Poniżej **dokładna transkrypcja tekstu z obrazu** (zamiana obrazu na tekst), z zachowaniem struktury i opisów: Anatomy of the .claude/ folder (commit .claude/ to git) your-project/ ├─ CLAUDE.md │ team instructions, committed ├─ CLAUDE.local.md │ personal overrides, gitignored ├─ .claude/ │ ├─ settings.json │ │ permissions + config, committed │ ├─ settings.local.json │ │ personal permissions, gitignored │ ├─ commands/ │ │ ├─ review.md │ │ │ → /project:review │ │ ├─ fix-issue.md │ │ │ → /project:fix-issue │ │ ├─ deploy.md │ │ → /project:deploy │ ├─ rules/ │ │ ├─ code-style.md │ │ ├─ testing.md │ │ ├─ api-conventions.md │ │ modular instruction files │ ├─ skills/ │ │ │ auto-invoked workflows │ │ ├─ security-review/ │ │ │ └─ SKILL.md │ │ ├─ deploy/ │ │ └─ SKILL.md │ ├─ agents/ │ ├─ code-reviewer.md │ ├─ security-auditor.md │ subagent personas Everything Claude needs to know about your project lives right here. Jeśli chcesz: * wersję **PL** (tłumaczenie), * konwersję do **Markdown / org-mode**, * albo rozbicie na **checklistę / dokumentację repo** — daj znać. Poniżej **wierna transkrypcja tekstu z obrazu** (zamiana obrazu na tekst), z zachowaniem nazw, hierarchii i opisów. *** THE $15/MONTH OPENCLAW STACK ### HOSTINGER / HETZNER — \~$5–7/mo 2–4 vCPU • 4–8GB RAM • Ubuntu * Docker * OpenClaw * App Version: 1.7.8? * Nodes.js + Skills * Browser (Chrome) * Tailscale VPN * Private network only * .openclaw/ (persistent) * config + memory + workspace *** ### MODEL PROVIDERS **PRIMARY** Kimi K2.5 (Moonshot AI) 256k ctx • SWE 78.6% • 3x boost Feb **FALLBACK #1** MiniMax M2.5 $0.30/$1.20 Mtok • SWE 80.2% • batch tasks **FALLBACK #2** Gemini 3 Flash sub-agents • parallel work **FALLBACK #3** GLM-4.7-Flash $0.00 • heartbeats + simple queries *** ### YOUR CHANNELS * WhatsApp * Telegram * Discord / Slack * Web UI * CLI *** ### COST SUMMARY TOTAL: ~$15–18/mo (dual VPS ~$12 + Kimi ~$3 + MiniMax ~$2 + OpenRouter ~$1) vs. $200/mo Claude Max *** Jeśli chcesz: * 🔁 **tłumaczenie na polski** * 🧱 **wersję w Markdown / org-mode** * 🧠 **rozpisanie tego jako architektury OpenClaw (runbook / SOP)** * 📦 **checklistę „jak to postawić od zera”** — daj znać, w jakiej formie. Poniżej **zamiana obrazu na tekst** – wierna transkrypcja plakatu **“40 AI TERMS EXPLAINED”**, z zachowaniem układu (hasło → definicja). Źródło widoczne na grafice: **Mindstream**. *** ## 40 AI TERMS EXPLAINED ### 1. Bias When an AI unfairly prefers some answers, often because of the data it was trained on. ### 2. Label A tag or answer given to data so AI knows what it is. ### 3. Model The final program that can do tasks after learning from data. ### 4. Training The process where AI learns from many examples to get better at tasks. ### 5. Chatbot A computer program that talks to people like a robot. *** ### 6. Dataset A big collection of information that AI learns from. ### 7. Token Words or pieces of words AI uses to read and write text. ### 8. Overfitting When AI learns the training data too well and performs worse on new examples. ### 9. AI Agent A software that does jobs for you using AI. ### 10. AI Ethics Making sure AI is used in ways that are right and fair to everyone. *** ### 11. Explainability How easily people can understand why an AI made a certain decision. ### 12. Inference When an AI uses what it learned to answer questions. ### 13. Turing Test A test to see if a computer can trick people into thinking it’s human. ### 14. Prompt The text or question you give an AI to get a response. ### 15. Fine-tuning Training an AI a bit more on special data to make it better at specific tasks. *** ### 16. Generative AI AI that can make new things like text, images, music, or code. ### 17. AI Automation Using AI to make tasks happen automatically without people doing them. ### 18. Neural Network Computer programs built a little like the human brain. ### 19. Computer Vision AI that helps computers “see” and understand images or videos. ### 20. Transfer Learning Using an AI trained for one job to help with another, related job. *** ### 21. Guardrails (in AI) Built‑in checks to stop AI from making mistakes or causing harm. ### 22. Open Source AI AI whose design and code are freely available for anyone to see or change. ### 23. Deep Learning AI that learns using brain‑like structures called neural networks with many layers. ### 24. Reinforcement Learning AI learns by trying things and getting rewards for good actions. ### 25. Hallucination (in AI) When AI makes up stuff that sounds true but is actually wrong. *** ### 26. Zero-shot Learning AI does a new task it wasn’t directly taught just by understanding the description. ### 27. Speech Recognition AI that turns spoken words into written text. ### 28. Supervised Learning AI learns from data that already has the correct answers labeled. ### 29. Model Context Protocol Rules on how AI shares context and information given to it. ### 