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Topics covered in this episode: NumFOCUS concerns leaping pytest debugger llm Extra, Extra, Extra, PyPI has completed its first security audit Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org Brian: @brianokken@fosstodon.org Show: @pythonbytes@fosstodon.org Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesdays at 11am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: NumFOCUS concerns Suggested by Pamphile Roy Write up of the current challenges faced by NumFOCUS, by Paul Ivanov (one of the OG of Scientific Python: Jupyter, Matplotlib, etc.) Struggling to meet the needs of sponsored and affiliated projects. In February, NumFOCUS announced it is moving in a new direction. NumFOCUS initiated an effort to run an election for open board seats and proposed changing its governance structure. Some projects are considering and actively pursuing alternative venues for fiscal sponsorship. Quite a bit more detail and discussion in the article. NumFOCUS covers a lot of projects NumPy, Matplotlib, pandas, Jupyter, SciPy, Astropy, Bokeh, Dask, Conda, and so many more. Michael #2: leaping pytest debugger llm You can ask Leaping questions like: Why am I not hitting function x? Why was variable y set to this value? What was the value of variable x at this point? What changes can I make to this code to make this test pass? Brian #3: Extra, Extra, Extra, 2024 Developer Summit Also suggested by Pamphile, related to Scientific Python The Second Scientific Python Developer Summit , June 3-5, Seattle, WA Lots of great work came out of the First Summit in 2023 pytest-regex - Use regexs to specify tests to run Came out of the '23 summit I'm not sure if I'm super happy about this or a little afraid that I probably could use this. Still, cool that it's here. Cool short example of using __init__ and __call__ to hand-roll a decorator. ruff got faster Michael #4: PyPI has completed its first security audit Trail of Bits spent a total of 10 engineer-weeks of effort identifying issues, presenting those findings to the PyPI team, and assisting us as we remediated the findings. Scope: The audit was focused on "Warehouse", the open-source codebase that powers pypi.org As a result of the audit, Trail of Bits detailed 29 different advisories discovered across both codebases. When evaluating severity level of each advisory, 14 were categorized as "informational", 6 as "low", 8 as "medium" and zero as "high". Extras Brian: pytest course community to try out Podia Communities. Anyone have a podia community running strong now? If so, let me know through Mastodon: @brianokken@fosstodon.org Want to join the community when it's up and running? Same. Or join our our friends of the show list, and read our newsletter. I'll be sure to drop a note in there when it's ready. Michael: VS Code AMA @ Talk Python [video] Gunicorn CVE Talk submissions are now open for both remote and in-person talks at the 2024 PyConZA? The conference will be held on 3 and 4 October 2024 in Cape Town, South Africa. Details are on za.pycon.org. FlaskCon 2024 will be happening Friday, May 17 inside PyCon US 2024. Call for proposals are now live! Joke: Debugging with your eyes
It's easy to get technical when you're writing copy about a highly technical subject. But that's when you lose your audience.Instead ask, "What problem is this solving for my customer?" And explain it in their language.That's one strategy inspired by Michael Crichton that we're exploring today with the CMO of Promo.com, Joel Horwitz. Together, we talk about writing in layman's terms, thoroughly researching the problem you're trying to solve, and learning something new about marketing every day.About our guest, Joel HorwitzJoel is an experienced High-Tech Marketing professional with a diverse background in data science & engineering, product strategy and digital marketing. Prior to Promo.com, he was the Chief Marketing Officer of AudioEye where he led the Go-To-Market team with a Product-Led Growth Strategy that helped grow the company from less than 300 customers to over 30,000 in a year. Prior to that, at IBM, he championed the value of a Digital Go-To-Market as the Global Vice President of Strategic Partnerships and Digital Offerings for IBM's Digital Business Group. In addition, his extensive background in Data + AI has helped him lead breakthrough customer experiences including the AudioEye Accessibility Solution, IBM Data Science Experience, Alpine Data Labs Modeler, Datameer Sheets for Hadoop, H2O.ai Sparkling Water, and more; through the introduction of platform partnerships, self-service offerings, and digital go-to-market.Joel holds an MBA in International Business from the University of Pittsburgh, an MS and BS in Nanotechnology from the College of Engineering at the University of Washington, Seattle, WA. He is a board member of NUMFocus, an advisor to a number of startups, and a volunteer in his local community. About Promo.comOriginally launched in 2016 as a B2B video creation and distribution platform, Promo.com has since won numerous awards, scored top customer reviews and has been deployed by Fortune 500 companies for social media marketing purposes for 10,000+ Brands. Promo.com's latest product, PromoAI Copilot, soft-launched in October 2023, gaining over 1,000 customers who are now using the Promo.com Platform. Its latest product, PromoAI Copilot is now available at Promo.com or on the OpenAI GPT Store.About Michael CrichtonMichael Crichton is the late award-winning author, screenwriter and filmmaker, having passed away in 2008. He's most known for having written Jurassic Park and having created ER. He was incredibly prolific. So he's also known for books, movies and TV shows like The Andromeda Strain, The Lost World, Westworld, and all the other Jurassic movies (Jurassic Park III, Jurassic World, etc.) He also wrote frequently under the pseudonym John Lange of Jeffery Hudson. He has sold 200 million books, and his books have been translated into 38 languages, and 13 of them have been made into movies. He has an Emmy and a Peabody among other awards.What B2B Companies Can Learn From Michael Crichton:Write in layman's terms. Even when it's a highly technical product or concept, write so the general reader can understand your topic. Joel says, “What makes Michael Crichton remarkable is his ability to explain highly complex and difficult ideas in a way that a nine year old can understand them. If you're in the high tech industry, you're working with cryptocurrencies, blockchain, artificial intelligence, machine learning, or large language models. All this stuff is very difficult to understand if you're a novice. So if you can communicate these ideas and not just explain them for what they are, but then to try to compel somebody to be interested in these ideas and then extrapolate a whole story and a whole vision of where this could take us, that to me is Remarkable.”Thoroughly research the problem you're trying to solve. Become an expert on the topic and then teach your audience about it. Explaining how your product solves the problem in detail and backing it up with research builds credibility as well as drives engagement and conversion. Joel says, “Ultimately, marketers are teachers. What are we really doing with content marketing? We're teaching people about how to think about a particular product area. A lot of the work goes into really making sure you've got the problem right that you need to solve. Not as much on the solutioning side. Usually it's like, ‘What is the problem that we're trying to actually solve here?' And researching that.” And he adds, “Not just reading, but actually, for example, going to the location or going out and actually talking to customers.”Learn something new about marketing every day. Ask questions and be intensely curious. Learn from your peers, from Google searches, or subscribe to a newsletter like Harry Dry's, Devin Reed's or Emily Kramer's. Joel says, “Constantly be learning, coming into things with a beginner's mindset. I think that's another big thing Michael Crichton does well. He asks a lot of good questions. My grandfather told me the smartest men and women ask the best questions. They act as if you don't know something because that's how you learn.”Quotes*”I was never one for the big unveil. I've always been like, ‘All right, what are the things that we can incrementally change and test to see if we're moving things in the right direction?'” - Joel Horwitz*”I think we often think of, ‘Who's that one ideal customer profile or who's that one champion that we need to target?' But these decisions, especially B2B, they're never made by a single person. It's almost always a team. And so it's really helpful for me to think about, ‘Who are the different personalities in the room that I'm speaking to?' Because I think if you can convince them or they can all see kind of their own story, their own journey, and how this product or how the solution is going to help them, I think you have a much better chance of getting their attention.” - Joel HorwitzTime Stamps[0:55] Meet Joel Horwitz, CMO of Promo.com[1:35] Why are we talking about Michael Crichton?[5:41] What does Joel's work at Promo.com entail?[8:27] Who is Michael Crichton?[13:22] What was Michael Crichton's creative process?[17:35] What makes Michael Crichton remarkable?[32:56] What B2B marketing lessons can we take from Michael Crichton?[50:13] What have Joel's favorite campaigns been over the years?[53:10] What's next for Promo.com?LinksLearn more about Michael CrichtonConnect with Joel on LinkedInLearn more about Promo.comAbout Remarkable!Remarkable! is created by the team at Caspian Studios, the premier B2B Podcast-as-a-Service company. Caspian creates both non-fiction and fiction series for B2B companies. If you want a fiction series check out our new offering - The Business Thriller - Hollywood style storytelling for B2B. Learn more at CaspianStudios.com. In today's episode, you heard from Ian Faison (CEO of Caspian Studios) and Meredith Gooderham (Senior Producer). Remarkable was produced this week by Jess Avellino, mixed by Scott Goodrich, and our theme song is “Solomon” by FALAK. Create something remarkable. Rise above the noise.
Logan Kilpatrick leads developer relations at OpenAI, supporting developers building with the OpenAI API and ChatGPT. He is also on the board of directors at NumFOCUS, the nonprofit organization that supports open source projects like Jupyter, Pandas, NumPy, and more. Before OpenAI, Logan was a machine-learning engineer at Apple and advised NASA on open source policy. In our conversation, we discuss:• OpenAI's fast-paced and innovative work environment• The value of high agency and high urgency in your employees• Tips for writing better ChatGPT prompts• How the GPT Store is doing• OpenAI's planning process and decision-making criteria• Where OpenAI is heading in the next few years• Insight into OpenAI's B2B offerings• Why Logan “measures in hundreds”—Brought to you by:• Hex—Helping teams ask and answer data questions by working together• Whimsical—The iterative product workspace• Arcade Software—Create effortlessly beautiful demos in minutes—Find the transcript for this episode and all past episodes at: https://www.lennyspodcast.com/episodes/. Today's transcript will be live by 8 a.m. PT.—Where to find Logan Kilpatrick:• X: https://twitter.com/OfficialLoganK• LinkedIn: https://www.linkedin.com/in/logankilpatrick/• Website: https://logank.ai/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Logan's background(03:49) The impact of recent events on OpenAI's team and culture(08:20) Exciting developments in AI interfaces(09:52) Using OpenAI tools to make companies more efficient(13:04) Examples of using AI effectively(18:35) Prompt engineering(22:12) How to write better prompts(26:05) The launch of GPTs and the OpenAI Store(32:10) The importance of high agency and urgency(34:35) OpenAI's ability to move fast and ship high-quality products(35:56) OpenAI's planning process and decision-making criteria(40:22) The importance of real-time communication(42:33) OpenAI's team and growth(44:47) Future developments at OpenAI(47:42) GPT-5 and building toward the future(50:38) OpenAI's enterprise offering and the value of sharing custom applications(52:30) New updates and features from OpenAI(55:09) How to leverage OpenAI's technology in products(58:26) Encouragement for building with AI(59:30) Lightning round—Referenced:• OpenAI: https://openai.com/• Sam Altman on X: https://twitter.com/sama• Greg Brockman on X: https://twitter.com/gdb• tldraw: https://www.tldraw.com/• Harvey: https://www.harvey.ai/• Boost Your Productivity with Generative AI: https://hbr.org/2023/06/boost-your-productivity-with-generative-ai• Research: quantifying GitHub Copilot's impact on developer productivity and happiness: https://github.blog/2022-09-07-research-quantifying-github-copilots-impact-on-developer-productivity-and-happiness/• Lesson learnt from the DPD AI Chatbot swearing blunder: https://www.linkedin.com/pulse/lesson-learnt-from-dpd-ai-chatbot-swearing-blunder-kitty-sz57e/• Dennis Yang on LinkedIn: https://www.linkedin.com/in/dennisyang/• Tim Ferriss's blog: https://tim.blog/• Tyler Cowen on X: https://twitter.com/tylercowen• Tom Cruise on X: https://twitter.com/TomCruise• Canva: https://www.canva.com/• Zapier: https://zapier.com/• Siqi Chen on X: https://twitter.com/blader• Runway: https://runway.com/• Universal Primer: https://chat.openai.com/g/g-GbLbctpPz-universal-primer• “I didn't expect ChatGPT to get so good” | Unconfuse Me with Bill Gates: https://www.youtube.com/watch?v=8-Ymdc6EdKw• Microsoft Azure: https://azure.microsoft.com/• Lennybot: https://www.lennybot.com/• Visual Electric: https://visualelectric.com/• DALL-E: https://openai.com/research/dall-e• The One World Schoolhouse: https://www.amazon.com/One-World-Schoolhouse-Education-Reimagined/dp/1455508373/ref=sr_1_1• Why We Sleep: Unlocking the Power of Sleep and Dreams: https://www.amazon.com/Why-We-Sleep-Unlocking-Dreams/dp/1501144324• Gran Turismo: https://www.netflix.com/title/81672085• Gran Turismo video game: https://www.playstation.com/en-us/gran-turismo/• Manta sleep mask: https://mantasleep.com/products/manta-sleep-mask• WAOAW sleep mask: https://www.amazon.com/WAOAW-Sleep-Sleeping-Blocking-Blindfold/dp/B09712FSLY—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe
JupyterCon 2023, the conference on all things Jupyter was held in Paris between 10-12 May 2023, followed by 2 days of hands-on "sprints". Jupyter is a very popular open source platform with tools such as Jupyter notebook/lab and driven by a very active community. There were a number of excellent talks from a range of different subjects. I had the pleasure to meet and talk to a number of people, see the interview list below.Order of Interviews: Leah Silen and Arliss Collins from Numfocus 02:04Franklin Koch (MyST) from Curvenote 04:59Nicolas Thiery (Paris-Saclay) 09:13Sarah Gibson (2i2c) 13:19Ana Ruvalcaba (Jupyter Executive Council) 18:57Fernando Perez (Jupyter Executive Council) 23:48Raniere de Silva (Gesis) 29:56Linkshttps://jupyter.org Jupyter projecthttps://jupyter.org/enhancement-proposals/79-notebook-v7/notebook-v7.html# Release notes for the new Jupyter Notebook v7https://jupyterlab.readthedocs.io/en/latest/getting_started/changelog.html#v4-0 Release notes for JupyterLab v4.0 (further incremental updates of v4 are available)https://www.youtube.com/@JupyterCon YouTube channel for JupyterCon 2023https://cfp.jupytercon.com/2023/schedule/ JupyterCon 2023 schedulehttps://www.outreachy.org Outreachy project https://numfocus.org Numfocus projecthttps://data.agu.org/notebooks-now/ Notebooks Now initiativehttps://myst-tools.org MyST tool for scientific and technical communicationUpcoming RSE conferences:https://rsecon23.society-rse.org UK RSE conference in Swansea 5-8 Sep 2023https://hidden-ref.org/festival-of-hidden-ref/ Hidden Ref in Bristol, UK, 21 Sep 2023https://un-derse23.sciencesconf.org Unconference of the German RSE society deRSE in Jena 26-28 Sephttps://us-rse.org/usrse23/ 1st face to face US RSE Conference in Chicago 16-18 Oct 2023Support the Show.Thank you for listening and your ongoing support. It means the world to us! Support the show on Patreon https://www.patreon.com/codeforthought Get in touch: Email mailto:code4thought@proton.me UK RSE Slack (ukrse.slack.com): @code4thought or @piddie US RSE Slack (usrse.slack.com): @Peter Schmidt Mastadon: https://fosstodon.org/@code4thought or @code4thought@fosstodon.org LinkedIn: https://www.linkedin.com/in/pweschmidt/ (personal Profile)LinkedIn: https://www.linkedin.com/company/codeforthought/ (Code for Thought Profile) This podcast is licensed under the Creative Commons Licence: https://creativecommons.org/licenses/by-sa/4.0/
Doc Searls and Dan Lynch talk with Tim Bonneman about how to help open-source science follow the road to success paved by open-source software development and build on lots of interesting and exciting work already going on. Hosts: Doc Searls and Dan Lynch Guest: Tim Bonnemann Download or subscribe to this show at https://twit.tv/shows/floss-weekly Think your open source project should be on FLOSS Weekly? Email floss@twit.tv. Thanks to Lullabot's Jeff Robbins, web designer and musician, for our theme music. Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Sponsors: kolide.com/floss bitwarden.com/twit
Doc Searls and Dan Lynch talk with Tim Bonneman about how to help open-source science follow the road to success paved by open-source software development and build on lots of interesting and exciting work already going on. Hosts: Doc Searls and Dan Lynch Guest: Tim Bonnemann Download or subscribe to this show at https://twit.tv/shows/floss-weekly Think your open source project should be on FLOSS Weekly? Email floss@twit.tv. Thanks to Lullabot's Jeff Robbins, web designer and musician, for our theme music. Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Sponsors: kolide.com/floss bitwarden.com/twit
Doc Searls and Dan Lynch talk with Tim Bonneman about how to help open-source science follow the road to success paved by open-source software development and build on lots of interesting and exciting work already going on. Hosts: Doc Searls and Dan Lynch Guest: Tim Bonnemann Download or subscribe to this show at https://twit.tv/shows/floss-weekly Think your open source project should be on FLOSS Weekly? Email floss@twit.tv. Thanks to Lullabot's Jeff Robbins, web designer and musician, for our theme music. Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Sponsors: kolide.com/floss bitwarden.com/twit
Doc Searls and Dan Lynch talk with Tim Bonneman about how to help open-source science follow the road to success paved by open-source software development and build on lots of interesting and exciting work already going on. Hosts: Doc Searls and Dan Lynch Guest: Tim Bonnemann Download or subscribe to this show at https://twit.tv/shows/floss-weekly Think your open source project should be on FLOSS Weekly? Email floss@twit.tv. Thanks to Lullabot's Jeff Robbins, web designer and musician, for our theme music. Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Sponsors: kolide.com/floss bitwarden.com/twit
