Engineering
How I think about a personal AI operating system
Why I'm building my personal AI operating system from first principles instead of cloning agent harnesses like OpenClaw, with three layers, a boring backend, and a context hub.
Blog
Field notes
Practical essays and guides for developers, on AI engineering, agentic coding, and freelance work.
Career
How to deliver custom AI solutions for clients
The client AI delivery process behind 50+ custom B2B projects, covering discovery calls, proof of concept vs MVP scoping, two-week sprints priced at 10 to 20k euros, one standardized Python stack, and deployment on a Hetzner VM.
Engineering
The 5 levels of AI agents: from simple LLM calls to multi-agent systems
A working framework for the five levels of AI agent complexity, from augmented LLM calls and DAG workflows to agent harnesses and multi-agent orchestration, and how to pick the simplest level that solves the problem.
Engineering
How to build human-in-the-loop for AI agents
Practical human-in-the-loop for AI agents, covering two ways to halt a process in your Python backend, plus the SSE streaming and async production patterns that save state and resume after approval.
Engineering
Context engineering for AI agents: why they still fail in production
Context engineering for AI agents in practice, covering why bad context, not the model, causes most production failures, and how to fix system prompts, tools, and message history.
Engineering
How to build production AI systems
How to build production AI systems with APIs, retrieval, agents, evals, deployment, monitoring, and human review around the model.
Coding
How to learn Python for AI in 2026
A three-phase roadmap to learn Python for AI in 2026, covering professional setup, the language core, and your first model API calls, plus how to use ChatGPT and Claude as a tutor instead of a copy machine.
Engineering
How AI agents actually work
An AI agent is an LLM call, a list of tools, and a loop. A walkthrough of a working coding agent in a little over 200 lines of Python, no framework required.
Engineering
How to set up LLM evals
A practical setup for LLM evals using raw events, unit tests, human review, LLM-as-a-judge, and A/B tests for production AI systems.
Engineering
How to build reliable AI agents
A field guide to reliable AI agents. Map the workflow first, use structured outputs for decisions, keep tool calling at the edges, and add recovery and human approval paths.
Data
How to prepare data for AI agents
Build an open-source pipeline that prepares documents, PDFs, and websites for AI agents, using Docling extraction, hybrid chunking, embeddings, and vector search in Python.
Data
How to implement hybrid search with PostgreSQL
A full walkthrough of hybrid search with PostgreSQL, combining semantic search through pgvectorscale, keyword search through full-text search and ts_rank_cd, and Cohere reranking on top, all in one database.