Maxim Salnikov
Privacy-first in-browser Generative AI web apps: offline-ready, future-proof, standards-based
#1about 3 minutes
A demo of client-side AI using the NPU
A computer vision application performs image classification directly in the browser without any backend calls by leveraging the device's Neural Processing Unit (NPU).
#2about 3 minutes
The case for privacy-first, on-device AI
On-device AI meets user demands for performance, privacy, and offline access while satisfying developer needs for a unified codebase and helpful abstractions.
#3about 3 minutes
Introducing the Web Neural Network (WebNN) standard
The emerging WebNN standard provides a model-agnostic, unified abstraction for near-native AI execution in the browser, designed around practical use cases.
#4about 4 minutes
Leveraging hardware like the CPU, GPU, and NPU
WebNN can access all available hardware, with the NPU offering a power-efficient alternative to the GPU for sustained AI workloads on mobile devices.
#5about 6 minutes
Getting started with the low-level WebNN API
To experiment with the emerging WebNN standard, developers must use canary browser versions and enable specific flags, but its low-level API can be complex.
#6about 7 minutes
Simplifying development with high-level AI frameworks
Frameworks like ONNX Runtime Web and Transformers.js provide higher-level, task-based abstractions over WebNN, making it easier for app developers to build AI features.
#7about 3 minutes
Best practices and the future of browser AI
Focus on user experience by providing fallbacks and progress indicators, and look ahead to upcoming built-in browser APIs like the Prompt API that abstract away model management.
#8about 2 minutes
Demo code and using web workers for performance
The demo applications are built as offline-ready Progressive Web Apps and use Web Workers to run intensive AI computations without freezing the main UI thread.
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