The Test Set by Posit Mon, 13 Jul 2026 11:16:13 -0400 https://www.buzzsprout.com/2512485 en-us © 2026 The Test Set by Posit yes 16387c8f-ec4f-50d3-8819-37835f85cac1 Posit, PBC episodic false 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.

]]> Buzzsprout (https://www.buzzsprout.com) Posit, PBC https://storage.buzzsprout.com/6jt3xxrm73x9tclstotha2du95wg?.jpg The Test Set by Posit https://www.buzzsprout.com/2512485 Michael Chow Hadley Wickham Wes McKinney Curiosity, duty, and existential dread — with Joe Cheng 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's 2012 self-doubt spiral that accidentally created Shiny
  • Why Joe almost quit working on AI entirely
  • The "loaded guns" problem with releasing AI tools
  • Hadley's blunt leadership style vs. Joe's people-pleasing
  • Nobody actually wanted to make Joe CTO?
  • Joe's take on curiosity, duty, and fear as motivators
]]> 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's 2012 self-doubt spiral that accidentally created Shiny
  • Why Joe almost quit working on AI entirely
  • The "loaded guns" problem with releasing AI tools
  • Hadley's blunt leadership style vs. Joe's people-pleasing
  • Nobody actually wanted to make Joe CTO?
  • Joe's take on curiosity, duty, and fear as motivators
]]> Posit Buzzsprout-19486971 Mon, 13 Jul 2026 11:00:00 -0400 3933 1 26 full false Confidently Incorrect — with Caitlin Colgrove 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 about building products for humans and agents at the same time.

What's inside

  • What Hex's Context Studio does, and why it's a data team's new job
  • More code is now written in Hex by agents than by humans
  • "My job is to vouch for the correctness of the answer" — redefining the data team
  • The vibe-coded CEO PR is coming for your data team (if it hasn’t already)
  • Soulsborne games as couple's therapy, aka, the Elden Ring co-op report
]]> 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 about building products for humans and agents at the same time.

What's inside

  • What Hex's Context Studio does, and why it's a data team's new job
  • More code is now written in Hex by agents than by humans
  • "My job is to vouch for the correctness of the answer" — redefining the data team
  • The vibe-coded CEO PR is coming for your data team (if it hasn’t already)
  • Soulsborne games as couple's therapy, aka, the Elden Ring co-op report
]]> Posit Buzzsprout-19418568 Mon, 29 Jun 2026 12:00:00 -0400 3609 1 25 full false The Bothness of It — with Alex Hillman 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 part of AI-assisted coding was always before you wrote a single line.

What's inside: 

  • Marketing is really just listening at scale
  • Building a 20-year relationship database from your sent folder
  • "Hot rod vs. plumbing" — the two kinds of software you build now
  • What early internet and the AI boom have in common
  • The case for reading 20-year-old engineering books with a coding agent
  • Karaoke philosophy as a framework for community building
]]> 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 part of AI-assisted coding was always before you wrote a single line.

What's inside: 

  • Marketing is really just listening at scale
  • Building a 20-year relationship database from your sent folder
  • "Hot rod vs. plumbing" — the two kinds of software you build now
  • What early internet and the AI boom have in common
  • The case for reading 20-year-old engineering books with a coding agent
  • Karaoke philosophy as a framework for community building
]]> Posit Buzzsprout-19349555 Mon, 15 Jun 2026 12:00:00 -0400 4442 1 24 full false The Code Doesn't Lie — with Mike Bostock 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 its own output is likely to be confidently wrong.

What's Inside

  • The 1998 visualization library that could only make bar charts
  • Why D3 hit #3 on GitHub, and what killed the gallery
  • What spreadsheets got right that notebooks ignored for years
  • "The agent can lie with text, but not with code"
  • Why Observable scrapped canvases and went back to notebooks
  • The penguin dataset that exposes AI
  • Strength training, tennis mind games, and a resurrected Stanford game
]]> 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 its own output is likely to be confidently wrong.

