#224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming

Travis Oliphant is a data scientist, entrepreneur, and creator of NumPy, SciPy, and Anaconda. Please support this podcast by checking out our sponsors: – Novo: https://banknovo.com/lex – Allform: https://allform.com/lex to get 20% off – Onnit: https://lexfridman.com/onnit to get up to 10% off – Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish oil – Blinkist: https://blinkist.com/lex and use code LEX to get 25% off premium EPISODE LINKS: Travis’s Twitter: https://twitter.com/teoliphant Travis’s Wiki Page: https://en.wikipedia.org/wiki/Travis_Oliphant NumPy: https://numpy.org/ SciPy: https://scipy.org/about.html Anaconda: https://www.anaconda.com/products/individual Quansight: https://www.quansight.com PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ YouTube Full Episodes: https://youtube.com/lexfridman YouTube Clips: https://youtube.com/lexclips SUPPORT & CONNECT: – Check out the sponsors above, it’s the best way to support this podcast – Support on Patreon: https://www.patreon.com/lexfridman – Twitter: https://twitter.com/lexfridman – Instagram: https://www.instagram.com/lexfridman – LinkedIn: https://www.linkedin.com/in/lexfridman – Facebook: https://www.facebook.com/lexfridman – Medium: https://medium.com/@lexfridman OUTLINE: Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time. (00:00) – Introduction (07:06) – Early programming (28:47) – SciPy (45:41) – Open source (57:23) – NumPy (1:34:39) – Guido van Rossum (1:46:57) – Efficiency (1:55:49) – Objects (2:02:47) – Numba (2:11:53) – Anaconda (2:16:20) – Conda (2:31:56) – Quansight Labs (2:35:32) – OpenTeams (2:43:05) – GitHub (2:48:35) – Marketing (2:53:13) – Great programming (3:04:03) – Hiring (3:08:01) – Advice for young people

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Lex Fridman?

  The following is a conversation with Travis Alafant, one of the most impactful programmers and data scientists ever. He created NumPy, SciPy, and Anaconda. NumPy formed the foundation of tensor-based machine learning in Python. SciPy formed the foundation of scientific programming in Python. And Anaconda, specifically with Conda, made Python more accessible to a much larger audience. Travis's life work across a large number of programming and entrepreneurial efforts has and will continue to have a measurable impact on millions of lives by empowering scientists and engineers in big companies, small companies,

• 0:43Lex Fridman?

  and open source communities to take on difficult problems and solve them with the power of programming. Plus, he's a truly kind human being, which is something that, when combined with vision and ambition, makes for a great leader and a great person to chat with. To support this podcast, please check out our sponsors in the description. As usual, I'll do a few minutes of ads now. No ads in the middle. I try to make these interesting, so hopefully you don't skip. But if you do, please still check out the sponsor links in the description. It does happen to be the best way to support this podcast. I use this stuff. I enjoy it. Maybe you will too.

• 1:24Lex Fridman?

  This show is brought to you by Novo, which is a business banking app. The process is simple. You sign up, they'll mail you a Novo debit card, and you get free ATM use. Honestly, if there's any industry that needs disruption, it's the old-school banking industry. And that's exactly what Novo does. It's backed by FDIC insurance, like the old-school banks. But there's no hidden fees, no monthly fees, or minimum balance requirements. It has an easy-to-use mobile app. You can apply in under 10 minutes. There's always human-powered customer service. I like the way that sounds, "human-powered." Free transfers, mailed checks and incoming wires.

• 2:06Lex Fridman?

  Integrates with other small business tools like Stripe, Shopify, QuickBooks and many more. Refunds all the ATM fees, like I said, thousands of dollars in exclusive perks. Go to banknovo.com/lex to sign up for free. That's banknovo.com/lex. I highly encourage you go there and support the disruption of the old school banking industry.

• 2:32Lex Fridman?

  This show is also sponsored by Allform, a furniture company. They ship to your home quickly, take it back for free if you don't like it in the first hundred days. It's easy to assemble, looks beautiful and classy and it feels amazing. I love it. I have one of their love seat. It's leather, it's black, it looks gorgeous, it feels great. I hung out with a lot of interesting people on that love seat. There's something about the dynamics of a love seat. You do have a little bit of space between you, but you're just close enough to where you have to contend with the challenges of human intimacy.

• 3:09Lex Fridman?

  For example, I hung out with Michael Malice on that love seat. Although he specifically refuses to acknowledge the emergent humanity in this particular computer's soul. Anyway, go to allform.com/lex. They're offering 20% off all orders if you go to allform.com/lex. There's small cottages, there are big ones and it's beautiful and it feels great. This episode is also brought to you by Onnit, nutrition supplement and fitness company. They make AlphaBrain, which is a nootropic that helps support memory, mental speed and focus. I take it when I have a difficult deep work session coming up

• 3:54Lex Fridman?

  and I really need help to get my mind to that place of clarity, of focus. I don't like to over-caffeinate myself and get jittery. I like what AlphaBrain does. It's like a steady focus that I get from it. I don't use it every day, so it's not part of my ritual. It's more like a jetpack, a super boost that I use when I know the session is going to be difficult, mentally challenging. Obviously, I first heard about Onnit on the Joe Rogan Experience. So it feels surreal to be doing an ad read for them now. But I've really enjoyed their stuff for a long time, so I'm proud and happy. Anyway, go to lexfridman.com/onit to get up to 10% off AlphaBrain. That's lexfridman.com/onit.

• 4:40Lex Fridman?

  This show is also brought to you by Athletic Greens and its new renamed AG1 Drink, which is an all-in-one daily drink to support better health and peak performance. It replaced the multivitamin for me and went far beyond that with 75 vitamins and minerals. There's so much good stuff in there. It tastes delicious. It's now like an integral part of my day. I drink it now at least twice a day. It makes sure that I get the nutritional base for all the crazy things I do in terms of athletic pursuits, in terms of long sessions of deep work, and also having keto or mostly a carnivore diet. So get the nutrition that my mind, my body needs, given all the diet,

• 5:26Lex Fridman?

  given all the hard work I have to undertake. Anyway, they will give you one month's supply of fish oil when you sign up at athleticgreens.com/lex. That's athleticgreens.com/lex. This episode is also brought to you by Blinkist, my favorite app for learning new things. Blinkist takes the key ideas from thousands of nonfiction books and condenses them down into just 15 minutes that you can read or listen to.

• 5:55Lex Fridman?

  I love reading slowly, reading through the whole book and thinking deeply through every page, through every paragraph. But coupled with that, I also love using Blinkist to pick which books I'm going to read next and using Blinkist to review the key insights from the books I've already read. So because I read so slowly and so deeply, I can't possibly afford to read a large number of books. So I rely on Blinkist to help me remember the key insights from books and to help pick future books. Go to blinkist.com/lex to start your free seven-day trial and get 25% off of a Blinkist Premium membership. That's blinkist.com/lex.

• 6:41Lex Fridman?

  This is the Lex Fridman Podcast, and here is my conversation with Travis Alafont.

• 7:06Lex Fridman?

  What was the first computer program you've ever written? Do you remember?

• 7:10Speaker 01

  Whoa, that's a good question. I think I was in fourth grade, just a simple loop in BASIC.

• 7:15Lex Fridman?

  BASIC.

• 7:16Speaker 01

  BASIC, yeah, on an Atari 800, Atari 400, I think, or maybe it was Atari 800. It was a part of a class and we just were just basic loops to print things out.

• 7:27Lex Fridman?

  Did you use go-to statements?

• 7:29Speaker 01

  Yes, yes, we used go-to statements.

• 7:32Lex Fridman?

  I remember in the early days, that's when I first realized there's like principles to programming, when I was told that don't use go-to statements. Those are bad software engineering, like it goes against what great, beautiful code is. And I was like, oh, okay, there's rules to this game.

• 7:49Speaker 01

  I didn't see that until high school when I took an AP Computer Science course. I did a lot of other kinds of just programming in TI. But finally, when I took an AP Computer Science course in Pascal.

• 8:00Lex Fridman?

  Pascal, wow.

• 8:01Speaker 01

  That's, yeah, it was Pascal. That's when I, oh, there are these principles.

• 8:04Lex Fridman?

  Not C or C++?

• 8:06Speaker 01

  No, I didn't take C until the next year in college, I had a course in C. But I haven't done much in Pascal, just that AP Computer Science course.

• 8:16Lex Fridman?

  Now, sorry for the romanticized question, but when did you first fall in love with programming?

• 8:21Speaker 01

  Oh, man, good question. I think actually when I was 10, you know, my dad got us a Ti, Timex Sinclair. And he was excited about the spreadsheet capability. And then, but I made him get the BASIC, the add-ons, we could actually program in BASIC. And just being able to write instructions and have the computer do something. And we got a TI 99, a TI 994A when I was about 12. And I would just, it had sprites and graphics and music, you could actually program, you could do music. That's when I really sort of fell in love with programming.

• 8:53Lex Fridman?

  So this is a full, like a real computer with like, uh, with memory and storage?

• 8:58Speaker 01

  Yeah.

• 8:59Lex Fridman?

  Processors or whatnot?

• 9:00Speaker 01

  Yeah.

• 9:00Lex Fridman?

  Cause you say TI.

• 9:01Speaker 01

  Yeah, the Timex Sinclair was one of the very first. It was a cheap, cheap, like, I think it was, well, it was still expensive, but it was two K of memory. We got the 16 K add-on pack. Uh, but yeah, it had memory and you could program it. You had the, in order to store your programs, you had to attach a tape drive. Remember that old, the sound that would play when you converted the, the modem would convert digital bits to audio file. It was set on a tape drive. Still remember that sound, but that was the storage.

• 9:29Lex Fridman?

  And what was the programming language? Do you remember?

• 9:31Speaker 01

  It was basic.

• 9:31Lex Fridman?

  It was basic.

• 9:32Speaker 01

  And then they had a VisiCalc. And so a little bit of spreadsheet programming in VisiCalc, but mostly just some basic.

• 9:37Lex Fridman?

  Do you remember what kind of things drew you to programming? Was it, uh, working with data? Was it video games?

• 9:44Speaker 01

  Math.

• 9:45Lex Fridman?

  Games?

• 9:46Speaker 01

  Math.

• 9:46Lex Fridman?

  Mathy stuff?

• 9:47Speaker 01

  Yeah, I, I've, I've always loved math and a lot of people think they don't like math because I think when they're exposed to it early, they, uh, it's about memory. You know, when you're exposed to math early, it's, you have a good short-term memory. You can remember your timetables. And I, I do have a reasonably, I mean, not perfect, but a reasonably long, um, little short-term memory buffer. And so I did great at timetables. I was like, oh, I get the math. But I started to really like math, just the, the, the problem solving aspect. And so computing was problem solving applied. And so that's always kind of been the, the draw, kind of coupled with the mathematics.

• 10:25Lex Fridman?

  Did you ever see the computer as like an extension of your mind, like something able to achieve?

• 10:31Speaker 01

  Not till later.

• 10:32Lex Fridman?

  Okay.

• 10:33Speaker 01

  Yeah, not, not then.

• 10:34Lex Fridman?

  It's just like a little set of puzzles that you can play with and you can, you could play with math puzzles and?

• 10:38Speaker 01

  Yeah, it was, it was too rudimentary early on. Like it was sort of, yeah, it was too, it was a lot of work to actually take a thought you'd have and actually get it implemented. And that's still work, but it's getting easier. And so, yeah, I would say that's definitely what's attracted me to Python is that that was more real. Right? I could think in Python. Speaking of foreign language, I only speak another language fluently besides English, which is Spanish. And I remember the day when I would dream in Spanish and you start to think in that language. And then you actually, I do definitely believe that language. Limits or expands your thinking. Uh, there are some languages that actually lead you to certain thought processes.

• 11:18Lex Fridman?

  Yeah, like, uh, so I speak Russian fluently and that's certainly, uh, a, a language that leads you down certain thought processes. Well, yeah, I mean, there's a, um, there's a history of the two world wars.

• 11:33Speaker 01

  Right.

• 11:34Lex Fridman?

  Of the, of millions of people starving to death or near to death throughout this history of suffering, of injustice, like this promise sold to the people and then the, the carpet or whatever swept from under them. It's like broken promises and all of that pain and melancholy is in the language, the sad songs, the, the sad hopeful songs, the over romanticized like, I love you, I hate you, the, the sort of, the swings between the, all the various, uh, spectrums of emotion. So that's all within the language, the way it's twisted, uh, poetry, there's a, there's a, there's a strong culture of rhyming poetry, so like the bards, like this, this thing, there's a musicality to the language too.

• 12:19Speaker 01

  Did Dostoevsky write in Russian?

• 12:21Lex Fridman?

  Mm hmm. Yeah. So like Dostoevsky, Tolstoy, all the, uh, all the.

• 12:27Speaker 01

  The ones that I know about, which are translated and I'm curious other translations.

• 12:31Lex Fridman?

  So Dostoevsky did not use the musicality of the language too much. So it actually translates pretty well because it's so philosophically dense that the story does a lot of the work, but there's a bunch of things that are untranslatable. Certainly the poetry is not translatable. I actually have a few conversations coming up offline and also on this podcast with people who've translated Dostoevsky. And that's in, for people who worked, who work in this field, know how difficult that is. Sometimes you can spend. You know, months thinking about a single sentence.

• 13:06Speaker 01

  Right.

• 13:07Lex Fridman?

  In in the context like, because there's just a magic captured by that sentence. And how do you translate it just in the right way? Because those words can be can be really powerful. There's there's a famous line, beauty will save the world from Dostoevsky. You know, there's so many ways to translate that. And you're right, the language gives you the tools with which to tell the story, but it also leads your mind down certain trajectories and paths.

• 13:32Speaker 01

  Yes.

• 13:33Lex Fridman?

  To where over time, as you think in that language, you become a different human being.

• 13:37Speaker 01

  Yes.

• 13:38Lex Fridman?

  Yeah.

• 13:38Speaker 01

  Yeah, that's a fascinating reality, I think, that is, I know people have explored that, but it's just rediscovered.

• 13:44Lex Fridman?

  Well, we don't, we live in our own like little pockets. Like, this is the sad thing is, I feel like, unfortunately, given time and given getting older, I'll never know the China, the Chinese world.

• 13:58Speaker 01

  Yeah.

• 13:58Lex Fridman?

  Because I don't truly know the language. Same with Japanese, I don't truly know Japanese and Portuguese in Brazil, that whole South American continent, like, yeah, I'll go to Brazil and Argentina, but will I truly understand the people if I don't understand the language? It's it's sad, because I wonder how much, how many geniuses we're missing? Because because so much of the scientific world, so much of the technical world is in English.

• 14:26Speaker 01

  Yes.

• 14:26Lex Fridman?

  And so much of it might be lost because there it's just we don't have the common language.

• 14:30Speaker 01

  I completely agree. I'm very much in that vein of there's a lot of genius out there that we miss. And it's sort of, we're sort of fortunate when it when it bubbles up into something that we can understand or process, there's a lot we miss. So that's why I tend to lean towards really loving democratization or things that empower people or, you know, a very resistant sort of authoritarian structures. Fundamentally, for that reason, it, well, several reasons, but it just hurts us.

• 14:59Lex Fridman?

  Yeah.

• 14:59Speaker 01

  We're worse off.

• 15:01Lex Fridman?

  So speaking of languages that empower you, so Python was the first language for me that that I could, I really enjoyed thinking in.

• 15:10Speaker 01

  Yeah.

• 15:10Lex Fridman?

  As you said.

• 15:11Speaker 01

  Sounds like you've shared my experience, too.

• 15:13Lex Fridman?

  So when did you first, do you remember when you first kind of connected with Python? Maybe you even fell in love with Python?

• 15:18Speaker 01

  It's a good question. It was a process, it took about a year. I first encountered Python in 1997. I was a graduate student studying biomedical engineering at the Mayo Clinic. And I had previously, I'd been involved in.

• 15:32Speaker 01

  Taking information from satellites, I was an electrical engineering student, used to taking information and trying to get something out of it, doing some data processing, getting information out of it. And I'd done that in MATLAB, I'd done that in Perl, I'd done that in scripting on a VMS, there's actually a VAX VMS system and they had their own little scripting tools around Fortran, done a lot of that. And then as a graduate student, I was looking for something and encountered Python. And because Python had an array, had two things that made me not filter it away. Because I was filtering a bunch of stuff, I was Yorick, I looked at Yorick, I looked at a few other languages that are out there at the time in 1997. But it had arrays, there's a library called numeric that had just been written in

• 16:15Speaker 01

  95, like not very, not too much earlier, by an MIT alum, Jim Huganen. And I went back and read the mailing list to see the history of how it grew. And it was very interesting, fascinating to do that, actually, to see how this emergent cooperation, unstructured cooperation happens in the open source world, that led to a lot of this collective programming, which is something maybe we might get into a little later, but what that looks like. What gap did numeric fill? Numeric filled the gap of having an array object. So instead of- There was no array object. There was no array, there was a one dimensional byte concept, but there was no n dimensional, two, three, four dimensional tensor, they call it now. I'm still in the category that a tensor is another thing, and it's just an empty array, we should call

• 16:59Speaker 01

  it, but kind of lost that battle. There's many battles in this world, some of which we win, some we lose. That's exactly right. So, but it was, it had no math to it. So numeric had math and a basic way to think in arrays. So I was looking for that, and it had complex numbers. A lot of programming languages, and you can see it, because if you're just a computer scientist, you think, ah, complex numbers are just two floats, so people can build that on. But in practice, a complex number is one of the significant algebras that helps connect a lot of physical and mathematical ideas, particularly FFT for an electrical

• 17:38Speaker 01

  engineer. And it's a really important concept, and not having it means you have to develop it several times, and those times may not share an approach. One of the common things in programming, one of the things programming enables, is abstractions. But when you have shared abstractions, it's even better. It sort of gets to the level of language of actually, we all think of this the same way, which is both powerful and dangerous, right? Because powerful in that we now can quickly make bigger and higher level things on top of those abstractions, dangerous because it also limits us as to the things we left, maybe left behind in producing that abstraction, which is at the heart of programming today and actually building around the programming world. So I think it's a fascinating philosophical topic.

• 18:21Lex Fridman?

  Yeah, that will continue for many years, I think.

• 18:23Speaker 01

  It'll continue for many years.

• 18:24Lex Fridman?

  As we build more and more and more abstractions.

• 18:26Speaker 01

  Yes, I often think about, you know, we have, we have a world that's built on these abstractions that, were they the only ones possible?

• 18:31Lex Fridman?

  Yeah.

• 18:32Speaker 01

  Certainly not, but they led to, you know, it's very hard to do it differently.

• 18:36Lex Fridman?

  Yeah.

