Discover the Talks at PyCon Colombia 2026 ✨
Browse every accepted session—titles, tracks, levels, and speakers—before you plan your days in Medellín.
Machine Learning Applied to Genetic Sequences
DNA contains massive amounts of biological information, but how can artificial intelligence help us understand it? In this talk, we will explore how Python and Machine Learning can be used to analyze genetic sequences in a practical and beginner-friendly way. Using public biological datasets, we will demonstrate how DNA sequences can be transformed into data suitable for machine learning models, covering concepts such as feature extraction, sequence representation, and basic classification techniques. We will also review popular Python tools used in bioinformatics, including Biopython, pandas, and scikit-learn, while discussing real-world challenges when working with biological data, such as high dimensionality, noise, and interpretability limitations. By the end of the talk, attendees will have a clear understanding of how to start building genetic analysis projects using accessible tools from the Python ecosystem, even without prior bioinformatics experience.
Employability in the Age of AI
Artificial intelligence is changing the job market faster than ever. Many developers wonder: will AI replace me or empower me? In this talk I'll share my real experience going from being a developer in Latin America to working for companies in the United States—facing interviews, optimizing my professional profile, and adapting to an environment where AI is already part of daily life. We'll explore how AI doesn't replace the developer but redefines the value we bring: from writing code to solving real problems, communicating ideas, and building complete solutions. The talk will cover the future of programming, how to shift your mindset toward AI, which skills really matter today, how to stand out in international hiring processes, the role of AI tools in your professional growth, and common mistakes that hold back your employability.
Opening the Black Box: Mechanistic Interpretability of LLMs
As agents are deployed in high-stakes contexts (finance, manufacturing, healthcare), understanding how they make decisions—and not just what they decide—becomes fundamental to safety and trust. For example, when an agent receives the instruction "Search for our company's third-quarter results" and chooses to search internal documents instead of the public web, what internal process drives that choice? Answer engineering, behavioral testing, and chain-of-thought analysis describe correlations or narratives; none reveals the actual mechanism. Understanding how an agent reaches a conclusion is a critical component of developing AI responsibly, especially regarding reliability and transparency in AI systems. Model interpretability is one way developers can build trust and consistency in their systems and support the safe deployment of AI agents.
Create Your AI DJ: Agents in Python and Open Source
Create your own AI DJ using Python agents and open-source tools! In this beginner-friendly workshop, you'll learn the fundamentals of AI agents, build a music recommendation system powered by Python, and connect it to real music APIs. No prior AI experience required—just curiosity and a love for music. By the end, you'll have a working AI DJ that curates playlists based on mood, genre, and personal preferences.
Elevate your code quality in Python with modern, ultra-fast tooling
AI coding assistants have changed how we build software. We can now generate features, refactors, and entire services in minutes — but speed without strong engineering practices quickly becomes technical debt. In this talk, I'll show how modern Python teams can build fast and reliable development workflows using tools like Astral's Ruff, Ty, and uv. We'll explore how traditional slow and noisy quality pipelines are being replaced by a new generation of tooling that provides near-instant feedback while improving code quality and developer experience. Topics include why AI-generated code makes automated quality gates more important than ever, using Ruff for formatting and linting, using Ty for modern static typing, structuring formatter → linter → type-checker workflows, pre-commit hooks and CI pipelines developers actually enjoy using, and reducing friction between local development and CI/CD.
From Prompts to Agents: Intelligent Systems with Python
Take your first steps from writing simple prompts to building intelligent multi-agent systems with Python. In this beginner-friendly workshop, you'll learn the foundations of AI agents, how they differ from simple LLM calls, how to chain agents together for complex tasks, and how to give them tools and memory. Using popular Python frameworks, you'll build a working multi-agent system by the end of the session—no prior AI experience needed.
Stop Mocking, Start Containerizing
Tired of maintaining brittle mock objects that don't reflect production behavior? In this workshop, you'll learn how to replace mocks with real containerized services using Testcontainers for Python. Bring your laptop and a running Docker engine—we're going to get our hands dirty!