What's Inside

  • The 1998 visualization library that could only make bar charts
  • Why D3 hit #3 on GitHub, and what killed the gallery
  • What spreadsheets got right that notebooks ignored for years
  • "The agent can lie with text, but not with code"
  • Why Observable scrapped canvases and went back to notebooks
  • The penguin dataset that exposes AI
  • Strength training, tennis mind games, and a resurrected Stanford game
]]> Posit Buzzsprout-19260417 Mon, 01 Jun 2026 10:00:00 -0400 4108 1 23 full false The Wonder-Driven Builder — with Paige Bailey 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 software is approaching zero. Bonus: installing solar-powered silent-film birdhouses as street art in San Francisco.

What's inside

  • From planetary science to TensorFlow, before it was GPU-capable
  • Geophysicists as early GPU adopters
  • The professors who said open-source wasn’t “real science”
  • Building silent-film birdhouses as San Francisco street art
  • Hiding Gemini API tests inside whimsical side projects
  • The right-tool-for-the-job case for mixing AI models
  • Why “taste” is the skill that matters when code costs nothing
]]> 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 software is approaching zero. Bonus: installing solar-powered silent-film birdhouses as street art in San Francisco.

What's inside

  • From planetary science to TensorFlow, before it was GPU-capable
  • Geophysicists as early GPU adopters
  • The professors who said open-source wasn’t “real science”
  • Building silent-film birdhouses as San Francisco street art
  • Hiding Gemini API tests inside whimsical side projects
  • The right-tool-for-the-job case for mixing AI models
  • Why “taste” is the skill that matters when code costs nothing
]]> Posit Buzzsprout-19196473 Mon, 18 May 2026 12:00:00 -0400 2743 1 22 full false Widgets Are Lego Bricks (and Other Things People Are Sleeping On) — with Vincent Warmerdam 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 strategy. Also: chickens die. The model doesn't know. This matters more than you think.

What's inside

  • How a spacey pun accidentally launched Vincent's career
  • Why Marimo's constraints make it better for LLMs, not just humans
  • The gorilla hiding in your dataset — and why the model missed it
  • Vibe coding vs. notebooks: three cells at a time as a discipline
  • Widgets as Lego bricks: reusable, composable, criminally underused
  • Cognitive debt, confirmation bias, and sycophantic data science
  • Why natural intelligence is still, actually, a pretty good idea
]]> 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 strategy. Also: chickens die. The model doesn't know. This matters more than you think.

What's inside

  • How a spacey pun accidentally launched Vincent's career
  • Why Marimo's constraints make it better for LLMs, not just humans
  • The gorilla hiding in your dataset — and why the model missed it
  • Vibe coding vs. notebooks: three cells at a time as a discipline
  • Widgets as Lego bricks: reusable, composable, criminally underused
  • Cognitive debt, confirmation bias, and sycophantic data science
  • Why natural intelligence is still, actually, a pretty good idea
]]> Posit, PBC Buzzsprout-19121648 Mon, 04 May 2026 11:00:00 -0400 4547 1 21 full false Everything's a Fad (Including This Podcast) — with Benn Stancil 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 creative work starts with cheating.

What's inside: 

  • Why the modern data stack was basically big data 2.0
  • The cultural flip from Nate Silver to Rick Rubin
  • Gas Town, tar pits, and the AI startup zero-sum game
  • Software is becoming content, and that changes things
  • Benn's creative process: Lorde lyrics, Codenames, and cheating
  • The boy band as a model for small-team collaboration
  • BI is (mostly) dead, and vibes might replace SQL
]]> 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 creative work starts with cheating.

What's inside: 

  • Why the modern data stack was basically big data 2.0
  • The cultural flip from Nate Silver to Rick Rubin
  • Gas Town, tar pits, and the AI startup zero-sum game
  • Software is becoming content, and that changes things
  • Benn's creative process: Lorde lyrics, Codenames, and cheating
  • The boy band as a model for small-team collaboration
  • BI is (mostly) dead, and vibes might replace SQL
]]> Posit, PBC Buzzsprout-19034085 Mon, 20 Apr 2026 10:00:00 -0400 5709 1 20 full false Deeply Unsexy: SQL's Redemption Arc — with Tristan Handy 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 with Claude now.

What's inside:

  • What analytics engineers *actually* do
  • SQL's journey from deeply unsexy to indispensable
  • How dbt turned source control into a source of truth
  • Building a tech community without the RTFM energy
  • AI agents on your data lake: permissions get personal
  • Will LLMs kill the open-source package ecosystem?
  • Edible gardening, welding dreams, and digital dysphoria
]]> 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 with Claude now.

