How I Filter AI News Without Losing My Mind (A 20-Minute Weekly System)
Eleven launches a day, most of them noise. This weekly filter keeps me informed enough to make decisions and oblivious enough to work.
AI news moved from a trickle to a firehose, and my first response was to drink from it: forty feeds, push notifications, a permanent background hum of announcements. My work got worse and my knowledge did not get better. The system I run now takes twenty minutes a week and, notably, I make better-informed decisions than I did at firehose exposure.
The core principle: filter for consequences, not for novelty. A new model release matters to me only if it changes what I should be using, buying, or building. Ninety percent of AI news fails that test by definition, including most of what gets the loudest coverage. Benchmarks are not consequences; pricing changes, deprecations, and capability jumps in tools I use are.
The weekly stack is deliberately small: one good digest newsletter, one community thread where practitioners report what actually changed, and one search I run myself for anything involving the tools I pay for. Twenty minutes, Saturday morning, coffee. Daily AI news is a slot machine; weekly is a briefing.
The rule that does the most work: nothing gets adopted from an announcement. Every tool that enters my stack does so after a boring-task trial, no matter how good the launch looked. This single rule filters more hype than any curated feed, because announcements and reality are different products with different incentives.
The fear underneath the firehose is falling behind. A year into the twenty-minute system, I can report the secret: almost none of the firehose mattered. The three or four things per year that actually change your work are exactly the things the weekly filter catches, and you get the other 361 days back.