --> NET
|
||
R6 --> ACP
|
||
R6 --> CREW
|
||
|
||
R7 --> PLAN
|
||
R7 --> DATA
|
||
R7 --> COST
|
||
|
||
R8 --> VOICE
|
||
R8 --> MON
|
||
|
||
R9 --> CTX
|
||
R9 --> MEM
|
||
R9 --> HIST
|
||
|
||
%% Deployment/tooling links
|
||
CLOUD --> AWS
|
||
CLOUD --> AZR
|
||
CLOUD --> GCP
|
||
LLM --> OA
|
||
LLM --> ANTH
|
||
LLM --> GEM
|
||
LLM --> GROK
|
||
STORE --> HF
|
||
|
||
### Jak czytać ten diagram (w 30 sekund)
|
||
|
||
* **CH → GW → AUTH**: kanały wejściowe + uwierzytelnienie (VPN/Tailscale, tokeny)
|
||
* **ORCH**: serce runtime — trzyma stan, robi routing, zbiera telemetrię
|
||
* **POL**: guardrails (co wolno agentowi, limity, allowlist narzędzi)
|
||
* **ROUTE → AG**: wybór agenta/persony (np. code-reviewer, ops-runner)
|
||
* **PLAN/ACT**: pętla agentowa (plan → akcje → obserwacja → korekta)
|
||
* **SK + ToolRunner**: Skills to Twoje procedury; ToolRunner to “wykonawca” narzędzi
|
||
* **MEM**: pamięć robocza + wektory (RAG) + SQL (fakty/metryki) + LTM (preferencje)
|
||
* **EVAL + OBS + RETRY**: jakość, obserwowalność, recovery/rollback
|
||
* **SCHED → JOB**: automaty (heartbeats, daily digests, housekeeping)
|
||
* **STATE → WS → CFG**: persistent workspace i konfiguracja `.openclaw/`
|
||
|
||
***
|
||
|
||
## Słownik mapowania “Twoje diagramy → OpenClaw klocki”
|
||
|
||
Żebyś mógł konsekwentnie przepisywać kolejne grafiki na runtime:
|
||
|
||
* **Skills** = procedury (`/project:*`, “auto-invoked workflows”, SOP-y)
|
||
* **Agents** = persony/subagenci (np. “security-auditor”, “code-reviewer”)
|
||
* **Tools** = integracje (API, shell, DB, web, pliki)
|
||
* **Memory** = STM + LTM + Vector + SQL (w zależności od danych)
|
||
* **Orchestrator** = routing + stan + error handling + cost/limits
|
||
* **Scheduler** = cron/beat (hourly/daily/weekly)
|
||
* **Observability** = logi, trace, metryki, alerty
|
||
* **Workspace** = katalog persistent (`.openclaw/`), manifesty, cache, artefakty
|
||
|
||
***
|
||
|
||
## Jeśli chcesz, dopasuję to do Twojego realnego setupu (bez “potwierdzania kroków”)
|
||
|
||
Powiedz tylko 3 rzeczy (jednym zdaniem):
|
||
|
||
1. kanały: **Telegram/CLI/WebUI/Slack?**
|
||
2. pamięć: **Vector (Qdrant/Chroma?) + SQL (Postgres/MSSQL?)?**
|
||
3. gdzie runtime: **VPS/Docker/Tailscale?**
|
||
|
||
A ja wtedy zrobię Ci:
|
||
|
||
* wersję Mermaid **1:1 pod Twój runtime**
|
||
* oraz **schemat katalogów `.openclaw/` + przepływ jobów** (też w Mermaid).
|
||
Pewnie — oto **mapa pojęć w Mermaid** (koncept-map), która łączy oba obrazki: **„Agentic AI Periodic Table”** + **„Types of AI Agents (1–9)”**.\
|
||
Zrobiłem ją w **flowchart** (najbardziej kompatybilny wariant) i **bez łamań linii w etykietach**, żeby nie wywoływać błędów rendererów.
|
||
|
||
|
||
|
||
### Jeśli chcesz wersję **ultra-minimalną** (jeszcze bardziej “bullet-proof” bez `subgraph`)
|
||
|
||
Napisz tylko: **„ultra-min”** — wygeneruję wariant jednopoziomowy, który przechodzi nawet przez najbardziej restrykcyjne renderery (Confluence/Obsidian itp.).
