The Test Set by Posit

Posit, PBC

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.

  1. -4 J

    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 file hierarchy standard hot take nobody asked for, and the pineapple-on-pizza hill he's willing to die on. Episode Notes Phillip Cloud (NVIDIA, Ibis project) joins Michael Chow, Wes McKinney, and Hadley Wickham to talk about his path from eye movement labs to pandas core team, why developer productivity tools have quietly gotten amazing, his brutally honest take on coding agents, and what it would actually take to impress him. Also: VisiData love, NixOS evangelism, and yard work as therapy. What’s Inside From eye movement labs and MATLAB to pandas core teamColumn multi-indexes: the feature nobody likes but Phillip neededWhy your command line tool better have a sweet TUIVisiData: the terminal data tool you're sleeping onAre we writing code for humans or for agents now?Phillip's AI skepticism journey: Cursor, Claude Code, and frustrationThe Numba CUDA test suite port that would finally impress him

    56 min

  2. 23 FÉVR.

    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 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 painfulWhy "just uv run it" became the killer featureHiring outside Python's ecosystem to build tools for itHis team said "we trust your PRs less now"Engineers screen-sharing their actual agent workflows at AstralThe "Lisp Curse" reborn: cheap code breaking open sourceIs Python the wrong language for an agentic world?

    1 h 35 min

  3. 9 FÉVR.

    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 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 committerSigns of a healthy open source community: people, dialogue, and turnoverArrow as critical infrastructure: DuckDB, Polars, Pandas, and the spec that unifies themManaging 4,000 open issues without losing your mindImposter syndrome in open sourceWhat the yoga mat teaches you about discipline and collaborationAI and the future of programming: 100x more software or 10x better software?

    1 h 1 min

  4. 26 JANV.

    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 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 mattersHow 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

    58 min

  5. 12 JANV.

    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 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 productivityEscaping busywork by building better toolsFrom R to Python to dashboards: Learning fast as a survival skillReality check on AI in the IDEWhy exploratory analysis still needs human intuitionThe 80/20 of coding: Automate the boring, protect the judgmentTeaching beginners and earning trustThe uncertain future of code

    57 min

  6. 15/12/2025

    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 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” operationsWhy expression systems won, and how they shaped Marco’s approach — with nods to Ibis, Polars, and PandasOpen source as social work: proactive outreach, trust-building, and a Discord-powered communityExtending Narwhals to new engines, starting with the Daft plugin

    52 min

  7. 01/12/2025

    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 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 mixologyBuilding Quarto extensions with JavaScript and AIWhen ChatGPT helps students learn (and when it doesn't)Applied stats meets history: analyzing social networks from the Polish RevolutionWhy remarkable, welcoming communities matter more than perfect code

    51 min

  8. 17/11/2025

    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 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 JamesHow solutions engineering became a natural pathway into product management at PositMulti-agent “bot posse” workflows and why context management mattersUsing AI the right way and why code literacy, critical thinking, and staying human are the real superpowers in an AI-saturated world

    42 min
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À propos

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.

Informations

  • Création
    Posit, PBC
  • Années d’activité
    2025 - 2026
  • Épisodes
    17
  • Classification
    Tous publics
  • Copyright
    © 2026 The Test Set by Posit
  • Site web de l’émission
    The Test Set by Posit
  • Fournisseur

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