Writing
AI Implementation
Why GitHub Wasn't Built for AI Agents (and What to Do About It)
GitHub assumes one person changes code and another reviews it. AI agents break that rhythm. What slows teams down, and the fixes that work.
To Branch or Not to Branch: Why We Build on Trunk With AI Agents
Long-lived branches feel safe, but with AI agents they multiply copies, collisions and lost work. Why we build on trunk, and when a branch still makes sense.
Two Hosts Who Don't Exist: How We Generate Our Podcast With Gemini 3.1
We moved our audio stack from OpenAI to Gemini. An honest guide to a two-host podcast on Gemini 3.1 multi-speaker TTS: what breaks, and how we fixed it.
From 17% to 97.8%: Making a Laptop-Sized AI Actually Reliable
A small on-device model started at 17% on a real task and ended at 97.8% with full speed intact. The biggest lever wasn't the model. It was the prompt.
Stop Measuring AI by Test-Pass Rate
Green tests prove the AI did what it tried, not that it was worth trying. The real metric: would a senior engineer merge it, and how to benchmark that.
91% Using AI. 11% Shipping. Which Are You?
Four stages of AI adoption: Experimenting, Piloting, Shipping, Compounding. A quick assessment to find where you are and what it takes to move forward.
Compounding Engineering: The 4-Step Loop
Most engineering makes the next feature harder. Compounding engineering flips it: a Plan, Delegate, Assess, Codify loop where each cycle makes the next easier.
Why 95% of AI Projects Fail (And What Mid-Market Companies Can Do About It)
MIT research says 95% of AI pilots fail, and 84% are leadership failures, not technology. Why mid-market projects stall, and how to get yours shipping.