Adapting to the Evolution of Software Engineering

PySanAntonio keynote from the president of the PyTexas Foundation

• Mason EggerSr Solutions Architect · Temporal Technologies

From Sensor to Signal: Building an Indoor Air Quality Platform With Python

Indoor air sensors generate plenty of data, but raw numbers rarely explain what is actually happening in a room. In this talk, we’ll build an air-quality monitor using MicroPython, FastAPI, and AI to detect unusual patterns, predict ventilation needs, and turn sensor readings into clear, useful insights. We’ll also look at the practical challenges of noisy sensor data, real-time updates, and deciding when simple rules are better than machine learning. Attendees will leave with a practical blueprint for building an intelligent embedded system entirely with Python.

• Samad AhmedFounder · Chamoy Labs

JOMO in the age of AI slop: the joy of missing out

Everyone's talking about what AI can do. This talk is about what it's doing to us our communities, our trust, our feeds. And why sometimes the best move is to just... not.

• Jordana NaftaliDesign Engineer

Experiments in Agentic Coding

Coding agents can behave in surprising ways for both experienced and new devs. Devs new to agentic coding may have frustrating experiences trusting code that agents generate, or be using agents in sub-optimal way. This talk demonstrates how agents work, why they sometimes do funny things, and how to put guardrails on Claude. The demonstrations will use prompt engineering experiments with personas to solve a strawman problem that all devs know and love -- FizzBuzz. First, silly personas (e.g., pretend you're a goose) will be used to solve FizzBuzz, and demonstrate phenomena like non-determinism, constitutional AI, and context. Next, it will progress to a more practical prompts (e.g. Django novice, Java expert, Senior Architect), and a more realistic example, by adding new requirements to FizzBuzz. This second set of demos will show how intrinsic training biases or reward hijacking can lead your agent to generate bad code. For Python specifically, a plain agent, not configured properly, will default to generating messy and unreadable tests. Along the way, it will show practical tips for using Claude (plan vs. edit mode, context management, memories, skills and sub-agents), and demonstrate what kind of prompts are needed to generate clean code. Sample skills and sub-agent MDs will be provided for users to try with their own code.

• Edwin JungStaff Engineer · Closinglock

Your Agent's requirements.txt Is a Lie

Coding agents write requirements.txt with total confidence and no memory of what is actually installed: inventing package names, guessing version pins, and blending APIs from different library eras. CI either fails opaquely or silently resolves to whatever the cache happens to have lying around. This talk treats that as a caching and provenance problem. Drawing on build-acceleration work with distributed compile farms, I will show what it takes to pin, content-address, and verify Python environments an agent proposes before any of that code reaches a real sandbox: hash-locked dependency resolution that treats the agent's guess as untrusted input to the cache key, and a fast-reject path that catches hallucinated package names before a slow install ever starts.

• Yossi EliazPrincipal Engineer · Incredibuild

AI Steering Wheel : Giving Humans Control of the Black Box

This talk introduces Operational Interpretability, a research area focused on bridging the gap between understanding AI systems and deploying them responsibly in the real world. By combining insights from mechanistic interpretability with human-readable explanations and compliance considerations, operational interpretability helps make AI systems more transparent, trustworthy, and practical. Attendees will learn how modern techniques allow us to look inside AI models, translate their behavior into understandable explanations, and use those insights to build AI systems that better serve people and society.

• Shayan AliFounder · Amber | Growth

Python Jeopardy

A quiz night for the Python crowd, and deliberately low stakes. No prep, no teams to sort out in advance, and nobody checking whether you write Python for a living or picked it up last month — turn up, shout answers, argue with the board. Two tickets to PyTexas go to the winners.