|
||
|
||
Gotowe ✅\
|
||
Poniżej masz **ULTRA‑MINIMALNĄ mapę pojęć w Mermaid**:
|
||
|
||
* ✅ **zero `subgraph`**
|
||
* ✅ **zero nowych linii w etykietach**
|
||
* ✅ **krótkie nazwy**
|
||
* ✅ **maksymalna kompatybilność (GitHub, Obsidian, Confluence, Mermaid Live)**
|
||
|
||
***
|
||
|
||
## Ultra‑minimalna mapa pojęć — Agentic AI
|
||
|
||
flowchart TB
|
||
AI["AI"] --> ML["ML"]
|
||
ML --> DL["Deep Learning"]
|
||
DL --> LLM["LLM"]
|
||
LLM --> AAI["Agentic AI"]
|
||
|
||
AAI --> PLAN["Planning"]
|
||
AAI --> TC["Tool Calling"]
|
||
AAI --> RAG["RAG"]
|
||
AAI --> MEM["Memory"]
|
||
AAI --> TEST["Testing"]
|
||
AAI --> CHAT["Chat"]
|
||
AAI --> DATA["Data"]
|
||
AAI --> SAFE["Safety"]
|
||
|
||
MEM --> STM["Short Memory"]
|
||
MEM --> LTM["Long Memory"]
|
||
|
||
AAI --> R1["Reflex Agent"]
|
||
AAI --> R2["Learning Agent"]
|
||
AAI --> R3["LLM Agent"]
|
||
AAI --> R4["Goal Agent"]
|
||
AAI --> R5["Chat Agent"]
|
||
AAI --> R6["Multi Agent"]
|
||
AAI --> R7["Utility Agent"]
|
||
AAI --> R8["Embodied Agent"]
|
||
AAI --> R9["Model Agent"]
|
||
|
||
***
|
||
|
||
### Jak używać tej wersji
|
||
|
||
* ✅ **Concept map / mental model**
|
||
* ✅ Slajdy / README
|
||
* ✅ Dokumentacja architektury
|
||
* ✅ Podstawa do dalszej rozbudowy (dodajesz węzły, nic nie psując)
|
||
|
||
Jeśli chcesz:
|
||
|
||
* 🔹 wersję **jeszcze mniejszą (1 ekran, \~6 węzłów)**
|
||
* 🔹 rozdzielenie na **biznes vs inżynieria**
|
||
* 🔹 wariant **pod OpenClaw / agent runtime**
|
||
* 🔹 eksport listy pojęć z tej mapy
|
||
|
||
— napisz, co robimy dalej.
|
||
|
||
Pewnie — zrobię wariant mapy pojęć **pod OpenClaw / agent runtime** (czyli: runtime + orkiestracja + skills + narzędzia + pamięć + kanały + obserwowalność + deploy). Poniżej masz **dwa poziomy**:
|
||
|
||
1. **Ultra‑minimal** (bullet‑proof, 1 ekran)
|
||
2. **Minimal‑PROD** (nadal kompatybilny, ale pokazuje realne klocki runtime)
|
||
|
||
> Oba warianty: ✅ bez `subgraph`, ✅ bez nowych linii w labelach, ✅ bez HTML, ✅ krótkie etykiety.
|
||
|
||
***
|
||
|
||
## 1) OpenClaw / Agent Runtime — ULTRA‑MINIMAL
|
||
|
||
flowchart TB
|
||
CH["Channels"] --> GW["Gateway"]
|
||
GW --> ORCH["Orchestrator"]
|
||
ORCH --> PLAN["Planner"]
|
||
ORCH --> EXEC["Executor"]
|
||
EXEC --> SK["Skills"]
|
||
EXEC --> TOOLS["Tools"]
|
||
ORCH --> MEM["Memory"]
|
||
MEM --> STM["Short"]
|
||
MEM --> LTM["Long"]
|
||
ORCH --> OBS["Observability"]
|
||
ORCH --> SCHED["Scheduler"]
|
||
ORCH --> STORE["Workspace"]
|
||
STORE --> CFG["Config"]
|
||
|
||
### Co tu jest “OpenClaw‑specific”
|
||
|
||
* **Gateway** = wejście z kanałów (CLI/Web/Telegram/Slack) + normalizacja wiadomości
|
||
* **Orchestrator** = router (kto/co/po co), kontrola stanu i błędów
|
||
* **Skills** = Twoje workflow “auto‑invoked” / wywoływane jawnie
|
||
* **Workspace + Config** = `.openclaw/` (persistent) + ustawienia runtime
|
||
|
||
***
|
||
|
||
## 2) OpenClaw / Agent Runtime — MINIMAL‑PROD (bardziej “realny”)
|
