How I Filter AI Signal From Hype

Between a Data Science background and trying to build something real in AI, I end up reading a lot of AI news — most of which doesn’t matter a week later. Here’s the filter I actually use before something earns a bookmark, let alone a post here.

Can I use it, not just read about it. A benchmark result is not the same as a capability I can point at a real problem. If I can’t try it against something concrete within a day or two, it stays in the “interesting, not yet relevant” pile.

Does it change what’s possible, or just what’s cheaper/faster. Both matter, but they’re different kinds of news. Incremental efficiency gains are useful; they’re not the same story as a new capability that didn’t exist before.

Who’s actually shipping vs. who’s announcing. Demos and papers are the start of a story, not the end of one. I try to wait for something that’s been used, not just unveiled.

This pillar will mostly be short reactions to specific developments, filtered through that lens — closer to notes than to news coverage.