Discover the Talks at PyCon Colombia 2026 ✨
Browse every accepted session—titles, tracks, levels, and speakers—before you plan your days in Medellín.
Leverage your Python skill using the Python interpreter
In this talk, I'll challenge the audience's mindset about Python. Python is not an interpreter, and in fact, there are multiple Python interpreters—each with its own architecture and purpose. I'll walk through Python's core internals and show how programming languages interact beneath the surface. We'll explore how to write better Python by understanding the garbage collector, what you can build using the AST, how to read and leverage the disassembler, and the practical implications of Python's transition from its old LL(1) parser to the current PEG parser. We'll also dive into lesser-known features of Python interpreters, what a PEP really is and how it shapes the language, and conclude with a deep look at Python without the GIL—what changes, what breaks, and how the core team removed it. Throughout the talk, I'll share personal stories, including battles caused by identical ASTs and the moment I believed I had discovered a way to speed up the Python interpreter itself.
From Vibe Coding to Spec-Driven Development with AWOS in Claude Code
Vibe coding works great until it doesn't. When AI agents start ignoring your architecture, making wrong assumptions about your stack, and producing code that compiles but misses the point, the problem isn't the model. It's the instructions. This talk introduces AWOS (Agentic Workflow Operating System), an open-source framework built by Provectus for Claude Code that brings Spec-Driven Development to AI-assisted coding. AWOS structures the development process into 8 phases, each with its own specialized agent and audience. What you'll see: a live demo building a conference talk management app. What you'll take home: a tool you can install with npx @provectusinc/awos and start using immediately.
Building a Transformer with Rust
Transformers are often perceived as incomprehensible giants. This talk aims to prove the opposite: they are not black boxes but elegant mechanisms that can be understood and mastered from their fundamentals. We present Molinete AI, a GPT-2-style model built strictly from scratch in Rust. No deep learning frameworks—just tensors, math, and full control. Inspired by Feste from Tag1 Consulting (trained on Shakespeare), this project poses a different challenge: training the network on Miguel de Cervantes's work to generate text in the style of the Golden Age. Throughout the session we'll break the model down piece by piece. With the support of a Manim animated presentation (over 4,000 lines of code), we'll make visible how information flows inside the network. We'll start from tokenization (BPE) and building basic operations, then dive into the core of the model: embeddings, causal mask, and Multi-Head Self-Attention. Finally, we'll explore the learning process, watching how gradients flow through the network during training. More than a demo, this talk aims to provide a clear, operational view of Transformers, connecting theory with a real from-scratch implementation.
NLP Without Labels: How to Cluster N Legal Processes of the Colombian State and Turn Chaos into a Production Classifier
What do you do when you have 600,000 legal complaints, zero labeled data, and a government entity waiting for results? This talk walks through the full process of building an unsupervised NLP classification system for the Procuraduría General de la Nación. Starting from raw administrative text—noisy, full of abbreviations and institutional jargon—I'll show how TF-IDF, truncated SVD, and KMeans combined to organize more than half a million records into 64 semantically coherent groups, without a single manual label. But clustering is only the starting point. I'll cover how clusters were validated, how a Logistic Regression classifier was trained on them to make the system deployable, and how the final pipeline was packaged in a .pkl that non-technical colleagues use in production today. Along the way we'll face real problems: elbow curves that don't behave, 1:20 size imbalances between clusters, and the tension between mathematical elegance and institutional usability. Because in the public sector, a model nobody uses isn't a model—it's a PDF gathering dust.
LangGraph and Strands Agents: Core Concepts, Patterns, and Tradeoffs
Dive deep into two powerful agentic frameworks—LangGraph and Strands Agents—and learn when to use each. This advanced workshop covers the core concepts behind both frameworks: state machines, graph-based orchestration, tool use, and memory management. We'll build the same agentic application in both frameworks, compare their strengths and limitations, and discuss the architectural trade-offs to help you choose the right tool for your production AI systems.
LLMs in Depth: How an LLM Works Mathematically (and Its Implementation with PyTorch)
Demystify the mathematics behind Large Language Models and implement them from scratch in PyTorch. This advanced workshop takes you through the complete mathematical foundations: attention mechanisms, transformer architecture, positional encodings, layer normalization, and training dynamics. For each mathematical concept, we'll write the corresponding PyTorch implementation—giving you a deep, hands-on understanding of how LLMs actually work under the hood.
Real-Time Voice Systems: Design and Architecture in 5 Levels
Voice systems have advanced rapidly in recent years, but most implementations still stop at demos: simple combinations of Speech-to-Text, language models, and Text-to-Speech that work in controlled environments but fail when facing real-world conditions. This talk proposes a different approach: understanding voice systems as an architecture that evolves through maturity levels, from basic prototypes to real-time production-ready systems. Through a 5-level framework, we'll walk the full path of a Conversational AI system: from integrating basic components, through orchestration challenges (streaming, latency, turn-taking), to less obvious but critical problems like audio quality, robustness, and user experience, reaching real-time architectures with technologies like LiveKit, and finally exploring where the future is headed with end-to-end systems and multimodal agents. The talk is based on real experience building voice systems in production and focuses on engineering decisions more than specific tools. Attendees will leave with a clear understanding of how to design modern voice systems with Python, what problems to anticipate, and how to structure their own architectures to build world-class conversational experiences.
