Wes McKinney @ Posit | Data Science Hangout

Hi everybody, welcome back to the Data Science Hangout. I'm Rachel, I lead Customer Marketing at Posit, and I'm so excited to have you joining us today. The Hangout is our open space to hear what's going on in the world of data across different industries, chat about data science leadership, and connect with others who are facing similar things as you. We get together here every Thursday at the same time, same place, so if this is your first time joining us, it is so nice to meet you. If it is anybody's first time, let us know in the chat, we'd love to say hi and and welcome anybody in who's joining for the first time today.

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I did just want to mention because we have quite a few people maybe joining for the first time that we do also have a LinkedIn group for the Data Science Hangout. Not always the easiest for ongoing conversations, but it's helpful to help connect with people that you meet here, maybe have met through the chat.

And I also did want to say real quickly before we get started here, and I know Curtis will share this in the chat, but the Call for Talks just recently opened for the POSIT conference this year, and so I just wanted to make that known and Curtis can share the link there in the chat. I guess one other quick note is if you are watching this recording sometime in the future and want to join us live, the link to add it to your calendar will be in the details below. And there's no rules and anybody has to stay on the whole time or talk, come and go as if it's your schedule. But with all that, thank you again for joining us and I am so excited to be joined by my co-host today, Wes McKinney, Principal Architect at POSIT. And Wes, I want to kick things off here by introducing yourself and your role, but also something you like to do outside of work too.

Wes's background and path to Posit

Yeah, I'm a software developer and an entrepreneur, so I've worked on a bunch of open source projects in and around data science and Python in particular. So started what became the Pandas Project about 16 years ago, and I've started a couple of companies and created some other companies oriented at providing financial funding for open source development, partly with POSIT's help. So worked on the Apache Arrow data infrastructure and computing layer for data science tools and database systems. And that I worked closely with POSIT when it was still RStudio throughout that project. And most recently spent a little over three years starting Voltron Data and getting it off the ground to a 130-person company or so.

And just last fall, I made the transition out of my full-time role at Voltron Data. So I'm still an advisor there on the advisory board, and I decided to rejoin up with more full-time with POSIT to kind of work to make awesome polyglot data science tools and open source software.

And what about something you like to do outside? Oh yeah, outside of work. Outside of work, I do a lot of yoga. So it turns out both Hadley and I are super into yoga, which is kind of just random and unrelated. So I do that. I like to cook and run and read books. And I still find a little bit of time for video games now and then, console video games. I have a soft spot for retro games. So every now and then we'll indulge in a playthrough of an old Super Nintendo game or something like that.

Love it. Oh, I know you shared a little bit about this in your intro, but to kick us off here, could you share a bit about your path and kind of decision to join POSIT?

Yeah. I mean, a lot of people who know me well know that I've been involved in kind of collaborating with RStudio, POSIT for a pretty long time. So I got in touch with, I met JJ well over, JJ Allaire, the CEO of POSIT well over a decade ago when RStudio was just getting started. And I started talking with Hadley about things that we could work on together that would help end the data science wars and looking out for opportunities to work together on things that would help create more reusable software and things that would help data science teams working with open source technology, be more productive, enable Python and R teams to work together more effectively.

And so there were a couple of things that happened. So firstly, I think, you know, Jupyter, you know, IPython started the IPython notebook that became Jupyter. And so there started to be work on notebooks and developer development environments that could be shared across Python and R. And then when I was interested in starting what became Apache Arrow, one of the first things I do was, one of the first things I did was reach out to Hadley about building a data format that would enable data to be shared between, you know, dplyr and R data frames and pandas data frames to make using R and Python together a lot faster and a lot simpler. So we got together in February 2016 and built what became Feather. So that was kind of the start of the more hands-on collaboration with RStudio at that point.

And then a couple years later, I partnered, you know, partnered up more formally with Posit to create Ursa Labs. So providing significant funding for Apache Arrow development, helping with the administration of Ursa Labs so that I could focus on building software as opposed to running a company. And so in a way, like, you know, in a way Posit essentially helped me incubate what became Ursa Computing and then Voltron Data. And so Posit is, you know, remains like a kind of shareholder and kind of an active, like an active participant in things we've been doing in Arrow and Voltron Data.