• 18:37Speaker 01

  Like, there's an inertia that's very hard to, you know, push out, push away from. There's, that has implications for things like, you know, the Julia language, which you have heard of, I'm sure. And the, I've met the creators and I like Julia. It's a really cool language, but they've struggled to kind of, against the, just the tide of like this inertia of people using Python and, and, you know, there's strategies to approach that, but nonetheless, it's a, it's a phenomenon. And sometimes, so I love complex numbers and I love arrays, so I, I looked at Python. And then I had the experience, I did some stuff in Python and I was just doing my PhD. So I was out, my focus was on, I was actually doing a combination of MRI and ultrasound and looking at a phenomenon called elastography, which is you push waves into the body and

• 19:22Speaker 01

  observe those waves, like you can actually measure them. And then you do mathematical inversion to see what the elasticity is. And so that's the problem I was solving is how to do that with both ultrasound and MRI. I needed some tool to do that with. So I was starting to use Python in 97. In 98, I went back, looked at what I'd written and realized I could still understand it, which is not the experience I'd had when doing Perl in 95, right? I'd done the same thing and then I looked back and I, I'd forgotten what it was even saying. Now, you know, I'm not saying, so I, that, that mean, hey, this may work. I like this. This is something I can retain without becoming an expert per se. And so that led me to go, I'm going to push more to this.

• 20:05Speaker 01

  And then that 98 was kind of the, when I started to fall in love with Python, I would say.

• 20:13Lex Fridman?

  A few peculiar things about Python, so maybe compared to Perl, compared to some of the other languages. So there's no braces.

• 20:21Speaker 01

  Yeah.

• 20:22Lex Fridman?

  So you space is used indentation, I should say, is used as part of the language.

• 20:28Speaker 01

  Yeah, right.

• 20:30Lex Fridman?

  So did you, I mean, that's quite a leap. Were you comfortable with that leap or were you just very open minded?

• 20:37Speaker 01

  Good question. I was open minded, so I was cognizant of the concern. And it definitely has, it has specific challenges, you know, cut and pasting. For example, when you're cut and pasting code, and if your editors aren't supportive of that, if you're putting it into a terminal. And particularly in the past, when terminals didn't necessarily have the intelligence to manage it now. Now, now iPython and Jupiter Notebooks handle that just fine, so there's really no problem. But in the past, it created some challenges, formatting challenges, also mixed tabs and spaces. If editors weren't, you weren't clear on what was happening, you would have these issues. So there were really concrete reasons about it that I heard and understood. I never really encountered a problem with it personally.

• 21:18Speaker 01

  Like it was occasional annoyances, but I really liked the fact that it didn't have all this extra characters. Right, that these extra characters didn't show up in my visual field when I was just trying to process, understanding a snippet of code.

• 21:32Lex Fridman?

  Yeah, there's a cleanness to it, but I mean, the idea is supposed to be that Perl also has a cleanness to it because of the minimalism of like how many characters it takes to express a certain thing. So it's very compact. But what you realize with that compactness comes, there's a culture that prizes compactness. And so the code gets more and more compact and less and less readable to a point where it's like,

• 21:58Lex Fridman?

  like to be a good programmer in Perl, you write code that's basically unreadable.

• 22:02Speaker 01

  Right.

• 22:03Lex Fridman?

  There's a culture like.

• 22:03Speaker 01

  Correct, and you're proud of it.

• 22:05Lex Fridman?

  Yeah, you're proud of it.

• 22:07Speaker 01

  Right, exactly. And it's like feels good and it's really selective. Like it means you have to be an expert in Perl to understand it. Whereas Python was allowed you not to have to be an expert. You didn't have to take all this brain energy. You could leverage, what I say, you could leverage your English Language Center, which you're using all the time. I've wondered about other languages, particularly non-Latin based languages, you know, Latin based languages with the characters are at least similar. I think people have an easier time, but I don't know what it's like to be a Japanese or a Chinese person trying to. Learn a different, um, different syntax, like what would computer programming look like in a, in, in that? I haven't looked at that at all, but it certainly doesn't, you know, leveraging your, your Chinese language center. I'm not sure Python or any programming does that, but that was a big deal.

• 22:52Speaker 01

  The fact that it was accessible, I could be a scientist. What I really liked is many programming languages really demand a lot of you and you can get a lot. You know, you do a lot if you learn it, but Python enables you to do a lot without demanding a lot of you. There's a, there's nuance to that statement, but it certainly was, it's more accessible. So more people could actually, as a, as a scientist, as somebody or engineer, who was trying to solve another problem besides point programming, I could still use this language and get things done and, and be happy about it. And I was also comfortable in C at that time. And MATLAB you did a little. And MATLAB I did a lot before that, exactly. So I was comfortable in, those three languages were really the tools I used during my studies and schooling.

• 23:35Speaker 01

  But to your point about language helping you think, one of the big things about MATLAB is it was, and APL before it. I don't know if you're, you remember APL? Nope. APL is actually the predecessor of array based programming, which I think is really an underappreciated. If I talk to people who are just steeped in computer programming, computer science, like most of the people that Microsoft has hired in the past, for example, Microsoft as a company generally did not understand array based programming, like culturally they didn't understand it. So they kept missing the boat, kept missing the understanding of what this, what this was. They've gotten better, but there's still a whole culture of folks that doesn't programming. That's, you know, that's, that's systems programming or web programming or lists and maps. And you know, what about an N dimensional array? Oh yeah, that's just an implementation detail.

• 24:19Speaker 01

  Well, you can think that, but then actually if you have that as a construct, you actually think differently. APL was the first language to understand that and it was in the sixties, right? The challenge of APL is APL had very dense, not only glyphs, like new characters, new glyphs. They even had a new keyboard because to produce those glyphs, this is back in the early days of computing when, you know, the query keyboard maybe wasn't as established, like, well, what, we could have a new keyboard, no big deal. But it was a big deal and it didn't catch on and the language, APL, very much like Perl, if people would pride themselves on how, how much could they write the game of life and. Thirty characters of APL. APL has characters that mean summation and they have adverbs.

• 25:02Speaker 01

  You know, they'd have adjectives and these things called adverbs, which are like methods, like reduction, reduction would be an adverb on an add operator. Right, so, but doing, using these tools, you could construct, and then you start to think at that level. You think in n dimensions, is something I like to say, and you start to think differently about data at that point. You know, now you're, it really helps.

• 25:22Lex Fridman?

  Yeah, I mean, outside of programming, if you really internalize linear algebra as a course, I mean, it philosophically allows you to think of the world differently.

• 25:31Speaker 01

  Yes.

• 25:32Lex Fridman?

  It's almost like liberating. You don't have to, you don't have to think about the individual numbers in the n dimensional array. You could think of it as an object in itself and all of a sudden this world can open up. You're saying MATLAB and APL were like the early, I don't know if many languages got that right ever.

• 25:49Speaker 01

  No, no, no, they didn't.

• 25:51Lex Fridman?

  Even still.

• 25:52Speaker 01

  Even still, I would say, I mean, NumPy is a inheritor of the traditions that, I would say, APLJ was another version that was, what it did is not have the glyphs, just have short characters, but still a Latin keyboard could type them. And then numeric inherited from that in terms of, let's add arrays plus broadcasting plus methods, reduction, even some of the language like rank is a concept that's in, that was in Python, is still in Python, for the number of dimensions, right? That's, that's different than say the rank of a matrix, which people think of as well. So it's, it came from that tradition, but NumPy is a very pragmatic, practical tool. NumPy inherited from numeric and we can get to where NumPy came from, which is the

• 26:36Speaker 01

  current array, at least current as of 2015, 2017. Now there's a ton of them over the past two or three years. We can get into that too.

• 26:45Lex Fridman?

  So if we just sort of linger on the early days of what was your favorite feature of Python? Do you remember like what? It's so interesting to linger on like the.

• 26:57Lex Fridman?

  What, what really makes you connect with a language? I'm not sure it's obvious to introspect that.

• 27:04Speaker 01

  No, it isn't. And I've, I've thought about that. It's just some length. I'm not, I, I think definitely the fact that I could read it later.

• 27:10Lex Fridman?

  Yeah.

• 27:11Speaker 01

  That I could use it productively without becoming an expert. Like other languages, I had to put more effort into.

• 27:16Lex Fridman?

  Right. That's like an empirical observation. Like you're not analyzing any one aspect of the language. It just seems time after time, when you look back, it's somehow readable.

• 27:25Speaker 01

  It's somehow readable. Then it, it was sort of, I could take executable English.

• 27:29Lex Fridman?

  Yeah.

• 27:30Speaker 01

  And translate it to Python more easily. Like I didn't have to go, there was no translation layer. As an engineer or as a scientist, I could think about what I wanted to do. And then the syntax wasn't that far behind it.

• 27:40Lex Fridman?

  Yeah.

• 27:41Speaker 01

  Right? Now, there was some, there are some, there's some warts there still. It wasn't perfect. Like there's some areas where like, ah, it would be better if this were different or if this were different. Some of those things got added to the language too. I was really grateful for some of the early pioneers in the Python ecosystem back. Cause Python got written in 91 is when the first version came out. But Guido was very open to users. And one of the sets of users were people like Jim Huganen and, and David Asher and Paul Dubois. And Conrad Hinson, these were people that were on the mailing list. And they were just asking for things like, hey, we really should have complex numbers in this language. So let's, you know, there's a J, there's a one J, right? And the fact they went the engineering route of J is interesting. I, I don't think that's entirely favorite engineers. I think it's because I is so often used as the index of a for loop.

• 28:27Speaker 01

  I think that's actually why.

• 28:29Lex Fridman?

  Probably.

• 28:29Speaker 01

  Right.

• 28:30Lex Fridman?

  I mean, there's, there's a pragmatic aspect.

• 28:31Speaker 01

  But the fact that complex numbers were there, I love that. The fact that I could write ndarray constructs and that reduction was there. Very simple to write summations and, and, and broadcasting was there. I could do addition of whole arrays. So that was cool. Those are some things I loved about it.

• 28:47Lex Fridman?

  I don't know what to start talking to you about because you've been you've created so many incredible projects that basically changed the whole landscape of programming. But okay, let's start with, let's go chronologically with SciPy. You created SciPy over two decades ago now.

• 29:03Speaker 01

  Yes, yes, I love to talk about SciPy. SciPy was really my baby.

• 29:07Lex Fridman?

  What is it? What was its goal? What is its goal? How does it work?

• 29:11Speaker 01

  Yeah, fantastic. So SciPy was effectively, here I am using Python to do stuff that I previously used MATLAB to use. And I was using numeric, which is an array library that made a lot of it possible. But I, you know, there's things that were missing, like I didn't have an ordinary differential equation solver I could just call, right? I didn't have integration. Hey, I wanted to integrate this function. Okay, well, I don't have just a function I can call to do that. These are things I remember being critical things that I was missing. Optimization, I just want to pass a function to an optimizer and have it tell me what the optimal value is. Those are things like, well, why don't we just write a library that adds these tools? And I started to post on the mailing list and there previously been, you know, people have discussed, I remember Conrad Henson saying, wouldn't it be great if we had this optimizer library?

• 29:55Speaker 01

  Or David Ash would say this stuff and, and I'm, you know, I'm a ambitious, ambitious is the wrong word, an eager. And probably more time than sense, I was, you know, a poor graduate student. My wife thinks I'm working on my PhD and I am, but part of a PhD that I loved was the fact that it's exploratory. You're not just, you know, taking orders, fulfilling a list of things to do, you're trying to figure out what to do. And so I thought, well, you know, I'm writing tools for my own use in a PhD, so I'll just start this project. And so in 99, 98 was when I first started to write libraries for Python. Effectively, when I fell in love with Python 98, I thought, oh, well, there's just a few things missing, like, oh, I need a reader to read DICOM files.

• 30:37Speaker 01

  I was in medical imaging and DICOM was a format that I want to be able to load that into Python. Okay, how do I write a reader for that? So I wrote something called, it was an I/O package, right? And that was my very first extension module, which is C. So I wrote C code to extend Python so that the Python I could write things more easily. That, that combination kind of hooked me. It was the idea that I could, here's this powerful tool I can use as a scripting language and a high level language to think about, but that I can extend easily. Easily in C, easily for me because I knew enough C. And then Guido had written a lang, I mean, the only, the hard part of extending Python was something called the way memory management works and you have to reference counting. And so there's, there's a tracking of reference counting you have to do manually. And if you don't, you have, you have memory leaks.

• 31:22Speaker 01

  And so that's hard. Plus then C, you know, it's just much more, you have to put more effort into it. It's not just, I have to now think about pointers and have to think about stuff that is different. I have to kind of, you're like putting a new cartridge in your brain. Like you're, okay, I'm thinking about MRI, now I'm thinking about programming and, and they're distinct modules you end up having to think about. So it's harder. And when I was just in Python, I could just think about MRI and high level writing. But I could do that and that kind of, I liked it. I found that to be enjoyable and fun. And so I ended up, oh, well, let me just add a bunch of stuff to Python to do integration. Well, and the cool thing is, is that, you know, the power of the internet, I just looking around and I found, oh, there's this net lib, which has hundreds of four chan routines that people had written in the sixties and the seventies and the eighties in four chan

• 32:07Speaker 01

  seventy seven, fortunately, it wasn't four chan sixties, it had been ported to four chan seventy seven. And four chan seventy seven is actually a really great language. Four chan ninety probably is my favorite four chan because it's also, it's got complex numbers, got arrays, and it's pretty high level. Now, the problem with it is you'd never want to write a program in four chan ninety or four chan seventy seven, but it's totally fine to write a subroutine in, right? And so, and then four chan kind of got a little off course when they tried to compete with C++ but at the time, I just want libraries that do something like, oh, here's an ordinary equation, here's integration, here's run cut integration, already done. I don't have to think about that algorithm. Well, you could, but it's nice to have somebody who's already done one and tested it. And so I sort of started this journey in ninety eight, really, if you look back at

• 32:50Speaker 01

  the manual list, there's sort of this, this productive era of me writing an extension module to connect run cut integration to Python and making an ordinary additional equation solver. And then releasing that as a package, so we call OD pack, I think I called it then, quad pack. And then I just made these packages. Eventually that became multi-pack because they're originally modular, you can install them separately, but a massive problem in Python was actually just getting your stuff installed. At the time, releasing software for me, like today, it's people think, what does that mean? Well, then it meant some poorly written webpage, I had some bad webpage up and I put a tarball, just a Gzip tarball of source code. That was the release.

• 33:32Lex Fridman?

  But, okay, can we just stand that?

• 33:34Speaker 01

  Sure.

• 33:34Lex Fridman?

  Because that, the community aspect of creating the package and sharing that.

• 33:40Speaker 01

  Yes.

• 33:40Lex Fridman?

  That's rare. That, to have, to both have the-

• 33:44Speaker 01

  What do you mean?

• 33:45Lex Fridman?

  At that time, so like the-

• 33:46Speaker 01

  Yeah, it was pretty early, yeah.

• 33:47Lex Fridman?

  So, well, not, not rare. Maybe, maybe you can, uh, correct me on this, but it seems like in the scientific community, so many people, you were basically solving the problems you needed to solve to process the particular application, uh, the, the data that you need. And to also have the mind that I'm going to make this usable for others, that's, um-

• 34:10Speaker 01

  I would say I was inspired. I'd been inspired by Linux, been inspired by, you know, Linus, Linus and him making his code available. And I was starting to use Linux at the time and I went, this is cool. So I'd kind of been previously primed that way. And generally, I was, I was into science because I liked the sharing notion. I liked the idea of, hey, let's, if collectively we build knowledge and share it, we can all be better off.

• 34:30Lex Fridman?

  Okay, so you were energized by that idea?

• 34:32Speaker 01

  So I was energized by that already.

• 34:33Lex Fridman?

  Yeah.

• 34:34Speaker 01

  Right? And I can't deny that. I was. I'm sort of, uh, had this very, I liked that part of science, that part of sharing. And then all of a sudden, oh, wait, here's something and, and here's something I could do. And then I, I slowly over years learned how to share better so that you could actually engage more people faster. One of the key things was actually giving people a binary they could install, right? So that wasn't just your source code. Good luck.

• 34:56Lex Fridman?

  Compile this and then-

• 34:57Speaker 01

  It's compiled, ready to install, just, you know, so, in fact, a lot of the journey from 98, even through 2012, when I started Anaconda, was about that. Like, it's why, uh, you know, it's really the, the key as to why a scientist with dreams of doing MRI research ended up starting a software company that installs software.

• 35:17Lex Fridman?

  I work with a few folks now that don't program. Like on the creative side, the video side, the audio side. And because my whole life is running on scripts, I have to try to get them. I'm having now the task of teaching them how to do Python enough.

• 35:32Speaker 01

  Yeah.

• 35:33Lex Fridman?

  To run the scripts. And so I've been actually facing this, whether it's on the con or some with the task of how do I minimally explain? Basically to my mom, how to write a Python script. And it's an interesting challenge. I have to, it's a to do item for me to figure out like, what is the minimal amount of information I have to teach? What are the tools you use? That one, you enjoy it to your effect of it.

• 35:55Speaker 01

  And they're related. Those are two related questions.

• 35:57Lex Fridman?

  And then the debugging, like the iterative process of running the script to figure out what the error is. Maybe even for some people to do the fix yourself.

• 36:06Speaker 01

  Yeah.

• 36:06Lex Fridman?

  So do you compile it? Do you like, how do you distribute that code to them? And it's interesting because I think it's exactly what you're talking about. If you increase the circle of empathy, the circle of people that are able to use your programs. You increase it, it's like effectiveness and its power. And so you have to think, you know, can I write scripts? Can I write programs that can be used by, by medical engineers, by all kinds of people that don't know programming? And actually maybe plant the seed, have them catch the bug of programming so that they start on the journey. And that's a huge responsibility and ultimately has to do with the Amazon one click buy.

• 36:50Lex Fridman?

  Like how frictionless can you make the early steps?

• 36:53Speaker 01

  Frictionless is actually really key. To grow in any community is every, any friction point, you're just gonna lose, you're gonna lose some people.

• 36:59Lex Fridman?

  Yeah.

• 36:59Speaker 01

  Right now, sometimes you may wanna intentionally do that. If you're early enough on, you need a lot of help. You need people who have the skills. You might actually, it's helpful. You don't necessarily have too much, too many users as opposed to contributors if the code, if you're early on. Anyway, there's sci fi started in 98, but it really emerged as this collection of modules that I was just putting on the net. People were downloading and you know, I think I got a hundred users, right? By the end of that year, but there, but the fact I got a hundred users and more than that, people started to email me with fixes. Like, and that was actually intoxicating, right? That was the. That was the, you know, here I'm writing papers and I'm giving conferences and I get people to say hello.

• 37:41Speaker 01

  But yeah, good job. But mostly it was, you're reviewed with, it's competitive. Yeah. You publish a paper and people are like, oh, it wasn't my paper, you know? I was starting to see that sense of academic life where it was so much, I thought that was a cooperative effort. But it sounds like we're here just to one up each other. Right. And, you know, it's not, it's not true across the board, but a lot of that's there. But here in this world, I was getting responses from people all over the world. You know, I remember Pierre Peterson in Estonia, right, was one of the first people. And he sent me back this make file because the first thing it is, yeah, your build thing stinks. And here's a better make file. Now, it is a complex make file. I don't think I, I never understood that make file, actually. But it worked and it did a lot more. And so I thought, thanks, this is cool.

