Data & AI

Machine assistance in the developer loop, and the engineering it still demands.

Updated 8 September 2026

• 4 min read

  Where smart coding assistants speed up your team, and where they should not go

  Coding assistants boost productivity and reduce time spent in code review. The risks lie in letting assistants generate or green-light the very code that needs human scrutiny.

• 4 min read

  Search Over Your Own Codebase: What Retrieval Adds and What It Cannot Fix

  Searching over your own codebase is a daily task for many developers. But as repositories grow, and the volume of code rises, so do the difficulties of keeping track of it all. Code…

• 4 min read

  Evaluating a Model Change Without Fooling Yourself: Metrics, Rubrics and Observability for AI systems

  Can your team tell whether the new prompt or model is really better? [Evaluating model changes is notoriously hard, because the evaluation requires a apples-to-apples test with the…

• 5 min read

  Running Models Locally on a Developer Machine: What It Is Good For

  Running AI models locally on a developer machine can be a practical choice when certain conditions are met, but it remains a constrained alternative to API access. Whether or not to…

• 3 min read

  AI Features Debugging: Logging Enough to Debug Without Storing Too Much

  Debugging AI-powered features can be difficult, especially when production outputs can not be explained later. Knowing what to log without violating privacy is a tough decision.…