Schedule

Let's take a nostalgic look at how the web has evolved from a simple website to a complex yet remarkably vibrant platform.

How to make a wow effect in Chrome 148 and why it's definitely time for you to read the WebGL articles from the list you saved for later.

Why use ONNX on the client at all? Comparison of server-side inference and client-side inference: latency, cost, and data privacy. When is it justified and when is it an anti-pattern?

Is Server State the bare minimum or the luxurious maximum for your projects? It’s time to find out!

How to use the Self-Profiling API to collect JavaScript profiles directly from user browsers, associate them with INP and other metrics, and then analyze them to find the real causes of performance issues.

How to develop projects with Claude/Codex and not go crazy. In the talk, I will tell you about the important features of Claude and show you how to apply the SDD approach in practice.

This is a story about how we built a system that allows us to check the technical quality of 100+ repos and roll out mass changes on them.

I want to highlight another perspective on UI that combines the purity of the functional paradigm with the reactivity of the reactive model: incremental computation. Let's explore this idea and its implementation in well-known JavaScript frameworks.

What do successful systems and shawarma have in common? How does scaling relate to choosing a sauce? What NFRs can help with choosing a topping? These and other questions will remain unanswered until we discuss solution design.

Let's explore how our beloved V8 works with code examples, see what happens to our code inside the engine, and discover what this TurboLion is and what it will bring us in the future!

In 45 minutes, we'll go through all the steps: from single prompts through rules and skills, delegation to subagents, and orchestration to autonomous tasks and dynamic workflows, where the system decides how to assemble itself for the task.

We'll try to figure out when micro frontends will help your app and when they'll complicate it unnecessarily.

I've been immersed in a range of corporate cultures and management manipulations, and I've seen their impact on people. And, of course, their motivation to keep working even when they see the real situation.

Large language models can generate convincing code, but they cannot reliably determine whether it actually works. In this talk, I will demonstrate two ways to give AI an engineering feedback loop: a formal specification based on types and category theory, and a runtime verification system in which generated code is instrumented, executed, inspected, and corrected before it reaches the user.

We will travel from Aristotle’s syllogisms and Church encodings to TypeScript, advanced prompting, and AI-agent architecture. Finally, I will share how I tested these approaches at Avito, including correctness metrics, iteration count, token usage, latency, cost, and the practical limitations of automated verification.

Analytics, tracing, monitoring, devtools — they are all so different, but they do the same thing — log aggregation. Let's talk about how to unify all of this and make life easier for yourself, LLM, and your business.

How we integrated external neural networks into our team.

Let's discuss the requirements for the layout and ui-kit and configure the Figma MCP + agent combination so that the code is generated based on your components and passes your review.

Interfaces without color theory are like JavaScript without algorithms.

The talk will guide developers from theory to practice, from generating the right shades to working with themes. It will introduce techniques that allow you to write more flexible color theory-based code right now.

I will tell you about the insights I gained during the development process. I will summarize the results achieved. I will show you the functionality and explain how to use it.

I will tell you what the Language Server Protocol is, how the editor and language server interact, what tasks it solves, and how to write your own language server.

The participant will leave with a mental and, for now, timeless model of "What the server doesn't know and why it won't find out" and criteria for choosing a strategy with an acceptable price.