We're so glad to launch our first podcast episode with Logan Kilpatrick! This also happens to be his first public interview since joining OpenAI as their first Developer Advocate. Thanks Logan!Recorded in-person at the beautiful StudioPod studios in San Francisco. Full transcript is below the fold.Timestamps* 00:29: Logan's path to OpenAI* 07:06: On ChatGPT and GPT3 API* 16:16: On Prompt Engineering* 20:30: Usecases and LLM-Native Products* 25:38: Risks and benefits of building on OpenAI* 35:22: OpenAI Codex* 42:40: Apple's Neural Engine* 44:21: Lightning RoundShow notes* Sam Altman's interview with Connie Loizos* OpenAI Cookbook* OpenAI's new Embedding Model* Cohere on Word and Sentence Embeddings* (referenced) What is AGI-hard?Lightning Rounds* Favorite AI Product: https://www.synthesia.io/* Favorite AI Community: MLOps * One year prediction: Personalized AI, https://civitai.com/* Takeaway: AI Revolution is here!Transcript[00:00:00] Alessio Fanelli: Hey everyone. Welcome to the Latent Space podcast. This is Alessio, partner and CTO in residence at Decibel Partners. I'm joined by my cohost, swyx writer editor of L Space Diaries. Hey.[00:00:20] swyx: Hey . Our guest today is Logan Kilpatrick. What I'm gonna try to do is I'm gonna try to introduce you based on what people know about you, and then you can fill in the blanks.[00:00:28] Introducing Logan[00:00:28] swyx: So you are the first. Developer advocate at OpenAI, which is a humongous achievement. Congrats. You're also the lead developer community advocate of the Julia language. I'm interested in a little bit of that and apparently as I've did a bit of research on you, you got into Julia through NASA where you interned and worked on stuff that's gonna land on the moon apparently.[00:00:50] And you are also working on computer vision at Apple. And had to sit at path, the eye as you fell down the machine learning rabbit hole. What should people know about you that's kind of not on your LinkedIn that like sort of ties together your interest[00:01:02] Logan Kilpatrick: in story? It's a good question. I think so one of the things that is on my LinkedIn that wasn't mentioned that's super near and dear to my heart and what I spend a lot of time in sort of wraps a lot of my open source machine learning developer advocacy experience together is supporting NumFOCUS.[00:01:17] And NumFOCUS is the nonprofit that helps enable a bunch of the open source scientific projects like Julia, Jupyter, Pandas, NumPy, all of those open source projects are. Facilitated legal and fiscally through NumFOCUS. So it's a very critical, important part of the ecosystem and something that I, I spend a bunch of my now more limited free time helping support.[00:01:37] So yeah, something that's, It's on my LinkedIn, but it's, it's something that's important to me. Well,[00:01:42] swyx: it's not as well known of a name, so maybe people kind of skip over it cuz they were like, I don't know what[00:01:45] Logan Kilpatrick: to do with this. Yeah. It's super interesting to see that too. Just one point of context for that is we tried at one point to get a Wikipedia page for non focus and it's, it's providing, again, the infrastructure for, it's like a hundred plus open source scientific projects and they're like, it's not notable enough.[00:01:59] I'm like, well, you know, there's something like 30 plus million developers around the world who use all these open source tools. It's like the foundation. All open source like science that happens. Every breakthrough in science is they discovered the black hole, the first picture of the black hole, all that stuff using numb focus tools, the Mars Rovers, NumFOCUS tools, and it's interesting to see like the disconnect between the nonprofit that supports those projects and the actual success of the projects themselves.[00:02:26] swyx: Well, we'll, we'll get a bunch of people focused on NumFOCUS and we'll get it on Wikipedia. That that is our goal. . That is the goal. , that is our shot. Is this something that you do often, which is you? You seem to always do a lot of community stuff. When you get into something, you're also, I don't know where this, where you find time for this.[00:02:42] You're also a conference chair for DjangoCon, which was last year as well. Do you fall down the rabbit hole of a language and then you look for community opportunities? Is that how you get into.[00:02:51] Logan Kilpatrick: Yeah, so the context for Django stuff was I'd actually been teaching and still am through Harvard's division of continuing education as a teaching fellow for a Django class, and had spent like two and a half years actually teaching students every semester, had a program in Django and realized that like it was kind of the one ecosystem or technical tool that I was using regularly that I wasn't actually contributing to that community.[00:03:13] So, I think sometime in 2021 like applied to be on the board of directors of the Django Events Foundation, north America, who helps run DjangoCon and was fortunate enough to join a support to be the chair of DjangoCon us and then just actually rolled off the board because of all the, all the craziness and have a lot less free time now.[00:03:32] And actually at PATH ai. Sort of core product was also using, was using Django, so it also had a lot of connections to work, so it was a little bit easier to justify that time versus now open ai. We're not doing any Django stuff unfortunately, so, or[00:03:44] swyx: Julia, I mean, should we talk about this? Like, are you defecting from Julia?[00:03:48] What's going on? ,[00:03:50] Logan Kilpatrick: it's actually felt a little bit strange recently because I, for the longest time, and, and happy to talk about this in the context of Apple as well, the Julie ecosystem was my outlet to do a lot of the developer advocacy, developer relations community work that I wanted to do. because again, at Apple I was just like training machine learning models.[00:04:07] Before that, doing software engineering at Apple, and even at Path ai, we didn't really have a developer product, so it wasn't, I was doing like advocacy work, but it wasn't like developer relations in the traditional sense. So now that I'm so deeply doing developer relations work at Open OpenAI, it's really difficult to.[00:04:26] Continue to have the energy after I just spent nine hours doing developer relations stuff to like go and after work do a bunch more developer relations stuff. So I'll be interested to see for myself like how I'm able to continue to do that work and I. The challenge is that it's, it's such critical, important work to happen.[00:04:43] Like I think the Julie ecosystem is so important. I think the language is super important. It's gonna continue to grow in, in popularity, and it's helping scientists and engineers solve problems they wouldn't otherwise be able to. So it's, yeah, the burden is on me to continue to do that work, even though I don't have a lot of time now.[00:04:58] And I[00:04:58] Alessio Fanelli: think when it comes to communities, the machine learning technical community, I think in the last six to nine months has exploded. You know, you're the first developer advocate at open ai, so I don't think anybody has a frame of reference on what that means. What is that? ? So , what do you, how did, how the[00:05:13] swyx: job, yeah.[00:05:13] How do you define the job? Yeah, let's talk about that. Your role.[00:05:16] Logan Kilpatrick: Yeah, it's a good question and I think there's a lot of those questions that actually still exist at OpenAI today. Like I think a lot of traditional developed by advocacy, at least like what you see on Twitter, which I think is what a lot of people's perception of developer advocacy and developer relations is, is like, Just putting out external content, going to events, speaking at conferences.[00:05:35] And I think OpenAI is very unique in the sense that, at least at the present moment, we have so much inbound interest that there's, there is no desire for us to like do that type of developer advocacy work. So it's like more from a developer experience point of view actually. Like how can we enable developers to be successful?[00:05:53] And that at the present moment is like building a strong foundation of documentation and things like that. And we had a bunch of amazing folks internally who were. Who were doing some of this work, but it really wasn't their full-time job. Like they were focused on other things and just helping out here and there.[00:06:05] And for me, my full-time job right now is how can we improve the documentation so that people can build the next generation of, of products and services on top of our api. And it's. Yeah. There's so much work that has to happen, but it's, it's, it's been a ton of fun so far. I find[00:06:20] swyx: being in developer relations myself, like, it's kind of like a fill in the blanks type of thing.[00:06:24] Like you go to where you, you're needed the most open. AI has no problem getting attention. It is more that people are not familiar with the APIs and, and the best practices around programming for large language models, which is a thing that did not exist three years ago, two years ago, maybe one year ago.[00:06:40] I don't know. When she launched your api, I think you launched Dall-E. As an API or I, I don't[00:06:45] Logan Kilpatrick: know. I dunno. The history, I think Dall-E was, was second. I think it was some of the, like GPT3 launched and then GPT3 launched and the API I think like two years ago or something like that. And then Dali was, I think a little more than a year ago.[00:06:58] And then now all the, the Chachi Beast ChatGPT stuff has, has blown it all outta the water. Which you have[00:07:04] swyx: a a wait list for. Should we get into that?[00:07:06] Logan Kilpatrick: Yeah. .[00:07:07] ChatGPT[00:07:07] Alessio Fanelli: Yeah. We would love to hear more about that. We were looking at some of the numbers you went. Zero to like a million users in five days and everybody, I, I think there's like dozens of ChatGPT API wrappers on GitHub that are unofficial and clearly people want the product.[00:07:21] Like how do you think about that and how developers can interact with it.[00:07:24] Logan Kilpatrick: It. It's absolutely, I think one of the most exciting things that I can possibly imagine to think about, like how much excitement there was around ChatGPT and now getting to hopefully at some point soon, put that in the hands of developers and see what they're able to unlock.[00:07:38] Like I, I think ChatGPT has been a tremendous success, hands down without a question, but I'm actually more excited to see what developers do with the API and like being able to build those chat first experiences. And it's really fascinating to see. Five years ago or 10 years ago, there was like, you know, all this like chatbot sort of mm-hmm.[00:07:57] explosion. And then that all basically went away recently, and the hype went to other places. And I think now we're going to be closer to that sort of chat layer and all these different AI chat products and services. And it'll be super interesting to see if that sticks or not. I, I'm not. , like I think people have a lot of excitement for ChatGPT right now, but it's not clear to me that that that's like the, the UI or the ux, even though people really like it in the moment, whether that will stand the test of time, I, I just don't know.[00:08:23] And I think we'll have to do a podcast in five years. Right. And check in and see whether or not people are still really enjoying that sort of conversational experience. I think it does make sense though cause like that's how we all interact and it's kind of weird that you wouldn't do that with AI products.[00:08:37] So we. and I think like[00:08:40] Alessio Fanelli: the conversational interface has made a lot of people, first, the AI to hallucinate, you know, kind of come up with things that are not true and really find all the edge cases. I think we're on the optimism camp, you know, like we see the potential. I think a lot of people like to be negative.[00:08:56] In your role, kind of, how do you think about evangelizing that and kind of the patience that sometimes it takes for these models to become.[00:09:03] Logan Kilpatrick: Yeah, I think what, what I've done is just continue to scream from the, the mountains that like ChatGPT has, current form is definitely a research preview. The model that underlies ChatGPT GPT 3.5 is not a research preview.[00:09:15] I think there's things that folks can do to definitely reduce the amount of hall hallucinations and hopefully that's something that over time I, I, again have full confidence that it'll, it'll solve. Yeah, there's a bunch of like interesting engineering challenges. you have to solve in order to like really fix that problem.[00:09:33] And I think again, people are, are very fixated on the fact that like in, you know, a few percentage points of the conversations, things don't sound really good. Mm-hmm. , I'm really more excited to see, like, again when the APIs and the Han developers like what are the interesting solutions that people come up with, I think there's a lot that can be explored and obviously, OpenAI can explore all them because we have this like one product that's using the api.[00:09:56] And once you get 10,000, a hundred thousand developers building on top of that, like, we'll see what are the different ways that people handle this. And I imagine there's a lot of low-hanging fruit solutions that'll significantly improve the, the amount of halluc hallucinations that are showing up. Talk about[00:10:11] swyx: building on top of your APIs.[00:10:13] Chat GPTs API is not out yet, but let's assume it is. Should I be, let's say I'm, I'm building. A choice between GP 3.5 and chat GPT APIs. As far as I understand, they are kind of comparable. What should people know about deciding between either of them? Like it's not clear to me what the difference is.[00:10:33] Logan Kilpatrick: It's a great question.[00:10:35] I don't know if there's any, if we've made any like public statements about like what the difference will be. I think, I think the point is that the interface for the Chachi B API will be like conversational first, and that's not the case now. If you look at text da Vinci oh oh three, like you, you just put in any sort of prompt.[00:10:52] It's not really built from the ground up to like keep the context of a conversation and things like that. And so it's really. Put in some sort of prompt, get a response. It's not always designed to be in that sort of conversational manner, so it's not tuned in that way. I think that's the biggest difference.[00:11:05] I think, again, the point that Sam made in a, a strictly the strictly VC talk mm-hmm. , which was incredible and I, I think that that talk got me excited and my, which, which part? The whole thing. And I think, I haven't been at open AI that long, so like I didn't have like a s I obviously knew who Sam was and had seen a bunch of stuff, but like obviously before, a lot of the present craziness with Elon Musk, like I used to think Elon Musk seemed like a really great guy and he was solving all these really important problems before all the stuff that happened.[00:11:33] That's a hot topic. Yeah. The stuff that happened now, yeah, now it's much more questionable and I regret having a Tesla, but I, I think Sam is actually. Similar in the sense that like he's solving and thinking about a lot of the same problems that, that Elon, that Elon is still today. But my take is that he seems like a much more aligned version of Elon.[00:11:52] Like he's, he's truly like, I, I really think he cares deeply about people and I think he cares about like solving the problems that people have and wants to enable people. And you can see this in the way that he's talked about how we deploy models at OpenAI. And I think you almost see Tesla in like the completely opposite end of the spectrum, where they're like, whoa, we.[00:12:11] Put these 5,000 pound machines out there. Yeah. And maybe they'll run somebody over, maybe they won't. But like it's all in the interest of like advancement and innovation. I think that's really on the opposite end of the spectrum of, of what open AI is doing, I think under Sam's leadership. So it's, it's interesting to see that, and I think Sam said[00:12:30] Alessio Fanelli: that people could have built Chen g p t with what you offered like six, nine months ago.[00:12:35] I[00:12:35] swyx: don't understand. Can we talk about this? Do you know what, you know what we're talking about, right? I do know what you're talking about. da Vinci oh three was not in the a p six months before ChatGPT. What was he talking about? Yeah.[00:12:45] Logan Kilpatrick: I think it's a little bit of a stretch, but I do think that it's, I, I think the underlying principle is that.[00:12:52] The way that it, it comes back to prompt engineering. The way that you could have engineered, like the, the prompts that you were put again to oh oh three or oh oh two. You would be able to basically get that sort of conversational interface and you can do that now. And, and I, you know, I've seen tutorials.[00:13:05] We have tutorials out. Yep. No, we, I mean, we, nineties, we have tutorials in the cookbook right now in on GitHub. We're like, you can do this same sort of thing. And you just, it's, it's all about how you, how you ask for responses and the way you format data and things like that. It. The, the models are currently only limited by what people are willing to ask them to do.[00:13:24] Like I really do think that, yeah, that you can do a lot of these things and you don't need the chat CBT API to, to build that conversational layer. That is actually where I[00:13:33] swyx: feel a little bit dumb because I feel like I don't, I'm not smart enough to think of new things to ask the models. I have to see an example and go, oh, you can do that.[00:13:43] All right, I'm gonna do that for now. You know, and, and that's why I think the, the cookbook is so important cuz it's kind of like a compendium of things we know about the model that you can ask it to do. I totally[00:13:52] Logan Kilpatrick: agree and I think huge shout out to the, the two folks who I work super closely with now on the cookbook, Ted and Boris, who have done a lot of that work and, and putting that out there and it's, yeah, you see number one trending repo on, on GitHub and it was super, like when my first couple of weeks at Open ai, super unknown, like really, we were only sort of directing our customers to that repo.[00:14:13] Not because we were trying to hide it or anything, but just because. It was just the way that we were doing things and then all of a sudden it got picked up on GitHub trending and a bunch of tweets went viral, showing the repo. So now I think people are actually being able to leverage the tools that are in there.