Performant Architecture for a Fast Gen AI User Experience

More and more developers are being asked to build Generative AI experiences into their web applications everywhere. However, the user's experience with Generative AI can sometimes be slow and frustrating. It’s been years since we would gladly wait over 10 seconds for a response to anything online. As AI processing gets faster, there are new limiting factors that could be negatively affecting your AI user experience.

In this talk, we will cover different ways to minimise laggy experiences in web applications when interacting with third-party Generative AI tools. We’ll be using a practical demonstration to show how you can reduce latency as we build one of my favourite sci fi gadgets, the babel fish.

Powerful generative AI features are quickly becoming a baseline in modern development. Potential blockers include privacy concerns, the need for a stable connection, and the costs associated with using or hosting models. However, we can now leverage generative AI directly in the browser on the user's device using emerging Web APIs like WebNN, combined with higher-level frameworks, for a better developer experience. In my session, I’ll discuss the current state of in-browser ML and AI features, compare the main players, and show you how to start building an offline-ready, future-proof, standards-based web application.

Don’t let Python developers have all of the fun with LLMs.

This talk is a gentle introduction to Generative AI and Large Language Models for front-end developers looking to build intelligent chatbots and applications. We’ll dive into Langchain.js, an open-source orchestration framework for building AI Applications.

We will start by exploring the basics of Generative AI (GenAI) and Large Language Models (LLMs), how they work, and cover some common pitfalls and how they can be avoided. We’ll take a closer look at vectors, retrievers, and how to build an agent capable of choosing tools to perform specific tasks.

About Adam: Adam Cowley is a dedicated developer with a keen interest in data and graph databases. Serving as the Senior Developer Advocate at Neo4j, Adam produces educational content for GraphAcademy, Neo4j’s free, self-paced, online learning platform. His technical experience spans two decades, developing websites, mobile apps and mixed reality experiences for budding startups to the world’s biggest companies

With the rapid adoption of AI models in software products, Chrome’s introduction of local-first, on-device AI models offers developers a powerful new way to build intelligent and fast AI features directly within the browser. This talk will explore Chrome’s Built-in AI models, their architecture, APIs, developer tooling, and also demonstrate how it supports low-latency AI experiences without relying solely on cloud-deployed models.

We’ll walk through the architecture behind Chrome’s on-device models, discuss real-world edge cases and common concerns from integrating the built-in AI APIs, and outline key system and compatibility requirements that every developer should know. This talk will also mention the evolution of Chrome’s AI ecosystem, from its early releases to current capabilities, along with a preview of upcoming features and insights from its developer community.

For advanced listeners in the audience, this talk will include Chrome’s hybrid AI support pairing with Firebase to extend applications with cloud-backed storage and processing, enabling developers to blend on-device intelligence with online workflows.

To keep this talk interactive and practical, I will use my open-source Chrome extension project built during the recently concluded Chrome AI hackathon as a demo, highlighting how the Prompt and Summariser APIs are being used in the Chrome extension.