April 10, 2026
The future is here. Explore artificial intelligence, large language models, computer vision, AI agents, and cutting-edge ML techniques. Learn how Python powers the AI revolution and how you can be part of it.
Keynote Speakers
Piotr Skalski
Open Source Lead, Roboflow
Piotr is Open Source Lead at Roboflow, building tools for computer vision developers. With 8 years in computer vision, he's created open source projects with over 70k GitHub stars, written 40+ blog posts, and produced 60+ YouTube videos covering key computer vision models. Known for his sports-focused projects including Football AI and Basketball AI.
Geoffrey Huntley
Open Source Engineer, Independent
Geoffrey Huntley is the creator of the Ralph Wiggum Loop, a brute-force AI agent technique that turns simple prompts into fully functional software through automated iteration. His recent work includes Loom (an AI orchestrator/software factory as an evolution of Ralph), the 'Cursed' programming language (a compiler written entirely by AI loops), and the 'How to Build a Coding Agent' workshop. Known for his viral activism with The NFT Bay, Geoff brings a unique mix of deep engineering rigor and chaotic creativity to the stage. He currently lives remotely on a goat farm in Australia.
What to Expect
Talks and workshops happening on AI Day
Reading the Mind of an LLM
What if you could watch an AI’s thought take shape? For years, LLMs have been impenetrable "black boxes," but we are finally beginning to find ways to see how the ghost in the machine actually works. This talk explores **mechanistic interpretability**, a subfield of AI that aims to understand the internal workings of neural networks. Mapping these internal "circuits" is not only just a philosophical curiosity - or duty: it is a high-stakes engineering necessity for safety, debugging, and trust.
Luca Baggi, Gabriele Orlandi
AI engineer @xtream and open source contributor, AI scientist at xtream
Measuring Experiments in LLMs: A/B Tests and Automated Testing
Even small changes in LLMs can impact output quality, safety, and user experience. In this talk, we’ll show how to log experiments with Langfuse, automate tests with Pytest, and enrich them using Hypothesis-generated random data scenarios. Participants will learn how to use code, tests, and data-driven A/B tests to improve LLM development.
Özge Çinko, Kader Miyanyedi
, I have been a backend developer for 4 years, working primarily with Python and Django. I enjoy sharing what I’ve learned at previous PyCon talks and through writing on Medium, helping others improve their coding and AI skills.
Spec-Driven Development with Kiro: From Software Developer to AI Agent Orchestrator
In this session, we explore Specification-Driven Development (SDD) and its implementation in the Kiro IDE. You will see how machine-readable specifications drive the entire lifecycle—from EARS-based requirements to automatically generated code, tests, and documentation. The session also highlights the industry shift from developers as coders to developers as orchestrators of AI agents, defining intent and architecture while AI executes and validates the implementation.
Haim Michael
Haim Michael is a software development trainer, entrepreneur, and lecturer with nearly 30 years of experience. He founded life michael (lifemichael.com), delivering professional training in Java, Python, JavaScript, Scala, Kotlin, and more. Haim has lectured at leading universities, including Bar-Ilan, HIT, Shenkar, and Technion, and has trained developers at top tech companies. He also organizes international developer conferences—XtremeJ, XtremeJS, and XtremePython—bringing global communities together. Earlier in his career, he developed over 200 mobile games and applications at Jacado for mobile phones, working with 200+ companies worldwide. He holds an MBA (summa cum laude) and multiple industry certifications, and he is passionate about teaching and shaping the future of software development.
From Training to Production: Lightweight ML Validation and Monitoring
Catch silent ML failures before they reach production! This session demonstrates lightweight, practical techniques to validate and monitor ML models across their lifecycle. Using tools like DeepChecks and Evidently AI, attendees will learn how to detect data drift, evaluate model quality, and ensure trust in their models with minimal effort.
Gift Ojeabulu
I’ve spent the last 6+ years at the intersection of AI/ML, SWE, developer advocacy, and community building. Most recently, I worked as an AI devrel advocate and content lead at Iterative.ai, the team behind the popular open source AI tools DVC and CML. I’ve built and scaled thriving AI communities, notably as co-founder of D.C.A, now the largest Data and AI community of Black professionals worldwide. A visionary data scientist whose work is transforming Africa's technological landscape. As the Co-founder of Data Community Africa, an advisory board member at DevNetwork (Artificial Intelligence), and AI Developer Advocate, Gift has emerged as a pivotal figure in democratizing data and AI across the continent. My crowning achievement, the African Data Community Newsletter, has become a beacon of knowledge sharing, reaching an impressive network of over 2500 subscribers spanning 45 countries and 8 U.S. states. This initiative has inspired his involvement at DatafestAfrica with 4 Conferences and 5+ hackathons in less than 4 years, now one of the continent's premier data and AI conferences, bringing together practitioners, researchers, and enthusiasts from across the globe. In Lagos, Gift's leadership of the MLOps community has revolutionized how organizations approach machine learning operations. Under his guidance, the community has become a hub for innovation in practical MLOps and Large Language Models (LLMs), fostering collaboration between industry leaders and emerging talents. His emphasis on open-source AI development has created new pathways for African developers to contribute to global technological advancement. Through strategic initiatives and unwavering dedication, Gift Ojeabulu continues to architect the future of Africa's data and AI ecosystem. His work exemplifies how individual leadership can catalyze continental transformation, making advanced technology accessible to communities that have historically been underserved in the global tech landscape.
What It Means to Be a CTO in an AI Startup Today
**I haven't written a single line of code in over a year.** As CTO of an AI startup, I've fully embraced vibe coding: orchestrating AI to generate production-ready code. 🔍 **Highlights** 1. **The paradigm shift:** from autocompletion to full project generation 2. **My 3-step workflow:** voice prompts, deep research, code generation 3. **The judgment trap:** why expertise matters more than ever You'll leave with practical techniques to ship 10x faster!
Fabien Vauchelles
Fabien Vauchelles brings 20+ years of experience in product design and software development. He holds a dual degree from Université Paris-Sud in Engineering and Master Research in Distributed Systems. A serial entrepreneur, Fabien has founded three companies including Zelros, where he served as CTO for six years building the AI platform from scratch. He is now CTO and Co-founder of an AI startup, applying vibe coding principles daily. Recognized internationally as an expert in AI, distributed systems, and data science, Fabien has spoken at conferences in 15+ countries on topics including web scraping, anti-bot systems, and open-source tooling. He is also the creator of Scrapoxy, a popular open-source web scraping tool.
XAI - Explainable AI tools and techniques
In this talk, I propose to discuss the problem of building explainable AI with the two approaches - causal vs correlational. I will talk about what mech interp in LLMs. As a way to understand how models answer questions by looking inside them and checking which neurons activate when. I will discuss Anthropic's open sourced a python module - circuit-tracer, the Neuronpedia portal , will also talk about my own work with "activation cube" data structure (this is not a standard - I came up with it)
Viraj Sharma
I am a passionate technologist with a strong interest in Python, artificial intelligence and Edge computing. I am currently studying in Class 9 at Presidium School, Delhi, INDIA. I have worked in areas such as torch.nn visualization, Anthropic technologies (MCP, Skills), large concept models, and TensorCore/CUDA benchmarks, Edge AI on raspbrry pi running small models with sensors. Recently I have been working on XAI (AI explaianability) and putting my work as a project on my AI Lab - modelrecon.com. As an active member of the Python community and AI communities, I enjoy learning from experienced developers and sharing my insights with others. I attend major tech events including PyCons, Linux Fests, OS Summits, GDG events, p99conf, and various AI conferences, where I actively present my projects and ideas.
Beyond Basic RAG: Boosting Accuracy with Hybrid Search and Fusion Algorithms.
Retrieval-Augmented Generation (RAG) often produces incorrect results when relying solely on vector-based similarity. While vector search is strong, it fails on exact keywords, acronyms, and domain-specific terms. This talk shows how to build high-accuracy RAG pipelines with Hybrid Search and Re-ranking using LangChain, combining vector and full-text retrieval and applying cross-encoder scoring to deliver more relevant context and reduce hallucinations.
Piti Champeethong
I've been working with databases and software development for 20 years. Currently, I'm a MongoDB senior consulting engineer based in Singapore. I've previously spoken at conferences such as PyCon Lithuania 2025, PyCon APAC 2025, PyCon SG 2025, PyCon Thailand 2025, and Global Azure Thailand 2025. I’m also part of the community leader team for the MongoDB and PyLanna (the Python) User Group in Thailand, which brings together over 3,000 developers.