• 38:25Speaker 01

  And that was my first kind of engagement with community development. But, you know, the process was, he sent me a patch file. I had to upload a new tar ball. And I just found I really loved that. And the, and the style back then was here's a mailing list. It's very, it wasn't as, it certainly weren't the tools that are available today. It was very early on, but I really started to, that's the whole year. I, I think I did about seven packages that year, right? And then by the end of the year, I collected them into a thing called multi-pack. So 99, there was this thing called multi-pack. And that's when a high school student, I know he was a high school student at the time, a guy named Robert Kern, took that package and made a Windows installer, right? And then, of course, a massive increase of usage. So by the way, most of this development was under Linux?

• 39:10Speaker 01

  Yes. Yes, it was on Linux. I was a Linux developer doing it on a Unix box. I mean, at the time, I was actually getting into, I had a new hard drive. I did some kernel programming to, to make the hard drive work. I mean, not programming, but modification to the kernel so I could actually get a hard drive working. I, I love that aspect of it. I was also, and you know, at school, I was building a, a cluster. I took Mac computers, like, and you put Yellow Dog Linux on them. They were, at the Mayo Clinic, they were just, they had all these Macs that were older. They were just getting rid of. And so I kind of got permission to go grab them together. I put about 24 of them together in a cluster in a cabinet and put Yellow Dog Linux on them all. And I wrote a C++. Programmed to do MRI simulation.

• 39:51Speaker 01

  That was what I was doing at the same time for my day job, so to speak. So I was loving the whole process. At the same time, I was, oh, I need a ordinary equation. That's why ordinary equations were key was because that's the heart of a block equation for simulating MRI is a OD solver. And so that's, I actually did that. Those happened at the same time. That's why kind of what you're working on and what you're interested in, they're coinciding. I was definitely scratching my own itch in terms of building stuff. And which helped in the sense that I was using it for me. So at least I had one user.

• 40:22Lex Fridman?

  Yeah.

• 40:23Speaker 01

  I had one person who was like, well, no, this is better. I like this interface better. And I had the experience of MATLAB to guide some of what those APIs might look like. But you're just doing yourself. You're building all this stuff. But the Windows installer, it was the first time I realized, oh yeah, the binary installer really helps people. And so that led to spending more time on that side of things. So around 2000, so I graduated my PhD in 2000, end of year, end of 2000. So 99, doing a lot of work there. 98, doing a lot of work there. 99, kind of spending more time on my PhD, you know, helping people use the tools, thinking about what do I want to go from here? There was a company, there was a guy, actually Eric Jones and Travis Vought. They were two friends who founded a company called N Thought. It's here in Austin, still here.

• 41:08Speaker 01

  And they, Eric contacted me at the time when I was a, I was a graduate student still. And he said, hey, why don't you come down? We want to build a company, you know, we want, we're thinking of, you know, a scientific company. And we want to take what you're doing and kind of add it to some stuff that he'd done. He'd written some tools. And then P R Peterson had done F to Pi. Let's come together and build, pull this all together and call it SciPy. So that's the origin of the SciPy brand. It came from, you know, multi-pack and a whole bunch of modules I'd written, plus a few things from some other folks. And then pull together in a single installer. SciPy was really a distribution of Python, masquerading as a library.

• 41:46Lex Fridman?

  How did you think about SciPy in context of Python, in context of numeric?

• 41:50Speaker 01

  Like, what? We saw SciPy as a way to make an R and D environment for Python, like, use Python, depended on numeric, so numeric was the array library we depended on. And then from there, extend it with a bunch of modules that allowed for... And at the time, the original vision of SciPy was to have plotting, was to have, you know, REPL environment, and kind of a whole, really a whole data environment, that you could then install and get going with. And that was kind of the thinking. It didn't really evolve that way, right? It sort of had a... For one, it's really hard to do massive scale projects in a... With open source collectives. Actually, there's a sort of intrinsic cooperation limit, as to which, you know, too many cooks in the kitchen, you know,

• 42:35Speaker 01

  you can do amazing infrastructure work. When it comes down to bringing it all together into a single deliverable, that actually requires a little more, a little more product management that is not, that doesn't really emerge from the same dynamic. So, it struggled, you know, struggled to get... Almost too many voices, it's hard to have everybody agree, you know, consensus doesn't really work at that scale. You end up with politics, you know, with the same kind of things that's happened in large organizations trying to decide on what to do together. So consensus building was still, was challenging at scale, as more people came in, right? Early on, it's fine, because there's nobody there, and so it works. But then as you get more successful, and more people use it, all of a sudden, oh, there's this, this scale at which this doesn't work anymore, and we have to come up with different approaches.

• 43:18Speaker 01

  So SciPy came out officially in 2001, was the first release. Most of the time, I remember the days of getting that release ready, it was a Windows installer, and there was, there were bugs on how, you know, the Windows compiler handled complex numbers, and you were, you were chasing segmentation faults. It was, it's a lot of work, there's a lot of... effort had nothing to do with my area of study. And at the same time, I had just gotten an offer. So he wondered if I wanted to come down and help him start that, you know, start that company with his friend. And I, at the time, I was like, I was intrigued, but I was square in a path, an academic path. And I had just got an offer to go and teach at my alma mater. So I took that tenure track position. And SciPy and kind of then I started work on SciPy as a professor, too. Okay.

• 44:02Speaker 01

  So that's, I left, got to Mayo Clinic, graduated, wrote my thesis using SciPy, wrote, you know, there's, there's images that were created. Now, the plotting tool I used was something from Yorick, actually. It was a plotting, a PLT, kind of a plotting language that I used. Yorick is a programming language? It was a programming language, had a plotting tool, Dislin. It had integration to Dislin. I ended up using Dislin plus some, some of the plotting from Yorick, linked to from Python. Anyway, it was a, people don't plot that way now, but this is before. And SciPy was trying to add plotting. Yeah. Right? It didn't have much success. Really, the success of plotting came from John Hunter, who had a similar experience to my experience, my kind of maverick experience as a

• 44:44Speaker 01

  person just trying to get stuff done and kind of having more time than, than money, maybe, right? And John Hunter created what? Matplotlib. He's the creator of Matplotlib? Yeah, so John Hunter was a, you know, he wasn't a student at the time, but he was an actor, he was working in quant field. And he said, we need better plotting. So he just went out and said, cool, I'll make a new project and we'll call it MatPlotLib. And he released in 2001, about the same time that SciPy came out. And it was separate library, separate install, use numeric, SciPy use numeric. And so SciPy, you know, 2001, we released SciPy and then Endthot created a conference called SciPy, which was brought people together to talk about the space. And that conference is still ongoing. It's one of the favorite conferences of a lot of people because it's, you know, it's changed over the years.

• 45:25Speaker 01

  But early on, it was, you know, a collection of 50 people who care about, scientists mostly, you know, practicing scientists who want to care about coding and doing it well and not using MATLAB. Or, and I remember being driven by, you know, I liked MATLAB, but I didn't like the fact that So I'm not opposed to proprietary software. I'm actually not an open source zealot. I love open source for the what it brings, but I also see the role for proprietary software. But what I didn't like was the fact that I would develop code and publish it and then effectively telling somebody here to run my code, you have to have this proprietary software.

• 45:57Lex Fridman?

  Right, and there's also culture around MATLAB as much because I've talked to a few folks in MathWorks, great MATLAB.

• 46:04Speaker 01

  Yeah.

• 46:05Lex Fridman?

  I mean, there's just a culture, they try really hard, but it just there's this corporate IBM style of culture that's like, or whatever, I don't want to say negative things about IBM, whatever, but there's a.

• 46:17Speaker 01

  No, it's really that connection. It's something I'm in the middle of right now is the business of open source. And how do you connect the ethos of cooperative development with the necessity of of creating profits, right? And like right now, today, you know, I'm still I'm still in the middle of that. That's actually the early days of me exploring this question. Because I was writing SciPy, I mean, as an aside, I also had, so I had three kids at the time, I have six kids now.

• 46:42Lex Fridman?

  Yeah.

• 46:42Speaker 01

  Got married early, wanted a family. I had three kids and I remember reading, I read Richard Stallman's post and I was a fan of Stallman. I would read his work. I liked this collective ideas he would have, certainly the ideas on IP law, I read a lot of his stuff. But then he said, you know, okay, well, how do I make money with this? How do I make a living? How do I pay for my kids? All this stuff was in my mind, young graduate student making no money, thinking I got to get a job. And he said, well, you know, I think just be like me and don't have kids, right? That's just don't, don't.

• 47:11Lex Fridman?

  That's his take on this?

• 47:12Speaker 01

  That was, that was, that was the what he said in that moment, right? That's the thing I read and I went, okay, this is a train I can't get on.

• 47:18Lex Fridman?

  Yeah. There has to be a way to preserve the culture of open source and still be able to make sufficient money to feed your kids.

• 47:24Speaker 01

  Yes, exactly. There's got to be, well, so that actually led me to a study of economics. Because at the time, I was ignorant and I really was. I'm actually, I'm embarrassed for educational system that they could let me, and I was valedictorian in my high school class and I did super well in college and like academically, I did great, right? But the fact that I could do that and then be clueless about this key part of life, it led me to go, there's a problem. Like I should, like I should have learned this in fifth grade. I should have learned this in eighth grade. Like, everybody should come out with a basic knowledge of economics.

• 47:56Lex Fridman?

  You're an interesting example because you've created tools that changed the lives of probably millions of people and the fact that you don't understand at the time of the creation of those tools, the basics economics of how like to build up a giant system is a problem.

• 48:10Speaker 01

  Yeah, it's a problem. And so during my PhD at the same time, this is basically 98, 99, at the same time, I was in a library, I was reading books on capitalism, I was reading books on Marxism, I was reading books on, you know, what is this thing? What is it? What does it mean? And I encountered a, basically what I encountered a set of writings from people that said they were the inheritors of Adam Smith, read Adam Smith for the first time, right? Which is the wealth of nations and kind of this notion of emergent, emergent societies and realized, oh, there's this whole world out here of people. And the challenge economics is also political, like, because economics, you know, people, different parties running for office, they'll, they want their economic friends,

• 48:52Speaker 01

  they want their economists to back them up, right? Or to be their, to be their magicians, like the magicians in Pharaoh's court, right? The people that are kind of say, hey, this is, you should listen to me because I've got the expert who says this. And so it gets really muddled, right? But I was looking at it from a, as a scientist going, what is this space? What does this mean? How do people, how does Paris get fed? How does, what is money? How does it work? And I found a lot of writings I really loved. I found some things that I really loved and I learned from that. It was writings from people like von Mises. He wrote a, he wrote a paper in 1920 that still should be read more than it is. It's got, I mean, it was the economic calculation problem of the socialist Commonwealth. It's basically in response to the Bolshevik Revolution in 1917. And his basic argument was, it's not going to work to not have private property. You're not

• 49:37Speaker 01

  going to be able to come up with prices. The bureaucrats aren't going to be able to determine how to allocate resources without a price system. And a price system emerges from people making trades. And they can only make trades if they have authority over the thing they're trading. And that that that creates information flow that you just don't have if you try to top down it.

• 49:56Lex Fridman?

  Right.

• 49:56Speaker 01

  Right. It's like, huh, that's a really good point.

• 49:59Lex Fridman?

  Yeah, the prices have a signal that's used and it's important to have that signal. When you're trying to build a community of productive people like you would in the software engineering.

• 50:08Speaker 01

  Yeah, the prices are actually an important signaling mechanism.

• 50:11Lex Fridman?

  Yeah.

• 50:12Speaker 01

  Right. And that money is just a bartering tool.

• 50:15Lex Fridman?

  Yeah.

• 50:15Speaker 01

  Right. So this is the first time I've encountered any of this concept, right? And the fact that, oh, this is actually really critical, like it's so critical to our prosperity. And that we're dangerously not learning about this, not teaching our children about this, you know?

• 50:31Lex Fridman?

  So you had the three kids, you had to make some.

• 50:32Speaker 01

  Had to make some money, right? Had to figure it out, but I didn't really care. I mean, I was never, I've never been driven by money, just need it.

• 50:38Lex Fridman?

  Yeah, right. To eat. So what, how did that resolve itself in terms of side by?

• 50:43Speaker 01

  So I would say it didn't really resolve itself. It sort of started a journey that I'm continuing on, I'm still on, I would say. I don't think it resolved itself, but I will say I went in wide, eyes wide open. Like I knew that there were problems with, you know, giving stuff away and creating the the market externalities that the fact that, yeah, people might use it and I might not get paid for it. And I'll have to figure something else out to get paid. Like, at least I can say I'm not bitter that a lot of people have used stuff that I've written. And I haven't necessarily benefited economically from it.

• 51:14Lex Fridman?

  Yeah.

• 51:15Speaker 01

  I've heard other people be bitter about that when they write or they talk like, oh, I should have got more value out of this. And I'm also, I want to create systems that let people like me, who might have these desires to do things, let them benefit. So it actually creates more of the same.

• 51:29Lex Fridman?

  Not to turn on your bitterness mojo, but there's some aspect, I wish there was mechanisms for me to reward whoever created side by and um by. Cause it brought so much joy to my life.

• 51:40Speaker 01

  I appreciate that.

• 51:41Lex Fridman?

  You know what I mean?

• 51:41Speaker 01

  The tip dark notion was there. I appreciate that. And I think.

• 51:44Lex Fridman?

  But there should be a very frictionless mechanism.

• 51:46Speaker 01

  frictionless mechanism. I totally agree. I would love to talk about some of the ideas I have, because I actually came across, I think I've come up with some interesting notions that could work. But they'll require, you know, anything that will work takes time to emerge, right? Like, things don't just turn overnight. That's definitely one thing I've also understood and learned is any fixes. That's why it's kind of funny. We often give credit to, you know, oh, this president gets elected and oh, look how great things have done. And I saw that when, when I had a transition in a condo, when a new CEO came in, right? And it's like the success that's happening, there's an inertia there.

• 52:18Lex Fridman?

  Yeah.

• 52:18Speaker 01

  Right?

• 52:19Lex Fridman?

  And sometimes the decision made like 10 years before is the reason why the success is see.

• 52:23Speaker 01

  Right, exactly. So we're sort of just running around taking credit for stuff.

• 52:27Lex Fridman?

  Credit assignment has like a delay to it.

• 52:29Speaker 01

  Yes.

• 52:30Lex Fridman?

  That is makes the credit assignment basically wrong more than right.

• 52:33Speaker 01

  Wrong more than right, exactly. And so I'm like, oh, this is, you know, that's the stuff I would, I would read a ton about, you know, early on. So I don't, I feel like I'm with you, like I want the same thing. I want to be able to, and honestly, not for personally, I've been happy. I've been, I've been happy. I feel like I don't have any, I mean, we've been done reasonably okay, but I've had to pursue it. Like that's, that's really what started my trajectory from academia is reading that stuff led me to say, oh, entrepreneurship matters. So I love software, but, but we need more entrepreneurs and I want to understand that better. So once I kind of had that, that virus infect my brain, it, even though I was on a trajectory to go to a tenure track position at a university

• 53:15Speaker 01

  and I was there for six years, I was kind of already out the door when I started and we can get into that, but.

• 53:22Lex Fridman?

  What can I just ask a quick question on, is there some design principles that were in your mind around sci pi? Like, was there some key ideas that were just like sticking to you that this is, this is just the fundamental ideas?

• 53:35Speaker 01

  Yeah, I would say so. I would think it's basically accessibility to scientists, like give them, give scientists and engineers tools that they don't have to think a lot about programming. So give them really good building blocks, give them functions that they want to call and sort of just the right length of spelling. You know, there's a one tradition in programming where it's like, you know, make very, very long names, right? And you can see it in some programming languages where the names get, you know, take half the screen and I, and in the Fortune world, characters had to be six, six letters early on, right? And that's way too, too much, too, too little. But I was like, I liked to have names that were informative, but short.

• 54:13Lex Fridman?

  So even though Python, this is a different conversation, but documentation is doing some work there. So when you look at great scientific libraries and functions, there's. There's a richness of documentation that helps you get into the details. The first glance at a function gives you the intuition of all it needs to do by looking at the headers and so on. But to get the depths of all the complexities involved, all the options involved, documentation does some of the work.

• 54:40Speaker 01

  Documentation is essential. Yeah, so that was actually a, so we thought about several things. One is we wanted plotting, we wanted interactive environment, we wanted good documentation. These are things we knew, we wanted. The reality is those took about 10 years to evolve, right? Given the fact that we didn't have a big budget, it was all volunteer labor. It was sort of, when N Thought got created and they started to, you know, try to find projects, people would pay for pieces and they were able to fund some of it. Not nearly enough to keep up with what was necessary. And I'm, no, no criticism, just simply the reality. I mean, it's, it's hard to start a business and then do consulting and also promote an open source project that's still fairly new. Cybo is fairly niche. We stayed connected all the, all while I was a student, sorry, a professor.

• 55:25Speaker 01

  I went to BYU and started to teach electrical engineering, all the applied math courses. I loved teaching signal processing, probability theory, natural magnetism. I was the, if you look at Rate My Professor, which my kids love to do, I, I wasn't, I got some bad reviews because people.

• 55:41Lex Fridman?

  What was the criticism?

• 55:42Speaker 01

  Um, I would speak too high, too high of a level. Like, I definitely had a calibration problem coming out of graduate work where I hate to be condescending to people. Like, I really have a ton of respect for people fundamentally. Like, my fundamental thing is I, I respect people. Sometimes that can lead to a, I was, I was thinking they were, they, they had more knowledge than they did. And so I would just speak at a very high level, assume they got it.

• 56:05Lex Fridman?

  But they need to rise to the standard that you set. I mean, that's one of the, some of the greatest teachers do that, you know?

• 56:11Speaker 01

  And I agree, and that was kind of what was inspiring me, but, but, you know, you also have to, I, I cannot say I was articulate of some of the greatest teachers, right? I was, you know, like, one, one classic example, when I first taught at BYU, my very first class, it was overheads, transparencies, overheads. Before projectors were really that common, I thought, transparencies, I'm writing my notes out. I go in, room's half dark, I just glaring through these transparencies. Here it is, here it is, here it is. And I gave a quiz after two weeks. No one knew anything, nothing I had taught had gotten anywhere.

• 56:47Speaker 01

  And I realized, okay, I'm not, this is not working. And so I took, put away the transparencies. And I turned around and just started using the chalkboard. And what it did is it slowed me down, right? The chalkboard just slowed me down and gave people time to process and to think. And then that made me focus. My writing wasn't great on the chalkboard, but I really love that part of like the teaching. So that, that entered SciPy's world in terms of, we always understood that there's a didactic aspect of SciPy. Kind of how do you take the knowledge and then produce it? The challenge we had was the scope, like ultimately SciPy was everything, right? And so 2001, when it first came out, people were starting to use it. No, this is cool. This is a tool we actually use. At the same time, 2001 timeframe, there was a little bit of like the Hubble Space Telescope.

