Discover the Talks at PyCon Colombia 2026 ✨
Browse every accepted session—titles, tracks, levels, and speakers—before you plan your days in Medellín.
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.
Provenance by Default: AI Media Pipelines in Python
A model can now generate a video that looks indistinguishable from one your camera recorded. The same is true for an image, a voice, or a song. As Python developers, we are building those pipelines — and we are also the ones who will be asked, very soon, to prove what came out of them. This talk is about building generative media pipelines in Python in a way that answers that question by default. We'll walk through Genblaze, an open-source SDK (github.com/backblaze-labs/genblaze, MIT licensed) that I work on at Backblaze, and use it as a vehicle to talk about the design problems any team faces when wiring AI generation into a real product. We will cover, with live code: the Pipeline pattern with a fluent Pipeline → Step → Run → Manifest API built on Pydantic v2; one API across eleven providers; provenance that survives the file with SHA-256-verified manifests embedded into PNG, JPEG, MP4, MP3, and WAV; privacy and policy controls; storage and replay; and agent loops with lineage. By the end, attendees will have a clear reference for how to architect generative-AI features in Python so that what did this system actually produce, and can I prove it? is a one-line answer instead of a ticket.
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.
Cost Optimization Strategies for GenAI with Python and AWS
Is it possible to scale Generative AI without project success compromising the organization's financial stability? This session will address how to transform the deployment of large language models (LLMs) through architecture design oriented toward operational efficiency. Instead of accepting high token consumption as an inevitable cost, we'll explore a sustainable cost model that lets you build intelligent, scalable applications without sacrificing profitability. Through a technical path centered on Python and AWS services, we'll analyze key strategies such as model arbitrage, where application logic dynamically decides which intelligence engine to use based on task complexity. We'll dive into how smart use of low-impact vector databases and semantic caching reuse prior knowledge, achieving significant infrastructure savings. Attendees will discover how implementing async flows and batch processing optimizes available resources. This talk is a practical guide for architects and developers looking to lead the transition from costly prototypes to production systems that are technically and economically viable.
From Expert Judgment to Autonomous Optimization: Encoding Human Expertise into LLM Judges with DSPy
A single misread clause in a reinsurance contract can mean millions in liability. Our LLM pipeline could extract and summarize these documents, but how do you know the output is actually correct? String matching fails ("USD 5,000,000" vs "$5M" scores zero), human review at scale is unaffordable, and a single LLM-as-judge prompt gives inconsistent, uncalibrated scores. The real bottleneck was never generation; it was evaluation. This talk shows how we solved it in two steps, both built entirely in Python. First, we encoded expert evaluation at scale using DSPy to distill judgments from five domain experts into a panel of calibrated LLM judges, each targeting a single quality dimension, weighted to reflect what experts actually care about. Then we closed the loop using DSPy's MIPROv2 and GEPA optimizers, wiring the judge panel as a fitness function and letting the system rewrite prompts autonomously, with regression guards and CI gates so humans review only the final score delta. The stack is Python-native: DSPy, MLflow, LiteLLM, Pydantic. You will leave with a concrete recipe for encoding expert knowledge into automated LLM evaluation and self-improving optimization, applicable to any domain where "correct" is nuanced.
Mateo Rios Querubin
Senior ML Engineer @ Provectus / Universidad EAFIT
Sebastián Gómez Ahumada
Middle ML Engineer @ Provectus
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 Notebook to Production: End-to-End MLOps on Databricks
Move beyond Jupyter notebooks and deploy machine learning models to production using MLOps best practices on Databricks. In this intermediate workshop, you'll learn to structure ML projects for production, implement CI/CD pipelines for models, manage experiments with MLflow, deploy models as REST APIs, and monitor them in production. We'll walk through a complete end-to-end example from data preparation to automated retraining.
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.
