JSNation 2026

Ever wonder how much of your codebase is just… hanging around, doing nothing? At Sentry, we did too - and the answer was more than we expected. In this talk, I’ll share how we used Knip, a powerful tool for detecting unused files, exports, and dependencies, to declutter our frontend codebase. You’ll learn about the practical steps we took to safely identify and remove dead code, how we integrated Knip into our workflows, about unexpected edge-cases and what we learned along the way. Whether you're maintaining a massive monolith or just looking to tidy up, this session will give you practical strategies - and maybe a little inspiration - to start decluttering your own codebase, one Knip at a time.

Ever wondered what it takes to power millions of voice conversations at ChatGPT's scale?When OpenAI needed infrastructure for ChatGPT's Advanced Voice Mode, they turned to LiveKit's open source Agents framework. Not a proprietary black box. Not a closed platform. Open source software that anyone can use, modify, and deploy.In this talk, I'll take you behind the scenes of building production voice AI infrastructure that handles millions of conversations:Why Open Source for Production AI – The technical and business reasons behind the choiceArchitecture Decisions – How we built for scale, reliability, and low latencyScaling to Millions of Calls – The challenges you don't anticipate until you hit themLessons Learned – What we'd do differently knowing what we know nowWhat's Possible Now – How you can use the same infrastructure for your projectsThis isn't a sales pitch, it's a technical deep dive with real production metrics, architectural diagrams, and honest discussions about trade-offs. You'll see the actual stack, understand the scaling challenges, and learn from our mistakes.Whether you're building your first voice agent or scaling to production, you'll walk away with insights from one of the largest voice AI deployments in the world. Because the infrastructure powering ChatGPT's voice mode is open source, and it's available to everyone.

The JavaScript ecosystem is moving towards a standardized module system, and Node.js is evolving to aid its adoption. What actually happens when Node.js loads an ES module today? This talk walks the full pipeline - resolution, loading, parsing, compilation, linking, instantiation, and evaluation. We will cover how the work is split between V8 and Node.js, where Node.js differs from browsers and bundlers, and the recent changes in Node.js that unlocks new patterns for better interop, performance, and customization in ESM. You will leave with a clearer mental model of how ESM is loaded in Node.js, shed obsolete knowledge and misconceptions, understand where to look when your modules don't work, and learn about the new capabilities that will help you write, run, and share ESM code better on Node.js today.

Code generation scales. Planning and review don't, limiting how fast AI-native teams can ship. This talk traces the evolution of developer tooling to the current explosion of coding agents, and makes the case for why the biggest opportunity now is helping engineers plan and review faster.

For over a decade, Node.js documentation relied on legacy tooling, despite being the critical source of truth for @types/node, Bun, Deno, and AI models. In this talk, we unveil the engineering behind @nodejs/doc-kit – the modular successor to Node’s documentation engine. Discover how we parse massive ASTs and leverage modern Web Standards to solve complex rendering and accessibility issues, creating a high-performance experience designed to delight humans and power the ecosystem.

Standardization pays off - but adoption is where teams bleed. An engineer’s view of adopting Netflix’s UI paved road tooling - Web Framework, Hawkins design system, and GenAI - covering the bumps (and fixes) with real-life examples and playbooks you can apply.

TanStack AI is an open-source project built to make it easy for developers to use AI in their applications and in this talk Alem will explain how they used AI to help them prototype concepts, solidify API's and ship the final library in under a month's time. Learn practical use-cases for AI in your day to day life through the lessons learned on the development of TanStack AI.

Memory leaks in single-page applications are often viewed as mere technical debt, but data shows they directly degrade user experience and engagement. In this talk, we introduce MemLab, an automated framework that identifies memory leaks by simulating user interactions and analyzing heap snapshots.