This talk demonstrates practical approaches to unified observability, where metrics, logs, traces, and profiles are integrated for rapid diagnostics in distributed systems. We will cover data correlation techniques using trace IDs and labels to enable instant navigation from errors to specific spans, setting up continuous profiling for preview environments, using flame charts for performance analysis, and leveraging dependency maps and service graphs to visualize architecture. Special attention is given to AI-specific aspects: applying AI assistants to automate root cause analysis and implementing AI Evals for systematic evaluation of the quality, correctness, and reliability of AI systems.

Low Latency in High-Load Systems: From Redis Pub/Sub to In-Memory Runtime — a Toy That Went Too Far In this talk, we will explore real-world experience in building a low-latency system for cross-exchange operations, where geography is just as important as algorithms. We will discuss why message brokers and classic microservices are not a good fit for HFT (High-Frequency Trading)-like scenarios, how in-memory state combined with regional runtime nodes provides predictable latency, and where the boundary lies between speed and consistency.

Team and service growth often leads to a situation where each individual decision seems right, but the system as a whole becomes complex, unpredictable, and difficult to maintain. In this talk, the speaker will explain how they and their team approached technology standardization, what they decided to unify versus what they intentionally left to team ownership, and why observability became not a goal, but a natural outcome of this approach.

When a system consists of dozens of independent repositories, a classic monorepo is not always the right fit. Dmytro will explain how a meta-repo built with Git submodules and AI-powered tools makes it possible to preserve service autonomy while working with them as a single ecosystem. You will see real-world scenarios where AI: • connects code, contracts, and documentation • assists with automated testing and integrations • simplifies collaboration for developers, QA, and product teams • accelerates prototyping and delivery

Knowing SOLID and the GoF patterns is like knowing the traffic rules: you can pass the exam and still crash your car. There’s a huge gap between “I know the Single Responsibility Principle exists” and “I understand why it matters right here.” In that gap live God objects, irreplaceable dependencies, and interfaces no one can actually implement. Using real mistakes from a large-scale project, we’ll explore the difference between knowledge and understanding, how frameworks can hide architectural issues, and why the only way to write maintainable code is to understand the “why,” not just memorize the “how.”

How to build your own environment for generative AI in a matter of hours — from idea to working solution? During the presentation, the De Novo team will share their practical experience in creating a controlled environment for GenAI, talk about architecture, security approach, and real-life use cases in Ukrainian companies.

Доповідь присвячена підходу масштабування бізнесу через продуктовий архітектурний принцип (Product-Oriented Architecture) — перехід від одного рішення до повноцінної екосистеми з 10+ незалежних продуктів протягом трьох років.

What can go wrong if you allow each service to access the database directly? In a startup, this seems like a quick and easy solution, but as the system scales, problems appear that no one could have guessed. In my talk, I'll share Solidgate's experience in transforming its architecture: from the chaos of direct connections to a service-based data access model. I will talk about the transition stages, bottlenecks, and how isolation affected infrastructure support. I will honestly show what worked and what didn't. In short, we will analyze the controversy of this talk.

The year is 2025. Ignoring the AI elephant in the JavaScript room is getting harder by the day. This beast, vibing along with our code, stomps all over the things we developers hold dear: clean code, architecture, our habits, and the beliefs about what's "right" and what's not. This knowledge cost me a few tens of thousands of dollars while working on a project that used AI to the max. And all I have left to show for it — is this talk. Of course, we won't be able to cover everything. But I desperately need a therapy session about the most painful part — how, through working with AI, I lost my sense of what architecture means to me... and what to do about it.

When human safety is at stake, technical reliability is not just a requirement. In this talk, we'll look at architecture, workloads, WebSocket solutions, Kubernetes scaling, and other technical aspects of creating an airborne alarm map. This is a story not only about code, but also about responsibility.