And so when I was considering kind of my next, you know, my next move after being, you know, full-time in a CTO role at Voltron Data, you know, the decision for me was given that Posit has expanded its umbrella of, you know, its products to support Python, you know, building for polyglot data science teams. I felt there was an opening for me to, you know, to use my experience and skills to really help bolster that effort, align the product offerings and create really amazing experiences for data science teams. So it ended up being like really great, you know, really great timing, you know, given that I was at a, you know, a crossroads in my career, like deciding, you know, how to allocate my time between, you know, the various projects that I work on. And so, you know, and I've been, you know, just an enormous fan of the company. And so it's great to kind of, you know, to be back and working more closely with, you know, with the team and to, you know, have the opportunity to work on software that has such a large impact on so many people.

The principal architect role

I know I introduced you as principal architect, but maybe it might be helpful to explain a bit about what that means. What is a principal architect?

Yeah, so I'm a, so at present, I'm a little bit of a, like a mixture between a, you know, very technical, technical product manager, and a, you know, senior software developer, software, software architect. So on one hand, I'm helping, helping provide, you know, feedback and like strategic, you know, guidance and alignment in Posit's product, you know, product roadmap, you know, features and identifying, you know, blind spots and things that, things that Posit needs to build in order to create, you know, really great tools and systems to support, in particular Python, Python focused or Python only data science teams.

I'm also, you know, as I'm finding my sea legs and the different projects going on in the company, I'm doing, you know, some strategic, strategic development, like kind of targeted, have high leverage, you know, development work on different, on different projects, that taking, taking advantage of my, my background and experience with tools like, like Pandas and kind of low level, you know, low level data work, like areas where stuff that I know really well, where I can jump into a project, provide some, some targeted, you know, targeted development assistance and, and help, help get, help accelerate projects, feature roadmap. It's only been, it's only been about 10 weeks. So, you know, we'll see where things are in another three months or another six months, you know, by the time of PositConf in, in August, but it's been, yeah, it's been really exciting.

And, you know, there's a lot of, there's so much stuff going on. So it's also an opportunity for me to really get up to speed on all the things that Posit's been doing in, in the last, you know, five years, while I've been busy, you know, working on, you know, we're working on Arrow, working on startups, but also to, to, to, to spend time listening, to get, you know, look, look at what data science teams are struggling with right now, what things are working well, like what things aren't working well, and where, like, where are the opportunities to, to, to build things that, that can, yeah, make the, you know, make, make things, make things better for, for data science teams.

Voltron Data and the composable data stack

Yeah. Well, so, so learning more about Voltron data, we could spend the rest of the hour just talking about, you know, talking about the company and, and all the things that it's doing. The, so, so we, we produced this, this really great, like, knowledge base called the Composable, Composable Codex that goes through, basically, the, the history of how, like, the, like, trends in, in data processing systems and how we are currently in an era that we're working towards, basically, modularizing the layers of the data stack to make things more interoperable and, and more, like, faster and more reusable.

Apache Arrow was one major piece of technology that was a, like, a missing, like, a missing key to being able to enable some of this, this modularization and composability. And last year, we, we worked with one of our development partners, Meta, and their, their data infrastructure team. They have a project called, called Velox that is helping, kind of, unify and modularize their query processing and query execution inside Meta's internal data platform. And so, we created a, a paper that is the, I, I might fumble the title, but it's the, the Composable Data Management System Manifesto. And, and so, I would highly encourage you to check out the, the Composable Codex, which is on the voltrondata.com website, as well as the, the VLDB paper, the Composable Data Management System Manifesto, which goes into, kind of, the, kind of, the technical, you know, foundations of, like, why, like, where we are in the development timeline of database systems, data science systems, data processing, and why we are working to create these, these modular and, and composable components for building next-generation data systems.

And so, Voltron Data just recently launched its distributed GPU-accelerated, you know, modular data engine, which, you know, we're, you know, working to incorporate into, you know, data infrastructure providers like, like HP. And, you know, we, we believe that employing accelerators like GPUs, but also custom silicon FPGAs is going to be a big trend in terms of reducing the carbon footprint of large-scale data processing and machine learning.

