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.

Partnership with AWS almost always starts with resistance: “it’s bureaucracy,” “we don’t have time,” “it doesn’t add product value.” In this talk, we’ll show how we transformed AWS programs (FTR, Ready, Delivery, Competencies) into a managed operating system for working with a portfolio of projects. We’ll break down how we gained support from both the business and engineering teams, how we aligned on priorities, and how we translated expectations into clear artifacts: evidence packs, templates, KPI formats, business outcomes, and enablement materials. We’ll explain how roles and ownership are structured, what operating rhythms we use, how quality gates and case selection criteria work, and how we respond to Amazon feedback like “be more specific” so that each next submission becomes stronger and faster.

When a partner submission system is already up and running, the main challenge becomes consistently turning findings into production changes and proving their impact with metrics. In this talk, I’ll walk through our practical cycle: finding → fix → impact validation → scaling the pattern across multiple teams. We focused on metrics that turn SRE into a manageable system. We measure the speed, stability, and cost of changes so that decisions are driven by data rather than intuition — with security now embedded as part of this pattern. We’ll break down how to avoid “local” fixes, make changes reproducible, and sustain progress continuously rather than as a one-time effort.

This is a story about real pain and the maturation of a logging system. We’ll examine how the lack of standards breaks observability and why Kubernetes became the point of no return, forcing us to rethink our entire logging approach. We’ll walk through the requirements and architectural decisions that helped us regain control. I’ll share hands-on experience in building controlled, production-grade log pipelines - without magic and without “silver bullet” tools. This is an honest story from real production environments.

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.

Within the Security Hardening initiative, we introduced an Access as Code approach for managing AWS permissions. Each repository is assigned a dedicated IAM role with permissions aligned to the principle of least privilege. Role management is centralized in a single repository, where each service is defined using a single YAML file. All changes go through pull requests and approvals, while Terraform and Atlantis automatically create or update IAM roles. As a result, we achieved scalable, auditable, and secure access management without direct access to AWS for engineering teams.