• 57:28Speaker 01

  The folks at Hubble had started to say, hey, Python, we're going to use Python for processing images from Hubble. And so Perry Greenfield was a good friend and running that program. And he had called me before I left BYU and said, you know, we want to do this, but numeric actually has some challenges in terms of, you know, it's not, the array doesn't have enough types. We need more operations, you know, broadcasting needs to be a little more settled. They wanted record arrays. They wanted, you know, record arrays are like a data frame, but a little bit different. But they wanted more structured data. So he had called me even early on then and he said, you know, hey, would you want to work on something to make this work? I said, yeah, I'm interested, but I'm going here and I, you know, we'll see if I have time. So in the meantime, while I was teaching and SciPy was emerging and I had a student, I was constantly, while I was teaching, trying to figure a way to fund this stuff.

• 58:13Speaker 01

  So I had a graduate student, my only graduate student, a Chinese fellow, Liu Hongzhe is his name, great guy. He wrote a bunch of stuff for iterative, iterative linear algebra, like got into writing some of the iterative linear algebra tools that are currently there in SciPy. And they've gotten better since, but this is in 2005. Kept working on SciPy, but Perry had started working on a replacement to numeric called Numarray.

• 58:40Speaker 01

  And in 2004, a package called End the Image. It was an image processing library.

• 58:45Lex Fridman?

  Nice.

• 58:45Speaker 01

  That was written for Numarray. And it had in it a morphology tool. I don't know if you know what morphology is. It's open dilations, close, you know, there was sort of this. As a medical imaging student, I knew what it was because it was used in segmentation a lot. And in fact, I'd wanted to do something like that in Python, in SciPy. But just had never gotten around to it. So when it came out, but it worked only on Numarray. And SciPy needed numeric. And so we effectively had the beginning of this split. And numeric and Numarray didn't share data. They were just two, so you could have a gigabyte of numeric data and a gigabyte of numeric data and they wouldn't share it.

• 59:22Lex Fridman?

  Yeah.

• 59:22Speaker 01

  And so you had these, then you had these scientific libraries written on top. I got really bugged by that.

• 59:27Lex Fridman?

  Yeah.

• 59:27Speaker 01

  I got really like, oh, man, this is not good. We're not cooperating now. We're not, we're sort of redoing each other's work and we're just this young community. So that's what led me, even though I knew it was risky because my, you know, I was on a tenter trap position. 2004 got reviewed. They said, hey, things are going OK. You're doing well. Paper's coming out. But you're kind of spending a lot of time on this open source stuff. Maybe do a little less of that and a little more of the paper writing and grant writing, which was naive. But it was definitely the time, you know, the thinking.

• 59:55Lex Fridman?

  It still goes on.

• 59:56Speaker 01

  Still goes on.

• 59:57Lex Fridman?

  You're basically creating a thing which enables science in the 21st century.

• 1:00:03Speaker 01

  Right.

• 1:00:03Lex Fridman?

  Um, maybe don't emphasize that so much in your free or tenure.

• 1:00:06Speaker 01

  Right. It illustrates some of the challenges.

• 1:00:09Lex Fridman?

  Yes.

• 1:00:10Speaker 01

  It, it, it does. It's, and it's, people mean well.

• 1:00:13Lex Fridman?

  Yeah.

• 1:00:13Speaker 01

  Like, but we've gotten broken in a bunch of ways.

• 1:00:16Lex Fridman?

  Certain, certain things, programming, understanding the role of software engineering programming.

• 1:00:20Speaker 01

  Exactly.

• 1:00:21Lex Fridman?

  Society is a little bit lagging.

• 1:00:22Speaker 01

  Exactly. Now, I was in an electrical engineering position.

• 1:00:24Lex Fridman?

  Right. That's even worse.

• 1:00:27Speaker 01

  There. Yeah, it was very, they were very focused. And so, you know, good people and I had a great time. I, I loved my time. I loved my teaching. I loved all the things I did there. The problem was the split was happening in this community I loved. Right? I saw people and I go, oh, my gosh, this is going to be. This is not great. And so I happened, you know, fate, I had a class I had signed up for. I was trying to build an MRI system. So I had a kind of a radio, instead of a radio, a digital radio class, it's a digital MRI class. And I had people sign up, two people signed up, then they dropped. And so I had nobody in this class. So, and I didn't have any other courses to teach. And I thought, oh, I've got some time. And I'll just write, I'll just write a merger of the American number array. Like, I'll basically

• 1:01:10Speaker 01

  take the numeric code base at the features number I was adding, and then kind of come up with a single array library that everybody can use. So that's where NumPy came from, was my thinking, hey, I can do this. And who else is going to? Because at that point, I'd been around the community long enough, and I'd written enough C code. I knew, I knew the structures. And I, in fact, my first contribution to numeric had been writing the C API documentation that went in the first documentation for NumPy, for numeric, sorry. This is Paul Dubois, David Asher, Conrad Henson, and myself. I got credit because I wrote this chapter, which is all the C API of numeric, all the C stuff. So I said, I'm probably the one to do it. Nobody else is going to do this. So it's sort of out of a sense of duty and passion, knowing that, I don't think

• 1:01:54Speaker 01

  my academic, I don't think the department here is going to appreciate this, but it's the right thing to do.

• 1:02:01Lex Fridman?

  Can we just linger on that moment?

• 1:02:03Speaker 01

  Yeah.

• 1:02:04Lex Fridman?

  The importance of the way you thought and the action you took, I feel is understated and is rare. And I would love to see so much more of it. Because what happens as the tools become more popular, there's a split that happens. And it's a truly heroic and impactful action to in those early, in that early split to step up. And it's like, great leaders throughout history, like, get, what is the Braveheart, like, get on a horse and rile the troops. Because I think that can have, make a big difference. We have TensorFlow versus PyTorch in the machine learning

• 1:02:43Speaker 01

  We have the same problem today.

• 1:02:45Lex Fridman?

  Yeah. I wonder.

• 1:02:46Speaker 01

  It's actually bigger.

• 1:02:47Lex Fridman?

  I wonder if it's possible to, in the early days, to rally the troops.

• 1:02:53Speaker 01

  It is possible, especially in the early days. The longer it goes, the harder, right? And the more energy in the factions, the harder. But in the early days, it is possible. And it's extremely helpful. And there's a willingness there. But But the but the challenge is there's usually not a willingness to fund it.

• 1:03:07Lex Fridman?

  Yeah.

• 1:03:07Speaker 01

  There's not a willingness to, you know, like, I was literally walking into a field saying, I'm gonna do this. And here I am, like, you know, I have five kids at home now.

• 1:03:18Lex Fridman?

  Pressure builds.

• 1:03:19Speaker 01

  Sometimes my wife hears these stories and she's like, you did what? I thought we were gonna, I thought you were actually on a path to make sure we had resources and money, but.

• 1:03:28Lex Fridman?

  Oh, wow.

• 1:03:28Speaker 01

  But again, there's a, there's an aspect, I'm a very hopeful person, I'm an optimistic person by nature. I love people, I learned that about myself later on.

• 1:03:39Speaker 01

  Part of my, my religious beliefs actually lead to that. And it's why I hold them dear, because it's actually how I feel about, that's why, it's what leads me to this, to these attitudes. Sort of this hopefulness and this sense of, yeah, it may not, it may not work out for me financially or maybe, but that's not the ultimate game. Like, that's a thing, but it's not, you know, that's not the scorecard for me. And so I just wanted to be helpful and I knew, and partly because these Sci-Pi conferences, because of the maintenance conversations. I knew there was a lot of need for this, right? And so I had this, it wasn't like I was alone in terms of no feedback. I had these people who knew, but it was crazy. Like, people who, at the time, said, yeah, we didn't think you'd be able to do it.

• 1:04:16Lex Fridman?

  Yeah.

• 1:04:17Speaker 01

  We thought it was crazy.

• 1:04:17Lex Fridman?

  And also instructive, like practically speaking, that you had a cool feature that you were chasing in the morphology, like the.

• 1:04:25Speaker 01

  Yes.

• 1:04:26Lex Fridman?

  Like, it's, it's not just like.

• 1:04:27Speaker 01

  End result.

• 1:04:28Lex Fridman?

  It's not some visionary thing, I'm going to unite the community. You, you were like.

• 1:04:32Speaker 01

  Correct.

• 1:04:32Lex Fridman?

  You were actually practically, this is what one person actually could do and actually build.

• 1:04:38Speaker 01

  Because that is important, because you can get over your skis.

• 1:04:41Lex Fridman?

  Yeah.

• 1:04:42Speaker 01

  You can definitely get over your skis. And I had, in fact, this almost got me over my skis, right? I would say, well, in retrospect, I hate looking back. We, I can tell you all the flaws with NumPy, right? When I go into it, I would, there's lots of stuff that I'm like, oh, man, that's embarrassing. That was wrong. I wish I had somebody stop me with a wet fish there.

• 1:04:58Lex Fridman?

  Yeah.

• 1:04:59Speaker 01

  Like, I needed, like, what I'd wished I'd had was somebody with more experience and certainly library writing and array library. There's, like, I wish I had me, I could go back in time and go, do this, do that, there's a Morton Bean. Because there's things we did that are still there, that are problematic, that created challenges for later. And, and I didn't know it at the time, didn't understand how important that was. And in many cases, didn't know what to do. Like, there was pieces of the design of NumPy, I didn't know what to do until five years ago. Now I know what they should have been, but I didn't know at the time and I couldn't get the help. Anyway, so I wrote it. It took about, it took four months to write the first version, then about 14 months to make it usable. But it was, it wasn't, it was that first four months of intense writing, coding, getting something out the door that worked.

• 1:05:45Speaker 01

  That was, it was, it was definitely challenging. And then the big thing I did was create a new type object called dType. That was probably the contribution. And then the fact that I added broad, not just broadcasting, but advanced indexing. So that you could do masked indexing and indirect indexing instead of just slicing.

• 1:06:04Lex Fridman?

  So for people who don't know, and maybe you can elaborate, NumPy, I guess the vision in the narrowest sense is to have this object that represents n-dimensional arrays. And like at any level of abstraction you want, but basically it could be a black box that you can investigate in ways that you would naturally want to investigate such objects.

• 1:06:28Speaker 01

  Yes, exactly. So you could do math on it easily.

• 1:06:30Lex Fridman?

  Math on it easily, yeah.

• 1:06:32Speaker 01

  So it had an associated library of math operations. And effectively, SciPy became an even larger set of math operations. So the key for me was, I was gonna write NumPy and then move SciPy to depend on NumPy. In fact, early on, one of the initial proposals was that we would just write SciPy and it would have the numeric object inside of it. And it'd be SciPy dot array or something. That turned out to be problematic because numeric already had a little mini library of linear algebra and some functions. And it had enough momentum, enough users that nobody wanted to, they wanted backward compatibility. One of the big challenges of NumPy was I had to be backward compatible with both numeric and numarray in order to allow both of those communities to come together.

• 1:07:14Speaker 01

  There was a ton of work in creating that backward compatibility that also created echoes in today's object. Like some of the complexity in today's object is actually from that goal of backward compatibility with these other communities. Which, if you didn't have that, you'd do something different, which is instructive because a lot of things are there. You think, what is that there for? It's like, well, it was, it's a remnant. It's an artifact of its historical existence.

• 1:07:40Lex Fridman?

  By the way, I love the empathy and the lack of ego behind that because I feel, you see that in the split in the JavaScript frameworks, for example, the arbitrary branching is.

• 1:07:51Lex Fridman?

  I think in order to unite people, you have to kind of put your ego aside and truly listen to others. Like, what do you love about Numerate? What do you love about numeric? Like, actually get a sense. We're talking about languages earlier, sort of empathize to the culture, the people that love something about this particular API, some, the naming style, or the, the use, the actual usage patterns, and like, truly understand them, and so that you can like, create that same draw in the united.

• 1:08:23Speaker 01

  I completely agree. And you have to also have enough passion that you'll do it. It can't be just like a perfunctory, "Oh yes, I'll listen to you, I'm really, I'll listen to you," and then, "I'm not really that excited about it." So it really is an aspect, it's a philosophical, like, there's a philia, there's a love of, a steaming of others. It's actually at the heart of what, it's sort of a life philosophy for me, right? That I'm constantly pursuing, and that helped, absolutely helped.

• 1:08:47Lex Fridman?

  Makes me wonder, in a philosophical, like, looking at human civilization as one object, it makes me wonder how we can copy and paste Travis's in this world.

• 1:08:56Speaker 01

  Well, in some aspects, maybe.

• 1:08:58Lex Fridman?

  Some aspects, right, right. Exactly. Well, I, it's like-

• 1:09:01Speaker 01

  It's a good question. How do we teach this? How do we, how do we encourage it? How do we lift it?

• 1:09:04Lex Fridman?

  Because so much of the software world, it's, it's giant communities, right? But it seems like so much is moved by, like, little individuals. You talk about, like, Linus Torvalds. It's like, can you, could you have not, could you have had Linux without him? Could you?

• 1:09:21Speaker 01

  Yeah, Guido and Python.

• 1:09:22Lex Fridman?

  Guido and Python.

• 1:09:23Speaker 01

  Python. I mean, the sci-fi community in particular, it's, like, I said we wanted to build this big thing, but ultimately we didn't. What happened is we had Mavericks and Champions like John Hunter, who created Matt Plotlib. We had Fernando Perez, who created iPython. And so we sort of inspired each other, but, and then it kind of, there's sort of a culture of, of this selfless, the stewardship mentality, as opposed to ownership mentality, with stewardship and, and, and community-focused-

• 1:09:48Speaker 01

  Community focused but intentional work, like not waiting for everybody else to do the work, but you're doing it for the benefit of others and not worried about what you're going to get. You're not worried about the credit, you're not worried about what you're going to get, you're worried about. I later realized that I have to worry a little about credit, not because I want the credit, because I want people to understand what led to the results. Like I don't, it's not about me, it's I want to understand this is what led to the result. So I think doing, and this is what had no impact on the result. Like, let's promote, this is just like you said, I want to promote the attributes that help make us better off. How do we make more of Wes McKinney? Like Wes McKinney was critical to the success of Python because of his creation of pandas,

• 1:10:28Speaker 01

  which is the roots of that were all the way back in numeric and numarray and numpy, where numpy created an array of records. Wes started to use that, almost like a data frame, except it's an array of records. And data frame, the challenge is, okay, if you want to augment it at another column, you have to insert, you have to do all this memory movement to insert a column. Whereas data frames became, oh, I'm going to have a loose collection of arrays. So it's a record of arrays that is a part of a data frame. And we thought about that back in the memory days, but Wes ended up doing the work to build it. And then also the operations that were relevant for data processing. What I noticed is just that each of these little things creates just another tick, another up.

• 1:11:12Speaker 01

  So numpy ultimately took a little while, about six months in, people started to join me. You know, Francesca Altad, Robert Kern, Charles Harris. And these people are many of the unsung heroes, I would say. People who are, you know, they sometimes don't get the credit they deserve because they were critical both to support, like, you know, it's hard and you want, you need some support. People need support and I needed just encouragement and they were helping and encouraged by contributing. And once, the big thing for me was when John Hunter. He had previously done kind of a simple thing called numerics to kind of move, you know, between the American number A, he had a little high level tool that would just select each one for matplotlib. In 2006, he finally said, we're going to just make numpy the dependency

• 1:11:56Speaker 01

  of matplotlib. As soon as he did that, and I remember specifically when he did that, I said, this, okay, now we've done it. Like, that was when I knew it was a success. Before then, it was still, you know, unsure. But that kind of started a roller coaster. And then 2006 to 2009. And then I've been floored by the, by what it's done. Like, I hadn't, I knew it would help. I had no idea how much it would help. Right? So.

• 1:12:21Lex Fridman?

  And it has to do with, again, the language thing. It just, people started to think in terms of numpy, like, and that opened up a whole new way of thinking. And part of the story that we cut, you kind of mentioned, but maybe you can elaborate, is it seems like at some point in this story, Python took over science and data science. And not bigger than that, the scientific community started to think like programmers, or started to utilize the tools of computers to do, like, at a scale that wasn't done with Fortran, like, at this gigantic scale, they started to opening their heart. And then Python was the thing.

• 1:13:06Lex Fridman?

  I mean, there's a few other competitors, I guess, but Python, I think, really, really took over.

• 1:13:11Speaker 01

  I agree. There's a lot of stories here that are kind of during this journey, because this is sort of the start of this journey in 2005, six. So, my tenure committee, I applied for tenure in 2006, 2007. It came back, I split the department. I was very polarizing. I had some huge fans, and then some people said, no way. Right? So it was very, I was a polarizing figure in the department. It went all the way up to the university president. Ultimately, my department chair had the sway and they didn't say no, they said, "Come back in two years and do it again." And I went, eh, at that point, I was like, I had this interest in entrepreneurship, this interest in not the academic circles, not the, like, how do we make industry work?

• 1:13:54Speaker 01

  So I do have to give credit to that exploration of economics because that led me, oh, I had a lot of opinions. I was actually very libertarian at the time and I'm still have some libertarian trends, but I'm more of a, I'm more of a collectivist libertarian.

• 1:14:10Lex Fridman?

  So you value broadly philosophically freedom?

• 1:14:13Speaker 01

  I value broadly philosophically freedom, but I also understand the power of communities, like the power of collective behavior. And so what's that balance, right? That makes sense. So by the time I was just, I gotta go out and explore this entrepreneur world. So I left academia. I said, "No, thanks." Called my friend Eric here, who his company was going. I said, "Hey, could I join you and start this trend?" And at that time, they were using SciPy a lot. They were trying to get clients. And so I came down to Texas. And in Texas is where I sort of, it's my entrepreneur world, right? I left academia and went to entrepreneur world in 2007. So moved here in 2007, kind of took a leap, knew nothing really about business, knew nothing about a lot of stuff

• 1:14:57Speaker 01

  there. There's, you know, for a long time, I've kept some connections to a lot of academics because I still value it. I still love the scientific tradition. I still value the essence and the soul and the heart of what is possible. Don't like a lot of the administration and the kind of, we can go into detail about why and where and how this happens, what are the challenges?

• 1:15:21Lex Fridman?

  I mean, I don't know, but I'm with you. So I'm still affiliated with MIT. I still love MIT because there's magic there.

• 1:15:29Speaker 01

  Yeah.

• 1:15:30Lex Fridman?

  There's people I talk to, like researchers, faculty, in those conversations and the whiteboard and just the conversation, that's magic there. All the other stuff, the administration, all that kind of stuff. Seems to, you don't, you don't wanna say too harshly criticize sort of bureaucracies, but there's a lag that seems to get in the way of the magic.

• 1:15:55Speaker 01

  Yeah.

• 1:15:55Lex Fridman?

  And I don't, I'm still have a lot of hope that that can change because I don't often see that particular type of magic elsewhere in industry. So like, we need that and we need that flame going.

