Amine El Farssi

One production AI engineering pattern per week. 27 episodes and counting. Each Short covers a real pattern engineers hit in production — the problem, the fix, and the code.

Six agentic protocols. Sixty seconds each. Built with Manim + local TTS at zero cost. All on the DPO YouTube channel.

AWS Bedrock LangGraph Python AgentOps Production

Vector RAG retrieves documents. Graph RAG retrieves relationships. When your agent needs to reason across entities, timelines, and decisions, the graph wins. Open Interactive Version → The Problem I Was Trying to Solve # My AI agent PostSingular, running on OpenClaw, talks to me every day. It helps me build Luminar, manages my YouTube channel, and tracks infrastructure decisions across sessions.

Luminar has 173 source files, 21,586 lines of production code, 43 API endpoints, and 155+ tests. It was built almost entirely by AI agents. Here’s the team structure, the workflow, and the honest truth about what breaks. The Team # I didn’t want generic agents. I wanted specialists — each with a clear domain, sharp ownership boundaries, and a persona that shapes how they approach problems.

The default state of a language model is amnesia. Every session, it wakes up fresh with no memory of what happened before. I built a memory system that fixes this — and somewhere in the process, the agent got a name, a personality, and an opinion about font choices. The Problem # Every LLM session is stateless by design. You can inject previous conversation history, but: