The Test Set by Posit
A Posit podcast for data science junkies, anomaly hunters, and those who play outside the confidence interval. Hosted by Michael Chow, with co-hosts Wes McKinney & Hadley Wickham.
Episodes
The Bus Has to Jump 50 Feet — Greg Swinehart
Greg Swinehart mocked up the first rstudio.org, and JJ Allaire and Joe Cheng sent it back for looking too good. He's been at it ever since, from hex stickers to Shiny's new look. He and Michael Chow get into the "JJ Allaire patented heat index," why the best design would have no words, and the four-person crew with a broken laptop that redesigned every Safeway. Plus, Michael&ap
You're the Base, AI Is the Exponent — with Tareef Kawaf
Posit CEO Tareef Kawaf’s theory about LLMs is that they work like an exponent on whoever you already are. Greater than one and you compound. Under one and you accelerate in the wrong direction. Michael, Wes, and Hadley chat with Tareef about what that means for data science, along with John Chambers' Prime Directive, why Tareef now goes up against his own board, and the cat he waited 40 years
Smuggled Gold and Untrustworthy Intuition — with Leland McInnes
Leland McInnes has spent his career building tools a lot data scientists take for granted (UMAP, HDBSCAN), and amusingly the applications of those tools keep getting stranger. On this episode of The Test Set, he tells Michael, Hadley, and Wes about teams tracking laundered gold through mineral impurities, and the time he helped casinos optimize slot machine layouts. Also, why nobody's intuiti
Nobody Remembers Your 20 Charts — with Ruth Milligan
Ruth Milligan has coached hundreds of conference speakers, curates TEDx Columbus, and wrote The Motivated Speaker. She's here to tell you that speaking is habitual, not natural. Michael, Wes, and Hadley get a live fifteen-second filler word hack, an uncomfortable truth about listening back to your own voice, and the three kinds of pitches Ruth has been reading for seventeen years.What's
Let the Agent Cook — with Trevor Manz
Trevor Manz went from measuring plant apertures by hand in a wet lab to building the notebook that lets coding agents take the wheel. The creator of anywidget and founding engineer at marimo (marimo.io/pair) popped into The Test Set to spill on reactive notebooks, why marimo pair threw out every MCP tool but one, and what agents really want out of a data environment. This conversation also feature
The Answer Was Never Us — with Leilani Battle
Leilani Battle studies how software shapes what we see and believe. The University of Washington professor and co-director of the UW Interactive Data Lab talks with Michael, Hadley, and Wes about an experiment that manipulated people using nothing but loading speed, and why AI models don't seem to recommend charts the way the community that studies charts actually does. Other highlights: rati
Curiosity, duty, and existential dread — with Joe Cheng
Joe Cheng is the CTO of Posit and the creator of Shiny. He joins Michael and Hadley to talk about why he almost walked away from AI work entirely over ethics concerns and what it takes to lead a team that didn't necessarily choose you. Plus, why saying yes to everyone is a worse strategy than it sounds. Bonus: Hadley calls out Joe's people-pleasing in real time.What's inside:Joe&apo
Confidently Incorrect — with Caitlin Colgrove
Caitlin Colgrove is the CTO of Hex, the data workspace for building and sharing data projects using SQL and Python that somehow counts a Sweetgreen chef as a power user. She joins Michael, Hadley, and Isabel to talk about what AI agents actually get wrong in data work (it's not the hallucinations, it's supreme overconfidence), why data teams aren't going anywhere, and how she thinks
The Bothness of It — with Alex Hillman
Alex Hillman built one of America's first co-working spaces, wrote a business book in tweets, and recently handed his inbox to a Claude Code agent — not to draft emails, but to notice when a friendship is going cold. In this episode, Alex, Michael, Wes, and Hadley dig into marketing for people who hate marketing, what 20 years of email reveals about your relationships, and why the hardest par
The Code Doesn't Lie — with Mike Bostock
Mike Bostock made D3 when the browser was still a joke. He built bl.ocks when people needed somewhere to share their work. Now he's building Observable — reactive notebooks with an AI that actually looks at what it made. In this episode: the three-GIF bar chart that launched 25 years of viz, why open source needs both intrinsic and extrinsic motivation, and why an agent that can't see it
The Wonder-Driven Builder — with Paige Bailey
Paige Bailey is a developer relations engineering lead at Google DeepMind. She's a geophysicist-turned-AI-engineer who was once told by her professors that building open-source libraries was a waste of time. We talk about her path from planetary science to TensorFlow, why statisticians have a hidden edge in the age of AI, and what it means to be a curious generalist when the cost of building
Widgets Are Lego Bricks (and Other Things People Are Sleeping On) — with Vincent Warmerdam
Vincent Warmerdam has been the first full-time hire at a startup, a spacey punster who accidentally got himself a job, a bartender at an Amsterdam comedy theater, and a Dutch bike tour guide — and he'll tell you all of it was career development. Now doing DevRel at Marimo, Vincent makes the case for reactive notebooks, Lego-brick widgets, and why "number go up" is not a data science
Everything's a Fad (Including This Podcast) — with Benn Stancil
Benn Stancil built Mode Analytics, spent a decade in the data trenches, and now writes some of the sharpest, funniest essays in the data world. On The Test Set, he talks about the cultural shift from Nate Silver to Rick Rubin why AI might kill the analytics dashboard, and what happens when a thousand startups all build the same thing. Plus: boy bands as a model for collaboration, and why the best