• 1:16:10Speaker 01

  I agree.

• 1:16:11Lex Fridman?

  And it's the same thing as, exactly as you said, it has the same kind of elements like the open source community does. And, but then if you, like, the reason I stepped away, the reason I'm here, just like you did in Austin, is like, if I wanna build one robot, I'll stay at MIT. But if I wanna build millions and make money enough to where I can explore the magic of that, then you can't. And I think that dance is.

• 1:16:39Speaker 01

  That translational dance has been lost a bit.

• 1:16:41Lex Fridman?

  Yeah.

• 1:16:42Speaker 01

  Right? And there's a lot of reasons for that. I'm not, I'm certainly not an expert on this stuff, like an opine like anybody else. But I, I realized that I wanted to explore entrepreneurship, which I, and really figure out, and it's been a driving passion for 20 years, 20, 25 years. How do we connect capital markets and company? Cause again, I fell in love with the notion, oh, profit seeking on its own is not a bad thing. It's actually a coordination mechanism for allocating resources that, you know, non, in an emergent way, right? That respects everybody's opinions, right? So this is actually powerful. So, so I say all the time, when I make a company and we do something that makes profit, what we're saying is, hey, we're collecting of the world's resources involuntarily. People are asking us to do something they like.

• 1:17:27Speaker 01

  And that's a huge deal. And so I really like that energy. So that's what I came to do and to learn and to try to figure out. And that's what I've been kind of stumbling through since for the past 14 years.

• 1:17:36Lex Fridman?

  And that's 2007.

• 1:17:37Speaker 01

  2007.

• 1:17:38Lex Fridman?

  And so you were still.

• 1:17:39Speaker 01

  So NumPy was just emerging, right? I, I, one thing I'd done, I'd done, it's worth mentioning because it emphasized the exploratory nature of my thinking at the time. I, I said, well, I don't know how to fund this thing. I've got a graduate student I'm paying for and I've got no funding for him. And I had done some fundraising from the public to try to get public fundraisers for my lab. I didn't really want to go out and just do the fundraising circuit, the way it's traditionally done. So I wrote a book and I said, I'm gonna write a book and I'm gonna charge for it. It was called Guide to NumPy. And so ultimately NumPy became documentation-driven development. Because I basically wrote the book and made sure the stuff worked, so the book would work. So it really helped, actually, make NumPy become a thing. So that doc, writing that book, and it was not a, I mean, it's not a page turner.

• 1:18:22Speaker 01

  I mean, NumPy is not a book you pick up and go, oh, this is great, over the fire. But it was, it's where you could find the details. Like, how'd all this work? And a lot of people loved that book. And so a lot of people ended up, so I, but I said, look, I, I needed, so I'm gonna charge for it. Uh, and I got some flack for that, not that much, just, just probably five angry messages, people, you know, yelling at me, saying I was, you know, bad guy for, for charging for this book. Was one of them Richard Thoma? No. Just kidding. No, I, I haven't really had any interaction with him personally, uh, like I said, uh, but, but there were a few, but, but actually surprisingly not. There was actually a lot of people like, no, it's fine, you know, you can charge for a book, that's no big deal. We know that's a way you can, you can try to make money around open source. So, so what I did, but I, I did it in an interesting way.

• 1:19:05Speaker 01

  I said, well, you know, kind of my ideas around, around IP law and stuff, I love the idea, you can share something, you can spread it. Like, once it's, the fact that you have a thing and copying is free, but the creation is not free. So how do we, how do you fund the creation and allow the copying, right? And then software's a little more complicated than that, because creation is actually a continuous thing. You know, it's not like you build a widget and it's done. It's sort of a process of emerging and continuing to, to, to create. But I wrote the book and had this market determined price thing. I said, look, I need, I think I said 250,000. If I make 250,000 from this book, it's, I'll make it free. So as soon as I get that much money, or I said five years, right? So there's a time limit, like it's not forever.

• 1:19:44Lex Fridman?

  I didn't know this story.

• 1:19:45Speaker 01

  Yeah, so I, I, I released it on this. And it's actually interesting because one of the people who also thought that was interesting ended up being Chris White, who was the director of DARPA project that we got funding through at Anaconda. And the reason he even called us back is because he remembered my name from this book and he thought that was interesting. And so even though we hadn't gone to the demo days, we applied and the people said, yeah, nobody ever gets this without coming to the demo day first. This is the first time I've seen it, but it's because I knew, you know, Chris had done this and had this interaction. So it did have impact. I was actually really, really pleased by the result. I mean, I ended up, I ended up in three years, I made 90,000. So sold 30,000 copies by myself. I just put it up on, you know, use PayPal and sold it.

• 1:20:27Speaker 01

  And those are my first taste of kind of, okay, this can work to some degree. And, and I, you know, all over the world, right? From Germany to Japan to, it was actually, it did work. And so I appreciated the fact that PayPal existed and I had a way to make, to get the money. The distribution was simple. This is pre-Amazon book stuff, so it was just publishing a website. It was the popularity of SciPy emerging and getting company usage. I ended up not letting it go to five years and not trying to make the full amount because, you know, a year and a half later, I was at Enthought. I had left academia, was at Enthought and I kind of had a full-time job. And then actually what happened is the documentation people, there's a group that said, hey, we want to do documentation for SciPy as a collective.

• 1:21:09Speaker 01

  And they were essentially needing the stuff in the book, right? And so they kind of asked, hey, could we just use the stuff in your book? And at that point I said, yeah, I'll just open it up. So that's, but it has served its purpose. And the money that I made actually funded my grad student. Like it was actually, you know, I paid him 25,000 a year out of that money.

• 1:21:30Lex Fridman?

  The funny thing is if you do a very similar kind of experiment now with NumPy or something like it, you could probably make a lot more.

• 1:21:37Speaker 01

  It's probably true.

• 1:21:38Lex Fridman?

  Because of the tooling and the community building.

• 1:21:41Speaker 01

  Yeah, I agree.

• 1:21:41Lex Fridman?

  Like the, and social media, that there, there's just a virality to that kind of idea.

• 1:21:46Speaker 01

  I agree, there'd be things to do. I've thought about that and, but, and, and really had thought about a couple of books or a couple of things that could be done there. And I just haven't, right? Even, I tried to hire a ghostwriter this, this year too, to see if that would help, but it, it didn't. Part of my problem is this, I've been so excited by a number of things that stemmed from that. Like, so I came here, worked at Enthought for four years. Graciously, Eric made me president, then we started to work closely together. We actually helped them buy out his partner. It didn't end great. Unfortunately, Eric and I aren't real, aren't friends now. I still respect him. I have a lot, I wish we were, but he didn't like the fact that Peter and I started Anaconda.

• 1:22:26Speaker 01

  Right? That was not, I mean, so there's two sides to that story, so I'm not going to go into it, right?

• 1:22:32Lex Fridman?

  Sure. But you, as human beings, and you wish you still could be friends.

• 1:22:37Speaker 01

  I do. I do. It saddens me.

• 1:22:40Lex Fridman?

  I mean, that's, that's a story of great minds building great companies.

• 1:22:46Speaker 01

  Yeah.

• 1:22:46Lex Fridman?

  Somehow it's sad that when there's that kind of...

• 1:22:49Speaker 01

  And I, I, I hold him in esteem. I'm grateful for him. I think he's, they're doing, you know, there's, Enthought still exists. They're doing great work. Helping scientists, they still run the SciPy conference. They're in the, they have an R and D platform they're selling now. That's a, a tool that you can go get today, right? So they've been a, Enthought has played a role in the SciPy, in, in, in supporting the community around SciPy. I would say. They ended up not being able to, they ended up building a tool suite to write GUI applications. Like, that's where they could actually make, that the business could work. And so the supporting SciPy and NumPy itself wasn't as possible. Like, they didn't, they tried. I mean, it was not just because, it was just because of the business aspect. So, and then I wanted to build a company that could do, that could get venture funding.

• 1:23:33Speaker 01

  Right? Better for worse. I mean, that's a longer story. We could talk a lot about that, but...

• 1:23:37Lex Fridman?

  And that's, that's where Anaconda came to be.

• 1:23:39Speaker 01

  That's where Anaconda came to be.

• 1:23:39Lex Fridman?

  So let me, let me ask you, it's a little bit for fun, because you built this amazing thing. And so let's, let's talk about, like, an old warrior looking over old battles. You've, you know, there's a sad letter in 2012 that you wrote to the NumPy mailing list, announcing that you're leaving NumPy.

• 1:24:00Speaker 01

  Yeah.

• 1:24:01Lex Fridman?

  And some of the things you've listed, some, some of the things you regret, or not regret necessarily, but some things to think about. If you could go back and you could fix stuff about NumPy, or both sort of in a personal level, but also like looking forward, what kind of things would you like to see changed?

• 1:24:19Speaker 01

  Good question. So I think there's technical questions and social questions right there.

• 1:24:24Speaker 01

  First of all, you know, I wrote NumPy as a service and I spent a lot of time doing it. And then other people came help make it happen. NumPy succeeded because of the work of a lot of people, right? So it's important for people to understand that. I'm grateful for the opportunity, the role I had, I could play. And I'm grateful that things I did had an impact. But they only had the impact they had because of the other people that came to the story. And so they were essential. But the way data types were handled, the way data types, we had array scalars, for example, that are really just a substitute for a type concept, right? So we had, array scalars are actual Python objects so that there's for every, for a 32-bit float or a 16-bit float or a 16-bit integer.

• 1:25:08Speaker 01

  Python doesn't have a natural, it's just a one integer, it's one float. Well, what about these lower precision types, these larger precision types? So we had them in NumPy so that you could have a collection of them, but then have an object in Python that was one of them. And there's questions about, like, in retrospect, I wouldn't have created those of it and improved the type system. Like, made the type system actually a Python type system as opposed to currently it's a Python 1 level type system. I don't know if you know the difference between Python 1, Python 2, it's kind of technical, kind of depth. But Python 2, one of its big things that Guido did, it was really brilliant, it was he actually, Python 1, all classes, new objects, were one.

• 1:25:49Speaker 01

  If he was a user, wrote a class, it was an instance of a single Python type called the class type, right? In Python 2, he used a meta typing hook to actually go, oh, we can extend this and have users write classes that are new types. So he was able to have your user classes be actual types and the Python type system got a lot more rich. I barely understood that at the time that NumPy was written. And so I essentially in NumPy, created a type system that was Python 1 era. It was every, every D type is an instance of the same type as opposed to having new D types be really just Python types with additional metadata. What's the cost of that? Is it efficiency, is it usability? It's usability primarily.

• 1:26:33Speaker 01

  The cost isn't really efficiency, it's the fact that it's clumsy to create new types. It's hard. And then one of the challenges, you want to create new types, you want a quaternion type, or you want to add a new posit type, or you want to, so it's hard. Now, and now, if we'd have done that well...

• 1:26:54Speaker 01

  When Numba came on the scene, where we could actually compile Python code, it would integrate with that type system much cleaner. And now, all of a sudden, you could do gradual typing more easily. You could actually have Python, when you add number, plus better typing, could actually be a, you'd smooth out a lot of rough edges.

• 1:27:11Lex Fridman?

  But there's already, there's like, but are you talking about from the perspective of developers within NumPy or users of NumPy?

• 1:27:18Speaker 01

  Developers of new, not really users of NumPy so much. It's the development of NumPy.

• 1:27:23Lex Fridman?

  I see you're thinking about like, how to design NumPy so that its contributors?

• 1:27:28Speaker 01

  Yeah, the contributors, it's easier.

• 1:27:30Lex Fridman?

  It's easier.

• 1:27:31Speaker 01

  It's less work to make it better and to keep it maintained. And where that's impacted things, for example, is the GPU. Like, all of a sudden, GPUs start getting added and we don't have them in NumPy. Like, NumPy should just work on GPUs.

• 1:27:45Lex Fridman?

  Right.

• 1:27:45Speaker 01

  The fact that we have to have to download a whole other object called CuPy to have arrays on GPUs is just an artifact of history. Like, there's no fundamental reason for it.

• 1:27:54Lex Fridman?

  Well, that's really interesting. If we could sort of go on that tangent briefly is you have PyTorch and other libraries like TensorFlow that basically tried to mimic NumPy. Like, you've created a sort of platonic form of what a multi-dimensional?

• 1:28:11Speaker 01

  Yeah, exactly. Well, and the problem was they didn't realize that.

• 1:28:14Lex Fridman?

  Yeah.

• 1:28:14Speaker 01

  Platonic form has a lot of edges. They're like, well, we should cut those out before we present it.

• 1:28:19Lex Fridman?

  So I wonder if you can comment, is there like a difference between their implementations? Do you wish that they were all using NumPy or like in this abstraction of GPU? And, sorry to interrupt, there's GPUs, ASICs. There might be other neuromorphic computing. There might be other kind of, or the aliens will come with a new kind of computer, like an abstraction that NumPy should just operate nicely over the things that are more and more and smarter and smarter with this multi-dimensional arrays.

• 1:28:49Speaker 01

  Yeah, yeah. I have, there's several comments there. We are working on something now called data-apis.org, data-api.org. You can go there today and it's, it's our answer. It's my answer, you know, it's not just me. It's me and Rolf and, and Athen and Aaron and a lot of companies are helping us at Quansight Labs. It's not unifying all the arrays, it's creating an API that is unified. So we do care about this and are trying to, trying to work through it. Actually, I had the chance to go and meet with the TensorFlow team and the PyTorch team and talk to them after exiting Anaconda, just talking about, because the first year after leaving Anaconda in 2018, I became deeply aware of this and realized that, oh,

• 1:29:31Speaker 01

  this split in the Array community that exists today makes what I was concerned about in 2005 pretty parochial. It's a lot worse, right? Now, there's a lot more people, so perhaps the industry can sustain more stacks, right? There's a lot of money, but it makes it a lot less efficient. I mean, but I've also learned to appreciate, it's okay to have some competition. It's okay to have different implementations, but it's better if you can at least refactor some parts. I mean, you're gonna be more efficient if you can refactor parts.

• 1:30:01Lex Fridman?

  It's nice to have competition over things.

• 1:30:04Speaker 01

  They're innovative.

• 1:30:05Lex Fridman?

  It's nice to have competition.

• 1:30:06Speaker 01

  They're innovative.

• 1:30:07Lex Fridman?

  Yeah, innovative, and then maybe on the infrastructure.

• 1:30:09Speaker 01

  Right.

• 1:30:10Lex Fridman?

  Whatever, however you define infrastructure, maybe it's nice to have come together.

• 1:30:15Speaker 01

  Exactly. I agree. And I think, but it was interesting to hear the stories. I mean, TensorFlow came out of the C++ library. Jeff Dean wrote, I think, there was basically how they were doing inference, right? And then they realized, oh, we could do this TensorFlow thing. That C++ library, then what was interesting to me was the fact that both Google and Facebook did not, it's not like they supported Python or NumPy initially. They just realized they had to. They came to this world and then all these were like, hey, where's the NumPy interface? Oh, then they kind of came late to it and then they had these bolt-ons. TensorFlow's bolt-on, I don't mean to offend, but it was so bad. It's the first time that I, I'm usually, I mean, one of the challenges I have is I

• 1:30:59Speaker 01

  don't criticize enough, in the sense that I don't give people input enough, you know, if.

• 1:31:04Lex Fridman?

  I think it's universally agreed upon that the bolt-ons on TensorFlow.

• 1:31:08Speaker 01

  But I went to, there was a talk given at Mallorca in Spain and a guy, great guy, came and gave a talk. I said, you should never show that API again at a PyData conference. Like, that was, that's terrible. Like, you're taking this beautiful system you've created and like you're corrupting all these poor Python people, forcing them to write code like that. Thinking they should. Uh, fortunately, you know, they adopted Keras as their, and that Keras is better. And so Keras TensorFlow is fine, is reasonable. But um, they bolted it on. Facebook did too, like Facebook had their own C++ library for doing inference. And they also had the same, you know, reaction, they had to do this. One big difference is Facebook, maybe because the way it's situated in the, in part of FAIR, part of their research library.

• 1:31:51Speaker 01

  TensorFlow is definitely used and you know, they have to make, they couldn't just open it up and let the community, you know, change what it is, because I guess they were worried about disrupting their operations. Facebook's been much more open to having community input on the structure itself. Whereas Google and TensorFlow, they're really eager to have user, community users. People use it and build the infrastructure, but it's much more walled. Like it's, it's harder to become a contributor to TensorFlow.

• 1:32:16Lex Fridman?

  And it's also, this is a very difficult question to answer. And I don't mean to be throwing shade at anybody, but you have to wonder, it's the Microsoft question of when you have a tool like PyTorch or TensorFlow, how much are you tending to the hackers and how much are you tending to the big corporate clients?

• 1:32:34Speaker 01

  Correct.

• 1:32:34Lex Fridman?

  And so, like the ones, so do you tend to the millions of people that are giving you almost no money? Or do you tend to the people, the few that are giving you a ton of money? I tend to um, stand with the people.

• 1:32:48Speaker 01

  Right.

• 1:32:49Lex Fridman?

  Because I feel like if you nurture the hackers, you will make the right decisions in the long term that will make the companies happy.

• 1:32:56Speaker 01

  I lean that way too. I totally agree.

• 1:32:58Lex Fridman?

  But then you have to find the right.

• 1:33:00Speaker 01

  But it's a balance.

• 1:33:01Lex Fridman?

  Yeah, it's.

• 1:33:01Speaker 01

  Because you can lean to the hackers and run out of money.

• 1:33:03Lex Fridman?

  Yeah, exactly. Exactly.

• 1:33:06Speaker 01

  Which has been some of the challenge I've faced.

• 1:33:08Lex Fridman?

  Yes.

• 1:33:08Speaker 01

  In the sense that, like I, like I would look at some of the experiments, like NumPy, the fact that we have this split is a factor of I wasn't able to collect more money towards NumPy development.

• 1:33:17Lex Fridman?

  Yeah.

• 1:33:18Speaker 01

  Right? I mean, I didn't succeed at, in the early days of getting enough financial contribution to NumPy, so they didn't make a work on it. Right? I couldn't work on it full time. I had to just catch an hour here, an hour there. And I basically not liked that. Like I've wanted to be able to do something about that for a long time and trying to figure out how, well, there's lots of ways. I mean, possibly one could say, you know, we had an offer from Microsoft, uh, early days of Anaconda. Uh, the 2014, they offered to come buy us, right? The problem was the right people at Microsoft didn't offer to buy us and they were still, they were, it was really a, we were like a second, they had really bought, they just bought R, the R company called, it was not R Studio, but it was another R company that was emergent.

• 1:34:00Speaker 01

  And it was kind of a, well, we should also get a Python play, but they were really doubling down on R. Right. And so it was like,

• 1:34:07Lex Fridman?

  It was where you would go to die. So it's not, it wasn't, it was before Sacha was there.

• 1:34:12Speaker 01

  Sacha had just started.

• 1:34:13Lex Fridman?

