What tasks should be delegated to AI right now, and what still requires human intervention? Using a streaming product as an example, we’ll discuss how AI copilots can help QA teams streamline technical routines, scale testing, accelerate releases, and free up time for people to focus on product research, UX, and complex user scenarios

Recently, Superhuman (formerly Grammarly) launched Superhuman Go, an AI assistant that works alongside you on every platform. To build it, we needed a scalable solution that supports an unlimited number of agents that dynamically shapes the user interface and looks similar across all supported desktop and mobile platforms. Join me to find out how we discovered solutions for this innovative new product.

Topics include: - Which growth challenges will define 2026. - How AI is changing the speed of MVP launches and product experiments — and why speed without strategic focus does not create sustainable growth. - Why CRO and performance marketing alone are no longer enough for scaling. - How to use AI for research, prototyping, product drafts, and faster solution launches.

This talk is about taming AI: the journey from a simple Plan Mode in Cursor/Claude to structured specification systems. We'll break down why GitHub Spec Kit turned out to be too heavyweight, how ADR (Architecture Decision Records) helps agents retain context across sessions, and why OpenSpec by Y Combinator (Fission-AI, W26) became sweet spot. The central thesis: code quality on output equals specification quality on input. Together we'll reflect on the transformation of the developer's role — from "coder" to "spec architect."

What if an engineer receives not a task, but a ready PR with context and a proposed solution? At Wix, we’re building remote agents — autonomous agents triggered by external events (Jira, Slack) that execute tasks in the background without human involvement and return results as context for developers. In this talk, I’ll cover: what remote agents are, how they’re architected, and how to integrate one into your own system. I’ll share real numbers — success and failure cases from our experience. Beyond that, the non-obvious parts: where agents break, why a spec-driven approach is critical for them to work, and what changes in team processes when part of the work is done by an agent. This talk is for those already working with AI tooling and thinking about the next step — from copilot to autonomy.

Vibecoding is one of the most effective tools for a product manager today. It not only enables the use of ready-made services to speed up workflows, but also allows you to build custom tools tailored to specific tasks, taking into account the product context and processes. This reduces unnecessary actions and repetitive manual work. In large teams, it’s about personal efficiency and workflow automation. At the R&D stage, it’s about the ability to independently build an MVP, get initial user feedback, and only then hand the solution over to development. In the talk, Maksym will explain how he uses vibecoding in his work as a product manager — from MVP development to building custom tools.

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Market expectations for developers are rising fast: today, clients expect not only basic prompt-writing skills but also hands-on experience with the Model Context Protocol (MCP). In this talk, we’ll show that MCP isn’t as complex as it seems. We’ll walk through the journey from a standard REST API to a fully functional MCP server. You’ll learn the key features of the protocol, the essential libraries, and the security considerations needed to enable AI to interact with your backend autonomously.

Oleksii Minakov and Vyacheslav Koldovskyy will face off in a spectacular battle, showcasing the most exciting and unexpected use cases of generative AI tools live on stage. Expect unconventional scenarios, creative experiments, and impressive real-world applications of AI that will engage both beginners and experienced professionals. The goal is not just to impress, but to expand your understanding of what modern artificial intelligence is truly capable of. Vyacheslav Koldovskyy: Agent Skills Superpowers Remember how Neo learned Kung Fu in The Matrix? The knowledge was simply uploaded into his mind. Unfortunately, that doesn’t work for humans yet — but it’s already possible for AI agents through Agent Skills. Agent Skills are essentially the equivalent of human skills — the ability to perform actions, achieve outcomes, or solve specific tasks. However, unlike humans, agents don’t need years of training. These skills can be created, combined, and reused almost instantly. In practice, Agent Skills are modular building blocks of behavior: individual functions, instructions, or scenarios that an agent can invoke when needed. They can range from simple actions (like processing text or retrieving information) to complex workflows (running analysis, interacting with APIs, or executing multi-step processes). The real “superpower” emerges when these skills work together. An agent can dynamically choose which skill to apply, chain them together, and adapt its behavior based on the context of the task. Moreover, if we define a sequence of actions not as deterministic code but as a set of agent skills, we effectively “program” the agent in a completely different paradigm — non-deterministic programming. Unlike traditional programs, this approach allows systems to adapt to context, make decisions under uncertainty, flexibly adjust behavior, and even improve over time. And that’s exactly why Agent Skills are the true superpower of AI agents — something we’ll explore together with Vyacheslav Koldovskyy.