In late October 2026 I posted a series of LinkedIn reflections covering video discovery, rapid prototyping, AI‑enabled execution, and feature prioritisation with RICE. The posts each laid out a problem, hypothesis, trade‑offs, metrics, and take‑aways to guide product decisions.
The new “Videos for you” section shows two video recommendations, sitting right between regular feed posts.
Key metrics I plan to track for the video discovery experiment include meaningful video discovery rate, qualified watch rate, follow conversion, feed satisfaction, and negative feedback on important content discovery.
Smart Revision Reminders: 500 users reached, 2 impact, 80% confidence, 2 person-months effort = 400
AI Doubt‑Solving Assistant: 800 users reached, 3 impact, 70% confidence, 6 person-months effort = 280
Personalized Study Dashboard: 400 users reached, 1 impact, 90% confidence, 2 person‑months effort = 180
I concluded that RICE scores are a useful filter but must be weighed against strategic considerations, compliance, and confidence thresholds before committing to build.