Mind-Reading with Intelligent & Adaptive UIs
summary
Claim: represent the UI as an executable state model, measure transition frequencies, and adapt the UI in real time. Implements this with X State and a single on-transition analytics hook, builds weighted graphs and decision trees, and explores higher-order Markov models and reinforcement learning agents running in-browser. Demo is a tic-tac-toe app where the agent learns human-like moves and the frequency heatmap updates live; used similar analytics in Visual Studio Live Share and Gatsby. Caveat: xstate-analytics has a reported memory-leak warning.
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chapters (15)
- 0:00Introduction
- 2:22Adaptive User Interfaces
- 5:11Adaptive User Interface Challenges
- 6:30Finite State Machines
- 7:48State Charts
- 9:54Weighted Graphs
- 11:30Adaptive UIs
- 14:38Higher Order Markov Models
- 15:55Reinforcement Learning in the Browser
- 18:18The Bad News
- 19:50Xdate Analytics
- 20:20Gatsby
- 20:41Resources
- 21:22Make your code do more
- 21:49Questions
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