[00:14:26] And, and Ted's written a bunch of amazing tutorials, Boris, as well. So I think it's awesome that more people are seeing those. And from my perspective, it's how can we take those, make them more accessible, give them more visibility, put them into the documentation, and I don't think that that connection right now doesn't exist, which I'm, I'm hopeful we'll be able to bridge those two things.[00:14:44] swyx: Cookbook is kind of a different set of documentation than API docs, and I think there's, you know, sort of existing literature about how you document these things and guide developers the right way. What, what I, what I really like about the cookbook is that it actually cites academic research. So it's like a nice way to not read the paper, but just read the conclusions of the paper ,[00:15:03] Logan Kilpatrick: and, and I think that's, that's a shout out to Ted and Boris cuz I, I think they're, they're really smart in that way and they've done a great job of finding the balance and understanding like who's actually using these different tools.[00:15:13] So, . Yeah.[00:15:15] swyx: You give other people credit, but you should take credit for yourself. So I read your last week you launched some kind of documentation about rate limiting. Yeah. And one of my favorite things about reading that doc was seeing examples of, you know, you were, you're telling people to do exponential back off and, and retry, but you gave code examples with three popular libraries.[00:15:32] You didn't have to do that. You could have just told people, just figure it out. Right. But you like, I assume that was you. It wasn't.[00:15:38] Logan Kilpatrick: So I think that's the, that's, I mean, I'm, I'm helping sort of. I think there's a lot of great stuff that people have done in open ai, but it was, we have the challenge of like, how can we make that accessible, get it into the documentation and still have that high bar for what goes into the doc.[00:15:51] So my role as of recently has been like helping support the team, building that documentation first culture, and supporting like the other folks who actually are, who wrote that information. The information was actually already in. Help center but it out. Yeah, it wasn't in the docs and like wasn't really focused on, on developers in that sense.[00:16:10] So yeah. I can't take the, the credit for the rate limit stuff either. , no, this[00:16:13] swyx: is all, it's part of the A team, that team effort[00:16:16] On Prompt Engineering[00:16:16] Alessio Fanelli: I was reading on Twitter, I think somebody was saying in the future will be kind of like in the hair potter word. People have like the spell book, they pull it out, they do all the stuff in chat.[00:16:24] GP z. When you talk with customers, like are they excited about doing prompt engineering and kind of getting a starting point or do they, do they wish there was like a better interface? ?[00:16:34] Logan Kilpatrick: Yeah, that's a good question. I think prompt engineering is so much more of an art than a science right now. Like I think there are like really.[00:16:42] Systematic things that you can do and like different like approaches and designs that you can take, but really it's a lot of like, you kind of just have to try it and figure it out. And I actually think that this remains to be one of the challenges with large language models in general, and not just head open ai, but for everyone doing it is that it's really actually difficult to understand what are the capabilities of the model and how do I get it to do the things that I wanted to do.[00:17:05] And I think that's probably where a lot of folks need to do like academic research and companies need to invest in understanding the capabilities of these models and the limitations because it's really difficult to articulate the capabilities of a model without those types of things. So I'm hopeful that, and we're shipping hopefully some new updated prompt engineering stuff.[00:17:24] Cause I think the stuff we have on the website is old, and I think the cookbook actually has a little bit more up-to-date stuff. And so hopefully we'll ship some new prompt engineering stuff in the, in the short term. I think dispel some of the myths and rumors, but like I, it's gonna continue to be like a, a little bit of a pseudoscience, I would imagine.[00:17:41] And I also think that the whole prompt engineering being like a job in the future meme, I think is, I think it's slightly overblown. Like I think at, you see this now actually with like, there's tools that are showing up and I forgot what the, I just saw went on Twitter. The[00:17:57] swyx: next guest that we are having on this podcast, Lang.[00:17:59] Yeah. Yeah.[00:18:00] Logan Kilpatrick: Lang Chain and Harrison on, yeah, there's a bunch of repos too that like categorize and like collect all the best prompts that you can put into chat. For example, and like, that's like the people who are, I saw the advertisement for someone to be like a prompt engineer and it was like a $350,000 a year.[00:18:17] Mm-hmm. . Yeah, that was, that was philanthropic. Yeah, so it, it's just unclear to me like how, how sustainable stuff like that is. Cuz like, once you figure out the interesting prompts and like right now it's kind of like the, the Wild West, but like in a year you'll be able to sort of categorize all those and then people will be able to find all the good ones that are relevant for what they want to do.[00:18:35] And I think this goes back to like, having the examples is super important and I'm, I'm with you as well. Like every time I use Dall-E the little. While it's rendering the image, it gives you like a suggestion of like how you should ask for the art to be generated. Like do it in like a cyberpunk format. Do it in a pixel art format.[00:18:53] Et cetera, et cetera, and like, I really need that. I'm like, I would never come up with asking for those things had it not prompted me to like ask it that way. And now I always ask for pixel art stuff or cyberpunk stuff and it looks so cool. That's what I, I think,[00:19:06] swyx: is the innovation of ChatGPT as a format.[00:19:09] It reduces. The need for getting everything into your prompt in the first try. Mm-hmm. , it takes it from zero shot to a few shot. If, if, if that, if prompting as, as, as shots can be concerned.[00:19:21] Logan Kilpatrick: Yeah. , I think that's a great perspective and, and again, this goes back to the ux UI piece of it really being sort of the differentiating layer from some of the other stuff that was already out there.[00:19:31] Because you could kind of like do this before with oh oh three or something like that if you just made the right interface and like built some sort of like prompt retry interface. But I don't think people were really, were really doing that. And I actually think that you really need that right now. And this is the, again, going back to the difference between like how you can use generative models versus like large scale.[00:19:53] Computer vision systems for self-driving cars, like the, the answer doesn't actually need to be right all the time. That's the beauty of, of large language models. It can be wrong 50% of the time and like it doesn't really cost you anything to like regenerate a new response. And there's no like, critical safety issue with that, so you don't need those.[00:20:09] I, I keep seeing these tweets about like, you need those like 99.99% reliability and like the three nines or whatever it is. Mm-hmm. , but like you really don't need that because the cost of regenerating the prop is again, almost, almost. I think you tweeted a[00:20:23] Alessio Fanelli: couple weeks ago that the average person doesn't yet fully grasp how GBT is gonna impact human life in the next four, five years.[00:20:30] Usecases and LLM-Native Products[00:20:30] Alessio Fanelli: I think you had an example in education. Yeah. Maybe touch on some of these. Example of non-tech related use cases that are enabling, enabled by C G B[00:20:38] T.[00:20:39] Logan Kilpatrick: I'm so excited and, and there's a bunch of other like random threads that come to my mind now. I saw a thread and, and our VP of product was, Peter, was, was involved in that thread as well, talking about like how the use of systems like ChatGPT will unlock like pretty almost low to zero cost access to like mental health services.[00:20:59] You know, you can imagine like the same use case for education, like really personalized tutors and like, it's so crazy to think about, but. The technology is not actually , like it's, it's truly like an engineering problem at this point of like somebody using one of these APIs to like build something like that and then hopefully the models get a little bit better and make it, make it better as well.[00:21:20] But like it, I have no doubt in my mind that three years from now that technology will exist for every single student in the world to like have that personalized education experience, have a pr, have a chat based experience where like they'll be able. Ask questions and then the curriculum will just evolve and be constructed for them in a way that keeps, I think the cool part is in a way that keeps them engaged, like it doesn't have to be sort of like the same delivery of curriculum that you've always seen, and this now supplements.[00:21:49] The sort of traditional education experience in the sense of, you know, you don't need teachers to do all of this work. They can really sort of do the thing that they're amazing at and not spend time like grading assignments and all that type of stuff. Like, I really do think that all those could be part of the, the system.[00:22:04] And same thing, I don't know if you all saw the the do not pay, uh, lawyer situation, say, I just saw that Twitter thread, I think yesterday around they were going to use ChatGPT in the courtroom and basically I think it was. California Bar or the Bar Institute said that they were gonna send this guy to prison if he brought, if he put AirPods in and started reading what ChatGPT was saying to him.[00:22:26] Yeah.[00:22:26] swyx: To give people the context, I think, like Josh Browder, the CEO of Do Not Pay, was like, we will pay you money to put this AirPod into your ear and only say what we tell you to say fr from the large language model. And of course the judge was gonna throw that out. I mean, I, I don't see how. You could allow that in your court,[00:22:42] Logan Kilpatrick: Yeah, but I, I really do think that, like, the, the reality is, is that like, again, it's the same situation where the legal spaces even more so than education and, and mental health services, is like not an accessible space. Like every, especially with how like overly legalized the United States is, it's impossible to get representation from a lawyer, especially if you're low income or some of those things.[00:23:04] So I'm, I'm optimistic. Those types of services will exist in the future. And you'll be able to like actually have a, a quality defense representative or just like some sort of legal counsel. Yeah. Like just answer these questions, what should I do in this situation? Yeah. And I like, I have like some legal training and I still have those same questions.[00:23:22] Like I don't know what I would do in that situation. I would have to go and get a lawyer and figure that out. And it's, . It's tough. So I'm excited about that as well. Yeah.[00:23:29] Alessio Fanelli: And when you think about all these vertical use cases, do you see the existing products implementing language models in what they have?[00:23:35] Or do you think we're just gonna see L L M native products kind of come to market and build brand[00:23:40] Logan Kilpatrick: new experiences? I think there'll be a lot of people who build the L l M first experience, and I think that. At least in the short term, those are the folks who will have the advantage. I do think that like the medium to long term is again, thinking about like what is your moat for and like again, and everyone has access to, you know, ChatGPT and to the different models that we have available.[00:24:05] So how can you build a differentiated business? And I think a lot of it actually will come down to, and this is just the true and the machine learning world in general, but having. Unique access to data. So I think if you're some company that has some really, really great data about the legal space or about the education space, you can use that and be better than your competition by fine tuning these models or building your own specific LLMs.[00:24:28] So it'll, it'll be interesting to see how that plays out, but I do think that. from a product experience, it's gonna be better in the short term for people who build the, the generative AI first experience versus people who are sort of bolting it onto their mm-hmm. existing product, which is why, like, again, the, the Google situation, like they can't just put in like the prompt into like right below the search bar.[00:24:50] Like, it just, it would be a weird experience and, and they have to sort of defend that experience that they have. So it, it'll be interesting to see what happens. Yeah. Perplexity[00:24:58] swyx: is, is kind of doing that. So you're saying perplexity will go Google ?[00:25:04] Logan Kilpatrick: I, I think that perplexity has a, has a chance in the short term to actually get more people to try the product because it's, it's something different I think, whether they can, I haven't actually used, so I can't comment on like that experience, but like I think the long term is like, How can they continue to differentiate?[00:25:21] And, and that's really the focus for like, if you're somebody building on these models, like you have to be, your first thought should be, how do I build a differentiated business? And if you can't come up with 10 reasons that you can build a differentiated business, you're probably not gonna succeed in, in building something that that stands the test of time.[00:25:37] Yeah.[00:25:37] Risks and benefits of building on OpenAI[00:25:37] swyx: I think what's. As a potential founder or something myself, like what's scary about that is I would be building on top of open ai. I would be sending all my stuff to you for fine tuning and embedding and what have you. By the way, fine tuning, embedding is their, is there a third one? Those are the main two that I know of.[00:25:55] Okay. And yeah, that's the risk. I would be a open AI API reseller.[00:26:00] Logan Kilpatrick: Yeah. And, and again, this, this comes back down to like having a clear sense of like how what you're building is different. Like the people who are just open AI API resellers, like, you're not gonna, you're not gonna have a successful business doing that because everybody has access to the Yeah.[00:26:15] Jasper's pretty great. Yeah, Jasper's pretty great because I, I think they've done a, they've, they've been smart about how they've positioned the product and I was actually a, a Jasper customer before I joined OpenAI and was using it to do a bunch of stuff. because the interface was simple because they had all the sort of customized, like if you want for like a response for this sort of thing, they'd, they'd pre-done that prompt engineering work for us.[00:26:39] I mean, you could really just like put in some exactly what you wanted and then it would make that Amazon product description or whatever it is. So I think like that. The interface is the, the differentiator for, for Jasper. And again, whether that send test time, hopefully, cuz I know they've raised a bunch of money and have a bunch of employees, so I'm, I'm optimistic for them.[00:26:58] I think that there's enough room as well for a lot of these companies to succeed. Like it's not gonna, the space is gonna get so big so quickly that like, Jasper will be able to have a super successful business. And I think they are. I just saw some, some tweets from the CEO the other day that I, I think they're doing, I think they're doing well.[00:27:13] Alessio Fanelli: So I'm the founder of A L L M native. I log into open ai, there's 6 million things that I can do. I'm on the playground. There's a lot of different models. How should people think about exploring the surface area? You know, where should they start? Kind of like hugging the go deeper into certain areas.[00:27:30] Logan Kilpatrick: I think six months ago, I think it would've been a much different conversation because people hadn't experienced ChatGPT before.[00:27:38] Now that people have experienced ChatGPT, I think there's a lot more. Technical things that you should start looking into and, and thinking about like the differentiators that you can bring. I still think that the playground that we have today is incredible cause it does sort of similar to what Jasper does, which is like we have these very focused like, you know, put in a topic and we'll generate you a summary, but in the context of like explaining something to a second grader.[00:28:03] So I think all of those things like give a sense, but we only have like 30 on the website or something like that. So really doing a lot of exploration around. What is out there? What are the different prompts that you can use? What are the different things that you can build on? And I'm super bullish on embeddings, like embed everything and that's how you can build cool stuff.[00:28:20] And I keep seeing all these Boris who, who I talked about before, who did a bunch of the cookbook stuff, tweeted the other day that his like back of the hand, back of the napkin math, was that 50 million bucks you can embed the whole internet. I'm like, Some companies gonna spend the 50 million and embed the whole internet and like, we're gonna find out what that product looks like.[00:28:40] But like, there's so many cool things that you could do if you did have the whole internet embedded. Yeah, and I, I mean, I wouldn't be surprised if Google did that cuz 50 million is a drop in the bucket and they already have the whole internet, so why not embed it?[00:28:52] swyx: Can can I ask a follow up question on that?[00:28:54] Cuz I am just learning about embeddings myself. What makes open eyes embeddings different from other embeddings? If, if there's like, It's okay if you don't have the, the numbers at hand, but I'm just like, why should I use open AI emitting versus others? I[00:29:06] Logan Kilpatrick: don't understand. Yeah, that's a really good question.[00:29:08] So I'm still ramping up on my understanding of embeddings as well. So the two things that come to my mind, one, going back to the 50 million to embed the whole internet example, it's actually just super cheap. I, I don't know the comparisons of like other prices, but at least from what I've seen people talking about on Twitter, like the embeddings that that we have in the API is just like significantly cheaper than a lot of other c.