What's inside:

  • What analytics engineers *actually* do
  • SQL's journey from deeply unsexy to indispensable
  • How dbt turned source control into a source of truth
  • Building a tech community without the RTFM energy
  • AI agents on your data lake: permissions get personal
  • Will LLMs kill the open-source package ecosystem?
  • Edible gardening, welding dreams, and digital dysphoria
]]> Posit, PBC Buzzsprout-18968901 Mon, 06 Apr 2026 09:00:00 -0400 3931 1 19 full false Your VP Is Doing a Rogue Analysis in Cursor Right Now — with Nell Thomas 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 Notes
What does it actually look like to run data infrastructure for millions of merchants while the entire industry reinvents itself in real time? Nell Thomas (VP of Data, Shopify) talks vibe-coded dashboards, political campaign data scarcity, blameless postmortems, and why no one should be locking in on an AI strategy just yet. Recorded live in Times Square.

What’s Inside

  • Mapping the modern data stack, role by role
  • Why data quality is still the #1 problem
  • What "good scrutiny" looks like on a data team
  • Vibe coded dashboards and the trust problem
  • Shopify's MCP for their data warehouse
  • The throwaway tech problem in political campaigns
  • Why the semantic layer is so damn hard
  • Sneakers!


]]> 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 Notes
What does it actually look like to run data infrastructure for millions of merchants while the entire industry reinvents itself in real time? Nell Thomas (VP of Data, Shopify) talks vibe-coded dashboards, political campaign data scarcity, blameless postmortems, and why no one should be locking in on an AI strategy just yet. Recorded live in Times Square.

What’s Inside

  • Mapping the modern data stack, role by role
  • Why data quality is still the #1 problem
  • What "good scrutiny" looks like on a data team
  • Vibe coded dashboards and the trust problem
  • Shopify's MCP for their data warehouse
  • The throwaway tech problem in political campaigns
  • Why the semantic layer is so damn hard
  • Sneakers!


]]> Posit, PBC Buzzsprout-18864065 Mon, 23 Mar 2026 07:00:00 -0400 4964 1 18 full false Sleeping Rats and Sociopathic Agents — with Phillip Cloud 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 team
  • Column multi-indexes: the feature nobody likes but Phillip needed
  • Why your command line tool better have a sweet TUI
  • VisiData: the terminal data tool you're sleeping on
  • Are we writing code for humans or for agents now?
  • Phillip's AI skepticism journey: Cursor, Claude Code, and frustration
  • The Numba CUDA test suite port that would finally impress him
]]> 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 team
  • Column multi-indexes: the feature nobody likes but Phillip needed
  • Why your command line tool better have a sweet TUI
  • VisiData: the terminal data tool you're sleeping on
  • Are we writing code for humans or for agents now?
  • Phillip's AI skepticism journey: Cursor, Claude Code, and frustration
  • The Numba CUDA test suite port that would finally impress him
]]> Posit, PBC Buzzsprout-18784399 Mon, 09 Mar 2026 07:00:00 -0400 3387 1 17 full false More productive but a lot less fun — with Charlie Marsh 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 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?
]]> 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?
]]> Buzzsprout-18696028 Mon, 23 Feb 2026 07:00:00 -0500 5714 1 16 full false Alenka Frim: What yoga teaches us about discipline and collaboration in data science 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 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?
]]> 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?
]]> Posit, PBC Buzzsprout-18631748 Mon, 09 Feb 2026 08:00:00 -0500 3674 1 15 full false Emily Riederer: Column selectors, data quality, and learning in public 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 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
]]> 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
]]> Posit, PBC Buzzsprout-18571612 Mon, 26 Jan 2026 17:00:00 -0500 3499 1 14 full false Rebecca Barter: Persistent learning, tool building, and ‘Will code even exist?’ 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 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
]]> 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
]]> Posit, PBC Buzzsprout-18486430 Mon, 12 Jan 2026 17:00:00 -0500 3407 1 13 full false Marco Gorelli: Narwhals, ecosystem glue, and the value of boring work 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” 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
]]> 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
]]> Buzzsprout-18358449 Mon, 15 Dec 2025 14:00:00 -0500 3101 1 12 full false Kelly Bodwin — Quarto hacks, AI in the classroom, and why R should stay weird 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 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
]]> 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
]]> Posit, PBC Buzzsprout-18282262 Mon, 01 Dec 2025 18:00:00 -0500 3069 1 11 full false James Blair: Part 2 — Solutions engineering, critical thinking, and staying human 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 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
]]> 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
]]> Posit, PBC Buzzsprout-18205771 Mon, 17 Nov 2025 18:00:00 -0500 2529 1 10 full false James Blair: Part 1 — Portfolios, practice, and staying curious 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 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 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
]]> Posit, PBC Buzzsprout-18128150 Mon, 03 Nov 2025 22:00:00 -0500 1771 1 9 full false Julia Silge: Part 2 — Glue work, licensing, and open source in the age of LLMs 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 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 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
]]> Posit, PBC Buzzsprout-18010491 Mon, 20 Oct 2025 17:00:00 -0400 1697 1 8 full false Julia Silge: Part 1 — Positron, pineapple pizza, and the art of iteration 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-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
]]> 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
]]> Posit, PBC Buzzsprout-17979941 Wed, 08 Oct 2025 17:00:00 -0400 2325 1 7 full false Michael Chow: From psychology and Python to constrained creativity 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 days thinking about how to make Python easier to use and more fun.

