AI for working
programmers.
Practical guides for programmers who can write code but haven't built with AI yet. From your first LLM call to production RAG, agents, MCP servers and evals. Every article comes with diagrams and code you can run. Free, no paywalls.
The topics
Each topic is a structured curriculum, not a blog. Built so you can go from zero to shipping production code, one article at a time.
AI Engineering
The core curriculum. Foundations, RAG, evals, production patterns. From "I've never called an LLM API" to "I shipped this to prod."
MCP Development
Build with the Model Context Protocol. Servers, clients, transports, integrations.
AI Agents
Build agents you can trust in production. Tool use, planning, state, retries, durable execution.
AI Coding Tools
Get real work out of Cursor, Claude Code and Devin Desktop. Context, prompting, reviewing AI code, team workflows.
LLM Infrastructure
Production LLM serving. vLLM, batching, GPU economics, autoscaling, caching. The infra behind AI at scale.
Voice & Real-time AI
Low-latency voice agents. STT, TTS, streaming, interruption handling, WebSockets. The backend behind real-time AI.
LLM Fine-Tuning
LoRA, dataset prep, eval-driven tuning. From notebook to production model.
How it works
Start with AI Engineering
AI Engineering is the core curriculum. Read it in order if you are new to AI, or jump to the article that matches what you are building.
Read what you need
Read in order if you are new to AI, or jump to the topic you are building right now.
New articles when they ship
Each article takes about 15 minutes and comes with diagrams and a runnable example. New ones show up in the RSS feed as soon as they ship.
Who this is for
Programmers who ship code in any language (backend, frontend, mobile, data) and now want to add AI to their toolkit, starting from zero.
Engineers who got an LLM call working and now want to do it well. Caching, evals, observability, the production stuff.
Experienced engineers who want depth, not tutorials. Explanations grounded in real code, with the decisions you'll actually face.
Frequently asked questions
Is this really free?
Yes. Every article is free, forever. No paywalls, no signup walls, no email harvesting.
What topics will you add?
AI Engineering, MCP Development, AI Agents, and AI Coding Tools are live. Still planned: LLM Fine-Tuning (LoRA, datasets), LLM Infrastructure (vLLM, batching, GPU economics), and Voice & Real-time AI (low-latency voice agents). New topics ship when they are ready.
I'm new to AI development. Is this for me?
Yes. Every article is written for someone who codes but is new to LLMs, RAG, embeddings, MCP, etc. Technical terms are defined when introduced. Start with the first article in either topic.
I'm an experienced engineer. Will I learn anything?
The articles are accessible but the depth is real. Each one ends with the production decisions you'll face and the trade-offs that matter. If you've shipped AI in production for years you'll skim some sections, and every article comes with code you can run to check the claims yourself.
How often do new articles ship?
There is no fixed schedule. Articles ship when they are ready, typically a few per month.
Who runs this site?
One engineer, as a side project. The articles are written with AI assistance, and every one comes with example code and tests you can run yourself. See the About section below.
About AI Engineer Path
AI Engineer Path is a learning resource for programmers building with AI. Written by one engineer who's shipped production AI, not consultants summarizing papers. Beginner-friendly to start; deep enough to take you to production.
The goal: write the resource I wish existed when I went from "I can ship a REST API" to "I just put an AI feature in front of real users." Clear explanations. Honest about what is hard. Free, because price should not be the reason anyone goes without it.