Engineering
The job-ready AI automation stack to learn in 2026
The five-layer AI automation stack I've used for years. Python and FastAPI, Postgres, React, model APIs, and Docker deployment. The layers jobs hire for, not the no-code tools.
Blog
Engineering
Designing, building, and shipping production AI systems: agents, platforms, evals, and deployment.
Engineering
How to build a private knowledge chatbot
A high-level architecture for building a private knowledge chatbot with Python, FastAPI, React, Supabase, retrieval, citations, model APIs, and a full implementation path.
Engineering
How to build webhooks for your AI platform
Webhook architecture for production AI systems. Verify signatures, store events, enforce idempotency, dispatch async workers, and recover failures.
Engineering
How to build your own AI platform
A practical architecture for a personal AI platform with webhooks, scheduled workflows, agents, durable events, and a tiered context hub.
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.
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.
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.