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

In this episode, we’re joined by Kelly Bodwin — candy corn defender, board game enthusiast, and Associate Professor of Statistics and Data Science at Cal Poly. We discuss her path from English and French to statistics, how she builds teaching tools and navigates AI in the classroom, and what it takes to keep a programming community weird in the best possible way.

Episode notes

Kelly is curious, collaborative, and unafraid to lean in on quirky. Kelly shares how she balances teaching three courses with master's student supervision, applied research projects spanning Polish history and beyond, and her belief that the best part of academia is the people. We also dive into the practical and philosophical challenges of staying current in a field that reinvents itself every few years.

What's inside

• Breakfast mixology
• Building Quarto extensions with JavaScript and AI
• When ChatGPT helps students learn (and when it doesn't)
• Applied stats meets history: analyzing social networks from the Polish Revolution
• Why remarkable, welcoming communities matter more than perfect code

This episode is Part 2 of our conversation with James Blair. He explains how he found his “accidental perfect fit” as a solutions engineer and how that role became a pipeline into product management. Get a peek into the AI-powered tooling he’s now building for the Posit ecosystem, and hear how he’s using Claude Code, Positron Assistant, and DataBot to generate synthetic, industry-specific demos on the fly — plus, why the real magic is keeping humans firmly in the loop. 

Episode notes

This is a story about listening deeply to users and using AI to make that listening scale. James explains what solutions engineers actually do, how that work shaped Posit’s product team, and how synthetic data plus agents are changing the way they build demos and teach data science. 

What’s inside

• What a solutions engineer really is and why the role was such a good fit for James
• How solutions engineering became a natural pathway into product management at Posit
• Multi-agent “bot posse” workflows and why context management matters
• Using AI the right way and why code literacy, critical thinking, and staying human are the real superpowers in an AI-saturated world

In Part 1 of our conversation with James Blair, we trace his delightfully non-linear path from childhood robotics dreams to journalism to R, with a few stops in between. We hear about the Shiny app that changed his career, plus a candid roundtable with Michael, Hadley, and Wes about whether a data-science master’s still pays off in the age of AI.

Episode notes

This is a story about staying hands-on and fiercely inquisitive — whether analyzing bike telemetry or in teaching data science. James shares how early experimentation with Shiny helped shape his career, and how curiosity (not credentials) still powers meaningful work in data science.

What’s inside

• A winding path from robotics to journalism to psychology to data science
• Discovering the power of applied stats
• The value (and limits) of a data-science master’s in a shifting AI landscape
• Fighting confirmation bias: good analysis resists the answer you want

In part two of our conversation with Julia Silge, we discuss how work actually ships: the boundaries, the glue, and the tools that turn noise into signal. From there, we go macro and wonder what the LLM era means for humanity’s contributions, plus how licensing is evolving to protect sustainability without abandoning openness.

Episode notes

Both practical and philosophical, this conversation spans workplace energy, team connective tissue, and the big questions LLMs have us asking in a shifting data science landscape.

What’s inside

• Julia’s system for turning scattered community signals (GitHub, Stack Overflow, discourse) into product insight
• The power of “glue” work, and where to find the wins
• From Stack Overflow to LLMs: What changed when communal Q&A became model fuel — and what that means for finding answers
• Licenses in a new era: Threading the needle between MIT-style generosity and elastic-style sustainability for platformed software
• Try Positron: Where to download, read docs, and give feedback

In part one of our conversation with Julia Silge, astronomer-turned–data-science leader, we explore why data science needs a different kind of IDE. Julia takes us inside Positron, Posit’s next-generation, data-scientist-first environment, and unpacks the day-to-day realities that make data science work unlike software engineering. Along the way, we get a first-hand account of a legendary pineapple-pizza protest and how to juggle multiple projects at once.

Episode Notes:

A behind-the-scenes tour of Positron and the workflows it’s built for, plus the stories, trade-offs, and team choreography required to ship an IDE on a living substrate. We talk extension ecosystems, upstream merges, data viewers, and more. Plus, Julia shares why applied systems (and messy, real-world data) are her happy place.

What’s Inside:

• The pineapple-pizza story that unexpectedly went viral — and what “context collapse” feels like from the inside
• Why Positron is a data-science-first IDE, optimized for analysis, not general software engineering
• Iteration vs. reproducibility: the central tension in data science workflows and how tooling can honor both
• Hadley’s cold-turkey move from RStudio, muscle memory, and finding the new ergonomic groove
• How Julia measures success by smoothing the boundaries between tools and teams
• The applied, people-and-process side of data science that keeps Julia energized