[00:29:30] Embeddings. Also the accuracy of some of the benchmarks that are like, Sort of academic benchmarks to use in embeddings. I know at least I was just looking back through the blog post from when we announced the new text embedding model, which is what Powers embeddings and it's, yeah, the, on those metrics, our API is just better.[00:29:50] So those are the those. I'll go read it up. Yeah, those are the two things. It's a good. It's a good blog post to read. I think the most recent one that came out, but, and also the original one from when we first announced the Embeddings api, I think also was a, it had, that one has a little bit more like context around if you're trying to wrap your head around embeddings, how they work.[00:30:06] That one has the context, the new one just has like the fancy new stuff and the metrics and all that kind of stuff.[00:30:11] swyx: I would shout a hugging face for having really good content around what these things like foundational concepts are. Because I was familiar with, so, you know, in Python you have like text tove, my first embedding as as a, as someone getting into nlp.[00:30:24] But then developing the concept of sentence embeddings is, is as opposed to words I think is, is super important. But yeah, it's an interesting form of lock in as a business because yes, I'm gonna embed all my source data, but then every inference needs an embedding as. . And I think that is a risk to some people, because I've seen some builders should try and build on open ai, call that out as, as a cost, as as like, you know, it starts to add a cost to every single query that you, that you[00:30:48] Logan Kilpatrick: make.[00:30:49] Yeah. It'll be interesting to see how it all plays out, but like, my hope is that that cost isn't the barrier for people to build because it's, it's really not like the cost for doing the incremental like prompts and having them embedded is, is. Cent less than cents, but[00:31:06] swyx: cost I, I mean money and also latency.[00:31:08] Yeah. Which is you're calling the different api. Yeah. Anyway, we don't have to get into that.[00:31:13] Alessio Fanelli: No, but I think embeds are a good example. You had, I think, 17 versions of your first generation, what api? Yeah. And then you released the second generation. It's much cheaper, much better. I think like the word on the street is like when GPT4 comes out, everything else is like trash that came out before it.[00:31:29] It's got[00:31:30] Logan Kilpatrick: 100 trillion billion. Exactly. Parameters you don't understand. I think Sam has already confirmed that those are, those are not true . The graphics are not real. Whatever you're seeing on Twitter about GPT4, you're, I think the direct quote was, you're begging to be disappointed by continuing to, to put that hype out.[00:31:47] So[00:31:48] Alessio Fanelli: if you're a developer building on these, What's kind of the upgrade path? You know, I've been building on Model X, now this new model comes out. What should I do to be ready to move on?[00:31:58] Logan Kilpatrick: Yeah. I think all of these types of models folks have to think about, like there will be trade offs and they'll also be.[00:32:05] Breaking changes like any other sort of software improvement, like things like the, the prompts that you were previously expecting might not be the prompts that you're seeing now. And you can actually, you, you see this in the case of the embeddings example that you just gave when we released Tex embeddings, ADA oh oh two, ada, ada, whichever it is oh oh two, and it's sort of replaced the previous.[00:32:26] 16 first generation models, people went through this exact experience where like, okay, I need to test out this new thing, see how it works in my environment. And I think that the really fascinating thing is that there aren't, like the tools around doing this type of comparison don't exist yet today. Like if you're some company that's building on lms, you sort of just have to figure it out yourself of like, is this better in my use case?[00:32:49] Is this not better? In my use case, it's, it's really difficult to tell because the like, Possibilities using generative models are endless. So I think folks really need to focus on, again, that goes back to how to build a differentiated business. And I think it's understanding like what is the way that people are using your product and how can you sort of automate that in as much way and codify that in a way that makes it clear when these different models come up, whether it's open AI or other companies.[00:33:15] Like what is the actual difference between these and which is better for my use case because the academic be. It'll be saturated and people won't be able to use them as a point of comparison in the future. So it'll be important to think about. For your specific use case, how does it differentiate?[00:33:30] swyx: I was thinking about the value of frameworks or like Lang Chain and Dust and what have you out there.[00:33:36] I feel like there is some value to building those frameworks on top of Open Eyes, APIs. It kind of is building what's missing, essentially what, what you guys don't have. But it's kind of important in the software engineering sense, like you have this. Unpredictable, highly volatile thing, and you kind of need to build a stable foundation on top of it to make it more predictable, to build real software on top of it.[00:33:59] That's a super interesting kind of engineering problem. .[00:34:03] Logan Kilpatrick: Yeah, it, it is interesting. It's also the, the added layer of this is that the large language models. Are inherently not deterministic. So I just, we just shipped a small documentation update today, which, which calls this out. And you think about APIs as like a traditional developer experience.[00:34:20] I send some response. If the response is the same, I should get the same thing back every time. Unless like the data's updating and like a, from like a time perspective. But that's not the, that's not the case with the large language models, even with temperature zero. Mm-hmm. even with temperature zero. Yep.[00:34:34] And that's, Counterintuitive part, and I think someone was trying to explain to me that it has to do with like Nvidia. Yeah. Floating points. Yes. GPU stuff. and like apparently the GPUs are just inherently non-deterministic. So like, yes, there's nothing we can do unless this high Torch[00:34:48] swyx: relies on this as well.[00:34:49] If you want to. Fix this. You're gonna have to tear it all down. ,[00:34:53] Logan Kilpatrick: maybe Nvidia, we'll fix it. I, I don't know, but I, I think it's a, it's a very like, unintuitive thing and I don't think that developers like really get that until it happens to you. And then you're sort of scratching your head and you're like, why is this happening?[00:35:05] And then you have to look it up and then you see all the NVIDIA stuff. Or hopefully our documentation makes it more clear now. But hopefully people, I also think that's, it's kinda the cool part as well. I don't know, it's like, You're not gonna get the same stuff even if you try to.[00:35:17] swyx: It's a little spark of originality in there.[00:35:19] Yeah, yeah, yeah, yeah. The random seed .[00:35:22] OpenAI Codex[00:35:22] swyx: Should we ask about[00:35:23] Logan Kilpatrick: Codex?[00:35:23] Alessio Fanelli: Yeah. I mean, I love Codex. I use it every day. I think like one thing, sometimes the code is like it, it's kinda like the ChatGPT hallucination. Like one time I asked it to write up. A Twitter function, they will pull the bayou of this thing and it wrote the whole thing and then the endpoint didn't exist once I went to the Twitter, Twitter docs, and I think like one, I, I think there was one research that said a lot of people using Co Palace, sometimes they just auto complete code that is wrong and then they commit it and it's a, it's a big[00:35:51] Logan Kilpatrick: thing.[00:35:51] swyx: Do you secure code as well? Yeah, yeah, yeah, yeah. I saw that study.[00:35:54] Logan Kilpatrick: How do[00:35:54] Alessio Fanelli: you kind of see. Use case evolving. You know, you think, like, you obviously have a very strong partnership with, with Microsoft. Like do you think Codex and VS code will just keep improving there? Do you think there's kind of like a. A whole better layer on top of it, which is from the scale AI hackathon where the, the project that one was basically telling the l l m, you're not the back end of a product[00:36:16] And they didn't even have to write the code and it's like, it just understood. Yeah. How do you see the engineer, I, I think Sean, you said copilot is everybody gets their own junior engineer to like write some of the code and then you fix it For me, a lot of it is the junior engineer gets a senior engineer to actually help them write better code.[00:36:32] How do you see that tension working between the model and the. It'll[00:36:36] Logan Kilpatrick: be really interesting to see if there's other, if there's other interfaces to this. And I think I've actually seen a lot of people asking, like, it'd be really great if I had ChatGPT and VS code because in, in some sense, like it can, it's just a better, it's a better interface in a lot of ways to like the, the auto complete version cuz you can reprompt and do, and I know Via, I know co-pilot actually has that, where you can like click and then give it, it'll like pop up like 10 suggested.[00:36:59] Different options instead of brushes. Yeah, copilot labs, yeah. Instead of the one that it's providing. And I really like that interface, but again, this goes back to. I, I do inherently think it'll get better. I think it'll be able to do a lot, a lot more of the stuff as the models get bigger, as they have longer context as they, there's a lot of really cool things that will end up coming out and yeah, I don't think it's actually very far away from being like, much, much better.[00:37:24] It'll go from the junior engineer to like the, the principal engineer probably pretty quickly. Like I, I don't think the gap is, is really that large between where things are right now. I think like getting it to the point. 60% of the stuff really well to get it to do like 90% of the stuff really well is like that's within reach in the next, in the next couple of years.[00:37:45] So I'll be really excited to see, and hopefully again, this goes back to like engineers and developers and people who aren't thinking about how to integrate. These tools, whether it's ChatGPT or co-pilot or something else into their workflows to be more efficient. Those are the people who I think will end up getting disrupted by these tools.[00:38:02] So figuring out how to make yourself more valuable than you are today using these tools, I think will be super important for people. Yeah.[00:38:09] Alessio Fanelli: Actually use ChatGPT to debug, like a react hook the other day. And then I posted in our disc and I was like, Hey guys, like look, look at this thing. It really helped me solve this.[00:38:18] And they. That's like the ugliest code I've ever seen. It's like, why are you doing that now? It's like, I don't know. I'm just trying to get[00:38:24] Logan Kilpatrick: this thing to work and I don't know, react. So I'm like, that's the perfect, exactly, that's the perfect solution. I, I did this the other day where I was looking at React code and like I have very briefly seen React and run it like one time and I was like, explain how this is working.[00:38:38] So, and like change it in this way that I want to, and like it was able to do that flawlessly and then I just popped it in. It worked exactly like I. I'll give a[00:38:45] swyx: little bit more context cause I was, I was the guy giving you feedback on your code and I think this is a illustrative of how large language models can sort of be more confident than they should be because you asked it a question which is very specific on how to improve your code or fix your code.[00:39:00] Whereas a real engineer would've said, we've looked at your code and go, why are you doing it at at all? Right? So there's a sort of sycophantic property of martial language. Accepts the basis of your question, whereas a real human might question your question. Mm-hmm. , and it was just not able to do that. I mean, I, I don't see how he could do that.[00:39:17] Logan Kilpatrick: Yeah. It's, it's interesting. I, I saw another example of this the other day as well with some chatty b t prompt and I, I agree. It'll be interesting to see if, and again, I think not to, not to go back to Sam's, to Sam's talk again, but like, he, he talked real about this, and I think this makes a ton of sense, which is like you should be able to have, and this isn't something that that exists right now, but you should be able to have the model.[00:39:39] Tuned in the way that you wanna interact with. Like if you want a model that sort of questions what you're asking it to do, like you should be able to have that. And I actually don't think that that's as far away as like some of the other stuff. Um, It, it's a very possible engineering problem to like have the, to tune the models in that way and, and ask clarifying questions, which is even something that it doesn't do right now.[00:39:59] It'll either give you the response or it won't give you the response, but it'll never say like, Hey, what do you mean by this? Which is super interesting cuz that's like we spend as humans, like 50% of our conversational time being like, what do you mean by that? Like, can you explain more? Can you say it in a different way?[00:40:14] And it's, it's fascinating that the model doesn't do that right now. It's, it's interesting.[00:40:20] swyx: I have written a piece on sort of what AGI hard might be, which is the term that is being thrown around as like a layer of boundary for what is, what requires an A real AGI to do and what, where you might sort of asymptotically approach.[00:40:33] So, What people talk about is essentially a theory of mind, developing a con conception of who I'm talking to and persisting that across sessions, which essentially ChatGPT or you know, any, any interface that you build on top of GPT3 right now would not be able to do. Right? Like, you're not persisting you, you are persisting that history, but you don't, you're not building up a conception of what you know and what.[00:40:54] I should fill in the blanks for you or where I should question you. And I think that's like the hard thing to understand, which is what will it take to get there? Because I think that to me is the, going back to your education thing, that is the biggest barrier, which is I, the language model doesn't have a memory or understanding of what I know.[00:41:11] and like, it's, it's too much to tell them what I don't know. Mm-hmm. , there's more that I don't know than I, than I do know . I think the cool[00:41:16] Logan Kilpatrick: part will be when, when you're able to, like, imagine you could upload all of the, the stuff that you've ever done, all the texts, the work that you've ever done before, and.[00:41:27] The model can start to understand, hey, what are the, what are the conceptual gaps that this person has based on what you've said, based on what you've done? I think that would be really interesting. Like if you can, like I have good notes on my phone and I can still go back to see all of the calculus classes that I took and I could put in all my calculus notebooks and all the assignments and stuff that I did in, in undergrad and grad school, and.[00:41:50] basically be like, Hey, here are the gaps in your understanding of calculus. Go and do this right now. And I think that that's in the education space. That's exactly what will end up happening. You'll be able to put in all this, all the work that you've done. It can understand those ask and then come up with custom made questions and prompts and be like, Hey, how, you know, explain this concept to me and if it.[00:42:09] If you can't do that, then it can sort of put that into your curriculum. I think like Khan Academy as an example, already does some of this, like personalized learning. You like take assessments at the beginning of every Khan Academy model module, and it'll basically only have you watch the videos and do the assignments for the things that like you didn't test well into.[00:42:27] So that's, it's, it's sort of close to already being there in some sense, but it doesn't have the, the language model interface on top of it before we[00:42:34] swyx: get into our lightning round, which is like, Quick response questions. Was there any other topics that you think you wanted to cover? We didn't touch on, whisper.[00:42:40] We didn't touch on Apple. Anything you wanted to[00:42:42] Logan Kilpatrick: talk?[00:42:43] Apple's Neural Engine[00:42:43] Logan Kilpatrick: Yeah, I think the question around Apple stuff and, and the neural engine, I think will be really interesting to see how it all plays out. I think, I don't know if you wanna like ask just to give the context around the neural engine Apple question. Well, well, the[00:42:54] swyx: only thing I know it's because I've seen Apple keynotes.[00:42:57] Everyone has, you know, I, I have a m M one MacBook Cure. They have some kind of neuro chip. , but like, I don't see it in my day-to-day life, so when is this gonna affect me, essentially? And you worked at Apple, so I I was just gonna throw the question over to you, like, what should we[00:43:11] Logan Kilpatrick: expect out of this? Yeah.[00:43:12] The, the problem that I've seen so far with the neural engine and all the, the Mac, and it's also in the phones as well, is that the actual like, API to sort of talk to the neural engine isn't something that's like a common you like, I'm pretty sure it's either not exposed at all, like it only like Apple basically decides in the software layer Yeah.[00:43:34] When, when it should kick in and when it should be used, which I think doesn't really like help developers and it doesn't, that's why no one is using it. I saw a bunch of, and of course I don't have any good insight on this, but I saw a bunch of rumors that we're talking about, like a lot of. Main use cases for the neural engine stuff.[00:43:50] It's, it's basically just in like phantom mode. Now, I'm sure it's doing some processing, but like the main use cases will be a lot of the ar vr stuff that ends up coming out and like when it gets much heavier processing on like. Graphic stuff and doing all that computation, that's where it'll be. It'll be super important.[00:44:06] And they've basically been able to trial this for the last, like six years and have it part of everything and make sure that they can do it cheaply in a cost effective way. And so it'll be cool to see when that I'm, I hope it comes out. That'll be awesome.[00:44:17] swyx: Classic Apple, right? They, they're not gonna be first, but when they do it, they'll make a lot of noise about it.[00:44:21] Yeah. . It'll be[00:44:22] Logan Kilpatrick: awesome. Sure.