  Just started.

• 1:34:14Speaker 01

  Right. And if the, and the offer was coming from someone two levels down from him.

• 1:34:17Lex Fridman?

  Got you.

• 1:34:18Speaker 01

  Right. And if it come from Scott Guthrie, so I got a chance to meet Scott Guthrie, great guy, I like him. If it offered to come from him, probably would be at Microsoft right now.

• 1:34:28Lex Fridman?

  That'd be fascinating. That'd be really nice, actually, especially given what Microsoft has since done for the open source community and all those.

• 1:34:35Speaker 01

  Yes, I think they're doing well. I really like some of the stuff they've been doing. They're still working and they've, you know, they've hired Guido now and they've hired a lot of Python developers.

• 1:34:43Lex Fridman?

  Guido's not Microsoft? I need to.

• 1:34:47Speaker 01

  Which he retired, then he came out of retirement and he's working on.

• 1:34:50Lex Fridman?

  I was just talking to him and he didn't mention this part.

• 1:34:52Speaker 01

  Well.

• 1:34:53Lex Fridman?

  I should, I should investigate this further. Cause I know he loved Dropbox, but I wasn't sure what he was doing, who he was up to.

• 1:35:00Speaker 01

  Well, he was kind of saying he'd retire, but, and it's, it's literally been five years since I last sat down and really talked to Guido. Right. Guido's a technology expert, right? He's a, so I, I came, I was excited because I'd finally figured out the type system for NumPy. I wanted to kind of talk about that with him and I kind of overwhelmed him.

• 1:35:18Lex Fridman?

  Could you stay in that just for a brief moment? Cause you're a fascinating person in the history of programming. He is a fascinating person. What have you learned from Guido about programming, about life?

• 1:35:32Speaker 01

  Yeah, yeah. A lot, actually. I've, I've been a fan of Guido's, you know, we had a chance to talk some, I wouldn't say, you know, we talk all the time, not really at all. But we talked enough to, I respect his, in fact, when I first started NumPy, one of the first things I did was I had, I asked Guido for a meeting with him and Paul Dubois in San Mateo. And I went and met him for lunch and basically to say, maybe we can actually, part of the strategy for NumPy was to get it into Python three and maybe be part of Python. And so we talked about that.

• 1:36:00Lex Fridman?

  That's a cool conversation.

• 1:36:00Speaker 01

  About that approach, right?

• 1:36:01Lex Fridman?

  I would have loved to be a fly in the wasp.

• 1:36:03Speaker 01

  That was, that was good. And, and over, over the years for Guido, I learned. So he was open, like he was willing to listen to people's ideas, right? And over the years, now generally, you know, I'm not saying universally that's been true, but but generally that's been true. So he's willing to listen. He's willing to defer, like on the scientific side, he would just kind of defer. He didn't really always understand what we were doing, like, and he'd defer. One place where he didn't enough was we missed a matrix multiply operator like that finally got added to Python, but about 10 years later than it should have. But the reason was because nobody, it takes, it takes a lot of effort. And I learned this while I was writing NumPy. I also wrote tools to Python, I became a Python dev and I added some pieces to Python, like the memory view object.

• 1:36:48Speaker 01

  I wanted the structure of NumPy into Python. So we didn't get NumPy into Python, but we got the basic structure of it in the Python, like, so you could build on it. Nobody did for a while, but eventually he database authors started to. And it was, it's a lot better they did. And also Antoine Petro and Stephane Kra actually fixed the memory view object because I wrote the underlying infrastructure in C, but the Python exposure was terrible until they came in and fixed it, partly because I was writing NumPy and it, NumPy was the Python exposure. I didn't really care about if you didn't have NumPy installed. Anyway, Guido opened up ideas, technologically, you know, brilliant, like really, I really got a lot of respect for him when I saw what he did with the, with the, this type class merger thing. It was actually tricky, right? And then, and then willing to share, willing to share his ideas.

• 1:37:33Speaker 01

  So the other thing, early on in 1998, I said, I start wrote my first extension module. The reason I could is because he'd written this blog post on how to do reference counting, right? And without it, I would have been lost, right? But he was, he was willing to at least try to write this post. And so he's been motivated, he's been motivated early on with Python, there's a computer science for everybody, kind of had this early on desire to, oh, maybe we should be pushing programming to more people. So he had this populist notion, I guess, or populist sense to learn that, that there's a certain skill, and I've seen it in other people too, of engaging with contributors sufficiently to, because when somebody engages with you and wants to contribute to you, if you ignore them, they go away. So building that early contributor base requires real engagement. With other people and he would do that.

• 1:38:19Lex Fridman?

  Can you also comment on this tragic stepping down from his position as the benevolent dictator for life over the wars, you know?

• 1:38:30Speaker 01

  The walrus operator?

• 1:38:31Lex Fridman?

  The walrus operator was the last battle. I don't know if that's the cause of it, but there's this, for people who don't know, you can look up, there's the walrus operator, which is, looks like a colon and equal sign.

• 1:38:44Speaker 01

  Yeah, colon, equal sign.

• 1:38:45Lex Fridman?

  And it actually does maybe the thing that you, that an equal sign should be doing.

• 1:38:52Speaker 01

  Yeah, maybe, right, exactly.

• 1:38:55Lex Fridman?

  But it's just historically, an equal sign means something else. It just means assignment. So he stepped down over this. What do you think about the pressure of leadership?

• 1:39:05Speaker 01

  It's something that you mentioned, the letter I wrote in umpire at the time. That was a hard time, actually. I mean, you know, there's been really hard times. It was hard, you know, you get criticized, right? And you get pushed and you get, not everybody loves what you do. Like anytime you do anything that has impact at all, you're not universally loved, right? You get some real critics and that's an important energy because it's impossible if you did everything right. You need people to be pushing, but sometimes people can get mean, right? People can, I prefer to give people the benefit of the doubt. I don't immediately assume they have bad intentions. And maybe for other, you know, maybe other, maybe that doesn't happen for everybody. They, for whatever reason, their past, their experience with people, they sometimes have bad, so they immediately attribute to you bad intentions. You're like, where did this come from?

• 1:39:50Speaker 01

  I mean, I'm definitely open to criticism, but I think you're misinterpreting the whole point. Cause I would get that, you know, certainly when I started Anaconda, you know, I've been, sometimes I say to people, I know I'm, I care enough about entrepreneurship to make some open source people uncomfortable. And I care enough about open source to make investors uncomfortable. So I sort of, you know, create, you create kind of doubters on both sides.

• 1:40:14Lex Fridman?

  So you have, and this is just a plea to the listener and the public. I've noticed this too, that there's a tendency, and social media makes this worse.

• 1:40:27Lex Fridman?

  When you don't have perfect information about the situation, you tend to fill the gaps with the worst possible, or at least a bad story that fills those gaps. And I think it's good to live life, maybe not fully naively, but filling in the gaps with the good, with the best, with the positive, with the hopeful explanation of why you see this. So if you see somebody like you trying to make money on a book about NumPy, there's a million stories around that that are positive. And those are good to think about, to project positive intent on the people. Because for many reasons, usually because people are good and they do have good intent.

• 1:41:10Lex Fridman?

  And also, when you project that positive intent, people will step up to that, too.

• 1:41:14Speaker 01

  Yes.

• 1:41:14Lex Fridman?

  So like, it has this-

• 1:41:16Speaker 01

  Great point.

• 1:41:16Lex Fridman?

  It has this kind of viral nature to it. And of course, with Twitter, early on, figured out on Facebook, is that they can make a lot of money and engagement from the negative.

• 1:41:27Speaker 01

  Yes.

• 1:41:27Lex Fridman?

  And so like, there's this, we're fighting this mechanism-

• 1:41:30Speaker 01

  I agree.

• 1:41:30Lex Fridman?

  Which is challenging.

• 1:41:31Speaker 01

  It's like easier.

• 1:41:32Lex Fridman?

  It's just easier to be-

• 1:41:33Speaker 01

  To be negative.

• 1:41:34Lex Fridman?

  And then for some reason, something in our minds really enjoys sharing that and getting all excited about the negativity.

• 1:41:41Speaker 01

  We do, yeah.

• 1:41:41Lex Fridman?

  But-

• 1:41:42Speaker 01

  Some protective mechanism, perhaps, that we're worried we're gonna eat if we don't.

• 1:41:45Lex Fridman?

  Exactly. For us to be effective as a group of people in a software engineering project, you have to project positive intent, I think.

• 1:41:51Speaker 01

  I totally agree, totally agree. And I think that's very, and so that happens in this, in the space. But Python has done a reasonable job in the past. But here is a situation where I think it's starting to get this pressure where it didn't. I was, I really didn't, I didn't know enough about what happened. I've talked to several people about it. And I know most of the steering committee members today, one person nominated me for that role, but it's the wrong role for me right now, right? I have a lot of respect for the Python developer space and the Python developers. I also understand the gap between computer science Python developers and array programming developers or science developers. And in fact, Python succeeds in the array space the more it has people in that boundary. And there's often very few. Like, I was playing a role in that boundary and, you know,

• 1:42:35Speaker 01

  working like everything to try to keep up with the, with the, even what Gita was saying, like- I'm a C programmer, but not a computer scientist. Like, I was a engineer and physicist and mathematician. And I don't, I didn't always understand what they were talking about and why they would have opinions the way they did. So you have to listen and try to understand. Then you also have to explain your point of view in a way they can understand. And that takes a lot of work. And that, that communication is always the challenge. And it's just what we're describing here about the negativity is just another form of that. Like, how do we come together? And it does appear we were wired anyway to at least have a, there's a part of us that will enemy, you know, friend, enemy. And, and we see, yeah, it's like, why are we wiring on the enemy front?

• 1:43:17Lex Fridman?

  Yeah.

• 1:43:18Speaker 01

  So, so why are we pushing that? Why are we promoting that so deeply?

• 1:43:21Lex Fridman?

  Assume friend until proven otherwise.

• 1:43:23Speaker 01

  Yes, yes.

• 1:43:24Lex Fridman?

  So, cause you have such a fascinating mind and all this. Let me just ask you these questions. So one interesting side on the Python history is the move from Python two to Python three. You mentioned move from Python one to Python two, but the move from Python two to Python three is a little bit interesting because it took a very long time. It, it broke in quite a small way backward compatibility. But even that small way seemed to have been very painful for people. Is there lessons you draw?

• 1:43:55Speaker 01

  Tons of lessons.

• 1:43:56Lex Fridman?

  From, uh, from how long it took and how painful it seemed to be?

• 1:44:00Speaker 01

  Yeah, tons of lessons. Well, I mentioned here earlier that Num, NumPy was written in 2005. It was in 2005 that I actually went to Guido to talk about getting NumPy to Python three. Like, my strategy was to, oh, we were moving to Python three, let's have that be. And it seems funny in retrospect because like, wait, Python three, that was in 2020, right? When we finally ended support for Python two, or at least 2017. The reason it took a long time, a lot of time, I think is because, well, one of the things is there wasn't much to like about Python three. 3.0, 3.1, it really wasn't until 3.3. Like, I consider Python 3.3 to be Python 3.0. It wasn't until Python 3.3 that I felt there's enough stuff in it to make it worth anybody using it.

• 1:44:44Speaker 01

  Right? And then 3.4 started to be, oh yeah, I want that. And then 3.5 as the matrix multiply operator. And now it's like, okay, we gotta use that.

• 1:44:51Lex Fridman?

  Plus the libraries that started leveraging some of the features of Python.

• 1:44:54Speaker 01

  Exactly.

• 1:44:55Lex Fridman?

  Yeah.

• 1:44:55Speaker 01

  So it really, the challenge was, it was, but it also illustrated a truism that, you know, it's when you have inertia, when you have a group of people using something, it's really hard to move them away from it. You can't just change the world on them. And Python 3, you know, made some, I think it fixed some things Guido had always hated. I don't think he didn't like the fact that print was a statement, wanted to make it a function. But in some sense, that's a bit of gratuitous change to the language. And you could argue, and there's, there are people have, but there was, one of the challenges was there wasn't enough features and too many just changes without features. And so the empathy for the end user as to why they would switch wasn't, wasn't there. I think also it illustrated just the funding realities, like Python wasn't funded.

• 1:45:39Speaker 01

  Like it was also a project with a bunch of volunteer labor, right? It had more people, so more volunteer labor, but it was still, it was funded in the sense that at least Guido had a job. And I've learned some of the behind the scenes on that now since, since talking to people who have lived through it. And maybe not on air, we can talk about some of that. But it's interesting to see, but Guido had a job, but he, but his full-time job wasn't just work on Python. Like he had other things to do.

• 1:46:03Lex Fridman?

  This is wild.

• 1:46:04Speaker 01

  It is wild, isn't it?

• 1:46:05Lex Fridman?

  It's wild how few people are funded.

• 1:46:08Speaker 01

  Yes.

• 1:46:08Lex Fridman?

  How much impact they have.

• 1:46:10Speaker 01

  Yes.

• 1:46:10Lex Fridman?

  Maybe that's a feature and not a bug. I don't know.

• 1:46:12Speaker 01

  Maybe, yes, exactly. At least early on, like it's sort of, I know, yeah.

• 1:46:16Lex Fridman?

  It's like Olympic athletes are often severely underfunded, but maybe that's what brings out the greatness.

• 1:46:22Speaker 01

  Perhaps. Yes, correct. No, exactly. Maybe this is the central part of it. Cause I do think about that in terms of, I currently have an incubator for open source startups. Like what I'm trying to do right now is create the environment I wished had existed when I was leaving academia with NumPy and trying to figure out what to do. I'm trying to create those opportunities and environments. So, uh, and that's, that's what drives me still is how do I make the world easier for the open source entrepreneur?

• 1:46:47Lex Fridman?

  So let me stay, I mean, I could probably stand up for a long time, but this is fun. Question. So Andre Karpati leads the Tesla Autopilot team and he's also one of the most like legit programmers I know. It's like he builds stuff from scratch a lot and that's how he builds intuition about how a problem works. He's built it from scratch and I always love that. And the primary language he uses is Python for the intuition building. But he posted something on Twitter saying that they got a significant improvement

• 1:47:26Lex Fridman?

  on some aspect of their like data loading, I think, by switching away from np dot square root. So the NumPy's implementation of square root to math dot square root. And then somebody else commented that you can you can get even a much greater improvement by using the vanilla Python square root, which is like

• 1:47:47Speaker 01

  Power 0.5?

• 1:47:48Lex Fridman?

  Power 0.5. And it's fascinating to me. I just wanted to

• 1:47:52Speaker 01

  Absolutely.

• 1:47:54Lex Fridman?

  So that was some shade throwing at some

• 1:47:56Speaker 01

  No, no.

• 1:47:57Lex Fridman?

  But also

• 1:47:58Speaker 01

  We're talking about.

• 1:47:59Lex Fridman?

  It's a good way to ask the trade off between usability and efficiency broadly in NumPy, but also on these like specific weird quirks of like a single function.

• 1:48:11Speaker 01

  Yep. So on that point, if you use a NumPy math function on a scaler, it's going to be slower than using a Python function on that scaler.

• 1:48:22Lex Fridman?

  Yeah.

• 1:48:23Speaker 01

  But because the the math object in NumPy is more complicated, right? Because you can also call that math object on an array. And so effectively it goes through a similar machine. There aren't enough of the which you would do in a you could do like checks and fast paths. So yeah, if you're basically doing a list, if you run over a list, in fact, for problems that are less than 1,000, even maybe 10,000 is probably the if you're going more than 10,000, that's where you definitely need to be using arrays. But if you're less than that, and for reading, if you're doing a reading process and essentially it's not compute bound, it's I/O bound. And so you're you're really taking lists of thousands at a time and doing work on it. Yeah, you could be faster just using Python, straight up Python.

• 1:49:07Lex Fridman?

  See, but also

• 1:49:09Speaker 01

  And then

• 1:49:09Lex Fridman?

  This is the, sorry to interrupt, but there's the fundamental questions when you look at the long arc of history. It's very possible that NP Duskrow is much faster.

• 1:49:20Speaker 01

  It could be.

• 1:49:21Lex Fridman?

  So like in terms of like, don't worry about it. It's the evils of over optimization or whatever. All the different quotes around that is, is uh, that sometimes obsessing about this particular little quirk is not, is not sufficient.

• 1:49:36Speaker 01

  Like for somebody like, uh, if you're, if you're trying to optimize your path, I mean, I agree, premature optimization creates all kinds of challenges. Right. Because now, but you may have to do it.

• 1:49:46Lex Fridman?

  I believe the quote is, it's the root of all.

• 1:49:48Speaker 01

  It's the root of all evil, right? Let's give Donald Newt, I think, or is he quoting somebody else?

• 1:49:53Lex Fridman?

  He's, well, Doc Newt is kind of like Mark Twain. People just attribute stuff to him. I don't.

• 1:49:57Speaker 01

  And we, and it's fine because he was brilliant. So, no, I was a LaTeX user myself, and so I have a lot of respect. And he did more than that, of course, but uh, yeah, I'm someone I really appreciate in the computer science space. Yeah, I don't, I think that's appropriate. There's a lot of little things like that where people actually, if you understood it, you go, yeah, of course that's the case.

• 1:50:16Lex Fridman?

  Yeah.

• 1:50:16Speaker 01

  Like, and the other part, and the other part I didn't mention, and number was a thing we wrote early on, and I was really excited by number because it's something we wanted. It was a compiler for Python syntax and I wanted it from the beginning of not writing NumPy because of this function question. Like taking, the, the power of arrays is really that you can write functions using all of it. It has implicit looping, right? So you don't worry about, I write this n dimensional for loop with, you know, four loops, for four statements. You just say, oh, big four dimensional array. I'm going to do this operation, this plus, this minus, this reduction. And you get this, it's called vectorization in other areas, but you can basically think at a high level and get massive amounts of computation done. With the added benefit.

• 1:51:00Speaker 01

  Of, oh, it can be paralyzed easily. It can be put in parallel. You don't have to think about that. In fact, it's worse to go decompose your, you write the for loops and then try to infer parallelism from for loops. So it's actually harder problem than to take the array problem and just automatically paralyze that problem. That's what, and, and so functions in NumPy are called universal functions, ufunks. So square root is an example of a ufunk. There are others, sine, cosine, add, subtract. In fact, one of the first libraries to SciPy was something called special, where I added vessel functions and like all these special functions that come up in physics. And I added them as ufunks so they could work on arrays. So I understood ufunks very, very well from day one inside of numeric. That was one of the things we tried to make better in NumPy was how do they work? Can they do broadcasting?

• 1:51:45Speaker 01

  What does broadcasting mean? But one of the problems is, okay, what do I do with a Python scalar? So what happens? The Python scalar gets broadcast to a zero dimensional array. And then it goes through the whole same machinery as if it were a 10,000 dimensional array. And then, and then it kind of unpacks the element and then does the addition. That's not to mention the function it calls, in the case of square root, is just the Seaborn's square root, right? In some cases, like Python's power, there's some optimizations they're doing for, that are, that can be faster than just calling the Seaborn's square root.

