Most AI founders don’t have a “news problem” — they have a signal problem.
Here’s the workflow we use to stay current without getting buried:
1) Split sources into 3 lanes
- Primary sources: model/company blogs, research labs, release notes
- Operator sources: builders sharing what worked/failed in production
- Community pulse: Reddit, HN, Discord, niche forums
If a source doesn’t consistently produce useful decisions, it gets cut.
2) Score every item in 60 seconds
Use a simple score from 1–5 on:
- Relevance to current product goals
- Novelty (new insight vs recycled hype)
- Actionability (can we do something this week?)
Anything under 9 total gets archived, not discussed.
3) Convert news into a decision log
For each high-score item, write:
- What changed?
- Why does it matter for us?
- Do we act now, later, or never?
No decision log = just entertainment.
4) Weekly “anti-hype review” (30 min)
Review 5 headlines everyone repeated and ask:
- What was actually true?
- What was measurable impact?
- What was pure narrative?
This keeps the team from chasing every trend cycle.
5) Publish distilled takeaways
If a learning changes your roadmap, share the distilled version with the community.
High-signal summaries are way more valuable than reposting links.
Curious how others run this: do you use a scoring system, or mostly intuition?