[00:44:22] Lightning Round[00:44:22] Logan Kilpatrick: So, so are we going to light. Let's[00:44:24] Alessio Fanelli: do it. All right. Favorite AI products not[00:44:28] Logan Kilpatrick: open AI. Build . I think synthesis. Is synthesis.io is the, yeah, you can basically put in like a text prompt and they have like a human avatar that will like speak and you can basically make content in like educational videos.[00:44:44] And I think that's so cool because maybe as people who are making content, like it's, it's super hard to like record video. It just takes a long time. Like you have to edit all the stuff, make sure you sound right, and then when you edit yourself talking it's super weird cuz your mouth is there and things.[00:44:57] So having that and just being able to ChatGPT A script. Put it in. Hopefully I saw another demo of like somebody generating like slides automatically using some open AI stuff. Like I think that type of stuff. Chat, BCG, ,[00:45:10] swyx: a fantastic name, best name of all time .[00:45:14] Logan Kilpatrick: I think that'll be cool. So I'm super excited,[00:45:16] swyx: but Okay.[00:45:16] Well, so just a follow up question on, on that, because we're both in that sort of Devrel business, would you put AI Logan on your video, on your videos and a hundred[00:45:23] Logan Kilpatrick: percent, explain that . A hundred percent. I would, because again, if it reduces the time for me, like. I am already busy doing a bunch of other stuff,[00:45:31] And if I could, if I could take, like, I think the real use case is like I've made, and this is in the sense of like creators wanting to be on every platform. If I could take, you know, the blog posts that I wrote and then have AI break it up into a bunch of things, have ai Logan. Make a TikTok, make a YouTube video.[00:45:48] I cannot wait for that. That's gonna be so nice. And I think there's probably companies who are already thinking about doing that. I'm just[00:45:53] swyx: worried cuz like people have this uncanny valley reaction to like, oh, you didn't tell me what I just watched was a AI generated thing. I hate you. Now you know there, there's a little bit of ethics there and I'm at the disclaimer,[00:46:04] Logan Kilpatrick: at the top.[00:46:04] Navigating. Yeah. I also think people will, people will build brands where like their whole thing is like AI content. I really do think there are AI influencers out there. Like[00:46:12] swyx: there are entire Instagram, like million plus follower accounts who don't exist.[00:46:16] Logan Kilpatrick: I, I've seen that with the, the woman who's a Twitch streamer who like has some, like, she's using like some, I don't know, that technology from like movies where you're like wearing like a mask and it like changes your facial appearance and all that stuff.[00:46:27] So I think there's, there's people who find their niche plus it'll become more common. So, cool. My[00:46:32] swyx: question would be, favorite AI people in communities that you wanna shout up?[00:46:37] Logan Kilpatrick: I think there's a bunch of people in the ML ops community where like that seemed to have been like the most exciting. There was a lot of innovation, a lot of cool things happening in the ML op space, and then all the generative AI stuff happened and then all the ML Ops two people got overlooked.[00:46:51] They're like, what's going on here? So hopefully I still think that ML ops and things like that are gonna be super important for like getting machine learning to be where it needs to be for us to. AGI and all that stuff. So a year from[00:47:05] Alessio Fanelli: now, what will people be the most[00:47:06] Logan Kilpatrick: surprised by? N. I think the AI is gonna get very, very personalized very quickly, and I don't think that people have that feeling yet with chat, BT, but I, I think that that's gonna, that's gonna happen and they'll be surprised in like the, the amount of surface areas in which AI is present.[00:47:23] Like right now it's like, it's really exciting cuz Chat BT is like the one place that you can sort of get that cool experience. But I think that, The people at Facebook aren't dumb. The people at Google aren't dumb. Like they're gonna have, they're gonna have those experiences in a lot of different places and I think that'll be super fascinating to see.[00:47:40] swyx: This is for the builders out there. What's an AI thing you would pay for if someone built it with their personal[00:47:45] Logan Kilpatrick: work? I think more stuff around like transfer learning for, like making transfer, learning easier. Like I think that's truly the way to. Build really cool things is transfer learning, fine tuning, and I, I don't think that there's enough.[00:48:04] Jeremy Howard who created Fasted AI talks a lot about this. I mean, it's something that really resonates with me and, and for context, like at Apple, all the machine learning stuff that we did was transfer learning because it was so powerful. And I think people have this perception that they need to.[00:48:18] Build things from scratch and that's not the case. And I think especially as large language models become more accessible, people need to build layers and products on top of this to make transfer learning more accessible to more people. So hopefully somebody builds something like that and we can all train our own models.[00:48:33] I think that's how you get like that personalized AI experiences you put in your stuff. Make transfer learning easy. Everyone wins. Just just to vector in[00:48:40] swyx: a little bit on this. So in the stable diffusion community, there's a lot of practice of like, I'll fine tune a custom dis of stable diffusion and share it.[00:48:48] And then there also, there's also this concept of, well, first it was textual inversion and then dream booth where you essentially train a concept that you can sort of add on. Is that what you're thinking about when you talk about transfer learning or is that something[00:48:59] Logan Kilpatrick: completely. I feel like I'm not as in tune with the generative like image model community as I probably should be.[00:49:07] I, I think that that makes a lot of sense. I think there'll be like whole ecosystems and marketplaces that are sort of built around exactly what you just said, where you can sort of fine tune some of these models in like very specific ways and you can use other people's fine tunes. That'll be interesting to see.[00:49:21] But, c.ai is,[00:49:23] swyx: what's it called? C C I V I Ts. Yeah. It's where people share their stable diffusion checkpoints in concepts and yeah, it's[00:49:30] Logan Kilpatrick: pretty nice. Do you buy them or is it just like free? Like open. Open source? It's, yeah. Cool. Even better.[00:49:34] swyx: I think people might want to sell them. There's a, there's a prompt marketplace.[00:49:38] Prompt base, yeah. Yeah. People hate it. Yeah. They're like, this should be free. It's just text. Come on, .[00:49:45] Alessio Fanelli: Hey, it's knowledge. All right. Last question. If there's one thing you want everyone to take away about ai, what would.[00:49:51] Logan Kilpatrick: I think the AI revolution is gonna, you know, it's been this like story that people have been talking about for the longest time, and I don't think that it's happened.[00:50:01] It was really like, oh, AI's gonna take your job, AI's gonna take your job, et cetera, et cetera. And I think people have sort of like laughed that off for a really long time, which was fair because it wasn't happening. And I think now, Things are going to accelerate very, very quickly. And if you don't have your eyes wide open about what's happening, like there's a good chance that something that you might get left behind.[00:50:21] So I'm, I'm really thinking deeply these days about like how that is going to impact a lot of people. And I, I'm hopeful that the more widespread this technology becomes, the more mainstream this technology becomes, the more people will benefit from it and hopefully not be affected in that, in that negative way.[00:50:35] So use these tools, put them into your workflow, and, and hopefully that will, and that will acceler. Well,[00:50:41] swyx: we're super happy that you're at OpenAI getting this message out there, and I'm sure we'll see a l
Guest Melissa Mendonça Panelists Richard Littauer | Amanda Casari Show Notes Hello and welcome to Sustain! The podcast where we talk about sustaining open source for the long haul. Today, we are so excited to have a wonderful guest, Melissa Mendonça, joining us. Melissa is a Senior Developer Experience Engineer at Quansight, where she focuses more on developer experience and contributor experience. Today, we'll hear all about Quansight and the focus for Melissa's role as a Developer Experience Engineer. Melissa tells us about a grant they are working on with CZI that focuses on NumPy, SciPy, Matplotlib, and pandas, she shares several ideas on what can be done to make people feel seen and heard, and we hear her thoughts on what the future of community management and community development looks like for people entering the role of these projects. Go ahead and download this episode now to hear more! [00:01:25] Melissa tells us her background and her role at Quansight. [00:03:41] When Melissa made the decision to switch from one role to another, Amanda asks if that was her plan or if she learned that the skills that she needed to get things done changed over time. [00:06:10] We find out what the focus is for Melissa's role as a Developer Experience Engineer and what she does on a day-to-day basis. [00:08:43] As Melissa was talking about her projects that they work on at Quansight, Amanda wonders if that's the majority of her portfolio, or if she works across different kinds of projects. We learn about the current grant they are working on with CZI that focuses on NumPy, SciPy, Matplotlib, and pandas. [00:13:18] We learn about the funding model and how sustainable it is. [00:16:20] Melissa shares some great ideas on how we can put more effort into making people feel seen and heard. [00:19:26] Melissa details some things she learned with the open source projects and things she recommends for others with large established projects. [00:22:44] Amanda talks about a 2020 paper that was released in nature called “Array programming with NumPy,” and Melissa gives us her perspective on what happened with the community in 2020, if things have changed, and what needs to be addressed. [00:27:09] Find out how CZI got involved with Melissa's work, what their goals are, and how she's changing in order to adapt towards those goals. [00:31:32] Melissa shares her thoughts on what the future of community management and community development looks like for people who are entering the role for those projects. [00:36:40] We hear more about Python Brasil 2022 that's coming up. [00:38:05] Find out where you can follow Melissa online and learn more about her work. Quotes [00:02:49] “Since Quansight is a company very focused on sustaining and helping maintain open source projects, we are trying to help new contributors, people who want to do the move from contributor to maintainer, understanding what that means, and how we can help them get there, and how we can help improve leadership in our open source projects.” [00:11:53] “This is one of the barriers that we want to break, is that making sure that people understand that these are important, they are core projects in the scientific Python ecosystem, but at the same time they are projects just like any other.” [00:12:17] “I think experience of working with projects that are so old and big has taught me a lot about the dynamics of how people work and how new people try to join these projects and how we can improve on that.” [00:16:41] “We need to make sure that people who do contribution outside of code are credited and that they are valued inside open source projects.” [00:18:20] “I think we should think about diversifying these paths for contribution, but for that we need to go beyond GitHub. We need to go beyond the current metrics that we have for open source, we need to go beyond the current credit system and reputation system that we have for open source contributions.” [00:30:38] “Community managers are not second-class citizens.” Spotlight [00:39:21 Amanda's spotlight is a 2014 paper from MSR called, “The Promises and Perils of Mining GitHub.” [00:40:48] Richard's spotlight is the book, Don't Sleep, There Are Snakes, by Daniel Everett. [00:41:52] Melissa's spotlights are Ralf Gommers and Scientific Python initiative. Links SustainOSS (https://sustainoss.org/) SustainOSS Twitter (https://twitter.com/SustainOSS?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor) SustainOSS Discourse (https://discourse.sustainoss.org/) podcast@sustainoss.org (mailto:podcast@sustainoss.org) Richard Littauer Twitter (https://twitter.com/richlitt?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor) Amanda Casari Twitter (https://twitter.com/amcasari?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor) Melissa Mendonça Twitter (https://twitter.com/melissawm) Melissa Mendonça LinkedIn (https://br.linkedin.com/in/axequalsb) Melissa Mendonça GitHub (https://melissawm.github.io/) Quansight (https://quansight.com/) Quansight Labs (https://labs.quansight.org/) Quansight Lab Projects (https://labs.quansight.org/projects) Quansight Labs Team (https://labs.quansight.org/team) Sustain Podcast-Episode 57: Mikeal Rogers on Building Communities, the Early Days of Node.js, and How to Stay a Coder for Life (https://podcast.sustainoss.org/guests/mikeal) Sustain Podcast-Episode 85: Geoffrey Huntley and Sustaining OSS with Gitpod (https://podcast.sustainoss.org/85) Advancing an inclusive culture in the scientific Python ecosystem (CZI grant for NumPy, SciPy, Matplotlib, and Pandas (https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063) Sustain Podcast-Episode 79: Leah Silen on how NumFocus helps makes scientific code more sustainable (https://podcast.sustainoss.org/79) NumPy (https://numpy.org/) SciPy (https://scipy.org/) Matplotlib (https://matplotlib.org/) pandas (https://pandas.pydata.org/) Sustain Podcast-Episode 64: Travis Oliphant and Russell Pekrul on NumPy, Anaconda, and giving back with FairOSS (https://podcast.sustainoss.org/guests/oliphant) Tania Allard Twitter (https://twitter.com/ixek?lang=en) Array programming with NumPy (nature) (https://www.nature.com/articles/s41586-020-2649-2) Python Brasil 2022 (https://2022.pythonbrasil.org.br/) “The Promises and Perils of Mining GitHub,” by Eirini Kalliamvakou, Georgios Gousios, Kelly Blincoe, Leif Singer, Daniel M. German, Daniela Damian (https://kblincoe.github.io/publications/2014_MSR_Promises_Perils.pdf) “The Promises and Perils of Mining GitHub,” by Eirini Kalliamvakou, Georgios Gousios, Kelly Blincoe, Leif Singer, Daniel M. German, Daniela Damian (ACM Digital Library) (https://dl.acm.org/doi/10.1145/2597073.2597074) Daniel Everett (Wikipedia) (https://en.wikipedia.org/wiki/Daniel_Everett#Don't_Sleep,_There_Are_Snakes:_Life_and_Language_in_the_Amazonian_Jungle) Excerpt: ‘Don't Sleep, There Are Snakes' (npr) (https://www.npr.org/2009/12/23/121515579/excerpt-dont-sleep-there-are-snakes?t=1661871384424) Ralf Gommers (GitHub) (https://github.com/rgommers) Scientific Python (https://scientific-python.org/) Credits Produced by Richard Littauer (https://www.burntfen.com/) Edited by Paul M. Bahr at Peachtree Sound (https://www.peachtreesound.com/) Show notes by DeAnn Bahr Peachtree Sound (https://www.peachtreesound.com/) Special Guest: Melissa Mendonça.
Hugo speaks with Peter Wang, CEO of Anaconda, about how Python became so big in data science, machine learning, and AI. They jump into many of the technical and sociological beginnings of Python being used for data science, a history of PyData, the conda distribution, and NUMFOCUS. They also talk about the emergence of online collaborative environments, particularly with respect to open source, and attempt to figure out the movings parts of PyData and why it has had the impact it has, including the fact that many core developers were not computer scientists or software engineers, but rather scientists and researchers building tools that they needed on an as-needed basis They also discuss the challenges in getting adoption for Python and the things that the PyData stack solves, those that it doesn't and what progress is being made there. People who have listened to Hugo podcast for some time may have recognized that he's interested in the sociology of the data science space and he really considered speaking with Peter a fascinating opportunity to delve into how the Pythonic data science space evolved, particularly with respect to tooling, not only because Peter had a front row seat for much of it, but that he was one of several key actors at various different points. On top of this, Hugo wanted to allow Peter's inner sociologist room to breathe and evolve in this conversation. What happens then is slightly experimental – Peter is a deep, broad, and occasionally hallucinatory thinker and Hugo wanted to explore new spaces with him so we hope you enjoy the experiments they play as they begin to discuss open-source software in the broader context of finite and infinite games and how OSS is a paradigm of humanity's ability to create generative, nourishing and anti-rivlarous systems where, by anti-rivalrous, we mean things that become more valuable for everyone the more people use them! But we need to be mindful of finite-game dynamics (for example, those driven by corporate incentives) co-opting and parasitizing the generative systems that we build. These are all considerations they delve far deeper into in Part 2 of this interview, which will be the next episode of VG, where we also dive into the relationship between OSS, tools, and venture capital, amonh many others things. LInks Peter on twitter (https://twitter.com/pwang) Anaconda Nucleus (https://anaconda.cloud/) Calling out SciPy on diversity (even though it hurts) (https://ilovesymposia.com/2015/04/03/calling-out-scipy-on-diversity/) by Juan Nunez-Iglesias Here Comes Everybody: The Power of Organizing Without Organizations (https://en.wikipedia.org/wiki/Here_Comes_Everybody_(book)) by Clay Shirky Finite and Infinite Games (https://en.wikipedia.org/wiki/Finite_and_Infinite_Games) by James Carse Governing the Commons: The Evolution of Institutions for Collective Action (https://www.cambridge.org/core/books/governing-the-commons/7AB7AE11BADA84409C34815CC288CD79) by Elinor Olstrom Elinor Ostrom's 8 Principles for Managing A Commmons (https://www.onthecommons.org/magazine/elinor-ostroms-8-principles-managing-commmons)
JuMP.jl is a an optimization library written entirely in the Julia language. And it's FAST! JuMP co-creator Miles Lubin join hosts David Amos and Randy Davila to discuss the inspiration behind JuMP, how JuMP become a NumFOCUS sponsored project, the journey to JuMP's version 1.0 release, and the future of the project. Special Guest: Miles Lubin.