The two dig into what drives Michael’s curiosity, how a “weird obsession with tables” turned into a beloved open source project, and the future of data science/scientists.


Episode Notes:
We explore Michael’s path from studying the mind to shaping the Python data science ecosystem. From adaptive learning platforms to Great Tables, Michael shares how following unexpected curiosities can spark tools and communities that last.


What’s Inside:

  • Michael’s pivot from an academic career in data science
  • Behind-the-scenes messiness of building data and learning platforms
  • Open source projects born out of zany, single-minded passions
  • Bringing beauty to rows and columns
  • Big-picture thoughts on where data science — and open source tooling — are headed
]]> 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 days thinking about how to make Python easier to use and more fun.

The two dig into what drives Michael’s curiosity, how a “weird obsession with tables” turned into a beloved open source project, and the future of data science/scientists.


Episode Notes:
We explore Michael’s path from studying the mind to shaping the Python data science ecosystem. From adaptive learning platforms to Great Tables, Michael shares how following unexpected curiosities can spark tools and communities that last.


What’s Inside:

  • Michael’s pivot from an academic career in data science
  • Behind-the-scenes messiness of building data and learning platforms
  • Open source projects born out of zany, single-minded passions
  • Bringing beauty to rows and columns
  • Big-picture thoughts on where data science — and open source tooling — are headed
]]> Posit, PBC Buzzsprout-17905852 Thu, 25 Sep 2025 17:00:00 -0400 4044 1 6 full false Roger Peng: Sustaining data science — in classrooms, code, and conversations 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 what makes for meaningful, lasting creative work.

What’s Inside:

  • Roger’s first analysis project and what it taught him about authorship and data
  • Roger’s advice for students testing the waters in data science
  • Why software has become the unifying language of modern statistics
  • The origins of “data science” as a field and a label
  • Reflections on Coursera, MOOCs, and opening education to the world
  • What keeps a podcast (and a career) going strong after a decade-plus
]]> 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 what makes for meaningful, lasting creative work.

What’s Inside:

  • Roger’s first analysis project and what it taught him about authorship and data
  • Roger’s advice for students testing the waters in data science
  • Why software has become the unifying language of modern statistics
  • The origins of “data science” as a field and a label
  • Reflections on Coursera, MOOCs, and opening education to the world
  • What keeps a podcast (and a career) going strong after a decade-plus
]]> Buzzsprout-17736089 Tue, 26 Aug 2025 10:00:00 -0400 2705 1 5 full false Mine Çetinkaya-Rundel: Teaching in the AI era — and keeping students engaged 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 way, the group dives into the joys and risks of coding by hand, the role of open source in the classroom, and what it’s like to work across both the R and Python communities.

