How to decide on the frontend decision tree
summary
A concrete workflow appears: a probability calculation framework implemented in JavaScript scores framework choices across cost popularity flexibility and time, and a data-driven task prioritisation scores stories by impact cost confidence per time to rank work. The talk uses numbers—about 35,000 decisions per day—to justify automating routine choices and demonstrates kNN training in JavaScript to refine recommendations; a live demo suffers connectivity hiccups but the model trains and yields ranked suggestions that replace guesswork.
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chapters (18)
- 0:00Intro
- 0:22Why
- 0:46It depends
- 1:33The Golden Circle
- 3:28Define
- 3:40Eisenhower
- 4:41Decision Phatak
- 6:12Action
- 6:46Which framework
- 7:43Map
- 8:00Demo
- 8:22First example
- 9:15Second example
- 9:55Impact Cost Confidence
- 10:20Realworld Scenario
- 11:17What now
- 11:38Experimenting with probability
- 12:45What next
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