And so, another project that I've been very involved with over the last decade and that's, has a, you know, a pretty good-sized team of Voltron Data is the Python IBIS project, which provides a unified data frame API to tons of different SQL and non-SQL backends. And that's one of the, kind of, the spokes of the strategy to facilitate the modularization of the, of the data stack. So, you can think about IBIS as being like dplyr for, kind of, like dplyr for Python. And so, we've, we've, you know, we've been put, we've put a lot of development work to, into maturing that, that, that tool in the last few years.

Managing multilingual Python and R teams

So, I work on a multilingual Python and R team, but it's not a software development team so much as just processes. We take processes that currently exist, ask how can we automate these, make them run faster, and get data to end users that need it for a variety of purposes. And one of the main problems that I've encountered in working on that team is, I'm an R user, I use projects in R, in R, EMD, and all the Python users, all six or seven of them, use conda environments. And is there any progress on helping those two different workflows, like, speak better together?

I know that it is, I know that it is, I don't know much about, about R, EMD, if that's, if that's how you pronounce it. But I know that it is possible to, it is, it is possible to manage an R dependency stack from conda. That's maybe not the, maybe not the answer that you were, that you were looking for. But there, there is, like, a bunch of work in recent times on improving the package management tools in general, particularly around cross-language, cross-language packaging.

So, like, one of the problems in the Python, in the Python world, is that there is the Python package index, and there's the pip installer tool. And so, the Python package index has the problem that it doesn't help you with non-Python dependencies, and developers have to do a lot of work in order to package up and deploy their packages as wheels on the Python package index. And so, for me, like, as a package developer, I've had to suffer a lot of hardship in order to get my software, like PyArrow, for example, on, on the Python package index. But, you know, basically pip and pipenv and Poetry, and there's, you know, various Python tools, they only work if, like, your dependency stack and what you're managing is something that lives on the Python package index. And so, if you have R or you have, like, other things that need to be installed for your full application, then these kind of pip centric or Python package index centric, index centric tools are not going to work for you.

And so, really, you know, we need to, we need to use cross, we need to use cross language or language agnostic package management tools that assist with kind of creating these reproducible, reproducible environments. I think Conda has the unfortunate association that people see it as, you know, basically a product of Anaconda, the company. But actually, I mean, nowadays, if you, you know, you can get a non-Anaconda distributed, you know, installation of Conda and Mamba, which is, like, a faster replacement for Conda. And there's a new packaging tool called Pixie. You're probably saying to yourselves, you know, why do we need another packaging tool? But I'm pretty excited about Pixie for a couple of reasons.

So, firstly, it enables you to manage your dependency stack, you know, without having to edit YAML files manually. So, like, you just add a dependency to your stack or remove a dependency, and it manages the YAML files for you, similar to NPM or Cargo, if anyone's used Rust. And so, I don't know that it's necessarily the answer that you're looking for, but I would, I would say that trying to, you know, look at things like Pixie or just, or even Conda, and trying to see if it's possible to create, you know, use those tools to manage both your R, you know, your R environments that you want to deploy or your, and your Python environments is one possible approach. There might be other approaches that I, that I'm not aware of, but if I were in that situation, that's what I would, I would try to do. And then if I can't get it to work, then I would write an angry blog post saying, like, I tried to do this and I failed.

So, maybe that might motivate the, that might motivate the developers to fix it. I've kind of made a personal, like, promise to myself to, like, never work on a Python packaging tool, because it's like, you know, we've all thought about it, because you get frustrated with the packaging and say, and you want to say, hey, I'm going to go fix this, but I've made a pledge that I will not, like, I will not, that's, you know, I might build a text editor before I build a Python packaging tool.

Yeah. Well, so, so I think this is, yeah, I don't have a simple answer for you right now, but I think you've hit the nail on the head that this is the kind of problem where, as a data scientist or as a system developer, you don't, this is not the thing that you want to have to spend your precious time sweating over, it's like how to make this just work in the production environment. And so I think it's important for, in an enterprise setting with the various controls that are in place in the production environment, I think it's critical to make that process easy for our only applications, which I think, to your point, I think Posit has made a big investment in making that seamless and just work for R, and so getting to that point with Python applications or applications that use Python and R, I think that's a critical thing.