• 1:52:18Lex Fridman?

  In the interpreter or in the?

• 1:52:20Speaker 01

  No, in the C code, in the Python runtime.

• 1:52:22Lex Fridman?

  In the Python, right? So they're, they really optimize it and they have the freedom to do that because they don't have to worry about.

• 1:52:28Speaker 01

  It's just a scalar.

• 1:52:28Lex Fridman?

  It's just a scalar.

• 1:52:29Speaker 01

  Right. They don't have to worry about the fact that, oh, this could be an object with many, you know, many pieces. They're not, the ufunk machinery is also generic in sense that typecasting and broadcasting, broadcasting is the idea of I'm going to go, I have a zero dimensional array. I have a scalar with a four dimensional array and I add them. Oh, I have to kind of coerce the shape of this guy to make it work against the whole four dimensional array. So it's the idea of I can do a one dimensional array against a two dimensional array and have it make sense.

• 1:52:57Lex Fridman?

  Well, that's what NumPy does is it challenges you to reformulate, rethink your problem.

• 1:53:01Speaker 01

  Yes.

• 1:53:01Lex Fridman?

  As a multi dimensional array problem versus like. Move away from scalers completely.

• 1:53:07Speaker 01

  Right, exactly.

• 1:53:08Lex Fridman?

  Yeah.

• 1:53:08Speaker 01

  Exactly. In fact, that's where some of the edge cases boundaries are is that, well, they're still there. And this is where array scalers are particular. So array scalers are particularly bad in the sense that they were written so that you could optimize the math on them. But that hasn't happened, right? And so their default is to coerce the array scalar to a zero dimensional array and then use the NumPy machinery. That's what, and you could specialize, but it doesn't happen all the time. So in fact, when we first wrote number, we do comparisons and say, look, it's a thousand x speed up. We're lying a little bit in the sense that, well, first do the 40x slowdown of using array scalers inside of a loop. Because if you just use Python scalers, you'd already be 10 times faster.

• 1:53:50Lex Fridman?

  Yeah.

• 1:53:51Speaker 01

  But then we would get 100 times faster over that using just compilation. But what we do is compile the loop from out of the interpreter to machine code. And then that's always been the power of Python is this extensibility so that you can, because people say, oh, Python's so slow. Well, sure, if you do all your logic in the runtime of the Python interpreter, yeah. But the power is that you don't have to. You write all the logic, what you do at the high level is just high level logic. And the actual calls you're making could be on gigabyte arrays of data, and that's all done at compiled speeds. And the fact that integration is, one, can happen, but two, is separable. That's one of the, the language like Julia says, we're going to be all in one. You can do all of it together. And then there's, the jury's out, is that possible?

• 1:54:34Speaker 01

  I tend to think that you're gonna, there's separate concerns there. You want to pre-compile them, but generally you will want to pre-compile your, some of your loops. Like SciPy is a compilation step. To install SciPy, it takes about two hours. If you have many machines, maybe you can get it down to one hour, but to compile those libraries takes about, takes a while. You don't want to do that at runtime. You don't want to do that all the time. You want to have this pre-compiled binary available that you're then just linking into. So there's real questions about the whole, you know, source code. Code is, running binary code is more than source code. It's create object code, it's the linker, it's the loader, it's the, how does that interpret it inside of a virtual memory space? There's a lot of details there that actually I didn't understand for a long time until I, you know, read books on the topic.

• 1:55:17Speaker 01

  And it led to, the more you know, the better off you are. And you can do more details, but sometimes it helps you with abstractions too.

• 1:55:26Lex Fridman?

  Well, the problem, as we mentioned earlier with abstractions, is you kind of sometimes assume. That whoever implemented this thing had your case in mind and found the optimal solution.

• 1:55:39Speaker 01

  Yes.

• 1:55:40Lex Fridman?

  Or like you assume certain things. I mean, there's a lot of

• 1:55:43Speaker 01

  Correct.

• 1:55:43Lex Fridman?

  One of the really powerful things to me early on. I mean, it sounds silly to say, but with Python, probably one of the reasons I fell in love with it is dictionaries.

• 1:55:54Speaker 01

  Yes.

• 1:55:55Lex Fridman?

  So I obviously probably most languages have some

• 1:55:59Speaker 01

  Mapping concept.

• 1:56:00Lex Fridman?

  Some mapping concept, but it felt like it was a first class citizen. And it was just my brain was able to think in dictionaries. But then there is the thing that I guess I still use to this day is ordered dictionaries. Because that seems like a more natural way to construct dictionaries. And and from a computer science perspective, the running time cost is not that significant. But there's a lot of things to understand about dictionaries. That the abstraction kind of doesn't necessarily incentivize you to understand.

• 1:56:32Speaker 01

  Right. Do you really understand the notion of a hash map and how the dictionary is implemented? But you're right. Dictionaries are a good example of an abstraction that's powerful. And I agree with you. I agree. I love dictionaries too. Took me a while to understand that once you do, you realize, oh, they're everywhere. And Python uses them everywhere too. Like it's actually constructed that one of the foundational things is dictionaries. And it does everything with dictionaries.

• 1:56:51Lex Fridman?

  Yeah.

• 1:56:52Speaker 01

  So it is, it's powerful. Ordered dictionaries came later. But it is very, very powerful. It took me a little while coming from just the array programming entirely to understand these other objects. Like dictionaries and lists and tuples and binary trees. I guess I wasn't a computer scientist, but I studied arrays first. And so I was very array centric. And you realize, oh, these others don't have purposes and value, actually.

• 1:57:16Speaker 01

  I agree.

• 1:57:16Lex Fridman?

  There's a friendliness about like one way to think about arrays is arrays are just like full of numbers. But to make them accessible to humans. And make them less air prone to human users. Sometimes you want to attach names, human interpretable names that are sticky to those arrays.

• 1:57:39Speaker 01

  Yes.

• 1:57:40Lex Fridman?

  That's how you start to think about dictionaries is.

• 1:57:42Speaker 01

  Yes, good point.

• 1:57:43Lex Fridman?

  You start to convert numbers into something that's human interpretable. And that's actually the tension I've had.

• 1:57:48Speaker 01

  Correct.

• 1:57:49Lex Fridman?

  With NumPy because.

• 1:57:50Speaker 01

  Correct.

• 1:57:51Lex Fridman?

  I've built so much tooling around human, uh, human interpretability and also protecting me from a year later not making the mistakes by being, I wanted to force myself to use English versus, um, numbers.

• 1:58:07Speaker 01

  Yes, so there's a, there's a project called label arrays. Like very early, it was recognized that, oh, we need, we, we, we're indexing NumPy with just numbers, all the columns and particularly the dimensions. I mean, if you have an image, you don't necessarily need to label each column or row. But if you have a lot of images or you have another dimension, you'd at least like to label the dimension as this is X, this is Y, this is Z, or this is, give us some human meaning or some domain sort of meaning. That was one of the impetus for pandas, actually, was just, oh, we do need to label these things. And label, label array was an attempt to add that, like a lighter weight version of that. And there's been, like, that's an example of something I think NumPy could add, could be added to NumPy. But one of the challenges again, how do you fund this?

• 1:58:49Speaker 01

  Like, like I said, one of the tragedies, I think, is that, so I, I, I never had the chance to, I was never paid to work on NumPy, right? So I've always just done it in my spare time, always taken from one thing, taken from another thing to do it. And, and at the time, I mean, today, it would be the wrong time today, like paying me to work on NumPy now would not be a good use of effort, but, but we are finally at Quansight Labs, I'm actually paying people to work on NumPy and SciPy, which is, I'm thrilled with, I'm excited by. Uh, I've wanted to do that, that's what I always wanted to do from day one, it just took me a while to figure out a mechanism to do that.

• 1:59:22Lex Fridman?

  Even like in the university setting, respecting that, like pushing students, young minds, young graduate students to contribute. And then figuring out financial mechanisms that enable them to contribute. And then sort of reward them for their innovative scientific journey, that that would be nice. But then also just just a better allocation of resources. Uh, you know, it's 20 year anniversary since nine 11 and I was just looking, we spent over $6 trillion in the, in the Middle East after nine 11 in the various efforts there. And sort of to put politics and all that aside, it's just, you think about the education system,

• 2:00:04Lex Fridman?

  all the other ways we could have possibly allocated that money. And to me, to take it back, the amount of impact you would have by allocating a little bit of money to the programmers that build the tools that run the world is fascinating. I mean,

• 2:00:25Speaker 01

  It is.

• 2:00:26Lex Fridman?

  I, I don't know. I think, again, there is some aspect to being broke as somewhat of a feature, not a bug, that you make sure that your values.

• 2:00:37Speaker 01

  Manage that.

• 2:00:38Lex Fridman?

  Right, no, I know. I, so I, but I don't think that's a big part. So it's like, I think you can, you can have enough money and actually be wealthy while maintaining your values.

• 2:00:48Speaker 01

  Agreed, agreed. There's an old adage that, you know, nations that trade together don't go to war together.

• 2:00:53Lex Fridman?

  Yeah.

• 2:00:53Speaker 01

  Right, I, I've often thought about, you know, nations that code together.

• 2:00:56Lex Fridman?

  Yeah.

• 2:00:58Speaker 01

  Because one thing I love about open source is it's, it's global, it's multinational. Like there aren't national boundaries. One of the challenges with business and open source is the fact that well, business is national. Like businesses are entities that are recognized in legal jurisdictions, right? And have laws that are respected in those jurisdictions and hiring. And yet the open source ecosystem is not, that's not, it's not there. Like currently, one of the problems we're solving is hiring people all over the world. Right, because we, it's, it's a global effort. And I've had the chance to work and I've loved the chance. I've never been to like Iran, but I once had a conference where I was able to talk to people there. Right, and talk to folks in Pakistan, never been there, but we had a, a call where there, and there are people there,

• 2:01:40Speaker 01

  like just scientists and normal people and, you know, and, and it's, there's a, there's a certain amount of humanizing, right, that gets away from the, like, we often get the memes of society that bubble up and then get, or get discussed, but the memes are not even an accurate reflection of the reality of what people are.

• 2:01:57Lex Fridman?

  Well, if you look at the major power centers that are leading to something like cyber war in the next few decades, it's the United States, it's Russia and China.

• 2:02:07Speaker 01

  Right.

• 2:02:08Lex Fridman?

  And those three countries in particular have incredible developers. So if they work together.

• 2:02:14Speaker 01

  Yeah.

• 2:02:15Lex Fridman?

  I think that's one way, the politicians can do their stupid bickering, but like the, there's a layer of infrastructure of humanity.

• 2:02:21Speaker 01

  Yeah.

• 2:02:22Lex Fridman?

  If they collaborate together, that, that I think can prevent major, uh, major military conflict, which would, I think, most likely happen at the cyber level versus the actual hot war level.

• 2:02:34Speaker 01

  You're right. No, I think that's good, that's a good prediction.

• 2:02:38Lex Fridman?

  Nations that code together, uh, don't go to war together.

• 2:02:41Speaker 01

  Don't go to war together. That's, that's a hope, right? That's one of the philosophical hopes, but yeah.

• 2:02:47Lex Fridman?

  So you mentioned, uh, the project of number, which is, um, fascinating. So from the early days, there was kind of a, a pushback on Python that it's not fast. You know, you see C plus, if you want to write something that's fast, you use C, C++. If you want to write something that's usable and friendly, but slow, you use Python. And so what is, uh, number? What is its goal? How does it work?

• 2:03:12Speaker 01

  Great. Yeah. Yes, that's what the argument. And the reality was people would write high level code and use compiled code, but there's still user story, use cases where you want to write Python, but then have it still be fast. You still need to write a for loop. Like before number, it was always, don't write a for loop. You know, write it in a vectorized way, you know, put it in an array. And often that'll, that can make a memory trade off, like quite often you can do it, but then you make, maybe you use more memory because you have to build this, you know, array of data that you don't necessarily need all the time. So number was, it started from a desire to have, uh, kind of a, a vectorized that worked. The vectorized was a, was a tool in Num, NumPy, it was released. You give it a Python function and it gave you a universal function, a U funk that would work on arrays.

• 2:03:55Speaker 01

  So get a function that just worked on a scaler. Like you could make a, like the classic case was a simple function that an if then statement in it. So, uh, sine X over X function, sync function. If X equals zero, return one, otherwise do sine X over X. The challenge is you don't want that loop pegged on in Python, so you want a compiled version of that. But the you funk, the vectorize in NumPy would just give you a Python function. So it would take the array of numbers and at every call do a loop back into Python. So it was very slow. It gave you the appearance of a you funk, but it was very slow. So I always wanted a vectorize that would take that Python scalar function and produce a you funk working on binary native code. So in fact, I had somebody work on that with PyPI and see if PyPI could be used to produce a you funk like that.

• 2:04:40Speaker 01

  Early on in 2009 or something like that, 2010, it didn't work that well. It was kind of pretty bulky. But in 2012, Peter and I had just started Anaconda. We had, I had just, I, I'd learned to raise money. And that's a different topic, but I'd learned to, you know, raise money from friends, family and fools, as they say. That's a good line. Oh, that's a good line. But, you know, so I, we were trying to do something. We were, we were trying to change the world. Peter and I are super ambitious. We wanted to make array computing and we had ideas for really what's still, is still the energy right now. How do you do at scale data science? And we had a bunch of ideas there, but one of them, I had just talked to people about LVM and I was like, there's a way to do this.

• 2:05:24Speaker 01

  I just, I went, I heard about my friend Dave Beasley at a compiler course. So I was looking at compilers like, and I realized, oh, this is what you do. And so I wrote a version of Numba that just basically mapped Python bytecode to LVM. Nice. Right? So, and the first version is like, this works and it produces code that's fast. This is cool for, you know, obviously a reduced subset of Python. I didn't support all of the Python language. There had been efforts to speed up Python in the past, but those efforts were, I would say, not from the array computing perspective, not from the perspective of wanting to produce a vectorize improvement. They were from the perspective of speeding up the runtime of Python, which is fundamentally hard because Python allows for some constructs that aren't, you can't speed up.

• 2:06:07Speaker 01

  Like it's this generic, you know, when it does this variable. So I, from the start, did not try to replicate Python's semantics entirely. I said, I'm going to take a subset of the Python syntax and let people write syntax in Python, but it's kind of a new language, really. So it's almost like for loops, like focusing on for loops. For loops, scalar arithmetic, you know, typed, you know, really typed language, a type subset. That was the key. So, but we wanted to add inference of types. So you didn't have to spell all the types out because when you call a function, So Python is typed, it's just dynamically typed. You don't tell it what the types are, but when it runs, every time an object runs, there's a type for the variables. You know what it is.

• 2:06:49Speaker 01

  And so that was the design goals of number were to make it possible to write functions that could be compiled and have them used for NumPy arrays, like the need to support NumPy arrays.

• 2:07:00Lex Fridman?

  And so how does it work? Do you add a comment within Python that tells to do, like, how do you help out the compiler?

• 2:07:06Speaker 01

  Yeah, so there isn't much, actually. You don't. It's kind of magical in the sense it just looks at the type of the objects and then does type inference to determine any of the other variables it needs. And then it was also because we had a use case that that could work early. Like, one of the challenges of any kind of new development is if you have something that to make it work, it's going to take you a long time. It's really hard to get out of the ground. If you have a project where there's some incremental story, it can start working today and solve a problem, then you can start getting it out there, getting feedback. Because number today, now number is nine years old today, right? The first two, three versions were not great, right? But they solved a problem and so people could try it and we could get some feedback on it.

• 2:07:50Lex Fridman?

  Not great and that it was very focused.

• 2:07:52Speaker 01

  Very fragile, very subset. The subset it would actually compile was small. And so if you wrote Python code and said, so the way it worked is you write a function and you say at jit. Use decorators. So decorator is just these little constructs that you decorate code with an at and then a name. The at jit would take your Python function and actually just compile it and replace the Python function with a another function that interacts with this compiled function. And it would just do that and it would, you know, we went from Python bytecode, then we went to AST. I mean, writing compilers is actually, I learned a lot about why computer science is taught the way it is because compilers can be hard to write. They use tree structures, they use all the concepts of computer science that are needed.

• 2:08:35Speaker 01

  And it's actually hard to, you can, it's easy to write a compiler and then have it be spaghetti code. Like, the passes become challenging and we ended up with three versions of number, right? Number got written three times.

• 2:08:48Lex Fridman?

  What programming language is number written in?

• 2:08:50Speaker 01

  Python.

• 2:08:51Lex Fridman?

  Wait, okay.

• 2:08:52Speaker 01

  Yeah, Python, so.

• 2:08:54Lex Fridman?

  Really?

• 2:08:55Speaker 01

  Yeah.

• 2:08:55Lex Fridman?

  Fascinating.

• 2:08:56Speaker 01

  Yeah, so Python, but then the whole goal of Numba is to translate Python bytecode to LVM. And so LVM actually does the code generation. In fact, a lot of times I'd say, yeah, it's super easy to write a compiler if you're not writing the parser, nor the code generator, right?

• 2:09:11Lex Fridman?

  So for people who don't know, LVM is the compiler itself, so you're.

• 2:09:15Speaker 01

  It's really badly named, Low Level Virtual Machine, which that part of it is not used. It's really low.

• 2:09:19Lex Fridman?

  Chris, he doesn't mean that.

• 2:09:23Speaker 01

  Love Chris, but the name makes you imply that the virtual machine is what it's all about. It's actually the IR and the library that the code generation. That's the real beauty of it. The fact that, what I love about LVM was the fact that it was a plateau you could collaborate on. Right? Instead of the internals of GCC or the internals of the Intel compiler, like, how do I extend that? And here's a place you could collaborate. And we were early, I mean, people had started before. It's a slow compiler, like, it's not a fast compiler. So for some kind of Jits, like, Jits are common in a language because, one, every browser has a JavaScript Jit. It does real-time compilation of the JavaScript to machine code.

• 2:10:03Lex Fridman?

  For people who don't know, Jit is Just-in-Time compilation.

• 2:10:06Speaker 01

  Thank you, yeah, Just-in-Time compilation. They're actually really sophisticated. In fact, I got jealous of how much effort was put into the JavaScript Jits.

• 2:10:13Lex Fridman?

  Yes, well, it's kind of incredible what they've done.

• 2:10:15Speaker 01

  Yes. I completely agree, I'm very impressed. But, you know, Numba was an effort to make that happen with Python. And so we used some of the money raised from Anaconda to do it. And then we also applied for this DARPA grant and used some of that money to continue the development. And then we used proceeds from service projects we would do. We get consulting projects on, that we would then use some of the profits to invest in Numba. So we ended up with a team of two or three people working on Numba. It was a fits and starts, right? And ultimately, the fact that we had a commercial version of it also we were writing. So part of the way I was trying to fund Numba is say, well, let's do the free Numba, and then we'll have a commercial version of Numba called Numba Pro. And what Numba Pro did is it targeted GPUs.

