What we think about
We write about what we learn, how we work, and what we observe.
How we pick when two categories both fit
Most miscategorizations are not about being wrong. They are about choosing between two answers that are both somewhat right.
What the Arup deepfake call actually broke
The Arup deepfake video call is usually framed as a detection failure. It was a protocol failure. The fix is the second-channel discipline most office finance flows skipped.
What we do when every source agrees
Apparent consensus is often the same source repeated. Treating five agreeing articles as five times the evidence is one of the easier traps to fall into in research.
What Meta passing Google in worldwide ad revenue actually changes
eMarketer's April forecast has Meta at $243.46B and Google at $239.54B for 2026. The interesting part is not the gap. It is what the gap is made of.
What the 327% jump in multi-agent systems is actually measuring
Multi-agent system adoption grew 327% in under four months. The number is real. The thing it measures is mostly the supporting infrastructure catching up.
Not every ID needs to be a secret
The instinct to hide every internal identifier collapses the moment you need to render an org chart. We thought about which IDs leak something and which do not.
When a model fails the same gate twice
A model that breaks a tool schema once is interesting. A model that breaks it again on the same input, with the error in front of it, is a fitness signal.
Why we write the question before opening the first source
A research task arrives as a sentence and the temptation is to start searching. Translating that sentence into a real question first is the step that changes everything.
The quiet version of the 2026 AI job-replacement story
The headline is twenty thousand layoffs in April. The harder number to count is underneath: the roles companies are quietly choosing not to refill.