Yeah, I mean I think the ideal thing is that you would have like a package, like essentially the equivalent of a package lock on Python so you know that what you develop and test locally is exactly what you're getting in the production environment, and I think some users may want to be, even be able to build the Docker image, build a Docker image locally and have some tooling around that so you can say this requirements file, this base Docker image, build this for me so that I can wire it up with my R application and use it through Reticulate or through a web API to know that, because to have something that works locally and then you deploy it and then maybe it works initially but then it gets broken because of something that was out of your control, that's not, that's no fun.

Lessons from bringing Python users to Posit tools

So I feel like Quarto and Shiny are the two big projects. I've used both of them. I went back and forth a lot with a very patient Posit developer on why my Python build was not compatible and I think he was a little taken aback by like how many rounds back and forth we were doing and like how broken my Python build was, whatever, whatever. So I feel like maybe that's one of the issues. Like in general, like what are the things that Posit has kind of taken away from like those like both fairly big tools and you know trying to pull in Python users and like how has that gone? Any lessons learned?

I think, I mean Quarto is definitely like a great example of if the project started out, I think the project started out like not working super closely with the Jupyter ecosystem and at some point while the project was still a little bit flying under the radar, they kind of reworked things to make it something that works really well for that like Jupyter-centric user. And so for example, there were people working on, like Jeremy Howard is one example of somebody who's a very Python-centric user working on deep learning and he wanted to write his book in Jupyter notebooks and then convert it into a Quarto website but also generate DocBook XML for O'Reilly Media. And I also, in my book, I got in touch with JJ and the Quarto developers about I would like to publish this book online and can you help me as Quarto something that can help?

And so I think that to understand the R community and the Python community I think are working in different, working from different starting places and those ecosystems have developed organically in different ways. And I think another thing that I often think about is the fact that the Python ecosystem on a relative basis has a lot, there's a much wider spectrum of types of users, the type of work that they are doing, the types of applications that they're building. And so I think in the R world, again, the generalization is not 100% valid but I would say the plurality of people working in R doing data science, data analysis, statistics, machine learning and in the Python world you have a lot more users that are doing some data work using some of these tools but that data analysis work might be incidental to some other aspect of their job, some other job responsibilities.

And that might be more software engineering or building production applications or building web services or there's many different things. Obviously you can do those things in R too but in terms of how these teams have developed inside companies, yeah, it's the teams and the people and the teams end up looking fairly different. I think that it's great that we have this opportunity for cross-pollination both in the open source libraries, I think the Python community has learned a lot from the R community in terms of API design, usability, user experience and I've tried to incorporate lessons from the Tidyverse and from dplyr and from ggplot2 into the things that I've built.

And I think also the R community has learned from things that the Python community has done also and so I think there's an enormous opportunity and the fact that we have a lot more active and more healthy dialogue and that we're not hearing as much about the language wars of Python sucks or R sucks or R is not a language. I haven't seen one of those blog posts in a while and so I think getting past the red team blue team bickering about who's programming language is better has enabled us to have just a lot more productive conversations about how we can build real tools for humans that are usable and accessible and how we can basically make the tent bigger and bring more.

I think getting past the red team blue team bickering about who's programming language is better has enabled us to have just a lot more productive conversations about how we can build real tools for humans that are usable and accessible and how we can basically make the tent bigger and bring more.

Because really the challenge that I see and that I think others see as well is that we're not working R versus Python, we're really like open source versus closed source proprietary software and so expanding the tent for open source and making open source the preferred and attractive way for businesses to go forward, that is our main challenge. And so it's fine if you have a bunch of open source software and an individual can get up and running but as soon as they want to go do that at work and they find that they're running into tons and tons of roadblocks or there's a lot of not so fun stuff that needs to be built to take free software that you can download on the internet or pull from GitHub and make it work in a business setting so I think we've made great progress but still a long road to go.

Developing a thick skin in open source

I'm just wondering as a person who has been sort of a driving force behind a lot of packages and platforms and things that have really wide visibility which most of us on this call do not do and have not experienced, do you feel like you had to develop a thick skin? Speaking of red team versus blue team and like all of the the feedback and the opinions that you get from all directions or that you know we see from all directions and if you did have to develop a thick skin how did you go about that?

I did. I will say that I don't think that I always handle the negative or petty feedback with Grace. I can think back on times where I became sort of upset or had an emotional reaction to something that somebody said. I remember one time Jeff Reback who's now got more contributions to pandas than I do and he was corresponding with somebody, an issue reporter on github and the person asked him if he was like a QA developer or a real developer. And so there are all these little comments that needle or that people complain about things and maybe they're missing the bigger picture or they don't realize that like yeah it's imperfect but you already worked really hard on what's there and you feel like they don't appreciate what you have built and they're only focused on like what's wrong with it.

