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24 in-depth ai agents articles on setup, real workflows, and production patterns — written for builders shipping with AI.
Your agents work on your laptop. Now what? This final guide covers everything you need to ship agents that run reliably in production: Docker containerisation, VPS vs serverless, structured logging, health checks, cost controls, and the monitoring setup that wakes you up before your users notice something broke.
Hermes by Nous Research is a self-hosted autonomous agent with persistent cross-session memory, scheduled cron tasks, and self-improving skills. At 175K GitHub stars in under 4 months, here is what it does, how it compares to Claude Code, and who should use it.
In Part 1 you built the loop. Now give it real tools: web search, code execution, and file writing - plus the error recovery patterns that separate demo agents from production ones. Copy-paste Python, no frameworks.
Most tutorials start with code. This one starts with the right mental model. Learn exactly what an AI agent is (and isn't), how it differs from a chatbot, and the four components every agent needs - with Python examples you can run today.
Your agent forgets everything the moment a session ends. This guide covers the three memory patterns every AI agent builder needs: in-context, external file, and vector database - with Python code for each.
Your single agent can now use tools and remember things. But one agent has a ceiling. This guide covers the three orchestration patterns that scale agents from demo to real work: pipeline, supervisor/worker, and fan-out. Python code for each.
Andrej Karpathy drew a hard line between vibe coding and agentic engineering at Sequoia Ascent 2026. Here's the full framework — spec design, diff review, eval loops — and why mastering it puts you beyond 10x.
Karpathy's Software 3.0 framework reframes how builders should think about AI products. The context window is your program, the LLM is the interpreter. Here's what that means — and what software should stop existing.
Karpathy published a pattern that fixes how builders manage knowledge. Instead of RAG, an LLM incrementally builds and maintains a persistent wiki. Here's the full architecture, operations, and how to start.
Google I/O 2026 delivered Jules async coding agent, ADK 1.0, Veo 3 in the API, Gemini Intelligence for Android, Firebase agent-native, and a new Gemini model generation. Here is the complete builder-focused breakdown of every announcement worth knowing.
Google I/O 2026 dropped Jules (async coding agent), ADK 1.0 (multi-language agent framework), Veo 3 in the API, and a 2M token Gemini model. Here is what actually matters for builders and what to do with it.
agentmemory adds persistent memory to Claude Code, Cursor, and Windsurf via MCP. 30-second setup, 95.2% recall, ~$10/year.
Level 1 runs a linter. Level 5 ships features end-to-end with no human in the loop. This guide breaks down each level with real examples, code, and the tools used at each stage — so you know exactly where to start.
What AI agents are, how the agent loop works, frameworks vs raw API, memory, multi-agent systems, and MCP. The 2026 builder's roadmap.
MCP is the protocol that lets Claude, Cursor, and any LLM call your own tools, databases, and APIs. This guide explains how it works and walks you through building a real MCP server from scratch in Python.
A complete tutorial on building an AI agent from scratch in Python — no LangChain, no framework. Just the Anthropic SDK, a tool-use loop, and ~60 lines of code that you fully understand and control.
LangChain has 80K stars. CrewAI has 20K. The raw Anthropic/OpenAI SDK is 60 lines. Which should you build your AI agent on? After shipping production agents in all three, here is the honest decision framework.
Build a working multi-agent system in Python — a coordinator agent delegates to specialized workers, handles failures, and synthesizes results. Complete code, real examples, no framework lock-in.
What if you had a chief of staff, research analyst, content writer, data analyst, and executive assistant — all working 24/7? Build your personal AI agent stack.
Part-time. While running Y Combinator. Garry Tan used Claude Code, gstack, and a multi-agent workflow to ship 600K+ lines in 60 days. Steal his exact stack and mental model.
AI lets a single person run marketing, sales, content, and growth that used to require a 5-person team. The complete GTM playbook for solopreneurs and small team leads.
The old bottleneck was your attention. AI agents change the math — run 10 workstreams simultaneously, make only the decisions that matter, and multiply your output by 10x.
Garry Tan open-sourced his entire AI development workflow. gstack turns Claude Code into a virtual engineering team with 23 specialist roles — CEO reviewer, QA lead, security officer, and more. Here's who it's for, how it works, and why it might change how you ship software.
Claude Code is Anthropic's CLI agent that reads your codebase, writes code across files, runs tests, and commits to git. Here's how to use it like a senior engineer on your team.