In this Stories in AI podcast, Dr. Andy Terrel, VP of Data and Algorithms at Xometry, gave me valuable perspectives on how corporations, product managers, and data scientists should view artificial intelligence. If you are looking to advance your career as a data scientist or just in AI, this is a must listen episode. Andy's Bio: Dr. Andy R. Terrel is the VP, Data, and Algorithms of Xometry, Inc. where he is bringing his experience building smart scalable data systems to the manufacturing industry. You will also find him leading the infrastructure committee of the NumFOCUS foundation. As a passionate advocate for open source scientific codes Andy has been involved in the wider scientific Python community since 2006, contributing to numerous projects in the scientific stack. Dr. Andy R. Terrel was previously a Research Associate Scientist for the Computational Hydraulics Group at the Institute for Computational Engineering and Science which is at the University of Texas at Austin and High-Performance Computing researcher at the Texas Advanced Computing Center. Andy's research included utilizing supercomputers with Python and studying methods for speeding up computational fluid dynamics. He graduated from the University of Chicago with a Ph.D. in Computer Science in 2010 and has been programming in Python for the last decade. Andy has contributed to numerous open-source projects notably the FEniCS Project and Sympy. Reach Andy at: http://andy.terrel.us https://www.linkedin.com/in/aterrel/ https://twitter.com/aterrel A note about our sponsors: Experian is the world's leading global information services company. We empower our clients to manage their data with confidence and build trusted relationships with consumers, using advanced analytics, decisioning technology and fraud prevention tools. We help businesses to make smarter decisions and thrive, lend more responsibly, and prevent fraud and financial crime. As the world's leading repository of consumer credit data, Experian is transforming data into solutions that facilitate transactions, ensure financial safety and improve the financial lives of millions of consumers around the world. Learn more at https://Experian.com.
Les références : Orekit Meilleures pratiques de la Core Infrastructure Initiative (CII) Formulaire d'auto-évaluation pour Orekit, 1er niveau (Orekit satisfait 100 % des critères) 2e niveau (Orekit satisfait 89 % des critères) 3ème niveau (Orekit satisfait 57 % des critères) Blog personnel de Sébastien Dinot Les 3 générations de fondations Libre à vous !, précisions sur la gouvernance ouverte La fondation NumFOCUS guides Open Source de Github Le TODO Group le compte d'organisation de CS sur githubVous pouvez commenter les émissions, nous faire des retours pour nous améliorer, ou encore des suggestions. Et même mettre une note sur 5 étoiles si vous le souhaitez. Il est important pour nous d'avoir vos retours car, contrairement par exemple à une conférence, nous n'avons pas un public en face de nous qui peut réagir. Pour cela, rendez-vous sur la page dédiée.
Guest Logan Kilpatrick Panelists Eric Berry | Justin Dorfman Show Notes Hello and welcome to Sustain! The podcast where we talk about sustaining open source for the long haul. We are very excited to have as our guest, Logan Kilpatrick, who is the Community Manager for the Julia Programming Language, a graduate student studying Software Engineering and Technology Law, and makes an exclusive announcement of another position he recently has taken on. Today, we are talking to Logan about the Julia Programming Language. We learn more about the role Major League Hacking played in the MLH Fellowship with Julia, why Logan is most interested in doing open source non-technical, his experience working at NASA, and the challenges he has with research papers. He also tells us about why the Julia community should not be using Slack, but maybe using Discord and Zulip in the future. Logan shares some parting advice about reaching out to people if there's opportunities that are interesting to you. Find out more and download this episode now! [00:00:22] Logan gives us a brief introduction of who he is, what he does, and what this new position is he has recently taken on. [00:01:52] NumFocus is the topic and how this all came to be for Logan, and why Julia as a programming language is so unique and special. [00:05:48] Justin brings up ML Hacks and Logan explains more about this. [00:08:04] Logan fills us in on what his Julia day-to-day tasks that he works on and his non-technical tasks so he can influence the next non-technical open source contributor. [00:11:51] Find out if the Julia Programming Language is using any tools to monitor their community engagement. Justin talks about something he uses called Orbit, which is a framework for building high gravity communities. [00:16:00] Find out the experience Logan had working with NASA! [00:18:49] Logan has so much going on in his life and Justin wonders how he finds time to do anything. [00:20:10] We learn why Logan has a bunch of challenges with research papers. [00:22:47] Eric wonders if people are not sharing the code for reasons that they don't want to give up intellectual property or that it's not completely well-formed and they just want to own it, but still want to share it. Logan gives his perspective on this. [00:25:17] Logan explains the different places you can find the Julia community and why they should not be using Slack. Eric wonders what is out there that we can use that people would adopt, and Logan talks about Discord, Zulip, and Forum Community. [00:29:09] Logan covers one more thing, going back to the convo they had about open source contributions and non-technical contributions. He also brings up Jono Bacon's book, People Powered. Quotes [00:04:46] “The estimate right now is something like a million developers or something like that, which is at a million users.” [00:05:53] “So, Major League Hacking is an incredible organization and they were sort of generous enough in the first iteration of the MLH Fellowship, which is just an opportunity for students to contribute to open source and get paid to do it by Major League Hacking and a bunch of peripheral organizations who support Major League Hacking.” [00:08:33] “I think my sort of general goal that has just come out recently for me is to make people understand that a non-technical contribution in open source is a viable way of contributing.” [00:08:58] “And the reason for that is I feel like there's more opportunities to do those non-technical contributions and there's more sort of missing pieces in the non-technical space.” [00:10:21] “Again, I think there's so much non-technical work that if someone doesn't step up and do it, it doesn't get done.” [00:20:21] “One of which is a lot of times folks don't release their code, which is sort of one of the missions of NumFocus and in a sense, “Open code equals better science.” [00:26:16] “To me, it's 100% evident and perfectly clear that we should not be using Slack.” [00:26:23] “Slack is a tool that is built for corporations to communicate with one another... It is not a tool for open source projects to be using.” [00:28:46] “In my personal opinion, Discord and Zulip will probably be the two that are fighting each other in the future with respect to places that communities go and meet.” [00:29:22] “I think something that is perhaps might be obvious to some people, might not be obvious to some people, but really, non-technical contributions in my opinion are the pathway to making a code contribution.” [00:30:58] “I think my parting suggestion for people that I always try to instill whenever I have the opportunity to talk to people that I don't know through the internet is take the opportunities to reach out to folks that you don't know if there's opportunities that are interesting to you.” Spotlight [00:31:55] Logan's spotlight is the tool Julia's visualization package Makie. [00:32:29] Eric's spotlight is a suite of tools called Setapp. [00:33:03] Justin's spotlight is Kid Pix. Links SustainOSS (https://sustainoss.org/) SustainOSS Twitter (https://twitter.com/SustainOSS?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor) SustainOSS Discourse (https://discourse.sustainoss.org/) Logan Kilpatrick Twitter (https://twitter.com/officiallogank?lang=en) Logan Kilpatrick Linkedin (https://www.linkedin.com/in/logankilpatrick) Julia Programming Language (https://julialang.org/) NumFocus (https://numfocus.org/) Major League Hacking (https://mlh.io/) Orbit-GitHub (https://github.com/orbit-love) [People Powered: How Communities Can Supercharge Your Business, Brand, and Teams by Jono Bacon](https://www.amazon.com/People-Powered-Communities-Supercharge-Business/dp/1400214882/ref=ascdf1400214882/?tag=hyprod-20&linkCode=df0&hvadid=385492364860&hvpos=&hvnetw=g&hvrand=6800069154212988263&hvpone=&hvptwo=&hvqmt=&hvdev=c&hvdvcmdl=&hvlocint=&hvlocphy=9052206&hvtargid=pla-903833266237&psc=1&tag=&ref=&adgrpid=79288121435&hvpone=&hvptwo=&hvadid=385492364860&hvpos=&hvnetw=g&hvrand=6800069154212988263&hvqmt=&hvdev=c&hvdvcmdl=&hvlocint=&hvlocphy=9052206&hvtargid=pla-903833266237) Sustain Podcast- Episode 84-“Jono Bacon on Building Sustainable Communities” (https://podcast.sustainoss.org/84) Sustain Podcast-Episode 79-“Leah Silen on how NumFocus helps makes scientific code more sustainable” (https://podcast.sustainoss.org/79) Zulip (https://zulip.com/) Discord (https://discord.com/) Forum Community (https://www.forumcommunity.net/) Makie (https://makie.juliaplots.org/dev/) Setapp (https://setapp.com/) Kid Pix (https://kidpix.app/) Credits Produced by Richard Littauer (https://www.burntfen.com/) Edited by Paul M. Bahr at Peachtree Sound (https://www.peachtreesound.com/) Show notes by DeAnn Bahr at Peachtree Sound (https://www.peachtreesound.com/) Special Guest: Logan Kilpatrick.
Abstract of the talk… In this talk, we will go over what Julia is, why you would want to learn it, and how to contribute to the ecosystem. Bio… Logan is the Community Manager for the Julia Programming Language, a member of the Board of Directors at NumFOCUS, and a Machine Learning Engineer. Outside of work, Logan is a graduate student at Harvard University and Northwestern Universities Pritzker School of Law. Key take-aways from the talk… Viewers will leave with a high-level understanding of the Julia ecosystem, the benefits the language provides, how to contribute to it, and more.
Guest Ewa Jodlowska, Rachel Lawson, Leah Silen, Ben Nickolls, Jory Burson, and Karen Sandler Panelists Duane O'Brien and Richard Littauer Show Notes Hello and welcome to Sustain! The podcast where we talk about sustaining open source for the long haul. Today, we're doing something a little different with this episode. We are giving you the audio recording of a round table that was recently hosted by Duane O'Brien and Richard Littauer, about the role of foundations in open source. Our panelists today are Ewa Jodlowska, Rachel Lawson, Leah Silen, Ben Nickolls, Jory Burson, and Karen Sandler. We'll spend time talking about foundations and associations in general, the kinds of things they do, the kinds of legal structures that they may have, and how they differ from each other. They explain about the work they've done for their projects and some services that they offer. And then we'll spend time talking about projects, when projects might think about reaching out to organizations, or when maintainers might think about bringing their projects to organizations. So, take a listen and enjoy! Go ahead and download this episode now! [00:00:40] Duane starts off with a quick overview of the conversations they'll be talking about. [00:01:36] Everyone gives a brief introduction of themselves, who they're representing, and what their organization does. [00:06:42] Duane asks the panelists for their responses to: What is a foundation, what isn't a foundation, and what are some of the differences between the types of organizations that you have. [00:10:58] Speaking on behalf of the Python Software Foundation, Ewa talks about what kinds of things they do for projects and we learn from Leah what fiscal sponsorship means. [00:13:07] Duane asks if there is anyone for whom their organization and their view of fiscal sponsorship is significantly different from what the others have described. Jory, Ben, and Karen share some things. [00:17:34] Duane asks the panelists to discuss about the times that their organizations have helped solve another kind of problem or member projects or for projects that later became members. And, when have they been able to step in and intervene on behalf of the project? [00:27:45] Find out what kinds of things the panelists look for from projects that apply to be a part of your organization and when do they think they're ready to come in. [00:31:56] For the maintainers of projects who are in charge of their project and are thinking it might be or wondering if it's time to start reaching out to foundations, Duane asks the panelists for some key indicators that they might look for that it's probably time to tag in some bigger help than they've had to date. [00:32:54] Richard brings up a question that was in the chat about mailing lists and why is mailing list important when considering whether you're going to take on a project into your foundation. [00:34:45] A question that was sent to Richard personally and not in the chat was, why do we think there are so many women in this space? [00:36:20] The next chat question Richard asks was, can everyone agree that most open source software foundation's purpose is not to support the public interest, but instead to support the interest of the members? [00:39:33] The panelists tell us what they are most excited about that might be coming up for them and what they want to plug on behalf of their organization. Links SustainOSS (https://sustainoss.org/) SustainOSS Twitter (https://twitter.com/SustainOSS?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor) Ewa Jodlowska Twitter (https://twitter.com/ewa_jodlowska?lang=en) Python Software Foundation (https://www.python.org/psf/) Python Software Foundation Campaign (donation page) (https://www.python.org/psf/donations/2021-q2-drive/) Rachel Lawson Twitter (https://twitter.com/rachel_norfolk?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor) Drupal (https://www.drupal.org/) Leah Silen Linkedin (https://www.linkedin.com/in/leah-silen-95733840) NumFOCUS (https://numfocus.org/) Ben Nickolls Twitter (https://twitter.com/benjam?lang=en) Open Source Collective (https://www.oscollective.org/) Jory Burson Twitter (https://twitter.com/jorydotcom?lang=en) OpenJS Foundation (https://openjsf.org/) Karen Sandler Twitter (https://twitter.com/o0karen0o?lang=en) Software Freedom Conservancy (https://sfconservancy.org/) Discover Drupal (https://www.drupal.org/association/discover-drupal) FundOSS (https://fundoss.org/) JavaScriptLandia (https://javascriptlandia.com/) OpenJS Foundation YouTube (https://www.youtube.com/c/OpenJSFoundation) NumFOCUS (https://numfocus.org/) PyData Global 2021 (https://pydata.org/global2021/) PyData YouTube (https://www.youtube.com/channel/UCOjD18EJYcsBog4IozkF_7w) Cloud68.co (https://cloud68.co/) Aspiration Tech (https://aspirationtech.org/) Indeed (https://www.indeed.com/) Credits Produced by Richard Littauer (https://www.burntfen.com/) Edited by Paul M. Bahr at Peachtree Sound (https://www.peachtreesound.com/) Show notes by DeAnn Bahr at Peachtree Sound (https://www.peachtreesound.com/) Special Guests: Benjamin Nickolls, Ewa Jodlowska, Jory Burson, Karen Sandler, Leah Silen, and Rachel Lawson.