Deeply Unsexy: SQL's Redemption Arc — with Tristan Handy
dbt Labs CEO Tristan Handy drops into The Test Set to map the fault lines between the data science world and the enterprise data world — and explain why analytics engineers are basically pissed-off data analysts who decided to organize the bookshelf. We get into SQL's glow-up, the community magic of dbt Slack, what AI agents mean for data warehouses, and why everyone's building iOS apps
Your VP Is Doing a Rogue Analysis in Cursor Right Now — with Nell Thomas
Nell Thomas has spent two decades in data — from equity research to the DNC to Facebook to leading a 400-person data org at Shopify. She walks Michael and Wes through the modern data stack role by role, gets honest about what AI is and isn't changing about data work, and admits the semantic layer has been her greatest leadership failure. Plus: Sneakers gets the respect it deserves.Episode Not
Sleeping Rats and Sociopathic Agents — with Phillip Cloud
Phillip Cloud has been shaping the Python data ecosystem since the early pandas days — and he has *opinions*. Now a principal engineer at NVIDIA leading the Ibis project, Phillip talks about how he stumbled into open source via an eye movement lab, why he prefers his coding agents cold and emotionless, and what happens when you ask an LLM for woodworking trig. Plus: terminal user interfaces, the f
More productive but a lot less fun — with Charlie Marsh
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 existenti
Alenka Frim: What yoga teaches us about discipline and collaboration in data science
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
Emily Riederer: Column selectors, data quality, and learning in public
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 be
Rebecca Barter: Persistent learning, tool building, and ‘Will code even exist?’
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 dashb
Marco Gorelli: Narwhals, ecosystem glue, and the value of boring work
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
Kelly Bodwin — Quarto hacks, AI in the classroom, and why R should stay weird
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 notesKelly is curious, coll
James Blair: Part 2 — Solutions engineering, critical thinking, and staying human
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
James Blair: Part 1 — Portfolios, practice, and staying curious
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 notesThis is a story about staying hands-on
Julia Silge: Part 2 — Glue work, licensing, and open source in the age of LLMs
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 notesBoth practical and philosophical, this conversation spans w
Julia Silge: Part 1 — Positron, pineapple pizza, and the art of iteration
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
Michael Chow: From psychology and Python to constrained creativity
For this episode, we turn the mic around. Wes McKinney takes over the interviewer’s chair to chat with his co-host, Michael Chow. Michael’s a principal software engineer at Posit, but he started out studying how people think — literally, with a PhD in cognitive psychology. Somewhere along the way, he got hooked on data science, helped build adaptive learning tools at DataCamp, and now spends his d
Roger Peng: Sustaining data science — in classrooms, code, and conversations
Michael, Hadley, and Wes welcome Roger Peng, professor of statistics and data science at UT Austin and co-host of Not So Standard Deviations. Together they trace Roger’s journey from early R adopter to pioneering online educator and prolific podcaster. The conversation ranges from the accidental rise of “data science” as a field, to the tension between research papers and software maintenance, to
Mine Çetinkaya-Rundel: Teaching in the AI era — and keeping students engaged
In this conversation, Mine Çetinkaya-Rundel, data science educator at Duke University and Posit, joins Michael, Hadley, and Wes to talk about teaching data science in a time when AI can write the code for you. Mine shares her journey from actuarial science to academia, the teaching philosophy behind the “whole game” approach, and her experiments using LLMs for instant student feedback. Along the w
Wes McKinney: Part 2 — The open source hustle and an insider view of Positron
In part two of our conversation with Wes McKinney, we dig into the challenges and realities of sustaining open source development. Wes shares how funding actually works (or doesn’t), why corporate buy-in is essential, and what it’s like building tools across languages, communities, and IDEs. We also talk about the Apache Software Foundation’s role in open governance and the origin of the Positron
Wes McKinney: Part 1 — Building Pandas, Arrow, and a speedrunning legacy
Wes McKinney’s fingerprints are all over the modern data stack — from inventing Pandas to co-creating Arrow. But before all that, Wes was organizing speedrun communities and hacking together better ways to wrangle datasets in finance. In this conversation, he shares his origin story and what makes good tools good. Stay tuned for part 2, coming soon.What’s Inside:How frustration with data work led
Hadley Wickham: Spreadsheets, bikes, and the accidental empire of R packages
Before Hadley Wickham became a pillar of modern data science, he was a spreadsheet-loving teenager making databases for his dad’s job. In this episode, he reflects on the early days of his involvement with R, the birth of tidyverse, and how real-world unpredictability — like a bear in a field — shapes data science.What’s Inside:Hadley’s first brush with R code … inside a Word docConsulting as a gr
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