Not everyone supports the new strategy. The AI initiative is being sabotaged. The architect is asking for two more quarters. Marketing wants to launch as early as next week. Sound familiar? The problem is that we often expect other people to follow the same logic we do. But in the product business, different “species” work side by side: those who champion people; those who champion experiments; those who champion stability; those who champion results. And until we learn to see these differences, any strategy risks remaining nothing more than a pretty slide. Using examples from product teams and AI transformations, we’ll explore how the framework of competing values works and how to use it for influence, negotiation, and change management. 📌 Key takeaways: Every stakeholder defends a value that is rational for them. “Everyone is right—but only partially.” How to read motives behind words and objections. Why the strongest products know how to switch between languages of influence. How to reduce resistance to change without pressure. How to build support around product decisions.

AI has already become part of the modern engineering landscape, but things are much more complicated in high-load production systems. GenAI works well in demos and copilots, but is it ready for real-time processing, heavy workloads, and critical production scenarios? In this panel discussion, we’ll talk about why AI has yet to become the standard for high-load architectures, where the line between ML and GenAI lies, why inference is expensive, and why FinOps is becoming a new headache for engineering teams. We’ll discuss on-prem vs. cloud for AI workloads and real-world production constraints.

A real-world-inspired architecture talk about embedding an AI agent into the operational workflow of a highload data pipeline. We walk through a cascade failure scenario: corrupted data enters the pipeline, Kafka queues get stuck, storage pressure grows, thousands of Kubernetes pods start failing and rescheduling, etcd degrades, and PostgreSQL becomes a secondary pressure point. Then we show how an agent built with AWS Bedrock AgentCore, LangChain, and MCP/Gateway could detect early signals, isolate corrupted messages, suggest human-approved fixes, protect cluster stability, and turn noisy telemetry into actionable recovery steps.

Just yesterday, AI was seen as a simple “assistant.” Today, it’s already reshaping hiring, salaries, career growth, and the role of the developer itself. Junior positions are disappearing, code generation is becoming cheaper, and companies are increasingly valuing adaptability and AI skills over years of experience. During this panel discussion, we’ll talk without rose-colored glasses: is AI really taking jobs, why senior-level experience no longer guarantees an advantage, who is winning the new AI race — engineers or prompt-native specialists — and whether software engineering itself is turning into a completely different profession. We’ll discuss what skills will actually matter for developers in the next 2–3 years, whether middle engineers will become the new juniors, and whether the Ukrainian IT market is adapting to AI-driven changes faster than the rest of the world.

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

Most systems start with push because it feels natural — the server knows when something changes, so it notifies clients immediately. But at scale, push embeds a hidden cost multiplier: connected users × open items × update rate. Every millisecond of freshness is paid for by every connected user, even those who aren't watching. This talk is based on a real-world production case study at DraftKings. We’ll explore how the data delivery model shapes the cost curve, rather than just the latency profile. We walk through: — the original push-based system and how it accumulated complexity over the years — the scaling pain points that made it unsustainable under peak load — the evaluation process that led to short polling as the answer — a zero-downtime migration strategy across four phases The results were counter-intuitive: a pull-based architecture with short polling outperformed push on data freshness at peak load, while achieving significant CPU and infrastructure cost reduction. The talk closes with a practical framework for deciding when push wins, when pull wins, and what question to ask first.

Communication is one of the key skills of a product manager, directly impacting the quality of decisions, team velocity, stakeholder trust, and career growth. It is often taken for granted: we all send messages, hold meetings, explain tasks, provide feedback, and negotiate with people every day. Because of this, many people assume their communication skills are fine. In practice, however, communication is often where the biggest pitfalls lie: unclear expectations, poorly phrased feedback, unnecessary conflicts, vague agreements, decisions lacking buy-in, and the feeling that the team “just doesn’t get it.” For a product manager, this is particularly critical, as their work relies heavily on the ability to explain, listen, coordinate, influence, and help others move in the same direction. In his talk, Ray will break down what strong communication looks like in product management: how to assess your own level more honestly, where mistakes most often occur, how to explain complex things in simple terms, and how to give feedback in a way that truly helps individuals and the team become stronger.

In this presentation, we will explore a practical use case of implementing effective infrastructure autoscaling using HPA, VPA, and Cluster Autoscaler. While working with standard VPA, we encountered several limitations, including a lack of flexibility in configuring calculation intervals and conflicts when running concurrently with HPA. Consequently, we decided to develop our own custom VPA controller. In our new solution, we: - Achieved stable coexistence of VPA and HPA on the same resources. - Implemented a filtering mechanism for transient CPU spikes during the pod startup phase. - Optimized the architecture by consolidating the functionality of three standard components into a single pod. - Leveraged the new In-Place Pod Resize capabilities introduced in Kubernetes 1.33. Key result: Optimized resource consumption and a 20–40% reduction in infrastructure costs.

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.