||
|
||
flowchart TB
|
||
CH["Channels"] --> GW["Gateway"]
|
||
GW --> AUTH["Auth"]
|
||
AUTH --> ORCH["Orchestrator"]
|
||
|
||
ORCH --> POL["Policy"]
|
||
ORCH --> ROUTE["Router"]
|
||
ROUTE --> AG["Agent"]
|
||
AG --> PLAN["Plan"]
|
||
AG --> ACT["Act"]
|
||
|
||
ACT --> SK["Skills"]
|
||
ACT --> TOOL["ToolRunner"]
|
||
|
||
TOOL --> EXT["APIs"]
|
||
TOOL --> FS["Files"]
|
||
TOOL --> DB["DB"]
|
||
TOOL --> WEB["Web"]
|
||
|
||
ORCH --> MEM["Memory"]
|
||
MEM --> STM["STM"]
|
||
MEM --> VEC["Vector"]
|
||
MEM --> SQL["SQL"]
|
||
MEM --> LTM["LTM"]
|
||
|
||
ORCH --> EVAL["Evals"]
|
||
ORCH --> OBS["LogsTraces"]
|
||
ORCH --> RETRY["RetryRollback"]
|
||
|
||
ORCH --> SCHED["Cron"]
|
||
SCHED --> JOB["Jobs"]
|
||
|
||
ORCH --> STATE["State"]
|
||
STATE --> WS["Workspace"]
|
||
WS --> CFG[".openclaw config"]
|
||
|
||
### Jak czytać ten diagram (w 30 sekund)
|
||
|
||
* **CH → GW → AUTH**: kanały wejściowe + uwierzytelnienie (VPN/Tailscale, tokeny)
|
||
* **ORCH**: serce runtime — trzyma stan, robi routing, zbiera telemetrię
|
||
* **POL**: guardrails (co wolno agentowi, limity, allowlist narzędzi)
|
||
* **ROUTE → AG**: wybór agenta/persony (np. code-reviewer, ops-runner)
|
||
* **PLAN/ACT**: pętla agentowa (plan → akcje → obserwacja → korekta)
|
||
* **SK + ToolRunner**: Skills to Twoje procedury; ToolRunner to “wykonawca” narzędzi
|
||
* **MEM**: pamięć robocza + wektory (RAG) + SQL (fakty/metryki) + LTM (preferencje)
|
||
* **EVAL + OBS + RETRY**: jakość, obserwowalność, recovery/rollback
|
||
* **SCHED → JOB**: automaty (heartbeats, daily digests, housekeeping)
|
||
* **STATE → WS → CFG**: persistent workspace i konfiguracja `.openclaw/`
|
||
|
||
***
|
||
|
||
## Słownik mapowania “Twoje diagramy → OpenClaw klocki”
|
||
|
||
Żebyś mógł konsekwentnie przepisywać kolejne grafiki na runtime:
|
||
|
||
* **Skills** = procedury (`/project:*`, “auto-invoked workflows”, SOP-y)
|
||
* **Agents** = persony/subagenci (np. “security-auditor”, “code-reviewer”)
|
||
* **Tools** = integracje (API, shell, DB, web, pliki)
|
||
* **Memory** = STM + LTM + Vector + SQL (w zależności od danych)
|
||
* **Orchestrator** = routing + stan + error handling + cost/limits
|
||
* **Scheduler** = cron/beat (hourly/daily/weekly)
|
||
* **Observability** = logi, trace, metryki, alerty
|
||
* **Workspace** = katalog persistent (`.openclaw/`), manifesty, cache, artefakty
|
||
|
||
***
|
||
|
||
## Jeśli chcesz, dopasuję to do Twojego realnego setupu (bez “potwierdzania kroków”)
|
||
|
||
Powiedz tylko 3 rzeczy (jednym zdaniem):
|
||
|
||
1. kanały: **Telegram/CLI/WebUI/Slack?**
|
||
2. pamięć: **Vector (Qdrant/Chroma?) + SQL (Postgres/MSSQL?)?**
|
||
3. gdzie runtime: **VPS/Docker/Tailscale?**
|
||
|
||
A ja wtedy zrobię Ci:
|
||
|
||
* wersję Mermaid **1:1 pod Twój runtime**
|
||
* oraz **schemat katalogów `.openclaw/` + przepływ jobów** (też w Mermaid).
|