And so I think the the feedback that you get in an open source project tends to only be only be negative feedback. Sometimes you get people telling you thank you like you know thanks for building this thing it's great I use it it's great but mostly you get the negative feedback people engage when there's something that something they don't they don't like or something that doesn't work something that's missing. And you know it is tough it is really tough.

In terms of building a thick skin yeah I think you know I think I don't know if anybody on this call has ever ever listened to or read David Foster Wallace's This is Water speech it was like a commencement speech you know from like 2005 or something and it encourages you to like be more empathetic or like more compassionate about like the subjective experience of other people. And so you know when you get this negative feedback like you have to realize that like it isn't always about you or like a criticism of you like it may actually be that that person is just having like a tough time they're having a bad day maybe they've got you know they've got everybody's got stress like everybody's got difficulties in their job or difficulties with their family. And so you know whenever you're you know whenever you're seeing this feedback you know you're you know you don't have that context of like that person and like what they're you know what they're dealing with in their life and so I think you have to kind of you have to take the negative feedback with the grain of salt.

And realize that you know when somebody's you know annoyed with your software not doing what they wish it did or being perfect in the ways that they wish for it to be perfect that you know that that comment is as much about them as it is about is about you and so you have to you know try try not to take it too personally. Um I do I do not read my amazon book reviews and so that was like I just I just decided not to do that. And um yeah and I I you know I try not to google myself and uh I try not to look at like I don't I rarely look at my um look at my you know my google results um I don't look up things on you know twitter I guess it's x now. Um but um yeah so social media is is you know not not a good place to get feedback um but you know every now and then there'll be comments on github that hurt. And and um yeah and over time like it it starts to hurt less and then after a while you become numb to it. Um I think Hadley is like totally numb to any any sort of feedback um and even to the point where you know um yeah he uh yeah it doesn't bother he doesn't mind you know being too too blunt with users it's like you know I'm sorry you don't like that but I'm not going to fix it basically.

LLMs and generative AI at Posit

Yeah sure thanks Rachel um just to curious Wes to hear your own kind of top of mind views on um how Posit intends to position itself around generative AI and LLMs maybe in particular um you know thinking about all the different ways that LLMs obviously rely on data even though they're not always transparent but you know what what are your thoughts around how Posit can meaningfully position itself as a tool for developing generative AI for testing and or monitoring generative AI responses and so on.

Yeah I I will I will will preface what I'm saying by by saying that that there yeah I think are aspects of this that are um I'm not sure what the phrase is above my pay grade but um I I do know that that um you know co-pilot is is a big deal for uh for for developer productivity and um you know making you know co-pilot available and you know easy to use and uh you know for people to opt in and use and you know kind of using the using the LLM features in a way that helps people be more you know be more productive and use the tool in the way that it's it's best you know it's best used which is you know you can use co-pilot to generate unit test cases for you and and help with repetitive tasks that you might otherwise like have to do a bunch of a bunch of typing and maybe make a lot more errors than co-pilot would make.

Um you know would make would make the first time obviously there's you know there's copyright you know copyright concerns and and sort of IP uh you know IP concerns and you know other concerns about you know misuse of um you know misuse of LLMs and um you know I do yeah I I am you know concerned about um you know about you know the ethical like ethical use of LLMs and kind of the potential harm potentially harmful effects you know on society. I think the the downside of you know the downside of making developers more productive is in the future we need fewer developers most likely and so that does have like a negative uh you know negative effect on on the workforce. Um you know but you would hope that at some point you know as we all become more productive that that we uh um you know we can work less I think that's always been like the sort of the dream of like increased productivity in the future uh means we don't have we all don't have to work as hard but um often you know the pointy haired bosses you know would prefer that we uh do twice as much work in the same amount of time uh with more with better tools.

