See how AI transforms software development

I’m an AI Agent Developer who has been coding with AI for the past six years,starting from the early days of GitHub Copilot. As one of Copilot and Cursor’s earliest users, I now primarily work on building coding agents and run workshops across different domains and speak frequently on AI productivity, helping developers unlock the full potential of AI-powered tools.

My expertise spans the full generative AI pipeline, from fine-tuning models and optimizing prompts to running rigorous evaluations and building scalable agents.

Grounded in a strong background in test architecture, I design AI agents that balance complexity with reliability. I focus on constantly refining and optimizing these systems to push the boundaries of what modern AI software can achieve

With 15+ years spanning distributed systems, real-time data infrastructure, and high-performance UIs across Google, Microsoft, Spotify, and Booking.com, Valerii is passionate about developer experience, low-latency systems, and bridging the gap between complex backend architectures and intuitive user-facing products.

Hands-On CTO | Software Architect | Principal Engineer | Consultant | Speaker | Mentor — AI Systems & Scalable Cloud Platforms

With over 10 years of software architecture experience and over two decades of hands-on software engineering experience, I design and build production-grade systems — from cloud-native architectures to AI-driven workflows — with a focus on scalability, reliability, and long-term maintainability.

Innokenty Bodrov is a systems analyst, product builder, and creator of AnalystCraft and the open-source TDPD Product Framework. He designs AI-assisted product development workflows that turn project context and user scenarios into executable acceptance criteria before coding agents begin implementation. His current work focuses on agentic development, traceability, and keeping human judgment at the architecture and UAT gates.

Rejin Joy is a Staff ML Engineer at Meta, where he enables and optimizes large-scale distributed training across hardware systems and builds production-scale recommender systems. Over the past decade he has worked at multiple organizations (Meta, AWS, MicroVision) across the breadth of ML domains in both modeling and infrastructure, with a deep interest in distributed-training infrastructure and large-scale Autonomous Perception Systems.