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World Congress 2025 • Aug 20, 2025 • Session details

Privacy-first in-browser Generative AI web apps: offline-ready, future-proof, standards-based

Maxim Salnikov

Ditch costly cloud APIs and build fully private, offline-ready generative AI web apps. Run models locally using the WebNN API to ensure zero latency and absolute data privacy.

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#1 about 4 min

In-browser computer vision and NPU usage demo

Running a client-side image recognition task locally without backend APIs demonstrates basic offline capabilities.

#2 about 3 min

The case for native AI in web browsers

Performance constraints and privacy considerations drive the need to standardize local machine learning capabilities.

#3 about 3 min

Introducing the Web Neural Network API standard

The WebNN standard emerges as a hardware-agnostic layer to deliver near-native model execution.

#4 about 2 min

Overview of the Edge AI ecosystem and tech stack

Architectural layers connect underlying hardware silicon progressively up to high-level JavaScript application frameworks.

#5 about 5 min

Hardware acceleration with processors and neural units

Targeting different computing hardware optimizes throughput requirements and power efficiency limits for local operations.

#6 about 3 min

Setting up experimental browser flags and hardware drivers

Testing upcoming local machine learning features requires specific environment overrides and updated system drivers.

#7 about 2 min

Working with low-level WebNN execution graph logic

Constructing raw node execution arrays exposes deep complexity for frontend developers relying on specification bindings.

#8 about 3 min

Leveraging ONNX Runtime Web for local model execution

Higher-level runtime utilities simplify model loading syntax while directly routing calculations to web backends.

#9 about 4 min

Simplifying local AI tasks using the Transformers.js framework

Triggering specific multimodal tasks locally enables granular caching logic and seamless javascript dataset integrations.

#10 about 2 min

Best practices for user experience and large model caching

Building graceful local interactions relies on transparent download notifications and clear application state indicators.

#11 about 2 min

Using built-in browser Prompt APIs for language models

Offloading execution securely skips manual payload bundling entirely by hooking into a vendor's embedded assistant feature.

#12 about 2 min

Running AI applications with PWAs and background web workers

Web architectures isolate intense calculation models strictly to background processes to preserve responsiveness.

Matching moments

2:54 min

Leveraging Chrome AI and nano models for web applications

Raymond Camden · Perf + AI

2:17 min

Accelerating local machine learning models via WebNN

Christian Liebel Christian Liebel · WWC Europe 2026

4:41 min

Exploring agentic browsers and artificial intelligence generation

Chris Heilmann +2 · LIVE

2:46 min

Enhancing native browser experiences with on-device generative AI

Chris Heilmann +2 · LIVE

2:19 min

Drawbacks of cloud dependencies and local inference benefits

Christian Liebel Christian Liebel · WWC 2025

53 sec

The shift toward client-side agentic web development

Jason Mayes · Coffee With Developers

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