30. Machine Learning A way for computers to learn things by looking at lots of examples. *** ### 31. AI (Artificial Intelligence) Technology that makes computers act smart, like humans do. ### 32. Unsupervised Learning AI finds patterns in data that has no labels. ### 33. LLM (Large Language Model) An AI model that understands and generates lots of text. ### 34. ASI (Artificial Superintelligence) An AI even smarter than the smartest humans ever. ### 35. GPU Special computer chips that help AI train and run faster. *** ### 36. NLP (Natural Language Processing) AI that understands and works with human language. ### 37. AGI (Artificial General Intelligence) A super‑smart AI that can learn anything a human can. ### 38. GPT A famous AI that writes text like a human. ### 39. API A way for different programs to talk to each other and use AI features. ### 40. Algorithm When an AI follows defined steps or rules to decide what to do. *** Na dole plakatu: Subscribe to Mindstream to learn AI for free — www.mindstream.news Jeśli chcesz: * ✅ **tłumaczenie PL → EN lub EN → PL (opisowe, nie literalne)** * ✅ **wersję w Markdown / org-mode (np. do repo lub notatek)** * ✅ **skróconą “ściągę” dla nietechnicznych** * ✅ **wersję dla inżynierów (mapowanie na LLM / agents / OpenClaw)** — powiedz w jakiej formie. 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). *** ## Why this infographic exists Most people think they understand AI. Until they realise they only recognise buzzwords. I’ve curated the **40 important AI terms** here to fix that gap. A few months ago, I noticed something strange. Smart professionals were using AI every day — but struggling to explain it clearly. They could say **“LLM”**, **“agents”**, or **“hallucinations”**. But they didn’t really understand what those mean or **why they matter**. That’s the difference between **using AI** and **thinking with AI**. So I broke it down here. *** ## Why this matters more than people realise * → **Clarity compounds faster** than just using AI tools * → **Understanding beats memorising prompts** * → **Vocabulary shapes how well you use AI** *** ## A few terms most people misuse daily * **Bias** → When training data tilts outputs * **Tokens** → How models read and write text * **Inference** → Using learned knowledge in real time * **Overfitting** → When AI memorizes instead of generalizing * **Hallucinations** → Confident answers without factual grounding *** ## Do’s ✅ * Learn **concepts before tools** * Connect terms to **real workflows** * Understand **limits**, not just capabilities * Revisit **fundamentals regularly** * Explain ideas **in your own words** *** ## Don’ts ❎ * Chasing tools without understanding basics * Confusing **AGI** with current **LLMs** * Ignoring **training and data quality** * Treating **prompts as everything** * Assuming AI is always correct *** ## Final thought If you want **leverage** from AI, you must **speak its language first**. To get a **quick scan of all 40 AI terms**, check the infographic below 👇 *** Jeśli chcesz, mogę: * ✳️ dopasować styl pod **LinkedIn / newsletter / README / landing page** * ✳️ skrócić do **wersji social (hook + CTA)** * ✳️ przerobić na **manifest / intro do kursu** * ✳️ zapisać całość w **org-mode lub Markdown** Powiedz, gdzie to publikujesz. 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**. *** # 50 Steps to Master AI **Go From Zero to Pro** ## Start **01** Grasp the basics of core types of AI (ANI, AGI, ASI) **02** Understand the history and evolution of artificial intelligence **03** Master essential AI concepts and terminology **04** Learn Python – the most used AI programming language **05** Understand core computer science principles (loops, data structures) **06** Get comfortable with statistics, probability, and distributions **07** Learn linear algebra and calculus fundamentals for ML **08** Understand machine learning fundamentals and real‑world applications **09** Differentiate types of learning: supervised, unsupervised, reinforcement **10** Explore key ML algorithms (regression, trees, clustering, etc.) **11** Build a basic machine learning project with scikit‑learn **12** Study training vs. testing, overfitting vs. underfitting **13** Learn feature engineering and data preprocessing techniques **14** Understand model evaluation metrics (accuracy, F1, AUC, etc.) **15** Learn feature engineering, preprocessing, layers, activation functions *** ## Transition **16** Learn common CNN architectures for vision (CNNs, RNNs, etc.) **17** Explore deep learning frameworks: TensorFlow and PyTorch **18** Implement a deep learning project (e.g. image classification) **19** Learn model tuning: hyperparameters, regularization, dropout **20** Learn model tuning, random forests, XGBoost, gradient boosting **21** Learn NLP basics: tokenization, POS tagging, embeddings **22** Work with NLP libraries: SpaCy, NLTK, HuggingFace