Let’s be honest: in most teams, performance optimization only becomes a priority when something is already on fire. And even then, it’s usually handled by one or two people. Not because others don’t care — but because real optimization requires a lot of time, context, and expertise. At Temabit, we decided to experiment with a different approach: delegating part of the optimization work to agents. In this talk, I’ll share what came out of it: how we learned to frame optimization tasks so agents can produce useful results, how we validate their suggestions, and why optimization turned out to be much harder to delegate than writing code — but potentially far more valuable. Real cases, honest lessons, no hype. And one key question: how realistic is it to trust agents with the performance of your product?

If engineers don’t think like product managers, PMs become the bottleneck. In many teams, engineers execute tasks while PMs carry strategy, context, and decisions alone. The result? Slow innovation, low ownership, and “feature factory” delivery. This talk explores we explore how PMs can redesign the system - not just the mindset - to build outcome-driven teams. You’ll learn: • Why feature factories emerge • How PM overload happens • How to push context and decisions down • How to create shared ownership of metrics and impact • Real examples of transforming delivery teams into product teams

AI development hits everyone, so hit us. Everybody wants AI agents to replace regular UIs. In this talk, I will describe the evolution of our multitool AI agent, built with Node.js on top of Google Vertex AI. I’ll dive into our journey of choosing the right models and scaling development through CI/CD, TDD, and performance monitoring. Is it even possible to achieve stable results for AI projects that can hallucinate and return various responses? Interestingly, we eventually decided to remove MCP servers and Zod schema validation—technologies often considered the "standard" for these tasks. Want to know why we moved away from them? Join my session to get these insights and ask your questions live!

- Professional deformation and its consequences. - Market bias and perception — how to find a balance? Let's talk about two important components of any experienced product leader's profile: their expertise, which can become a burden, and the difference in the dynamics of the development of that expertise and the market to which it relates. There will certainly be more questions than answers, but the search for answers will be an interesting adventure for your “homework.”

AI video generation services are evolving extremely fast, effectively opening up a new way of creating video — at a moment when the standards, language, and rules of this industry are still being formed. It feels like a reinvention of cinema: there is no single “right” approach yet, but it’s already clear what prevents generation from becoming a controlled production process. I will share ideas on how existing platforms can be improved, drawing on directing experience: which director-level needs a product should account for, where modern services most often break the pipeline, and what product changes could significantly increase the quality of the final result. The focus is on a platform that works equally well for two audiences: experienced filmmakers who need control and predictability, and “native AI” creators who started their journey in video through generation and have no traditional production background. For both groups, I will outline the logic of a pipeline: "idea → generation → rough edit → export", including the minimum viable editing inside the service and professional export to Premiere or DaVinci for finalization.

Talking about something abstract is easy and fun — you can sound smart, and no one can really verify your claims because, well, it’s abstract. In a world where every other person is now an “AI visionary,” I want to speak about AI in a максимально concrete way, using my own work at GitLab as the example. It’s a perfect case. While many people describe a world of pink ponies — greenfield projects built from scratch — I want to share the hardcore brownfield reality: what actually happens when you introduce AI into a project that has existed for years. Here’s what we’ll cover: - How AI and JavaScript get along (or, more accurately, don’t) - My setup and “secret sauce” for agentic development - What I’ve achieved together with Opus (my favorite model) - How much JavaScript code I still write by hand - What prevents us from reaching the mythical 10x productivity — and what I’m doing about it My goal is simple: after this case study, everyone should walk away with a clear understanding of what they can implement in their own project to make life better — starting now.

Most people use artificial intelligence primarily as a productivity tool — to complete tasks faster and more efficiently. But can AI also help us grow faster and become better versions of ourselves? On the one hand, AI as a plan designer helps define the steps needed to achieve personal goals. AI as a reflective partner can analyze your voice journal — structuring chaotic thoughts, identifying patterns, and uncovering unexpected insights — and enable deeper reflection on articles, books, and videos. AI as a sparring partner creates a safe space to practice skills in simulations, from job interviews and difficult conversations to improving your English. AI as a tutor allows you to acquire new knowledge and skills at your own pace. On the other hand, there is a risk of the illusion of competence — how can you be sure you have truly mastered knowledge and skills with the help of AI if you are not an expert in the field? Moreover, some studies point to cognitive atrophy resulting from outsourcing thinking and relying on AI as a crutch for reasoning. So, is using AI about accelerated growth — or gradual degradation? During the talk, the speaker will present the results of a practical experiment with real cases and explore whether AI can help us become better versions of ourselves.

Design is what gets to people—not what sits in files and internal meetings. To make that happen, you need to present and sell your work. The client, stakeholders, dev team, and everyone else who'll build it needs to buy in. I'll share what I've learned working with product companies, studios, agencies, entrepreneurs, and decision-makers. I'll cover: • Why a good-looking, logical design isn't enough to get it approved and built • How to structure your presentation so people actually get it • When screen sharing Figma works fine, and when it kills the vibe • How to think about who you're presenting to, what they need, and why—so you spend less time on revisions and ship faster

About the different stages of a designer’s career and the challenges we face at each of them. Answers to questions such as: what truly makes a designer great; what kind of education a designer needs and whether it’s possible without it; why designers need politics and how to present their work; how to work in a team; how to give non-toxic feedback; how to understand yourself and find your own “kung fu” — and much more.

Today, a portfolio is not just a collection of beautiful visuals — it’s a reflection of a designer’s way of thinking. We’ll talk about how to create a portfolio that tells a story, demonstrates the depth of design decisions, and shows the real value a designer brings to a team. The talk will feature real-world examples, common mistakes, and practical tips to help designers make their portfolios clear, compelling, and relevant to today’s market.