I will discuss modern video generation capabilities from text prompts and AI-assisted editing, leveraging my 17 years of video production experience. I will showcase key platforms and tools, share case studies of integrating AI into creators’ and product managers’ workflows, outline product opportunities and workflows for developers and video makers, and highlight priority directions for the development of AI-based video services.

Can you trust the cloud? Confidential Computing enables data protection even in fully controlled environments.

A practical case of using a computer vision system for automated visual inspection in a pharmaceutical production line. The system ensures the quality and consistency of vial contents, reducing human error and improving efficiency in a highly regulated environment.

As an infrastructure provider, we often see that potential clients are somewhat disoriented when it comes to choosing the right accelerators. The technology evolves rapidly, and questions like “What’s out there?”, “How do these cards differ?”, and “Which ones are better?” are completely valid. But the selection criteria are far from simple. In this talk, we’ll explain key concepts such as compute-bound vs memory-bound models, arithmetic intensity, and model quantization—factors that are crucial when choosing the right hardware for your workload.

Kernel is currently the leading producer of sunflower oil and one of the largest agroholdings in Ukraine. What business challenges are they addressing, and why is ML a must-have? This talk explores the development of the data science team at Kernel—from early experiments in Google Colab to building minimal in-house infrastructure and eventually scaling up through an infrastructure partnership with De Novo. The session will highlight their work on crop yield forecasting, the positive results from testing on H100, and how the speed gains enabled the team to solve more business tasks.

A talk by an AI solutions integrator on how they structure their internal business processes and tasks using AI. Learn how a model can process business requirements in just 15 minutes—something that would typically take a data science team two weeks to complete.

Let’s explore Computer/Browser/Mobile Use agents. We’ll start with the APIs provided by OpenAI and Claude that support such use cases. Then, we’ll recall how LLMs and VLMs are trained, what Reinforcement Learning (RL) is, and how it can be applied in this context. We'll also look into some recent open-source agent models, and discuss how to evaluate these agents effectively.

With AI becoming increasingly present in our everyday lives, the latest advancements in the field now make it easier than ever to integrate it into our software projects. In this session, we’ll explore how machine learning models can be embedded directly into front-end applications. We'll walk through practical examples, including running basic models such as linear regression and random forest classifiers, all within the browser environment. Once we grasp the fundamentals of running ML models on the client side, we’ll dive into real-world use cases for web applications—ranging from real-time data classification and interpolation to object tracking in the browser. We'll also introduce a novel approach: dynamically optimizing web applications by predicting user behavior in real time using a machine learning model. This opens the door to smarter, more adaptive user experiences and can significantly improve both performance and engagement. In addition to the technical insights, we’ll also touch on best practices, potential challenges, and the tools that make browser-based machine learning development more accessible. Whether you're a developer looking to experiment with ML or someone aiming to bring more intelligence into your web apps, this session will offer practical takeaways and inspiration for your next project.

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.