The Test Set by Posit

Charlie Marsh built Ruff, uv, and Ty — the tools that mass-fixed Python's worst pain points. Now he's grappling with what happens when agents start writing most of the code. In this episode, Charlie gets real about his team trusting his PRs less, the gnarly middle of coding with agents, and whether Python is even the right language for an agentic future. It's honest, a wee existential, and deeply relatable if you ship code for a living.
Episode Notes
Charlie Marsh is the founder and CEO of Astral — the company behind Ruff, uv, and Ty. He sits down with Michael and Wes to talk about what it's actually like building with coding agents every day, why his team's code review dynamics completely changed, and big open questions about code quality, open source community, and Python's future nobody has answers to yet.
What’s Inside
How Ruff convinced mass adoption when switching tools is painful
Why "just uv run it" became the killer feature
Hiring outside Python's ecosystem to build tools for it
His team said "we trust your PRs less now"
Engineers screen-sharing their actual agent workflows at Astral
The "Lisp Curse" reborn: cheap code breaking open sourceIs 
Python the wrong language for an agentic world?

Alenka Frim went from teaching yoga full-time to becoming a committer and PMC Member on Apache Arrow. In this episode, Alenka joins The Test Set hosts to talk about how Arrow grew from spec to critical infrastructure, and why she started contributing to a project she had never even used. She reflects on imposter syndrome, the discipline of showing up (on the mat and in GitHub), and how agents are changing what it means to write code. Plus: managing 4,000 open issues without losing your mind.

Episode Notes
Alenka's path into Arrow is unconventional: Sshe wasn't looking for a job, she wasn't using the tool, and she'd spent the previous five years focusing on mind-body fitness. But open source felt like the right place to learn, have fun, and figure things out, so she jumped in. What followed was a journey from her first R bindings to becoming a PMC member on one of the most critical pieces of data infrastructure in the world.

What’s Inside
Alenka's journey, from yogi to Arrow committer
Signs of a healthy open source community: people, dialogue, and turnover
Arrow as critical infrastructure: DuckDB, Polars, Pandas, and the spec that unifies them
Managing 4,000 open issues without losing your mindImposter syndrome in open source
What the yoga mat teaches you about discipline and collaborationAI and the future of programming: 100x more software or 10x better software?

Emily Riederer writes Python with an R accent, and we’re all comfortable with it. In this episode, Emily reflects on her journey through R, Python, and SQL — from lessons learned in averaging default values (oops, we're not all rich!) to discovering that column selectors are way cooler than they sound. She weighs in on the delicate art of learning in public, why frustration often makes the best teacher, and how to find your niche by solving the boring problems. Oh, Oh, and the crew casually drops that she's keynoting posit::conf 2026!
Episode Notes
Emily’s had a wild ride through modeling, data engineering, machine learning, and back again, and she knows a thing or three about the evolution of SQL tooling (from nightmare multi-page scripts to the dbt renaissance). She reveals how building internal packages became her gateway to making work enjoyable. Plus: the surprising Stata origins of column selectors, the eternal struggle of naming packages across R and Python, and why watching people code teaches you more than any tutorial ever could. The conversation gets real about imposter syndrome and the magic of tacit knowledge.
What’s Inside
Why real-world data is chaos, not truthThe path from modeling to data engineering (and back)
What a data pipeline really is (extract, load, transform) and why organization matters
How dbt changed the SQL game Learning by watching: Tacit knowledge and coding over the shoulder Imposter syndrome and learning in public 
Building internal tools to escape busyworkposit::conf 2025 keynote preview

Rebecca Barter, senior data scientist at Arine and adjunct assistant professor at the University of Utah, refuses to work on things she doesn’t care about. Lucky for us, she cares about a lot, most of all impact. In this episode, Rebecca joins The Test Set to talk about learning fast, building better tools, and staying motivated and adaptable.
She shares how moving between R, Python, SQL, and dashboards reshaped how she thinks about expertise. Plus a reflection on her recent posit::conf talk, “AI: Hype, Help, or Hindrance.”
Episode Notes
Rebecca digs into what it’s really like to work with AI every day and why humans still rule, especially in exploratory data analysis. She explains how tool building can be the fastest way out of busywork and how teaching beginners sharpened her ability to communicate clearly. The conversation circles a bigger question too: As AI keeps improving, are we headed toward a future where code looks completely different … or maybe disappears altogether?
What’s Inside
Why motivation matters even more than productivity
Escaping busywork by building better tools
From R to Python to dashboards: Learning fast as a survival skill
Reality check on AI in the IDE
Why exploratory analysis still needs human intuition
The 80/20 of coding: Automate the boring, protect the judgment
Teaching beginners and earning trust
The uncertain future of code

You’ve probably used Narwhals without realizing it. It’s the compatibility layer helping apps and libraries like Plotly play nice with Pandas, Polars, Arrow, and more — while keeping computation native instead of converting everything to Pandas. In this episode, Marco Gorelli explains how his weekend experiment turned into essential ecosystem infrastructure and why data types, not APIs, are where interoperability gets tricky. Plus what it takes to build trust and community around an open-source project.

Episode Notes
Marco shares the Narwhals origin story (including the meme-powered name), the hard edge cases that live in data types and null semantics, and why he’s cautious about using AI for code generation when correctness hinges on tiny details. We also jam on proactive “GitHub surfing,” conference talks as trust-building exercises, celebrating contributors, and how early commit messages capture the genuine excitement of building something new.

What’s Inside
Narwhals 101: You’ve probably used it (even if you didn’t know it)
The real interoperability traps: data types, null semantics, and “looks-the-same” operations
Why expression systems won, and how they shaped Marco’s approach — with nods to Ibis, Polars, and Pandas
Open source as social work: proactive outreach, trust-building, and a Discord-powered community
Extending Narwhals to new engines, starting with the Daft plugin