Pulto A Spatial Jupyter client for the Apple Vision Pro
(30 Minutes)
By: Joshua Herman
Experience Level: Intermediate
Slides Link

This is a talk about making a Jupyter client for the Apple Vision Pro targeting the VisionOS operating system. It has largely been implemented in Swift but there is also an extended Jupyter server to manage 3D models, Gaussian Splats and Point Cloud data. It has been vibe coded a great part of the implementation will be discussed.

• Apple Human Interface Guidelines (for the Vision Pro)
• Complying with App Store policies (what can be done on devices)
• Interfacing with Jupyter Remotely
• Rendering custom cells.

Inference at Scale: Transcribing Millions of Insurance Calls with Whisper and Azure ML
(25 Minutes)
By: Jimmy Scray
Experience Level: Intermediate

Transcribing a few audio files with Whisper is easy. Transcribing millions of recordings efficiently, reliably, and cost-effectively is a very different problem.

In this talk, I'll dive into the Python code and infrastructure behind a large-scale speech transcription platform built for the insurance industry. Starting from a notebook prototype, we'll explore how the system evolved into a distributed inference pipeline running across thousands of GPU workers.

Rather than focusing on machine learning theory, we'll focus on inference engineering: benchmarking CPU and GPU workloads, maximizing throughput, orchestrating jobs with Azure Machine Learning, handling spot-instance interruptions, and writing resilient Python code that can recover from failures and resume processing automatically.

Along the way, I'll share benchmark results, architecture decisions, code examples, and the lessons learned while processing millions of real-world recordings.

If you're interested in Python, distributed systems, performance optimization, or production machine learning infrastructure, this talk will show what happens after the model is trained.

Mini Shai Hulud is no longer just an npm problem
(20 Minutes)
By: eevelweezel
Experience Level: Novice

Mini Shai Hulud is self-propagating malware that steals credentials from developer machines and CI/CD pipelines.  It was first reported infecting npm packages in 2025, but as of May 2026, it has spread to PyPI.  This talk will cover how Shai Hulud works and some of the mitigation strategies discussed at PyCon. 

You're Not Being Replaced You're Being Multiplied.
(40 Minutes)
By: Joshua Herman
Experience Level: Intermediate
Slides Link

Everyone has this deep rooted existential fear that AI are taking software developers / engineers jobs. Not only that epistologically when we write code with AI or when working with others they may introduce deficiencies, regressions and problems with readability of committed code.  

We will address these fears and problems with going through techniques on writing a AGENTS.md file and other similar files like CLAUDE.md . These are guides for agents that work with your code and it introduces rules on how your code should be treated when you are working on them and you can even commit these files to any code repo and other people who use things like codex and claude code can read them. These can be used to perform tests before regressions exist and undo them, look for bugs and security holes and even enforce style rules in your code and applications. Last we will go over ways to generate diagrams

https://agents.md

Units are Types: Let's Treat Then That Way
(20 Minutes)
By: Emmanuel I. Obi
Experience Level: Intermediate
Slides Link

At SREcon 2026, I showed how untyped infrastructure configs fail silently and how dimensional analysis catches those failures at definition time. But catching errors in human-written configs is only half the story. AI agents are now generating configs, writing IaC, computing dosages, and scaling parameters autonomously. They make the same unit mistakes humans do, faster and more confidently.

This talk extends the SREcon argument into agentic territory. I’ll show how ucon’s MCP server turns dimensional analysis into a verification primitive that any AI agent can call not as a unit converter, but as an algebraic safety net. I'll walk through real agent workflows where structured error responses (dimensional vectors, got/expected pairs, likely-fix hints) enable a model to self-correct in a single retry, and demonstrate how kind-of-quantity enforcement catches errors that no existing unit library can even represent. The core claim: small models paired with algebraic verification outperform large models without it and the infrastructure to prove that should be a tool call, not a training objective.

You Can Start a Tech Club in Your Community
(45 Minutes)
By: Brianne Caplan
Experience Level: Novice

You don’t need a nonprofit, funding, or a perfect curriculum to start a tech club—just a starting point.

Today, access to computer science education is still deeply uneven. Only about 60% of U.S. high schools offer foundational computer science, and participation gaps persist for girls, Black, and Latino students. Further, most students are unlikely to pursue tech pathways unless they are exposed to computer science early in school and in ways that feel relevant to their lives.

In this talk, I’ll show how developers, students, and professionals are starting tech clubs in their communities using the skills they already have, and how Code Your Dreams supports people in actually doing it. We’ll walk through how to go from idea → first session, how to teach beginners (even if you’ve never taught before), and how to build something that lasts.

I’ll share real examples of clubs launched in schools and community spaces, what worked, what didn’t, and how small efforts can turn into real community impact.

If you’ve ever thought, “I’d love to give back, but I don’t know where to start,” this is for you.

What You’ll Learn:

• Why computer science education is still important in the age of AI
• How to launch a club or get involved in your community
• Ways to teach coding / tech and keep participants engaged
• How Code Your Dreams can support you in getting started

The Job Market, a Tale of Pain
(20 Minutes)
By: Hugo Seguin
Experience Level: Novice

In this talk, I share what it was really like spending seven months trying to land a job in today’s market, applying to both data analyst and data engineer roles. I’ll walk through how I approached it and how I changed my techniques along the way.

I’ll talk honestly about what actually helped me get interviews, what I found didn’t work, and the mistakes I made early on and currently. That includes how I handled resumes, technical interviews, networking, and figuring out what companies are really looking for when they hire for data roles.

I’ll also share the resources and tools I wish I knew about from the beginning things that would have saved me time, energy, and a lot of trial and error. The goal of this talk is to give practical, experience-based advice so others can approach their job search with a clearer plan and avoid some of the frustration I ran into.

Marimo: The next generation notebook
(30 Minutes)
By: Henry Cuzco
Experience Level: Novice

Many people in the data science community are familiar with jupyter notebooks and use them regularly as part of their day-to-day tooling. While jupyter notebooks have a lot of extensions and are a great way to combine documentation with code, they don't come without drawbacks. One of the biggest drawbacks is the execution order of the cells. The other drawback is looking at the code changes between commits when trying to upload notebooks to GitHub. To solve these shortcomings, marimo notebooks were created. This talk will demonstrate marimo notebooks' functionality, the modes in which you can run them, and other marimo features.

SQL Testing: Python Tools, Patterns, and Automation
(45 Minutes)
By: Paul Zuradzki
Experience Level: Intermediate

This talk is a practical introduction to SQL testing using Python. We’ll cover why testing SQL can be tricky, compare strategies for preparing a SQL test environment and preparing data, how to check for schema breakages, data validation checks, automated formatting, and automated testing in local or remote environments. Attendees will leave with patterns and code examples to make SQL pipelines more reliable and maintainable.