Guest Leah Silen Panelists Eric Berry | Justin Dorfman | Alyssa Wright | Richard Littauer Show Notes Hello and welcome to Sustain! Today, our special guest is Leah Silen, who is the Executive Director of NumFOCUS. She has been the primary driver behind the organization and execution of its programs including fiscal sponsorship, the PyData event series, and DEI initiatives. We learn what NumFOCUS does, how it works in terms of scientific research, who provides the funding, and the diversity, equity, and inclusion support that NumFOCUS provides projects. Leah talks about the importance of Grant Management and Community Management needed to help projects in the future, and a “Sustain Exclusive” announcement is made by Leah on something NumFOCUS is in the early stages of building. Go ahead and download this episode now to find out what it is! [00:01:16] Leah explains what NumFOCUS does, how it works, and what scientific open source means. [00:03:22] Since NASA researchers use NumFOCUS for sponsored projects, Justin asks if there are any sponsored projects on Mars right now. [00:05:18] Leah tells us about NumFOCUS being a project foundational to scientific research. [00:05:54] We learn about Leah's art background and becoming one of the founding members of NumFOCUS. [00:07:21] There are maintainers of forty-two projects and Leah explains who the typical maintainer is of the NumFOCUS ecosystem. [00:08:14] Find out what a typical week looks like for Leah at NumFOCUS. [00:10:37] Richard is curious how Leah sees the future of this sort of organization as we're seeing more of them, and if she's just going to keep growing until there's hundreds of projects under her or will there be more or less. [00:13:12] We learn who provides funding at NumFOCUS since they have nine staff members. Justin wonders how NumFOCUS is diversifying their income and Leah makes an announcement about something NumFOCUS is building and it's a “Sustain Exclusive!” [00:16:11] Justin asks if NumFOCUS ever joins forces with the PSF. [00:16:55] Leah mentioned the diversity, equity, and inclusion support that NumFOCUS provides projects, she describes how it's important for project sustainability, and the conversations there have been. [00:19:59] Richard wonders about the process of taking on a new project. [00:23:25] Leah tells us how they deal with the maintenance of scientific projects. [00:25:24] We learn the moon-shot idea of NumFOCUS, besides just making sure all these projects run smoothly, and what the goal is. [00:26:42] Leah tells us what she's most excited about in terms of providing better stuff to projects in the near future. [00:29:20] Community Manager and Developer Advocate is discussed. [00:31:20] Find out where you can follow Leah and NumFOCUS on the internet. Quotes [00:04:00] “Many of the leaders in that project work for a division of NASA that have been directly involved in Mars Roemer images and things like that, as well as Astro Pi, another one of the projects that's widely used by the astronomy community.” [00:05:18] “We many times speak of NumFOCUS projects as being very foundational to scientific research.” [00:10:59] “We have to make sure that as the number of projects that we're sponsoring are affiliated with NumFOCUS grows, that the organization is able to scale with that.” [00:12:20] “And there's so many areas that we don't address that we could address for our projects, you know just handling the legal aspect, grant management, helping them with we have a contributor diversification and research program.” [00:12:35] “So working on DEI initiatives that's woven through everything we do and helping our projects with that.” [00:23:58] “But that's one reason we really want to work and focus on diversifying the contributor base. Also, with contributors who are across different domains and in different areas.” [00:24:08] “So, if a project comes and applies to NumFOCUS and everyone is at one university, we don't consider that open, so there has to be contributors spread out no more than two employed, whether that's a university or whether that's a for-profit entity.” [00:26:50] “So, I think projects, a lot of the things that NumFOCUS does can be related to Community Management but definitely when you're talking about more of an internal project community.” [00:27:20] “I think that is probably one of the things that is most needed across projects is every project having a Community Manager to really look at their internal communities as well as interactions with their user base.” Spotlight [00:32:05] Alyssa's spotlight is Community Managers. [00:32:44] Eric's spotlight is Doom Emacs. [00:33:21] Justin's spotlight is Lipgloss by Charm. [00:33:42] Richard's spotlight is IDLE. [00:34:09] Leah's spotlight is Sustain Diversity Working Group. Links NumFOCUS (https://numfocus.org/) NumFOCUS Twitter (https://twitter.com/NumFOCUS?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor) info@numfocus.org (mailto:info@numfocus.org) leah@numfocus.org (mailto:leah@numfocus.org) “5 qualities of outstanding open source community managers” by Jason Blais (https://opensource.com/article/20/9/open-source-community-managers) Doom Emacs-GitHub (https://github.com/hlissner/doom-emacs) Lipgloss-Charm (https://github.com/charmbracelet) Charm Twitter (https://twitter.com/charmcli?lang=en) IDLE (https://docs.python.org/3/library/idle.html) Sustain Working Groups (https://sustainoss.org/working-groups/) Credits Produced by Richard Littauer (https://www.burntfen.com/) Edited by Paul M. Bahr at Peachtree Sound (https://www.peachtreesound.com/) Show notes by DeAnn Bahr at Peachtree Sound (https://www.peachtreesound.com/) Special Guest: Leah Silen.
When explaining Bayesian statistics to people who don’t know anything about stats, I often say that MCMC is about walking many different paths in lots of parallel universes, and then counting what happened in all these universes. And in a sense, this whole podcast is dedicated to sampling the whole distribution of Bayesian practitioners. So, for this episode, I thought we’d take a break of pure, hard modeling and talk about how to get involved into Bayesian statistics and open-source development, how companies use Bayesian tools, and what common struggles and misperceptions the latter suffer from. Quite the program, right? The good news is that Peadar Coyle, my guest for this episode, has done all of that! Coming to us from Armagh, Ireland, Peadar is a fellow PyMC core developer and was a data science and data engineer consultant until recently – a period during which he has covered all of modern startup data science, from AB testing to dashboards to data engineering to putting models into production. From these experiences, Peadar has written a book consisting of numerous interviews with data scientists throughout the world – and do consider buying it, as money goes to the NumFOCUS organization, under which many Bayesian stats packages live, like Stan, ArviZ, PyMC, etc. Now living in London, Peadar recently founded the start-up Aflorithmic, an AI solution that aims at developing personalized voice-first solutions for brands and enterprises. Their technology is also used to support children, families and elderly coping with the mental health challenges of COVID-19 confinements. Before all that, Peadar studied physics, philosophy and mathematics at the universities of Bristol and Luxembourg. When he’s away from keyboard, he enjoys the outdoors, cooking and, of course, watching rugby! Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ (https://bababrinkman.com/) ! Thank you to my Patrons for making this episode possible! Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll and Nathaniel Burbank. Visit https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;) Links from the show: "Matchmaking Dinner" announcement: https://twitter.com/alex_andorra/status/1351136756087734272 (https://twitter.com/alex_andorra/status/1351136756087734272) How to get acces to "Matchmaking Dinner" episodes: https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) Peadar on Twitter: https://twitter.com/springcoil (https://twitter.com/springcoil) Peadar's website: https://peadarcoyle.com/ (https://peadarcoyle.com/) Peadar on GitHub: https://github.com/springcoil (https://github.com/springcoil) Interviews with Data Scientists -- A discussion of the Industy and the current trends: https://leanpub.com/interviewswithdatascientists/ (https://leanpub.com/interviewswithdatascientists/) Aflorithmic -- Personalized Audio SaaS Platform: https://www.aflorithmic.ai/ (https://www.aflorithmic.ai/) Peadar's PyMC3 Primer: https://product.peadarcoyle.com/ (https://product.peadarcoyle.com/) This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy Support this podcast
Panelists Eric Berry | Justin Dorfman | Alyssa Wright | Richard Littauer Guest Travis Oliphant | Russell Pekrul Show Notes Hello and welcome to Sustain! Today, we have two guests from OpenTeams in Austin, Travis Oliphant and Russell Pekrul. Travis is the CEO and Russell is the Program Manager and the Founder and Director of FairOSS. We learn all about what OpenTeams and FairOSS are and how they work. Also, Travis tells us about the non-profit he started called NumFOCUS. Other topics discussed are dependencies and how their values are assigned, NumPy and SciPy, and building relationships with companies, which Russell mentions there is a bit of a “chicken and egg” problem here. There is some incredible advice and fascinating stories shared today so go ahead and download this episode now! [00:01:10] We find out what OpenTeams is and how it works. Travis also tells us when he wrote NumPy and SciPy and when he started OpenTeams. [00:07:18] Travis tells us about a non-profit he started with a bunch of people called NumFOCUS so there could be a home for the fiscal sponsor for open source projects. [00:09:24] Russell tells us what FairOSS is and how it works. [00:11:32] Alyssa asks Russell how does he first see the dependencies and then how does he assign that value? He mentions BackYourStack as a starting point. [00:13:00] Eric brings up one of the problems he’s found with trying to fund up open source is that it’s very difficult to solve the problem on more a grand scale. He wonders how Travis and Russell make the impact they want with the magnitude of problems they see. A key piece Travis brings up that they recognize is there’s a data gap and projects have to be participating. Alyssa wonders if projects are aware of their dependencies. [00:17:22] Richard asks about the dependency graph that they are making. He wonders how do you go down the stack and look all the way at the base and how do you judge the usefulness of what dependencies really matter for what code matters for the business proposition? Richard also wonders if anyone has done equity stuff for open source maintainers. [00:23:06] Alyssa is interested in learning more about how Travis and Russell are building the relationships with these companies and what we can do to help. [00:26:35] Alyssa asks Travis and Russell to talk about why this, why now, with this being a time of economic contraction, why is this important? Also, why have they been seeing traction during what can be difficult times for a lot of companies? [00:27:40] Eric asks if Travis can give an example of a project that he feels does that well, that doesn’t have to go through and do it twice, essentially. [00:29:48] Alyssa brings up investments around open source start-ups and how they start with a commitment towards open source and once the investment happens there’s a pivot. She wonders if Travis could talk about how this type of sustainability is shifting that model of these investments. Travis tells a story about speaking to the Founder of SaltStack and how their views matched. [00:34:03] We find out where you can learn more about FairOSS and follow them on this journey, invest, and join in. Spotlight [00:34:52] Justin’s spotlight is Curiefense, which extends Envoy proxy to protect all forms of web traffic. [00:35:15] Alyssa’s spotlight is Pixel8.earth. [00:36:06] Eric’s spotlight is OctoPrint. [00:36:53] Richard’s spotlight is Michael Oliphant’s work. [00:37:36] Russell’s spotlight is Conda. [00:38:20] Travis’s spotlight is Matplotlib. Quotes [00:03:25] “We were connecting and creating a social network long before the social networks started. That was the early days of social networks and it was addicting.” [00:04:14] “New libraries are starting to be written on numarray and we had SciPy written on numeric and there was this fork in this flegging scientific community in Python.” [00:21:18] “So that was a very exciting day. Actually, I remember I told my wife you know the problem I’ve been searching on for twenty years, I finally figured it out. I’ve been trying to figure out twenty years how to make this work, and I finally figured it out. I had to go start several companies and start a venture fund and get involved in finance and cap tables to really pull it off, but that got me excited. Now I also said, but we’re at the base of Mount Everest, like all we’ve got to do is climb to the top of this mountain and we’re there.” [00:22:44] “So you basically have a company and its value is spread to all the values of the projects. You have a bunch of those, have a thousand of those, that each add incrementally the value of a project. Invert the matrix and every project now has a linear dependency on companies that effectively you created an index fund out of every project.” [00:24:52] “The idea is if you can get open source contributors to recognize that they want to work only for companies that are participating people want to hire open source contributors. They’re some of the best people to bring into your company.” [00:25:21] “We found that companies would absolutely sponsor PyData and the reason they would is because they’re trying to hire people. They wanted to hire the best developers and they would. So, they really didn’t care so much about the projects they started, but they wanted the people.” [00:27:10] “Go make an open source project, then get somebody or connect with somebody who’s going to help you build a company that they’ll vest in and build something else. So, you basically have to do it twice.” [00:28:34] “I’ve had the chance to work at companies large and small, go in and see that’s used to do x, and realized it’s added billions of dollars of value to a lot of work for the world. And yet, the same time NumPy struggled, not enough funding to maintain itself.” [00:30:15] “I spoke to the founder of SaltStack that just got acquired by VMware. I spoke to him about his view and it was amazing how much it matched mine, in a sense that he recognized that open source is you build some of the value and you use it. The way you need to make money is to build something that uses it but isn’t the open source.” [00:32:41] “It’s not you’re monetizing open source, you’re empowering, you’re sustaining open source, by selling and connecting the economic value to the functional value that’s there.” [00:33:04] “There will still be challenges. I’m not naïve. Every new thing comes with a whole set of new challenges.” Links OpenTeams (https://openteams.com/about) FairOSS (https://faiross.org/) FairOSS, PBC Twitter (https://twitter.com/faiross_pbc) FairOSS Community (https://community.faiross.org/login) Travis Oliphant Twitter (https://twitter.com/teoliphant?lang=en) Anaconda Dividend Program (https://www.anaconda.com/blog/sustaining-the-open-source-ds-ml-ecosystem-with-the-anaconda-dividend-program) Quansight (https://www.quansight.com/) NumFOCUS (https://numfocus.org/) BackYourStack (https://backyourstack.com/) Dask (https://dask.org/) SaltStack (https://www.saltstack.com/) SciPy (https://www.scipy.org/) NumPy (https://numpy.org/) Curiefense (https://www.curiefense.io/) Pixel8.earth Ambassador Program (https://pixel8earth.medium.com/kicking-off-the-pixel8-earth-ambassador-program-80a87a70fb3a) OctoPrint (https://octoprint.org/) Michael Oliphant’s work (https://langev.com/index.php/author/moliphant/Michael+Oliphant) Conda (https://github.com/conda/conda) Matplotlib.com (https://matplotlib.org/) Credits Produced by Richard Littauer (https://www.burntfen.com/) Edited by Paul M. Bahr at Peachtree Sound (https://www.peachtreesound.com/) Show notes by DeAnn Bahr at Peachtree Sound (https://www.peachtreesound.com/) Special Guests: Russell Pekrul and Travis Oliphant.
