4:00 pm ET
“Build your castle, dig your moat: AI sovereignty, provenance and compliance”
with Dawn WagesYour intelligent application is your castle, and your security practices are the moat that protects it. Inside your castle, you must aim for full visibility into what you’re running and why, with freedom to iterate without vendor rate limits or surprise API changes. Your moat creates your security perimeter, ensuring no proprietary data leaves your castle and enforcing best practices including data provenance, cryptographically signed models, evaluation tools, build pipelines and reproducible environments.
Build on your infrastructure, answer to your requirements, scale on your terms.
In this Python Exchange you’ll learn…
- What AI sovereignty actually means for your stack and your business
- How to evaluate self-hosted, local LLMs
- Overview of supply chain security controls for data and code artifacts – provenance, signatures and compliance measures, opacity and trust signals
Meet Dawn Wages
Dawn Wages is the Director of Community and Developer Relations at Anaconda, responsible for the most popular AI and ML Python distribution in the world. She is a software engineer, ethical open source advocate, and community leader who previously served as Chair of the Python Software Foundation. Her work emphasizes inclusive practices and sustainable growth in open source ecosystems, combining technical knowledge with attention to equity, sovereignty, and developer collaboration.
When not working on Python, she enjoys watching Star Trek in Philadelphia with her wife and two dogs.
Recent Events
#45 Diátaxis in practice - and in the wild
Daniele Procida — March 25, 2026
You let ideas loose and then they have a very interesting life of their own!
It’s nearly ten years since I first began writing and talking about the ideas that shaped the Diátaxis documentation approach. In that time, I’ve seen Diátaxis adopted widely, including in contexts I had not even anticipated. I’m aware of hundreds of software projects that use it.
I now have a much stronger sense of how it’s interpreted and used, especially when those ideas are picked up by people that I haven’t met or spoken to.
The lessons learned from seeing what happens when people get hold of those ideas have helped me understand the problems it’s actually solving - not always the ones I’d expected. It has also given me insight into the aspects of the framework that are liable to be misunderstood, or interpreted too rigidly.
Watch on Youtube#44 From Visualization to Conversational Data Exploration with HoloViz
Philipp Rüdiger — February 25, 2026
We will look at how the evolution of HoloViz reflects broader trends in the Python ecosystem over the last decade. We start in the early days of interactive data visualization, when tools like Bokeh and Plotly challenged static plotting and made it possible to explore large datasets dynamically in Python. HoloViz emerged in this moment, focused on composable, high-level abstractions that treated interactivity as a core part of data analysis.
We then move into the shift from notebooks to data applications, even before frameworks like Dash and Streamlit emerged, we created Panel as a way to quickly share analyses with other users. Panel was developed to bridge analysis and application building, allowing Python users to structure, deploy, and share interactive workflows without splitting their codebase or leaving the open-source scientific stack. This transition marked a turning point for HoloViz, transforming it from a set of visualization tools into a platform for building production data apps.
The conversation closes with the current wave of change driven by large language models. As “vibe coding” has emerged as an alternative way to quickly prototype applications, we discuss how HoloViz and Panel in particular are staying relevant. Additionally, we take a look at the latest HoloViz project, Lumen, providing an approach to make analysis more accessible without sacrificing structure, transparency, or trust. We discuss how HoloViz is responding by combining conversational interfaces with auditable, Python-based execution that remains fully extensible and open source, and what this transition means for the project as the Python ecosystem continues to evolve.
Watch on Youtube#43 Efficient Statistical Modeling for Particle Physics Using Computational Graphs in Python
Dr. Giordon Stark — January 28, 2026
Statistical modeling is central to discovery in particle physics, yet the tools commonly used to define, share, and evaluate these models are often complex, fragmented, or tightly coupled to legacy systems. In parallel, the scientific Python community has developed a variety of statistical modeling tools that have been widely adopted for their performance and ease of use, but remain under-utilized in particle physics. We attempt to bridge this gap with a lightweight python framework that calculates likelihood ratios through the construction and evaluation of computational graphs. With modularity, auto-differentiability, and computational efficiency in mind, we designed the framework to integrate with modern scientific computing ecosystems while providing a clean, well-documented, and extendable API. This implementation makes published particle physics results more transparent, reproducible, and accessible for reanalysis. We present the initial framework, validate its results against established calculations, examine its performance relative to existing systems, and outline future development plans.
Watch on YoutubeAbout Us
At Don’t Use This Code, we want to create a unique opportunity to see Python succeed and thrive within the National Labs! We propose creating a new resource for scientists, researchers, and technical staff to support their use of Python and to build a strong, lasting community for Python users within the Department of Energy National Labs. Disclaimer: The Python Exchange is an independent group of Python enthusiasts who wish to see the use of Python and open-source computing thrive within the National Lab system. This group is not sponsored by or affiliated with the Department of Energy.