Transformers **23** Build a basic NLP model (text classification, summarization, etc.) **24** Understand computer vision basics: pixels, channels, filters **25** Explore OpenCV for image processing fundamentals *** ## Advanced **26** Implement a computer vision project using CNNs **27** Study transfer learning and pre‑trained models (ResNet, etc.) **28** Build your own NLP or CV project using HuggingFace or TorchVision **29** Learn data annotation and augmentation techniques **30** Use cloud services (AWS/GCP) for training and inference at scale **31** Understand generative models: GANs, VAEs, diffusion models **32** Implement a generative AI project (e.g. Deepfake, art) **33** Dive into reinforcement learning fundamentals and learning policies **34** Create a basic RL environment and agent using OpenAI Gym **35** Learn optimization algorithms: SGD, Adam, RMSProp *** ## End **36** Explore AutoML and hyperparameter tuning tools (Optuna, Keras Tuner) **37** Study explainable AI (XAI) and interpretability tools (LIME, SHAP) **38** Build your AI project portfolio with diverse applications **39** Explore multimodal AI combining text, images, audio **40** Understand the ethical implications of AI (bias, fairness, misuse) **41** Deploy models using Flask, FastAPI, or Streamlit **42** Serve models via APIs using Docker & cloud services **43** Explore MLOps (CI/CD pipelines, monitoring, model versioning) **44** Learn time series forecasting (ARIMA, Prophet, LSTMs) **45** Stay updated with AI research (arXiv, blogs, conferences) **46** Join AI competitions (Kaggle, Zindi, DrivenData) **47** Contribute to open‑source AI projects on GitHub **48** Follow AI research: arXiv, Google Research, DeepMind **49** Read top AI books, newsletters, and blogs to stay updated **50** Teach AI to others or write about your AI journey *** Jeśli chcesz: * ✅ **tłumaczenie na polski** * ✅ **wersję w org‑mode / Markdown (np. roadmapa learningowa)** * ✅ **przeróbkę na plan 6–12 miesięcy** * ✅ **dopasowanie pod praktyka (LLM / agents / MLOps zamiast researchu)** — napisz, w jakiej formie i dla kogo. Poniżej **wierna transkrypcja tekstu z obrazu** – zamiana grafiki **„Agentic AI Tech Stack”** na tekst, z zachowaniem struktury i nazw narzędzi. *** # Agentic AI Tech Stack *(Analytics Vidhya)* *** ## 1. Deployment and Infrastructure * groq * AWS * together.ai * baseten * Modal * Fireworks AI * Replicate *** ## 2. Evaluation and Monitoring * LangSmith * MLflow * Weights & Biases * Ragas * Deepchecks * Fairlearn * Holistic AI * Hugging Face * Phoenix *** ## 3. Foundation Models * Claude 3.7 Sonnet * Mistral AI * Cohere * Gemini 2.5 Pro * LLaMA 4 * Claude 4 *** ## 4. Orchestration Frameworks * LangChain * DSPy * Microsoft AutoGen * Adaflow * LlamaIndex * Haystack * LiteLLM * Dify * Ray *** ## 5. Vector Databases * Milvus * Qdrant * Redis * pgvector * Vald * Chroma * Pinecone * Elasticsearch * Vespa * Weaviate *** ## 6. Embedding Models * Voyage AI * Nomic * fastText * Hugging Face * OpenAI * spaCy * Vector Flow * Cohere *** ## 7. Data Ingestion and Extraction * Scrapy * Docling * BeautifulSoup * Diffbot * Firecrawl * LlamaParse * Amazon Textract * Apache Tika *** ## 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
status + summary] P3 --> P4[Notify Telegram
success or failure] end H --> P D --> P W --> P P4 --> R[You see results in Telegram topics
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
with manifest] AB1 --> AB2 --> AB3 end PROT --> AB AB --> GD[Google Drive
with retention policy:
old backups auto-pruned] AB --> REPO[Code Repo
Auto-synced via hourly git push] REPO --> GH[GitHub
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,
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
Raw capture of everything that happened] DN --> WS[Weekly Synthesis
Distill patterns and preferences] WS --> LTM[Long-Term Memory
Stable preferences, learned behaviors] M --> LRN[.learnings/
Corrective patterns
so mistakes don't repeat] LRN --> LTM LTM --> OUT[Claude gets better over time
without being retrained] *** ## 4) Remote work setup + Development Flow + Fast Ops (SSH Terminal) flowchart LR A["Mac Studio
(wherever you are)"] --> B1[Cursor SSH Remote] A --> B2[Direct SSH Terminal] A --> B3["TeamViewer
(fallback)"] B1 --> C["MacBook Air
(always-on, at home)
claw runs here 24/7"] B2 --> C B3 --> C C --> STABLE[Live runtime
stays stable
the whole time] subgraph DEV[Development Flow] direction TB D1[1. Work in isolated git worktree
changes don't affect live system] D2[2. Make targeted edits
New skills, prompt tweaks, bug fixes] D3[3. Run validation scripts
check logs, verify behavior] D4[4. Commit and sync
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 `
` 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** `
` * **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** `
` / `\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).