The Compounding Asset Most Engineers Throw Away Every time you finish a project and close the last PR, something valuable gets deleted. Not the code. Not the tests. The reasoning. The “why we did it this way” that lived inside your head, inside a thread of Slack messages, inside a context window that got cleared…
The Capability Trap: Why Your LLM’s Confidence Problem Won’t Be Solved With More Data OpenAI just published a study on how 700 million people use ChatGPT. Seven hundred million. That number alone will send half the industry rushing to the same conclusion: collect more, train harder, scale faster. I think that instinct is wrong, and…
Model Accuracy Is a Lie You Tell Yourself Before Shipping I used to be the engineer who stayed up until 2am squeezing another 0.3% out of a validation set. I thought that was the job. Then I shipped enough production systems to realize something uncomfortable: the models that got replaced fastest were often the most…
Your Dev Environment Is Not a Vault I have watched AI engineers obsess over model security, prompt injection defenses, and output filtering while their local machines sit wide open. The LiteLLM supply chain attack from March 2026 is the clearest possible illustration of where the real risk actually lives, and most of the people I…
The Discipline Nobody Teaches Every engineer I know is tracking the wrong thing. Lines of code shipped. Features deployed. Model latency shaved by another 12 milliseconds. These are easy to measure, which is probably why we measure them. But the skill that actually separates good AI engineers from great ones is almost impossible to put…
Assumption Debt: The Silent Killer of AI Products Most AI products don’t fail because the model was bad. They fail because the team spent six months optimizing for the wrong thing, and by the time anyone noticed, the cost of changing course felt unbearable. I’ve watched this happen repeatedly, and I’ve done it myself. The…
Harvard’s ML Systems Curriculum Is Free Now. Here’s What That Actually Means. Prof. Vijay Janapa Reddi just open-sourced the entire Harvard CS249r ML Systems course. The textbook is on GitHub. The curriculum is public. The thing that costs tens of thousands of dollars per credit hour to sit near in Cambridge, Massachusetts is now free…
