Concepts

The mental models that make a personal operating system work. Understand the why before the how.

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10 AI Agent Failure Modes: Why Agents Break in Production The documented ways AI agents fail: hallucination cascades, context overflow, tool calling errors, and 7 more. Diagnosis patterns and fixes for each. Agent Checkpointing: Save, Restore, and Rewind Autonomous Work How checkpoint systems enable long-running agent workflows by saving state periodically, allowing recovery from failures and rollback from bad decisions Agent Guardrails: Input/Output Validation for Autonomous Systems How to implement runtime guardrails that validate agent inputs, filter outputs, and enforce business rules. Covers NeMo Guardrails, layered checking, and production patterns. Agent Handoffs: When and How to Transfer Control Learn when to use agent handoffs vs single agents. Includes code patterns from OpenAI Swarm and practical implementation examples. Agent Memory Systems How AI agents implement memory: short-term context, long-term storage, vector retrieval, and the architecture that ties it together. Agent Observability How to implement distributed tracing, logging, and monitoring for AI agents using OpenTelemetry and purpose-built tools like Langfuse and Braintrust. Agentic Design Patterns: ReAct, Reflection, Planning, Tool Use When to use ReAct loops, self-critique, task decomposition, and tool calling in AI agents. A practical pattern library for building effective agent systems. AI Memory Compression Techniques for compressing AI observations into retrievable semantic summaries that fit in context windows Browser Agents AI that clicks, types, and scrolls your browser autonomously to complete web-based tasks Building an AI Second Brain Transform AI from chatbot to persistent knowledge partner Context Rot: When More Tokens Mean Worse Results LLM performance degrades predictably as context windows fill up. Learn why this happens, how to detect it, and practical strategies to maintain output quality. Context Window Management Keep your AI sharp by managing what fits in its working memory Digital Gardens Non-linear personal knowledge sites that grow over time. Not blogs. Notes evolve publicly without pressure to be finished. Episodic Memory for LLM Agents Give AI agents memory of specific past events with temporal context. The missing piece between semantic facts and procedural rules in the CoALA framework. Graph Memory for Personal AI Knowledge graphs track relationships between people, projects, and time that vector databases miss. Build AI memory that understands context across sessions. Human-on-the-Loop Move from approving every AI action to supervising agents that act autonomously, escalating only when confidence drops or risk rises. Hybrid Retrieval: When RAG Meets Long Context Combine RAG retrieval with long-context windows strategically instead of treating them as competing approaches Hybrid Search: Combining Keyword and Semantic Retrieval Vector search misses exact matches. Keyword search misses concepts. Hybrid search with reciprocal rank fusion combines both for personal knowledge bases. Late Chunking: Context-Aware Document Splitting for Better Retrieval Process entire documents through embedding models before splitting to preserve cross-chunk context that traditional chunking destroys Learning in Public Stop lurking. Create learning exhaust: blogs, videos, talks. The fastest way to level up is documenting what you learn where others can find it. Local LLM Runtimes: When to Use Ollama vs vLLM Ollama excels for single-user development with simple setup. vLLM delivers 20x higher throughput for production multi-user deployments. Choose based on your workload. Malleable Software Software as clay you reshape, not appliances you consume MCP Server Composition Connect your AI agent to multiple MCP servers at once, combining calendar, database, files, and search through one protocol Memory Attribution and Provenance Track where AI memories came from, when they were created, and how much to trust them Memory Consolidation and Forgetting How AI agents consolidate short-term observations into long-term storage using sleep-inspired patterns, plus when and what to forget. Model Quantization: Running 70B Models on a Laptop Reduce model precision from 32-bit to 4-bit to run large language models locally. Covers k-quants, GGUF, and choosing the right quantization level. Multi-Agent Knowledge Management When a single AI can't handle your PKM needs, specialized agents working together can automate capture, processing, and synthesis. Personal Search: Searching Your Own Data Build a search engine for your notes, journals, tweets, and memories instead of the web Plan Mode in Claude Code Research before coding to avoid wrong approaches and hallucinations Preference Learning: AI That Adapts to You How AI systems infer your preferences from interactions and adapt without configuration. Covers POPI, Mem0, LaMP benchmarks, and building preference-aware systems. Principles for AI Delegation What to delegate to AI and what to keep human Prompt Engineering for Agent Coding Structure prompts that make AI coding agents 30-40% more effective Prompt Engineering Patterns That Work Five proven patterns to get better results from LLMs: chain-of-thought, few-shot, ReAct, prompt chaining, and role prompting with examples Prompt Injection in Skill Files AI agents read project files like CLAUDE.md or AGENTS.md. These files can contain hidden instructions that hijack the agent. Here's how the attack works. Self-Evolving Agents Build AI agents that improve through structured feedback capture, automated evaluation, and continuous retraining loops Self-Updating Instructions (Procedural Memory) Build AI agents that modify their own operating instructions based on experience, feedback, and observed failures Small Bets Stop betting everything on one startup. Build a portfolio of small projects that compound over time. Subagent Patterns: Parallel, Sequential, Background Three dispatch patterns for delegating work to AI subagents and when to use each one. Terminal-Native AI Coding: Neovim with Claude Code Run Claude Code alongside Neovim in split terminal panes. Keep your vim keybindings while getting AI assistance. No VS Code required. The Architecture of a Personal OS Personal OS architecture: interface, agent, memory, integration, and tool layers. Build your AI system incrementally in 4 weeks The Jagged Frontier AI capabilities have irregular boundaries. Some tasks it nails, others it fails spectacularly. Learn to map the frontier for your work. The Three-Layer Workflow Match your AI tool to the task: tab completion for most work, agents for multi-file changes, reasoning for architecture Tool Routing: How AI Agents Pick Which Function to Call Modern agents route between dozens of tools using semantic matching, LLM-as-router, hierarchical patterns, and fallback chains. Patterns for scoring, selection, and MCP sampling. Tool Use Patterns: How LLMs Call External Tools Function calling, MCP protocol, and ReAct patterns for AI tool use. Learn when to use each approach and how to implement them. Vibe Coding Building software through conversation instead of keystrokes Vision-Based Web Automation: Why Screenshots Are Replacing Selectors How computer vision and multimodal LLMs enable browser agents that see pages like humans instead of parsing brittle DOM structures. Voice-First Note Capture: Whisper to Structured Markdown Use whisper.cpp for local transcription, then LLM post-processing to convert rambling voice memos into structured notes with headers, bullet points, and action items What is a Personal Operating System? What is a personal operating system? Learn how AI agents like Claude Code manage your tasks, memory, calendar, and decisions autonomously

48 concepts