#Architecture

stateless agents: why your AI shouldn't remember you

AI stopped being a tool. it became infrastructure

the personal AI OS is no longer a thought experiment. it's a deployment pattern. and the human-in-the-loop is vanishing faster than anyone projected.

CrewAI vs LangGraph: choose a multi-agent framework by workflow shape

CrewAI fits role/task crews and event-driven flows. LangGraph fits explicit state, loops, interrupts, persistence, and replay. Use neither when one agent and a few tools is enough.

agentic loops: observe, plan, act, verify

the core pattern of autonomous agents. simple in theory, messy in practice, and full of ways to fail.

what is MCP? the Model Context Protocol explained

what is MCP (Model Context Protocol)? a non-technical explanation of how MCP works, why it matters, and how it connects AI agents to the world.

context engineering

context engineering is the discipline of crafting optimal context for AI agents — memory, retrieval, compression, and instruction design.

AI agent orchestration

how multiple AI agents coordinate work — supervisor patterns, hierarchical delegation, swarm architectures, and when each makes sense.

agent-first design

how to structure repositories so AI agents can actually work in them. the code isn't just for humans anymore.

Agent Memory Systems

How AI agents implement memory: short-term context, long-term storage, vector retrieval, and the architecture that ties it together.

MCP Server Stacking: One Data Source is Data, Two is Intelligence

Combine multiple MCP servers to unlock cross-domain insights that no single server can provide on its own.

Debug Your RAG Pipeline Before Users Notice

Monitor retrieval-augmented generation systems with OpenTelemetry tracing. Find whether bad answers come from retrieval, context, or generation.

AI Memory Compression

Techniques for compressing AI observations into retrievable semantic summaries that fit in context windows

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.

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.

Ollama vs vLLM vs llama.cpp vs LM Studio: choose the local runtime by job

Choose a local LLM runtime by workload: Ollama for laptop/dev, llama.cpp for GGUF control, LM Studio for a desktop UI, and vLLM for multi-user serving.

Subagent Patterns: Parallel, Sequential, Background

Three dispatch patterns for delegating work to AI subagents and when to use each one.

Multi-Agent Knowledge Management

When a single AI can't handle your PKM needs, specialized agents working together can automate capture, processing, and synthesis.

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.

llama.cpp setup guide: build, run, and troubleshoot GGUF models locally

Build llama.cpp, load a GGUF model, run the CLI or server, and verify the install with one smoke test and troubleshooting table.

The Architecture of a Personal OS

Personal OS architecture: interface, agent, memory, integration, and tool layers. Build your AI system incrementally in 4 weeks

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

Malleable Software

Software as clay you reshape, not appliances you consume

ACE Framework for Personal AI

Implement David Shapiro's six-layer cognitive architecture to give your Claude Code assistant mission, values, and strategic context

MCP Server Composition

Connect your AI agent to multiple MCP servers at once, combining calendar, database, files, and search through one protocol

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.

Hybrid Retrieval: When RAG Meets Long Context

Combine RAG retrieval with long-context windows strategically instead of treating them as competing approaches

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.

Claude Code Plugins System

Extend Claude Code with plugins for custom tools, MCP servers, and workflows

What is a Personal OS?

A Personal OS is the user's control plane for memory, identity, permissions, tools, workflows, actions, receipts, and rollback across AI agents and devices.

Building Your First MCP Server

Create custom MCP servers to extend Claude with your own tools

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.

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.

Self-Evolving Agents

Build AI agents that improve through structured feedback capture, automated evaluation, and continuous retraining loops

Agent Observability

How to implement distributed tracing, logging, and monitoring for AI agents using OpenTelemetry and purpose-built tools like Langfuse and Braintrust.

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.