But um so I don't know if that answers your question but I think you know integrating I think integrating Copilot into uh into uh Posit's product offerings is you know I think that's table stakes in in in 2024 and so we can have LLM assisted LLM assisted data exploration uh development testing um and um yeah I think I'm interested in like other sorts of LLM assisted like workflows around data like actual data analysis like helping you like actually ask better questions about the data I think that's one interesting area of of research. Um so not just like you know how do I make you know show me how to make this plot with ggplot2 but actually like what are some other way what are some other ways that questions that I could ask are like ways that I could look at this data that might bring more like more insights I think that's a pretty interesting uh an interesting area um kind of like a LLM powered LLM powered like research assistant kind of thing.

Yeah no I think you're talking about ways in which LLMs can help make you know R or Python use more productive I was almost thinking the opposite like how um Posit as as a form of IDE uh or development workspace could help in the creation um and augmentation of say open source LLM models and maybe it's not cool for that you know maybe other tools are really more well suited to that use case um which is fine too just but maybe in the future there's a place for Posit in that in that space.

Yeah I mean I I know that um I mean the primary language of of doing LLM research and implementations is um you know is is Python and um I mean I think there's some you know C++ development and probably some you know Rust development here and there um but I think to the extent that um you know I think we're like we're we're building development environments that that assist teams that are doing LLM doing LLM development or collaborating on LLM development um I don't know that we have any specific plans or at least specific plans that we're able to talk about around like tooling to support LLM model development specifically but I think I do think it's an interesting and an important area.

Getting started with Python

Yeah um I if you if you've never worked in if you've never worked in Python before um I guess my advice is always um you know to find something concrete that you would like to do with to do with Python. It could be something like really mundane like uh you know if if you're curious about you know I don't know like what what you're spending your money on Amazon or something like that I get really curious about my personal finances so but find some problem that's relevant to your life. And um I think there's there's a number of great books that um that help with you know learning Python from a from a beginner standpoint like um um you know automate the boring stuff with Python. I know the author of that book and um you know there's you know there's some other kind of you know introductory Python books just for the Python language.

I have a book that's now in its third edition called Python for Data Analysis that has if you're you know interested in learning about data analysis and data science um it has like kind of a quick start in Python. It doesn't go into object oriented development or building you know serious software in Python but if you're looking to learn just enough Python to use Pandas and get uh get up and running with you know Jupyter notebooks and uh and work with data I think I think it's a great resource for that. And so I wrote the book intending to for it to be like a quick start for somebody who has some basic programming experience um but wants to use Python for uh for kind of data data analysis data science.

Advice for building a company

Yeah it's it's it's very it's very difficult um I've my route my route to building companies has been um you know definitely different from you know from a lot of people because I've been I've really focused on the on the technology and then kind of retrofitting like a business model and uh and a corporate structure like around like in support of of the of the open source projects. And so for me the process has been build build an open source software project um build critical mass uh start engaging with users of that project and then learn from learn from those users like well outside of the open source project and solving the kind of low-level technology problems like what are the the next set of problems that need to be solved you know around that.

So it could be just like in in the case of like in the case of Voltron data for example like the first product that we launched was um like an enterprise like an enterprise support and open source partnership program for Apache Arrow because we recognized that there were businesses that were incorporating um Arrow into their into their own products and systems and they needed to have a reliable partner and a private channel to discuss issues that they encountered using the open source software in their in their internal development. And to create a structure where we could align on development initiatives within the open source project but that they wanted to put funding behind but in like a more structured way where they're like there's a contract and you know like kind of deliverables and timelines and you know recently concrete resourcing and all of the you know kind of things that you would you would need around like a commercial you know kind of enterprise enterprise contract.

Um I think that other people start companies that are more you know they start with identifying like identifying a business problem and conceptualizing a product that solves that problem and so kind of starting from more like the product on down and you say okay given what the product needs to do and so like the technology that you build to create to deliver that product ends up being like an incidental something incidental that you know in a sense like it's like the iceberg thing like there's this bit of ice that you see above the water like that's the product but then there's all this other stuff that you build underneath and so from the user standpoint you know they don't need to know that much about you know how it's built just that it works.

