TL;DR
Use RadarAI as the signal layer, then run a short weekly review: shortlist → classify → decide one action → document with source links.
Time box: 25 minutes per week
Collect (10 min) → Classify (5 min) → Decide one action (5 min) → Document (5 min). Set a timer; when time’s up, pick one action and close.
Step 1: Collect signals (10 minutes)
- Scan Updates and pick 5 items with clear impact.
- Skim GitHub Trends for 2 OSS momentum signals.
- Use Skills to watch tools you actually use.
Step 2: Classify (5 minutes)
- Capability jump: new model/tool makes a workflow possible
- Breaking change: API/behavior shifts that can hurt production
- Pattern: repeated feature motif across multiple products
Step 3: Decide one action (5 minutes)
- Prototype (1–2 hours)
- Benchmark (compare two options)
- Interview (validate user expectation shift)
Step 4: Document (5 minutes)
Write one decision note: “We will adopt/watch/ignore X because …” and attach source links for future review.
Copyable template (doc or Notion)
## Weekly AI monitoring — [Date] **Shortlist (5 items):** [Item 1], [Item 2], … **Classification:** Capability jump / Breaking change / Pattern (per item) **One action:** [e.g. "Run 1h benchmark of X by Friday"] **Source link:** [URL] **Next review:** [Next week date]
Checklist: Do / Don’t
- Do: Use one signal layer; time-box 25 min; shortlist then pick one action; document with source link; revisit next week.
- Don’t: Mix 10 tabs and feeds in one session; skip the “one action” step; document without a primary source link; extend the time box “just to finish.”
Boundaries and exceptions
This workflow assumes you want a weekly cadence and one action. If you need daily alerts for a critical dependency (e.g. a breaking API change), add a separate, narrow channel (e.g. one feed or one repo watch), but keep the main routine weekly. If your role is not builder/PM/founder (e.g. pure research with no product decisions), a reading-only habit may be enough—skip the “one action” but still time-box to avoid doomscrolling.
Recommended proof pages
- Methodology — how RadarAI curates signals
- Compare — choose tools based on workflow
- Best-of — shortlist alternatives
- FAQ — quotable answers