What’s Inside:

  • How a career in actuarial science led Mine to the world of data science and teaching
  • The “whole game” approach to learning and how it helps students stay motivated
  • Building an LLM-powered feedback tool for low-stakes assignments
  • Balancing AI assistance with the need for hands-on coding experience
  • The shared DNA of R and Python scientific computing communities
  • The hidden value of live coding, pair programming, and seeing the process — not just the output
]]> 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 way, the group dives into the joys and risks of coding by hand, the role of open source in the classroom, and what it’s like to work across both the R and Python communities.

What’s Inside:

  • How a career in actuarial science led Mine to the world of data science and teaching
  • The “whole game” approach to learning and how it helps students stay motivated
  • Building an LLM-powered feedback tool for low-stakes assignments
  • Balancing AI assistance with the need for hands-on coding experience
  • The shared DNA of R and Python scientific computing communities
  • The hidden value of live coding, pair programming, and seeing the process — not just the output
]]> Posit, PBC Buzzsprout-17655580 Mon, 11 Aug 2025 18:00:00 -0400 3287 1 4 full false Wes McKinney: Part 2 — The open source hustle and an insider view of Positron 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 IDE.

What’s Inside:

  • Why passion isn’t enough for open source to scale
  • Apache Arrow’s origin story and how it was pitched
  • How open governance enables trust between competitors
  • The thinking behind Positron, Posit’s next-gen IDE
  • Polyglot programming – Designing tools that bridge the R/Python divide
  • LLMs and data UX: Why modern IDEs need to serve both humans and models
  • Day-to-day coding, advising, investing, and context-switching
  • Metalheads unite
]]> 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 IDE.

What’s Inside:

  • Why passion isn’t enough for open source to scale
  • Apache Arrow’s origin story and how it was pitched
  • How open governance enables trust between competitors
  • The thinking behind Positron, Posit’s next-gen IDE
  • Polyglot programming – Designing tools that bridge the R/Python divide
  • LLMs and data UX: Why modern IDEs need to serve both humans and models
  • Day-to-day coding, advising, investing, and context-switching
  • Metalheads unite
]]> Buzzsprout-17579004 Mon, 28 Jul 2025 21:00:00 -0400 1593 1 3 full false Wes McKinney: Part 1 — Building Pandas, Arrow, and a speedrunning legacy 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 Wes to build pandas (and leave a PhD)
  • A nostalgic dive into the GoldenEye speedrunning scene
  • Why read_csv performance is a deeply personal crusade
  • Lessons from convincing friends to quit finance and go open source
  • Founding startups, launching Arrow, and the Ibis origin story
  • The beauty of letting contributors take the reins
  • Shout-out to Philip Cloud, pandas’ resident pun master
  • Why open communities win — and what it takes to build them
]]> 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 Wes to build pandas (and leave a PhD)
  • A nostalgic dive into the GoldenEye speedrunning scene
  • Why read_csv performance is a deeply personal crusade
  • Lessons from convincing friends to quit finance and go open source
  • Founding startups, launching Arrow, and the Ibis origin story
  • The beauty of letting contributors take the reins
  • Shout-out to Philip Cloud, pandas’ resident pun master
  • Why open communities win — and what it takes to build them
]]> Posit, PBC Buzzsprout-17504185 Mon, 14 Jul 2025 17:00:00 -0400 1402 1 2 full false Hadley Wickham: Spreadsheets, bikes, and the accidental empire of R packages 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 doc
  • Consulting as a grad student — and learning what people really want from stats
  • How messy Excel sheets inspired the tidy data revolution
  • Writing R packages as a form of self-defense (and productivity)
  • The secret sauce of building the tidyverse team
  • On focus, burnout, and saying “no” to GitHub pull requests
  • Current obsession: using LLMs  to make data science faster, easier, and more fun
  • How writing books is a form of tidying ideas, and how a Shiny textbook led to a custom bike
]]> 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 doc
  • Consulting as a grad student — and learning what people really want from stats
  • How messy Excel sheets inspired the tidy data revolution
  • Writing R packages as a form of self-defense (and productivity)
  • The secret sauce of building the tidyverse team
  • On focus, burnout, and saying “no” to GitHub pull requests
  • Current obsession: using LLMs  to make data science faster, easier, and more fun
  • How writing books is a form of tidying ideas, and how a Shiny textbook led to a custom bike
]]> Buzzsprout-17423698 Mon, 30 Jun 2025 13:00:00 -0400 1708 1 1 full false