Stop Guessing: Build Feedback Loop for Prompt Engineering
Most teams iterate on prompts by eyeballing outputs and hoping for the best. This talk presents a different approach: connect your prompt to a measurable downstream outcome, build a test set, and let metrics drive improvements. I'll walk through a complete feedback loop - from prompt to retrieval metrics to automated error analysis to LLM-powered iteration - and show how this turns prompt engineering from guesswork into something you can actually measure and improve systematically.
Tadas Goberis
Dentist turned engineer with 4 years in software and nearly 3 years in AI. Currently Lead AI Engineer at Trimble, where I build production agentic systems.
Beyond the Prompt: Building Research Agents in Python
LLMs can answer questions, but can they conduct research? Real research requires planning, data gathering, synthesis, and refinement. This talk explores Python patterns for orchestrating multi-step workflow to create autonomous research agents. We'll deconstruct the agentic architecture, from DAG-based planning to reflection loops, and show how to implement these concepts using practical Python patterns.
Simona Skiotytė
As a Software Developer for the Generative AI platform at Nasdaq, Simona Skiotytė specializes in architecting the integration of Generative AI systems on AWS. Key projects include the integration of a research system that automates query generation, iterative web search, and multi-stage report synthesis, all orchestrated in AWS. Simona's experience extends to building secure and scalable cloud infrastructure. She architected a multi-tenant data platform with comprehensive test coverage and audit logging. Simona also developed a framework for an AWS cross-account data ingestion into vectorstore featuring complex IAM roles, presigned URLs for secure file management with KMS encryption, and lifecycle orchestration using AWS Step Functions. Additionally, she built a CRUD API for managing version-controlled, reusable Generative AI prompt templates. Simona's foundation in data science comes from her time at ETH Zürich, where she applied statistical and mathematical models to large-scale biological datasets, utilizing dimensionality reduction, clustering, and regression modeling. This background provides a strong analytical lens through which Simona approaches the construction of Generative AI systems.
When Space Weather Breaks Your GPS: Building an Explainable Early Warning System
Have you ever happened to use GPS and realised that it is not working properly? The Sun could be responsible. We'll see a **real-world forecasting system** designed to predict a Space Weather phenomenon affecting GNSS accuracy and radio communications. The system is based on **CatBoost** and integrates space- and ground-based data. The talk focuses on **model design and evaluation choices**, showing how interpretability and uncertainty-aware forecasting can be combined in a real-time operational pipeline.
Vincenzo Ventriglia
A results-driven data professional, focused on hype-free solutions tailored to business needs. I currently create value at the **National Institute of Geophysics and Volcanology**, where I develop machine learning models in the **Space Weather** domain. My work is complemented by finding the hidden stories in data and make them accessible to stakeholders. I studied Physics in Italy (Napoli) and Germany (Frankfurt am Main), previously worked in Analytics within the strategic division of the world's largest professional services network, as well as in the Data Science department of Italy’s leading publishing group. I am also an organiser of **PyData Roma Capitale**, actively involved in building the local Python and data science community. Outside of work, I enjoy theatre, discussing finance, and learning new languages.
Engineering Complex AI solutions: Observability and Testing of multi-Agent Solutions
As AI agents evolve from simple chatbots to complex multi-agent systems utilizing Model Context Protocol (MCP), manual validation becomes impossible. During this talk, I will demonstrate the process of architecting a quality assurance loop for these solutions. No theoretical fluff: I will focus on the practical analysis of automated pipeline results, interpreting Langfuse reports for cost and performance, and ensuring reliability from an AI Architect/System Engineer perspective.
Dmitri
Lead systems engineer with more than 20 years experience in IT: - 3 years in AI-based solutions development; - 8 years of S-SDLC methodology implementation; - 7 years of security architecture development; - 4 years Experience in the development of the scalabale solution for the non-functional testing using private clouds AWS based APIs - solid knowledge of building secure and resilient architectures in multi-cloud environments - 9 years of experience in consulting services for the external and internal EPAM accounts - 6 years of experience in project and team management - 8 years of experience in solutions architectures development and deployment - 10 years security concepts and tests development including regular security audits - 8 years of experience of developing and deploying Continuous Delivery and Continuous Integration concepts - Experience in processes development - 10 years developing and deploying Unix/Linux based infrastructures
