Schedule

Tools like Playwright MCP (or AI) or Claude Code can generate tests for your code.Cursor can give you testing suggestions about your codebase that sounds logical within seconds also.Testing has never felt this easy before.Until the need for a full scale, seamless are effective testing strategy for your product arises.How do you know if these generated tests and suggestions really fit your standards and needs, despite looking so good?How do you, as the lead engineer, balance the generated tests across different testing quadrants, between the “what” and the “when” of a testing automation pillar and design a scalable architecture for your team, and beyond?How do you combine these AI tools to architect a scalable AI-driven testing workflow and still fully own your team’s code quality?Join my talk and let’s find out.

JavaScript engines power the web, but what does it actually take to build one, and even contribute to the ones used by billions?In this talk, I'll share the journey of creating Boa, a JavaScript engine written in Rust, and what it taught me about how JavaScript really works under the hood. We'll explore how code is parsed and executed, and the surprising complexity hidden inside the ECMAScript specification.But building an engine wasn't just a technical challenge, it was also about building a community. I'll share lessons from leading an open-source project, growing contributors, and navigating the realities of maintaining a language runtime.Finally, we'll look at how this work extended beyond Boa, culminating in contributions to V8, the engine that powers Chrome, through the implementation of Temporal in Rust. This is the story of how an open-source project can evolve from experimentation to influencing the broader JavaScript ecosystem.

As the pendulum of web development swings back towards the server, streaming has become increasingly popular. Specifically, out-of-order streaming.Let's build our very own simplified version to explore how it works, what problems we are trying to solve, and what this future of web development looks like.

Billion of users are starting their searches on ChatGPT instead of Google.For JavaScript developers, this isn't just a trend, it's a new runtime: ChatGPT Apps. These new apps serves as a replacement for traditional web and mobile applications: interactive, tool-powered experiences living inside the world's fastest-growing AI platform. They benefit from packing an LLM right within the application.Giants have already shipped their own: Booking, Expedia, Accor, Figma, Uber... Now it's time to build yours!I built Skybridge, an open-source TypeScript framework for building them, and it comes packed with everything you'd expect from a modern JS dev environment.

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.

Modern web development still relies on a strong separation between HTML/CSS for UI and Canvas/WebGL for rendering. In practice, combining both often leads to complex synchronization logic and a series of workarounds.In this talk, we’ll first walk through how these problems are typically solved today — including scroll synchronization, frame loop timing, scissor-based rendering, and mapping DOM elements into a WebGL scene — and why these approaches tend to be fragile and hard to scale.Using a set of experiments and websites developed at basement.studio, we’ll show these patterns in practice, combining real layouts with shaders and interactive effects.From there, we’ll introduce HTML-in-Canvas and show how it simplifies many of these problems — not as a finalized solution, but as a direction that removes the need for most of the synchronization and rendering workarounds we rely on today.We’ll also cover the challenges of building UI directly in WebGL, where layout, text, and interactivity become difficult to manage, especially in environments like WebXR.

Claude Code is no longer just a coding assistant, it's becoming an operating system for software development. In this talk, I'll walk through how reusable templates, agents, hooks, skills, and MCP integrations can transform Claude Code into a structured, composable workflow engine. Drawing from building aitmpl.com and shipping these patterns in production at scale, I'll show how developers can stop prompting from scratch and start building on top of a shared, evolving component layer.

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.

Join the "Ecosystem Performance" initiative to explore our mission to revolutionize the JavaScript ecosystem. We tackle bloated, slow, and outdated dependencies in popular open-source libraries, replacing them with optimized alternatives or crafting our own superior solutions. Our goal is a faster, more secure web, enhancing the developer experience by reducing node_modules overhead and improving overall efficiency. Discover what we've achieved so far, our ongoing efforts, and how you can contribute to shaping a leaner, more performant future for web development.

Yarn is the latest tool to join the Rust revolution. But is rewriting JavaScript tooling in Rust a necessity or just a trend? In this talk we'll dive into Yarn's recent updates to understand why they made the switch and how it went. We will discuss whether a fully Rust-based toolchain is inevitable and, crucially, what role Node.js will play in this new landscape.

Sooner or later, every team faces the same question: how do we migrate from legacy code to a modern framework? Should we take the “big bang” approach and rewrite everything from scratch, or should we migrate progressively, piece by piece? In this talk, I’ll share real-world stories from large-scale frontend migrations and highlight the trade-offs of both strategies. We’ll explore the risks of freezing development for months, the complexity of running two worlds in parallel, and the decision-making criteria that help teams pick the right path.

Code migrations are repetitive, time-consuming, and tools like codemods struggle to handle complex transformations without extensive manual effort. What if there was a smarter way to tackle large-scale migrations? In this talk, we explore how LLMs can take the heavy lifting out of such complex code migration and share practical learning and insights.

Most technical talks teach you how to write clean, scalable, maintainable code. This one does not. Here you will learn how to write code that confuses, deceives, and breaks things in unexpected ways... on purpose.

After training and coaching more than 400 tech leads across companies and industries, one pattern keeps repeating: when tech leads struggle, it’s rarely a technical gap.In this talk, Anemari Fiser shares the most common challenges tech leads bring into coaching sessions - overload, unclear expectations, ownership confusion - and assumed alignment and why “getting better technically” almost never fixes them.Drawing from real coaching and training examples, the talk explores where tech leads get stuck, what they think the problem is, and the small but meaningful shifts that help them move forward.