PyChat: PyData global CfP is opened JupyterCon 2020 Virtual Event PyTorch joins NumFOCUS as an Affiliated Project PyGotham TV - last days for CfP (July 5th) Python Ireland remote meetup events > volunteers for Lightning Talks Blog about Pickle: Pickle's nine flaws Mid Meet - Hall of Fame Interview with Waylon Walker (Twitter & dev.to)- Data driven solution enabler, creator of find-kedro and kedro-static-viz
Talk Python To Me - Python conversations for passionate developers
On this episode, we are going to weave a thread through three different areas of Python programming that at first seem unlikely to have much in common. Yet, the core will be the same throughout. I think this is a cool lesson to learn as you get deeper into programming and a great story to highlight it. We are going to meet Ravin Kumar who wrote Python code and data science tooling for oil rig tool manufacturer, a rocket company, and a hip multilocation restaurant chain. Links from the show Ravin on Twitter: @canyon289 PyMC3: pymc.io Arviz project: arviz-devs.github.io/arviz pystan project: pystan.readthedocs.io NumFocus: numfocus.org Bayesian Decision Making: canyon289.github.io open-aerospace project: open-aerospace.github.io SweetGreen: sweetgreen.com Get notified when Bayesian Computation In Python is out: docs.google.com/forms Bayesian Analysis with Python Book: packtpub.com Sponsors Sentry Error Monitoring, Code TALKPYTHON Linode Talk Python Training
PyCon US go online! Subscribe to their YouTube channel to catch all the talks PyLadies also has YouTube Channel Python Ireland's 1st meetUp is tonight talking about App Performance Monitoring in Python. PyCon Australia and PyCon Africa are also going online, details to be announced. Scikit-image join NUMFocus! PyPI Highlight: Foxdot - Make music with Python FastAPI - a high-performance framework, easy to learn, and fast to code
In episode 28, we interviewed Leah Silen from the NumFocus organization. She introduced us to the goals and the mission of the organization. We then had a discussion about the different levels of support provided by the organization to its member projects. She informed us about the legal, financial, technological and logistical support that can be provided by NumFocus. Following that, we asked her about the revenue sources of the organization as well as the possible influence from the corporate sponsors over the decisions and governance of the organization. We also discussed of the requirements to become part of NumFocus including details about the application process. We had a brief discussion about the history of the project and the evolution of the scope of projects that are part of the organization. After discussing the governance of the organization, we concluded the interview with our usual questions. 00:00:00 Intro 00:00:18 Introducing Leah Silen 00:02:28 Goals and mission of NumFocus 00:03:06 Examples of supported projects 00:03:39 Status of sponsorded and affiliated projects 00:05:04 Advantages of one status over the other 00:05:48 Legal challenges for open source scientific projects 00:07:19 Financial support for scientific open source projects 00:10:13 Assistance to apply for external grants 00:11:01 Paying developers from outside of US? 00:11:43 Revenue sources for NumFocus 00:12:21 Levels of corporate sponshorships 00:13:14 The influence of corporate sponsors 00:13:56 Motivations of corporate sponsors 00:15:02 Some of the sponsors of the NumFocus organization 00:16:05 Technological support for projects 00:17:03 Events previously supported by the organization 00:18:09 The kind of support that can be provided for events 00:19:22 Requirements for new projects 00:21:10 Clarification about the meaning of being a scientific oriented project 00:23:11 Requirements about the team size and strong governance within projects 00:24:58 The application process 00:26:18 Duration of support 00:26:44 Timeframe to receive a response for an application 00:28:30 Feedback in the case of rejection 00:29:12 Are there downsides of becoming a part of NumFocus? 00:30:32 Additional administrative overhead? 00:32:01 Location of NumFocus staff members 00:33:00 Foudation of NumFocus and initial projects 00:34:05 Opening to projects outside of the Python ecosystem 00:34:54 Favourite project? 00:35:46 Initial role in NumFocus 00:36:53 Term duration for positions at the board of directors 00:37:24 Selection process for the board of directors 00:38:31 Leah's vision about FLOSS and its importance for the openness of science 00:39:04 Negative impacts of FLOSS 00:39:33 Most notable scientific discovery in recent years 00:39:59 Favourite text processing tool 00:40:37 A topic in science about which she changed her mind about 00:41:36 Anything else we forgot to ask about? 00:42:54 How to contact Leah Silen 00:43:14 Outro
Revisit the early days of Travis Oliphant's contributions to scientific Python and by extension Python's relevance to AI. Catch-up on the high energy, current efforts of the creator of NumPy, SciPy, Numba, and Conda. Travis also founded the NumFOCUS Foundation. NumFOCUS is backing Jupyter and Pandas. In this Episode, we break our 26.1 Minutes rule for a really great and important chat with this legend of Data Science. Enjoy an extra few minutes.
In this episode I talk with Matt Rocklin. Matt is best known for his work on Dask, a parallel computing package built into the PyData stack. After working on open source software at Anaconda and NVIDIA he now founded his own company centered around Dask called Coiled Computing. In this episode we talk about the insights into open source he gained through his career, what Dask is and how it is funded, and then of course his new company.Links:https://twitter.com/mrocklinhttps://dask.orghttps://coiled.iohttps://matthewrocklin.comhttps://rapids.aihttps://pangeo.orghttps://prefect.ioThanks to my Patrons for their support, especially:Daniel GerlancRichard CraibJonathan NgSupport me here to get early access: https://www.patreon.com/twiecki PyData is a registered trademark of NumFOCUS, Inc.Support the show (https://www.patreon.com/twiecki)
conda-forge is community led collection of recipes, build infrastructure and distributions. Conda-forge currently build conda packages for Linux, Mac, Windows, ARM, and Power8 architectures. Conda-forge has 1400 members in its GitHub organization and >7000 repositories. The conda-forge channel has about 80 million downloads a month, and growing. Conda-forge is an official NumFOCUS project.
Bradley and Karen discuss and critique the new initiative by the Linux Foundation called CommunityBridge. The podcast includes various analysis that expands upon their blog post about Linux Foundation's CommunityBridge. Show Notes: Segment 0 (00:36) Conservancy helped Free Software Foundation and GNOME Foundation begin fiscal sponsorship work. (07:50) Conservancy has always been very coordinated with Software in the Public Interest, which is a FOSS fiscal sponsor that predates Conservancy. (08:26) Conservancy helped NumFocus get started as a fiscal sponsor by providing advice. (08:53) The above are all 501(c)(3) charities, but there are also 501(c)(6) fiscal sponsors, such as Linux Foundation and Eclipse Foundation. (10:00) Bradley mentioned that projects that are forks can end up in different fiscal sponsors, such as Hudson being in Eclipse Foundation, and Jenkins being associated with a Linux Foundation sub-org. (10:30) Bradley mentioned that any project — be it SourceForge, GitHub, or Community Bridge — that attempts to convince FOSS developers to use proprietary software for their projects is immediately suspect (12:00) Open Collective, a for-profit company seeking to do fiscal sponsorship (but attempting to release their code for it) is likely under the worst “competitive” threat from this initiative. (19:50) Segment 1 (21:23) Projects that use CommunityBridge are required to act in the common business interest of the Linux Foundation members. (27:30) Board of Directors seats at the Linux Foundation are for sale, according to their by-laws. (28:50) Bradley advises that you should not put anything copylefted into CommunityBridge — given Linux Foundation's position on copyleft and citing the ArduPilot/DroneCode example. (29:50) CommunityBridge appears to only allow governance based on the “benevolent dictator for life model” (31:40), at least with regard to who controls the money (34:30) Bradley mentioned the LWN article about Community Bridge. (33:22) Segment 2 (36:54) Karen mentioned that CommunityBridge also purports to address diversity and security issues for FOSS projects. (37:00) Bradley mentioned the code hosted on k.sfconservancy.org and also the Reimbursenator project that PSU students wrote. (42:00) Segment 3 (42:44) Bradley and Karen discuss (or, possibly don't) discuss what's coming up on the next episode. Fact of the matter is that this announcement wasn't written yet when we recorded this episode and we weren't sure if 0x65 would be released before or after that announcement was released. We'll be discussing that topic on 0x66. Send feedback and comments on the cast to . You can keep in touch with Free as in Freedom on our IRC channel, #faif on irc.freenode.net, and by following Conservancy on on Twitter and and FaiF on Twitter. Free as in Freedom is produced by Dan Lynch of danlynch.org. Theme music written and performed by Mike Tarantino with Charlie Paxson on drums. The content of this audcast, and the accompanying show notes and music are licensed under the Creative Commons Attribution-Share-Alike 4.0 license (CC BY-SA 4.0).
Bradley and Karen discuss and critique the new initiative by the Linux Foundation called CommunityBridge. The podcast includes various analysis that expands upon their blog post about Linux Foundation's CommunityBridge. Show Notes: Segment 0 (00:36) Conservancy helped Free Software Foundation and GNOME Foundation begin fiscal sponsorship work. (07:50) Conservancy has always been very coordinated with Software in the Public Interest, which is a FOSS fiscal sponsor that predates Conservancy. (08:26) Conservancy helped NumFocus get started as a fiscal sponsor by providing advice. (08:53) The above are all 501(c)(3) charities, but there are also 501(c)(6) fiscal sponsors, such as Linux Foundation and Eclipse Foundation. (10:00) Bradley mentioned that projects that are forks can end up in different fiscal sponsors, such as Hudson being in Eclipse Foundation, and Jenkins being associated with a Linux Foundation sub-org. (10:30) Bradley mentioned that any project — be it SourceForge, GitHub, or Community Bridge — that attempts to convince FOSS developers to use proprietary software for their projects is immediately suspect (12:00) Open Collective, a for-profit company seeking to do fiscal sponsorship (but attempting to release their code for it) is likely under the worst “competitive” threat from this initiative. (19:50) Segment 1 (21:23) Projects that use CommunityBridge are required to act in the common business interest of the Linux Foundation members. (27:30) Board of Directors seats at the Linux Foundation are for sale, according to their by-laws. (28:50) Bradley advises that you should not put anything copylefted into CommunityBridge — given Linux Foundation's position on copyleft and citing the ArduPilot/DroneCode example. (29:50) CommunityBridge appears to only allow governance based on the “benevolent dictator for life model” (31:40), at least with regard to who controls the money (34:30) Bradley mentioned the LWN article about Community Bridge. (33:22) Segment 2 (36:54) Karen mentioned that CommunityBridge also purports to address diversity and security issues for FOSS projects. (37:00) Bradley mentioned the code hosted on k.sfconservancy.org and also the Reimbursenator project that PSU students wrote. (42:00) Segment 3 (42:44) Bradley and Karen discuss (or, possibly don't) discuss what's coming up on the next episode. Fact of the matter is that this announcement wasn't written yet when we recorded this episode and we weren't sure if 0x65 would be released before or after that announcement was released. We'll be discussing that topic on 0x66. Send feedback and comments on the cast to . You can keep in touch with Free as in Freedom on our IRC channel, #faif on irc.freenode.net, and by following Conservancy on identi.ca and and Twitter. Free as in Freedom is produced by Dan Lynch of danlynch.org. Theme music written and performed by Mike Tarantino with Charlie Paxson on drums. The content of this audcast, and the accompanying show notes and music are licensed under the Creative Commons Attribution-Share-Alike 4.0 license (CC BY-SA 4.0).
NumFocus: The team behind Jupyter and much more!
This week, Hugo speaks with Reshama Shaikh, about women in machine learning and data science, inclusivity and diversity more generally and how being intentional in what you do is essential. Reshama, a freelance data scientist and statistician, is also an organizer of the meetup groups Women in Machine Learning & Data Science (otherwise known as WiMLDS) and PyLadies. She has organized WiMLDS for 4 years and is a Board Member. They’ll discuss her work at WiMLDS and what you can do to support and promote women and gender minorities in data science. They’ll also delve into why women are flourishing in the R community but lagging in Python and discuss more generally how NUMFOCUS thinks about diversity and inclusion, including their code of conduct. All this and more.LINKS FROM THE SHOWDATAFRAMED GUEST SUGGESTIONSDataFramed Guest Suggestions (who do you want to hear on DataFramed?)FROM THE INTERVIEWReshama’s BlogReshama on TwitterList of Relevant Conferences (and Code of Conduct info)NYC PyLadies meetupCode of Conduct for NeurIPS and Other Stem OrganizationsNumFOCUS Diversity & Inclusion in Scientific Computing (DISC)NumFOCUS DISCOVER Cookbook (for inclusive events)fastai deep learning notesWiMLDS (Women in Machine Learning and Data Science)NYC WiMLDS meetupTo start a WiMLDS chapter: email info@wimlds.org and more info at our starter kit.WiMLDS WebsiteGlobal List of WiMLDS Meetup ChaptersWiMLDS Paris: They run their meetups in English, so knowledge of French is not required. FROM THE SEGMENTSDataCamp User Stories (with David Sudolsky ~17:27 & ~31:50)Boldr WebsiteOriginal music and sounds by The Sticks.
We’re talking with Gina Helfrich the Communications Director for NumFOCUS about their story and history, the impact of open code on science, the difference between sponsored and affiliated projects, corporate backing, the back story of their education and events program PyData, and the struggles of storytelling and fundraising.
We’re talking with Gina Helfrich the Communications Director for NumFOCUS about their story and history, the impact of open code on science, the difference between sponsored and affiliated projects, corporate backing, the back story of their education and events program PyData, and the struggles of storytelling and fundraising.
PyDataMCR - Episode 0 - NUMFOCUS with Dr Gina Helfrich Welcome to the official PyDataMCR podcast, In this episode we give an introduction to PyData,NUMFOCUS and some of the organisers. The mission of NumFOCUS is to promote sustainable high-level programming languages, open code development, and reproducible scientific research. We accomplish this mission through our educational programs and events as well as through fiscal sponsorship of open source scientific computing projects. We aim to increase collaboration and communication within the data science and scientific computing community. PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. Dr Gina Helfrich is the Communications Director and Program Manager for Diversity & Inclusion at NumFOCUS, a non-profit that supports better science through open code. Show notes: Why Women Are Flourishing In R Community But Lagging In Python - https://bit.ly/2EacceY Discover Cookbook - https://bit.ly/2PLJyS5 Numfocus blog - numfocus.org/blog Numfocus donation link/membership - https://bit.ly/2S4JDSH Corporate sponsor - numfocus.org/sponsors Conda-forge - conda-forge.org ropenSci - ropensci.org QuantEcon - quantecon.org OpenJournals - www.theoj.org Astropy -astropy.org Cantera -cantera.org jump built with Julia - juliaopt.org/JuMP.jl/0.17/quickstart.html Schoolbus project - https://bit.ly/2LkfUm0 Google slides transcription - https://bit.ly/2PJSkzE Safia Abdallah - twitter.com/captainsafia Stuff Mom Never Told You episode featuring Gina - stuffmomnevertoldyou.com/podcasts/spill-your-salary-secrets.htm Mesa - github.com/projectmesa/mesa Book recommendation from Bertil and David: https://amzn.to/2QB6HMg Our Sponsor Cathcart Associates is a technology recruitment company with offices in Leeds and Manchester covering all things tech, but with an experienced team focusing on Data Science in the North West. We’re good at what we do. We understand what our candidates do, and what our clients need, and we really care about making sure you both get what you want. We’ve been sponsoring PyDataMCR since its inception because we’re nice guys and we like pizza. Check out our website to get in touch – cathcartassociates.com Contact Twitter - twitter.com/pydatamcr Slack - bit.ly/2v60ieu Meetup - meetup.com/PyData-Manchester/
Fernando Perez is best-known as the creator of IPython and co-founder of Project Jupyter: a set of open-source data science tools that some may consider to be the equivalent of the bat & ball to the sport of baseball. Today, you really can’t play the game of data science without Jupyter Notebooks and our guest today is one of Jupyter's leads and originators (see here for the rest of the amazing team). Fernando is also an Assistant Professor in Statistics at UC Berekely, Researcher at the Berekely Institute for Data Science, and Founding Board Member of the NumFOCUS foundation — the community that creates the SciPy stack, along with virtually every other notable open source data science tool out there. This conversation was recorded in-person with Fernando in his office on UC Berekely’s campus, and it turned out to be the most humanizing, energizing, and down-to-earth interview I’ve had so far. Some of the many topics we covered include: what Fernando wanted to be while growing up in Medellin (Me-de-jean), Colombia the function that formal education played in his learning of data science the story behind IPython and Project Jupyter and it’s evolution over the past 10 years lessons learned about technical competence and human character from his mentors over the years what a “computational narrative” means to him and why it’s principles are key to data storytelling Fernando’s experience teaching a 650-student course (part of a pair of courses that are the largest of it's kind) as part of the Berekely Institute of Data Science Enjoy the show! Show Notes: https://ajgoldstein.com/podcast/ep7/ Fernando’s Twitter: https://twitter.com/fperez_org AJ’s Twitter: www.twitter.com/ajgoldstein393/
Brian Granger is an associate professor of physics and data science at Cal Poly State University in San Luis Obispo, CA. His research focuses on building open-source tools for interactive computing, data science, and data visualization. Brian is a leader of the IPython project, co-founder of Project Jupyter, co-founder of the Altair project for statistical visualization, and an active contributor to a number of other open-source projects focused on data science in Python. He is an advisory board member of NumFOCUS and a faculty fellow of the Cal Poly Center for Innovation and Entrepreneurship.
Todd Gamblin – a computer scientist at Lawrence Livermore National Lab – tells Nadia and Mikeal all about bringing open source to his peers in the national labs. They discuss what it’s like to open source a project inside the government, how Todd found contributors for Spack, why he got involved with NumFOCUS, and much more.
Todd Gamblin – a computer scientist at Lawrence Livermore National Lab – tells Nadia and Mikeal all about bringing open source to his peers in the national labs. They discuss what it’s like to open source a project inside the government, how Todd found contributors for Spack, why he got involved with NumFOCUS, and much more.
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