Um I haven't built too many of like those kinds of companies but um I've seen many people you know work on them uh so I think learning from you know learning from your users um you know being open-minded um you know have strong opinions but loosely held opinions so be willing to change your mind and and learn from learn from feedback. And um I think you know I've benefited greatly from mentorship and help from uh from many others you know from the generation you know the generation above me um I'm I'm 38 I've been doing uh entrepreneurial things for you know the past 11 or 12 11 or 12 years um you know I had my 20s I benefited greatly from uh from folks who you know had you know 10 or 20 years of experience on me in in entrepreneurship and uh in open source software. And so you know I learned a lot from people in the Python community um around open source community building project development culture um and you know I think without that that mentorship and that helping help from from others who who'd been on the path you know before me um it I would have uh you know not yeah it would have been harder for me to to get where I get where I am now so standing on the standing on the shoulders of giants certainly.

Most memorable career advice

Um I would say I I would say for me yeah probably the most frequent advice that I give to other people is to uh is is to focus on uh you know what you're is to focus on like people and and relationships so in investing in your relationships with people that you want to work with for a long time and also viewing your working relationships with a with a long-term mindset um not just like you know what value to we do we have to each other right now in the project that we were working on together but also like what you know what value might these working relationships have you know have in the future.

And so for me both in a entrepreneurial setting but also in an open source software setting I've taken that mindset and how I interact with other open source developers from this kind of long-term long-term relationship building uh standpoint so it might be that um you know there's no opportunity to collaborate with somebody or to work with somebody right now but there might be in five years and so it's kind of this balancing act of like how do you you know how do you spend your time or like who do you decide who to collaborate with and like where to spend you know your your uh you know eight or ten hours of working hours in a day.

Um and um but you know I think finding people that you like working with people that you feel inspired and productive around like who who you know help to generate ideas and helps you feel kind of motivated and productive about what you're working on and um you know and finding and cultivating those relationships and people that you want to work with for a long time and so you know I I have people that I've worked with you know actively off and on for over over a decade and I I treasure those I treasure those relationships. And so I think I've definitely seen other people who you know are working in a more you know transactional mindset and thinking more short term about you know their relationships um or treating you know kind of people as more um kind of uh interchangeable parts you know who you know serve kind of a short-term purpose to achieve like some you know business goal or some you know complete some task that you have in front of you. Um but um you know kind of taking a more people and human centric like mindset towards towards that I think for me at least has been um yeah it's been a lot more rewarding and I think has has been you know more valuable long term and so I always encourage other people to to to you know to the extent that they're able to to take that approach as well.

I've definitely seen other people who you know are working in a more you know transactional mindset and thinking more short term about you know their relationships um or treating you know kind of people as more um kind of uh interchangeable parts you know who you know serve kind of a short-term purpose to achieve like some you know business goal. Um but um you know kind of taking a more people and human centric like mindset towards towards that I think for me at least has been um yeah it's been a lot more rewarding and I think has has been you know more valuable long term.

What's ahead at Posit

Well that's a big that's a big question um I personally like I'm you know I think this is for me this has been like one of the first opportunities I've had to um in the last several years to get my get my head above water and take a more like a look a more uh like a you know kind of my data science walkabout you know so to speak and learn learn more about what everybody else has been building and doing in the last you know five or six years where I've been like I've you know I really had my head down and been really focused on um the Arrow project and its and its ecosystem.

And um and so you know I'm I'm you know really impressed with with everything that I've everything that I see that's you know that's been built uh you know in the last few years around you know developer tools and productivity and environments and you know tooling that supports all these different all these different types of work. Um and so yeah I think um you know I'm also looking you know sort of thinking longer term about you know like like what are my next big projects going to look like and you know like what else could I work on aside from continuing to nudge along you know the projects that I've uh worked on in the past um you know to help uh reach kind of the next you know uh kind of the next plateau of like progress and growth in the ecosystem.

So so I'm in a you know I'm in a learning mode I think that um you know I think that the people that I'm working with at Posit um you know it's a very stimulating environment and a place for me to learn and and um and you know come up with new ideas and so um yeah I'm really excited to see what that uh you know what that holds for me personally and also you know for the company and for the and for the data science ecosystem more generally.

Thank you so much Wes. I really tried to cram a few questions into that last one and you did a great job there. I really appreciate you joining us to to share your experience. This has been great. Thanks everybody for for hanging out with me for an hour. I enjoyed it so thank you. Yeah thank you all so much for joining us today. I know there were a few people joining for their first time ever today for the hangout so I did just want to let people know again we do have these every Thursday at 12 eastern time. If you use the short link I just shared in the chat you can add them to your calendar. Come whenever it fits your schedule.