Every title that reaches Netflix's millions of subscribers passes through a gauntlet of decisions — some made by rules, some by ML models, and some by human reviewers. And with Netflix's catalog growing faster than ever, getting all of them to work together reliably, at scale, is one of the hardest problems in production systems engineering.In this talk, we'll share how we rethought workflow orchestration from the ground up to build a framework where rule-based automation, ML models, and human-in-the-loop review aren't just bolted together — they're first-class citizens in the same pipeline. We'll get into the real challenges: routing decisions across heterogeneous components, isolating failures so a single bad signal doesn't cascade, and closing the feedback loop across the entire system. Along the way, we'll show why the architectural choices that make this work today are exactly what make AI agent integration tomorrow feel like a natural evolution — not a retrofit.If you've ever tried to build a production pipeline that doesn't fall apart when one piece changes — this one's for you.

The hype around AI coding agents is very real, and autonomous coding systems are improving fast. In this talk, we'll explore the "Ralph Wiggum" method, where AI agents run in persistent iteration cycles until tasks are actually complete. You will learn about backpressure mechanisms to let the LLM self-correct and retry without human intervention. We will discuss how to structure the prompts, practical patterns for turning test failures into actionable AI feedback, and honest insights about when this approach works best versus where it falls flat.

AI coding assistants are great at completing small tasks or features. But what do you do when you're working with more complex codebases and need to build in-depth features that require upfront planningThis talk explores spec-driven development as a solution to this problem. I'll show you how modern AI coding assistants can help break down complex tasks into three distinct phases. We'll look at the real-world tradeoffs of this approach, and most importantly, how you can use it in your own projects right away.

Monorepo Metamorphosis: Reducing CI Load by Over 65% with Nx, pnpm & Predictive Build GraphsDoes your CI feel slower every time your monorepo grows? In this talk, I’ll share how we transformed a 200+ project JavaScript/TypeScript monorepo by applying predictive dependency graphs, Nx task-hashing, pnpm’s content-addressable workspace, and horizontally scaled runners to eliminate redundant work and shrink build+test cycles by more than 65%. If you’re looking to break the cycle of ever-increasing CI times and unlock real delivery velocity, this session provides a clear and practical blueprint.

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 infrastructure. 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.

Auth bugs rarely show up as “login is down.” They show up as redirect loops, stale sessions, broken refresh flows, and permission leaks after “small” changes. This talk walks through one real incident pattern and delivers a repeatable playbook for debugging fast and adding guardrails that keep auth stable in production.

Black Friday is one of the most challenging times for any system, as traffic can increase by four to ten times within a short period. Teams often prepare by running performance tests, but they may not be using the right testing method.In this talk, I will present a real-life situation where a system seems fine during testing but crashes when faced with actual conditions.The main problem isn't the system itself, but the way the test was set up.This topic is especially important for teams developing and expanding web applications, especially during high-traffic events like Black Friday.It bridges the gap between performance testing strategies and real-world system performance, while also considering business impact.

JavaScript uses Garbage Collection (GC) for memory management. As a result, many developers that use JavaScript based systems, such as NodeJS, assume that they’re free of memory issues. Unfortunately this is incorrect, and many NodeJS-based services suffer from memory management problems such as leaks and excessive GC CPU cost. But finding, understanding, and resolving memory issues can be very challenging, because a problem in any part of the application impacts the application as a whole.

AI is writing code faster than ever. But there's a gap: the gap between "it works" and "it works correctly."As a QA Automation Engineer with 9 years of experience, I've developed the instinct to see what breaks before it breaks. AI generates clean code that hides catastrophic bugs in plain sight. This talk teaches you "the tester's eye" – the ability to spot edge cases, boundary conditions, state issues, and silent failures that AI confidently creates.You're reviewing AI code with developer eyes trained to see "does this solve the problem?" Testers see something different: "what are all the ways this could fail?" I'm teaching you to see both.

Every minute added to your CI pipeline costs real money and real developer time. At ~45 MRs per day and ~340 pipeline runs, a single minute of pipeline overhead translates to nearly 6 hours of lost engineering time daily. This talk presents the complete playbook we used to reduce our Merge Train pipeline from about an hour to about 22 minutes — a ~64% reduction — while improving CI health from the low 80s to the low 90s percent. Covering compute instance migration (with real benchmark data from 10 runs per configuration), service test extraction, CloudWatch optimisation, linter caching, flaky test detection, and cost analysis, attendees leave with a prioritised framework for their own CI optimisation efforts.

JWTs (JSON Web Tokens) are everywhere – frontends, backends, microservices – and for good reason: they're easy to pass around, self-contained, and standardized. But while JWTs can be a solid fit for authentication, using them for authorization is a decision that comes with serious pitfalls – especially in distributed systems.In this talk, we’ll explore the technical and security limitations of JWT-based authorization and explain why they're fundamentally incompatible with the needs of modern applications. From the infamous ""New Enemy Problem"" described in Google’s Zanzibar paper to the vague semantics of scope claims and the difficulty of revoking tokens in-flight, we’ll unpack the real-world consequences of treating JWTs as your AuthZ layer.

AI agents are almost everywhere, but deploying one yourself still feels like it requires a PhD in infrastructure. What if it didn't? This talk breaks down what AI agents actually are, how they work, and why Cloudflare's platform makes it ridiculously easy to build and ship your own. Everything written with the language you love (or hate): JavaScript.We'll cover the agent loop, introduce MCP (Model Context Protocol) as the glue that connects agents to real world tools, and walk through Cloudflare's stack, from Durable Objects for stateful execution to Workers AI for inference.By the end, you'll deploy your own agent using a single command from a shared GitHub repo.