Build UIs that learn - Discover the powerful combination of UI and AI
What if your UI could predict a user's next click? Learn to build self-training apps that prefetch code and eliminate loading delays entirely.
#1about 1 minute
The performance problem of large single-page applications
Large component codebases in single-page applications can lead to significant loading delays and a poor user experience.
#2about 3 minutes
Using lazy loading and prefetching to improve performance
React's lazy loading API defers component loading, and dynamic imports can prefetch resources before they are explicitly requested by the user.
#3about 3 minutes
Predicting user actions with machine learning sequences
User navigation can be modeled as a sequence prediction problem, where a machine learning model predicts the next click based on past behavior.
#4about 2 minutes
Designing a neural network for click prediction
An LSTM (Long Short-Term Memory) neural network is well-suited for sequence prediction, using one-hot encoding to represent user actions without false correlations.
#5about 1 minute
Implementing the prediction model with TensorFlow.js
TensorFlow.js allows for building and running machine learning models directly in the browser using a sequential API to stack network layers.
#6about 1 minute
Training the model asynchronously with user data
The model is trained asynchronously in the background using the `fit` command, allowing the application to adapt to user behavior over time without blocking the UI.
#7about 3 minutes
Architecting a solution with the React Context API
A centralized architecture using two React contexts, one for prediction logic and one for prefetching, decouples the prediction source from the prefetching action.
#8about 3 minutes
Implementing the context-based prefetching logic in code
A custom `PredictionLink` component uses the `useContext` hook to listen for predictions and trigger prefetching via the prefetch context.
#9about 3 minutes
Summary of the intelligent prefetching technique
Combining React's code splitting with a browser-based machine learning predictor creates a powerful system for improving application performance by prefetching resources.
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