AI Coding Tip 032 - Build a Dark Factory Pipeline
TL;DR: Run your pipeline like a dark factory: automated, sampled, and policed by an adversarial model. Common Mistake ❌ You let one model write a pull request, then hand the same model (or a suspicio
I am Maximiliano Contieri I've been working as a software engineer and teaching for about 25 years. I write about software design, refactoring, test driven development, clean code and code smells.
TL;DR: Run your pipeline like a dark factory: automated, sampled, and policed by an adversarial model. Common Mistake ❌ You let one model write a pull request, then hand the same model (or a suspicio
TL;DR: Reasoning models already verify and pace themselves, so drop those prompts and set real effort, scope, length, autonomy. Common Mistake ❌ You still write prompts for a model that evolved and s
TL;DR: Turn repeatable skill steps into tested scripts instead of prompts, so behavior stays deterministic and cheap. Common Mistake ❌ You ask the AI to repeat the same multi-step task through free-f
TL;DR: Assign a different model to each pipeline stage since none excels at planning, coding, reviewing, and testing alike. Common Mistake ❌ You open one chat with your favorite model and ask it to p
TL;DR: Wire a skill into your company's live systems so every answer carries a source and a freshness check. Common Mistake ❌ As a single contributor, you can build a personal Second Brain, and it wo
TL;DR: Wire your standards into hooks, skills, and a judge, so the harness blocks violations before a human opens the diff. Common Mistake ❌ You paste your coding standards into AGENTS.md and trust t
Know who speaks before the skill runs TL;DR: Always define a clear role at the top of every skill file so you know whose perspective drives the execution. Common Mistake ❌ You write a skill full of
Everyone is talking about Loop Engineering. Apparently, you don't need to program anymore. TL;DR: Loop Engineering is the hottest AI workflow pattern of 2026. But it hides a dirty secret. The Tweet
TL;DR: You write tests that touch every line but verify nothing, creating false confidence in a broken system. Problems 😔 False confidence Hidden production defects Misleading metrics Wasted tes
TL;DR: Add a PITFALLS.md next to every SKILL.md so your AI never repeats the same mistake twice. Common Mistake ❌ You write a great SKILL.md. The AI follows it well most of the time. Then it does som
TL;DR: Spawn a fresh subagent after every task to check your rules, because the AI that did the work can't audit itself. Common Mistake ❌ You write a detailed AGENTS.md with strict mandatory rules. T
TL;DR: Tell your AI to split work into small reviewable pull requests before it writes any code. Common Mistake ❌ You ask your AI agent to build a feature. The agent opens a 2,000-line pull request t
TL;DR: Install your harness before prompting: the structure you set up first is what turns an impulsive AI into a safe, steerable collaborator. Common Mistake ❌ You open a fresh chat and type a bare
TL;DR: Merging code you don't understand creates comprehension debt that compounds until your team can no longer maintain it. Common Mistake ❌ You ask the AI to implement a feature. The code looks cl
TL;DR: Use Obsidian with Markdown notes, YAML metadata, and direct AI file access to build a Second Brain with LLMs that gives your tools persistent project context. Common Mistake ❌ You paste a snip
TL;DR: Tell the AI your reason before your request to get solutions that match your real constraints. Common Mistake ❌ You send commands to the AI without context. "Refactor this." "Optimize this que
TL;DR: Dictate your prompts instead of typing them to speak twice as fast, create richer context, and unlock mobility. Common Mistake ❌ You write detailed prompts by hand, word by word, staying at yo
TL;DR: Ask the AI to write a program that analyzes your data instead of pasting all your data into the prompt. Common Mistake ❌ You have 50 complex JSON files. You paste them all into the chat and as
TL;DR: Improve the AI tools, rules, skills, and workflows you use in every pull request so your team and future agents can learn, reproduce, and improve on every change. Common Mistake ❌ You write cl
Don't let your most important instructions drown in context noise TL;DR: Bury critical rules and AI models ignore them. Use explicit markers to force compliance. Common Mistake ❌ You write a long sk