Monthly Archives: July 2026

26 AI Personas reviewing code

26 reviewers, one codebase — what a single persona’s perspective misses

26 reviewers, one codebase — what a single persona’s perspective misses How do you make sure a code review actually covers everything that matters, not just what one reviewer happens to think of? A code review has a default shape: one reviewer, reading for correctness, occasionally performance. That shape isn’t wrong. It’s just narrow, and the narrowness is invisible until …

AI Security Checks

Presence check versus a completeness check

Most code review asks one question: does this look right? A security review that only asks that question will miss a debug flag sitting where a config value should be, a validation function that exists in the codebase but never gets called, or a mitigation that only handles the obvious path. The alternative is to ask a different question: what …

Why is “What Year Are You In?” Is One of the Hardest Questions in Education

Why is “What Year Are You In?” Is One of the Hardest Questions in Education Imagine two students. One lives in Dublin. The other lives in Tokyo. They’re the same age. They’re both studying mathematics, science and English. Both are preparing for university. So they’re at the same educational stage… …aren’t they? Not necessarily. Ask someone in Ireland what year …

human oversight in AI

Suggestions, not commands — designing human oversight into an AI pipeline

When you build an AI enrichment pipeline, the default temptation is to automate the output. Suggestions go in, classifications come out, humans correct errors when they surface. It’s faster to build and faster to run. . It’s also wrong — at least for a system where the classification decides what a learner can find. . What the pipeline actually does …