• 2:10:58Speaker 01

  So we had the very first CUDA Jit and the very first at-Jit compiler that in 2012 through 13, you could run not just a U-Funk on CPU, but a U-Funk on GPUs. And it would automatically paralyze it and get 1000x speed.

• 2:11:13Lex Fridman?

  And that's an interesting funding mechanism because. You know, large companies or larger companies care about speed.

• 2:11:23Speaker 01

  Speed, exactly.

• 2:11:24Lex Fridman?

  In just this way. So it's exactly a really good way.

• 2:11:28Speaker 01

  Yeah, there's been a couple of things you know people will pay for. One, they'll pay for really good user interfaces. Right? And so I'm always looking for what are the things people will pay for that you could actually adapt to the open source infrastructure? One is definitely user interfaces. The second is speed.

• 2:11:41Lex Fridman?

  Yeah.

• 2:11:42Speaker 01

  Like a better runtime, faster runtime.

• 2:11:43Lex Fridman?

  And then when you say people, you mean like a small number of people pay a lot of money, but then there's also this other mechanism that.

• 2:11:49Speaker 01

  That's true.

• 2:11:49Lex Fridman?

  A ton of people pay.

• 2:11:51Speaker 01

  That's true.

• 2:11:51Lex Fridman?

  A little bit. First, I gotta, we mentioned Anaconda, we mentioned friends, family and fools. So Anaconda is yet another, so there's a company, but there's also a project.

• 2:12:03Speaker 01

  Correct.

• 2:12:04Lex Fridman?

  That is exceptionally impactful in terms of, for many reasons, but one of which is bringing a lot more people into the

• 2:12:15Lex Fridman?

  into the community of folks who use Python. So what is Anaconda?

• 2:12:21Speaker 01

  Yeah.

• 2:12:21Lex Fridman?

  What is its goals?

• 2:12:23Speaker 01

  Yeah.

• 2:12:23Lex Fridman?

  Maybe what is Conda versus Anaconda?

• 2:12:26Speaker 01

  Yeah, I'll tell you a little bit of the history of that because Anaconda, we wanted to do, we wanted to scale Python. Because we, you know, that was the goal. Peter and I had the goal of when we started Anaconda, we actually started as Continuum Analytics was the name of the company that started. It got renamed Anaconda in 2015, but we, we said, we want to scale analytics. NumPy is great. Pandas is emerging, but these need to run at scale with lots of machines. The other thing we wanted to do was make user interfaces that were web. We wanted to make sure the web did not pass by the Python community, that we had a ways to translate your data science to the web. So those are the two kind of technical areas. We thought, oh, we'll build products in this space. And that was the idea.

• 2:13:07Speaker 01

  Very quickly in, but of course, the thing I knew how to do was to do consulting to make money and to make sure my family and friends and fools that had invested didn't lose their money. So it's a little different than if you take money from a venture fund. If you take money from a venture fund, the venture fund, they want you to go big or go home. They're kind of like expecting nine out of 10 to fail or 99 out of 100 to fail. It's different. I was I was at a barbell strategy. I was like, I can't fail. I mean, I may not do super well, but I cannot lose their money. So I'm gonna do something I know can return a profit. But I want to have exposure to an upside. So that's what happened in a con. And we didn't. There was lots of things we did not well in terms of that structure. And I've learned from since and have it better.

• 2:13:48Speaker 01

  But we've, uh, we did a really good job of kind of attracting the interest around the area to get good people working. And then get funnel some money on some interesting projects. Super excited about what came out of our energy there, like a lot did.

• 2:14:01Lex Fridman?

  So what are some of the interesting projects?

• 2:14:03Speaker 01

  So Dask, Numba, Bokeh, Condor. There was a data shader, panel, Holovis.

• 2:14:11Speaker 01

  These are all tools that are extremely relevant in terms of helping you build applications, build tools, build faster code. There's a couple I'm forgetting. Oh, Jupiter Lab. Jupiter Lab came out of this, too.

• 2:14:23Lex Fridman?

  Fascinating, actually. Okay, so, uh, Bokeh does plotting?

• 2:14:27Speaker 01

  Bokeh does plotting. So Bokeh was one of the foundational things to say, I want to do plot in Python, but have the things show up in a web.

• 2:14:33Lex Fridman?

  Right. That's right. That's right. That's right. And plotting to me, still, with all due respect to Matplotlib and Bokeh, is feels like still an unsolved problem, not solved problem.

• 2:14:45Speaker 01

  It is. It's a it's a big problem.

• 2:14:47Lex Fridman?

  Right, because you're. I mean, I don't know. It's visualization broadly, right?

• 2:14:53Speaker 01

  I think we've got a pretty good API story around certain use cases of Python, of plotting. But there's a difference between static plots versus interactive plots versus I'm a end user. I just want to write a simple. For, you know, Panda started the idea of here's a data frame, I want to dot plot. I'm just going to attach plot as a method to my object. Which was a little bit controversial, right? But works pretty well, actually, because there's a lot less you have to pass in, right? You could just say, here's my object. You know what you are. You tell the visualization what to do. So that and there's things like that that have not been, you know, super well developed entirely. But Bokeh was focused on interactive plotting. So you could, it's a short path between interactive plotting and application, dashboard application.

• 2:15:37Speaker 01

  And there's some incredible work that got done there, right? And it was a hard project because then you're basically doing JavaScript and Python. So we wanted to tackle some of these hard problems and try to and just go after them. We got some DARPA funding to help, and it was super helpful. Funny story there. We actually did two DARPA proposals, but one we were five minutes late for. And DARPA has a very strict cutoff window. And so I, we had two proposals, one for the Bokeh and one for actually Numba and the other work.

• 2:16:04Lex Fridman?

  Which one were you late for?

• 2:16:05Speaker 01

  The Foundation on the Miracle work. So Bokeh got funded. Fortunately, Chris let us use some of the money to fund still some of that other foundational work. But it wasn't as, yeah, he, his hands were tied. He couldn't do anything about it. Uh, that was a whole interesting story.

• 2:16:20Lex Fridman?

  So one of the incredible projects that you worked on is Condor.

• 2:16:24Speaker 01

  Yes.

• 2:16:24Lex Fridman?

  So what is Condor?

• 2:16:26Speaker 01

  Yeah, Condor, it was early on, like I said, with SciPy. SciPy was a distribution mask and a library. And he said, talking, heard me talking about compiler issues and trying to get the stuff shipped and the fact that people can use your libraries if they have it. So for a long time, we'd understood the packaging problem in Python. And one of the first things he did at Anaconda, at Continuum Analytics, became Anaconda, was organize the PyData ecosystem in conjunction with NumFocus. We actually started NumFocus with some other folks in the community, the same year we started Anaconda. I said, we're going to build a corporation, but we also got to reify the community aspect and build a nonprofit. So we did both of those.

• 2:17:04Lex Fridman?

  Can we pause real quick and, and can you say what is PyPy, the Python package index, like this whole story of packaging in Python?

• 2:17:14Speaker 01

  Yeah, that's what I'm going to get to, actually. This is exactly the journey I'm on. It's to sort of explain packaging in Python. I think it's best expressed through the conversation I had with Guido at a conference, where I said, so, you know, packaging is kind of a problem. And Guido said, I don't ever care about packaging. I don't, don't use it. I don't, I don't install new libraries. I'm like, I guess if you're the language creator and if you need something, you just put it in the, put it in the distribution, maybe you don't worry about packaging. But Guido has never really cared about packaging, right? And never really cared about the problem of distribution. It's somebody else's problem. And that's a fair position to take, I think, as a language creator. In fact, there's a philosophical question about, should you have different development packaging managers? Should you have a package manager per language? Is that really the right approach? I think there are some answers of. It is appropriate to have development tools.

• 2:17:59Speaker 01

  And there's an aspect of development tool that is related to packaging. And every language should have some story there to help their developers create.

• 2:18:07Lex Fridman?

  So you should have language specific.

• 2:18:08Speaker 01

  Language specific development tools that relate to package managers. But then there's a very specific user story around package management that those language specific package managers have to interact with and currently aren't doing a good job of that. That was one of the challenges that not seeing that difference and it still exists in the difference today. Konda always was a user. I'm going to use Python to do data science. I'm going to use Python to do something. How do I get this installed? It was always focused on that. So it didn't have like a develop. You know, classic example is pip has a pip develop. It's like, I want to install this into my current development environment today. Now, Konda doesn't have that concept because it's not part of the story.

• 2:18:47Lex Fridman?

  For people who don't know, pip is a Python specific.

• 2:18:52Speaker 01

  Specific.

• 2:18:53Lex Fridman?

  Packaging manager, package manager.

• 2:18:56Speaker 01

  Right.

• 2:18:56Lex Fridman?

  That's exceptionally popular. That's probably like the default thing.

• 2:19:01Speaker 01

  It's the default user. Yeah, it's sort of the story there emerged because what happened is in 2012, we had this meeting at the Google, Google Plex. And Guido was there to come talk about what we're going to do. How are we going to make things work better? And Wes McKinney, me, Peter, Peter has a great photo of me talking to Guido. And he pretends we're talking about this story. Maybe we were, maybe we weren't. But we did at that meeting talk about it and ask Guido, Guido, what? We need to fix packaging in Python. Like people can't get the stuff. And he said, go fix it yourself. I don't think we're going to do it. All right.

• 2:19:30Lex Fridman?

  The origin story right there.

• 2:19:31Speaker 01

  All right, you said, okay, you said to do this ourselves. So at the same time, people did start to work on the packaging story in Python. It just took a little longer. So in 2012, kind of motivated by our training courses we were teaching, like how do we, very similar to what you just mentioned about your mother. Like it was motivated by the same purpose. Like, how do we get this into people's hands? And it's this big, long process. It takes too expensive. It was actually hurting NumPy development because I would hear, people were saying, don't make that change to NumPy because I just spent a week getting my Python environment. And if you change. If you change it on Py, I have to reinstall everything. And reinstalling is such a pain, don't do it. I'm like, wait, okay, so now we're not making changes to a library because of the installation problem that it'll cause for end users. Okay, there's a problem with pack, there's a problem with installation, we've got to fix this.

• 2:20:15Speaker 01

  So we, we, we said, we're going to make a distribution of Python. And we'd previously done that, previously done that at Endthought. I wanted to make one that we give away for free, that everyone could just get. Like, that was critical, that we could just get it, you know, it wasn't tied to a product, it was just you could get it. And then, we had constantly thought about, well, do we just leverage RPM? Do we, but the challenge had always been, we want a package manager that works on Windows, Mac OS X, and Linux the same. Right? And it wasn't there, like, you don't have anything like that.

• 2:20:42Lex Fridman?

  And for people who don't know, RPM is

• 2:20:45Speaker 01

  Red Hat's package manager.

• 2:20:47Lex Fridman?

  Operating system specific package manager.

• 2:20:49Speaker 01

  Correct, it's an operating specific, yes.

• 2:20:51Lex Fridman?

  So do you create, the, the, the design question is, do you create an umbrella package manager that

• 2:20:57Speaker 01

  Yes.

• 2:20:57Lex Fridman?

  Cross operating system?

• 2:20:58Speaker 01

  Yes, that was the decision.

• 2:21:00Lex Fridman?

  And a neighboring design question is, do you also create a package manager that spans multiple programming languages?

• 2:21:06Speaker 01

  Correct, exactly. That was the world we faced, and we decided to go multiple operating systems, multiple, and programming language independent. Because even Python, and particularly what was important, was SciPy has a bunch of FORTRAN in it, right? And Scikit-learn has links to a bunch of C++. There's a lot of compiled code. And the Python package manager, especially early on, didn't even support that. So in 2000, so we, we wrote, we, we released Anaconda, which was just a distribution of libraries, but we started to work on Conda in 2012. First version of Conda came out in early 2013, 2000, summer of 2013. And it was a package manager, so you could say, Conda install Scikit-learn. In fact, that was the, Scikit-learn was a fantastic project that emerged. Kind of, it was the, the classic example of the scikit.

• 2:21:51Speaker 01

  I told, talked to you earlier about SciPy being too big to be a single library. Well, what the community had done is said, let's make scikit. And there's scikit- images, there's scikit-learn, there's a lot of scikit. And it was a fantastic move, you know, that the community did. I didn't do it. I was like, okay, that's a good idea. I didn't like the name. I didn't like the fact you typed scikit image. I was like, that's gonna be simpler. SK learn. We gotta make that smaller. I don't like typing all this stuff from imports. So I was kind of a pressure that way. But I love the energy and love the fact that they went out and they did it. And lots of people, Jared Millman, and then of course, Gail, and there's people I'm not even naming. That scikit learn really emerged as fantastic project.

• 2:22:29Lex Fridman?

  And the documentation around that is also incredible.

• 2:22:31Speaker 01

  It was incredible. Exactly.

• 2:22:32Lex Fridman?

  I don't know who did that, but they did a great job.

• 2:22:34Speaker 01

  A lot of people in NRIA, a lot of people, a lot of European contributors. Andreas, there's some Andreas in the US. There's a lot of just people I just adore. I think are amazing people. Awesome use of scipy, right? I love the fact that they were using scipy effectively to do something I love, which is machine learning. But couldn't install it.

• 2:22:53Lex Fridman?

  Cause there's so many pieces involved.

• 2:22:55Speaker 01

  So many dependencies, right? So our use case of Conda was con install scikit learn, right? And it was the best way to install scikit learn in 2013 to really 2018, 17, 18. Pip finally caught up. I still, I still think it's you should con install scikit learn instead of pip install scikit learn. But you can pip install scikit learn. The issue is the package they created was wheels and pip does not handle the multi-vendor approach. They don't handle the fact you have C++ libraries you're depending on. They just stop at the Python boundary. And so what you have to do in the wheel world is you have to vendor. You have to take all of the binary and vendor it. Now, if your change happens in early dependency, you have to redo the whole wheel.

• 2:23:38Speaker 01

  So TensorFlow is a good example of a, you should not pip install TensorFlow. It's a terrible idea. People do it because the popularity of pip. Many people think, oh, of course, that's how I install everything in Python.

• 2:23:49Lex Fridman?

  Yeah, this is one of the big challenges. You know, you take a GitHub repository or just a basic blog post. The number of time pip is mentioned over Conda is like 100x to one.

• 2:24:00Speaker 01

  Correct, correct.

• 2:24:01Lex Fridman?

  So they just have to.

• 2:24:01Speaker 01

  And that was increasing. It wasn't true early because pip didn't exist. Like Conda came first.

• 2:24:06Lex Fridman?

  So but that's like the long tail of the internet documentation user generated so that like you think. How do I install Google? How do I install TensorFlow? You're just not going to see Konda in that first page.

• 2:24:17Speaker 01

  Correct, exactly.

• 2:24:18Lex Fridman?

  And that

• 2:24:19Speaker 01

  Today, you would you would have in 2016, 2017.

• 2:24:24Lex Fridman?

  And it's sad because you saw the Konda solves a lot of usability issues.

• 2:24:29Speaker 01

  Correct.

• 2:24:29Lex Fridman?

  Like for especially super challenging thing, I don't know. One of the big pain points for me was just on the computer vision side, OpenCV.

• 2:24:37Speaker 01

  Yes.

• 2:24:37Lex Fridman?

  Installation that

• 2:24:38Speaker 01

  Perfect example, yes.

• 2:24:39Lex Fridman?

  I think I don't know if Konda solved that one.

• 2:24:42Speaker 01

  Konda has an OpenCV package.

• 2:24:43Lex Fridman?

  I don't know. I certainly know PIP has not solved. I mean, there's complexities there because

• 2:24:50Speaker 01

  Right.

• 2:24:51Lex Fridman?

  I actually don't know. I should probably know a good answer for this, but you know, if you compile OpenCV with certain dependencies, you'll be able to do certain things. So there's this kind of flexibility of what you like, what options you compile with. And I don't think it's trivial to do that in with Konda or

• 2:25:12Speaker 01

  So Konda has a notion of variance of a package. You can actually have different compilation versions of a package. So not just the version is different, but oh, this is compiled with these optimizations on it. So Konda does have an answer.

• 2:25:22Lex Fridman?

  As flavors.

• 2:25:23Speaker 01

  Has flavors, basically.

• 2:25:24Lex Fridman?

  Well, PIP, as far as I know, does not have flavors.

• 2:25:27Speaker 01

  No, PIP generally hasn't thought deeply about the binary dependency problem, right? And that's why fundamentally it doesn't work for the sci-fi ecosystem. It barely, you can sort of paper over it and duct tape and it kind of works until it doesn't and it falls apart entirely. So it's been a mixed bag, like, and I've been having lots of conversations with people over the years because again, it's an area where if you if you understand some things, but not all the things, but they've done a great job of community appeal. This is an area where I think Anaconda as a company needed to do some things in order to make Konda more community centric, right? And this is a, I talk about this all the time. There's a balance between

• 2:26:09Speaker 01

  you have, every project starts with what I call company backed open source, even if the company is yourself, it's just one person, just doing business as. But ultimately for products to succeed virally and become massive influencers, they have to create, they have to get community people on board. They have to get other people on board. So it has to become community driven. And a big part of that is engagement with those people, empowering people, governance around it. And there was, and what happened with Conda in the early days, PIP emerged and we did, we did do some good things. Conda Forge, Conda Forge community is sort of the community recipe creation community. But Conda itself, I still believe, and Peter is CEO of Anaconda, he's my co-founder. I ran Anaconda till 2017, 2018.

• 2:26:53Lex Fridman?

  Is Peter still in it?

• 2:26:53Speaker 01

  Peter's still in Anaconda, right? We're still great friends. We're great friends. We talk all the time. I love him to death. There's a long story there about like why and how and we could cover in some, some other podcasts perhaps.

• 2:27:03Lex Fridman?

  Yeah.

• 2:27:04Speaker 01

  It's sort of a more, maybe a more business focused one, but, but this is one area where I think Conda should be more community driven. Like he, he should be pushing more to get more community contributors to Conda and let, let, let the, not, like Anaconda shouldn't be fighting this battle.

• 2:27:20Lex Fridman?

  Yeah.

• 2:27:21Speaker 01

  Right? It's actually, it's really a developers, like you said, like help the developers and then they'll actually move us the right direction.

• 2:27:27Lex Fridman?

  Well, that was the problem I have is many of the cool kids I know don't use Conda and that to me is confusing.

• 2:27:33Speaker 01

  It is confusing. It's a really a matter of, Conda has some challenges. First of all, Conda still needs to be improved. There's lots of improvements to be made. And that, it's that aspect of wait, who's doing this? And the fact that then the PyPI really stepped up. Like they were not solving the problem at all. And now they, they kind of got to where they're solving it for the most part. And then effectively you could get, like Conda solved a problem that was there and it still does and it's still, you know, there's still great things it can do. But, and we still use it all the time at Quansight and with other clients, but with, but you can kind of do similar things with PIP and Docker. Right? So especially with the web development community, that part of it again is this, is the, there's a lot of different kind of developers in the Python ecosystem.

• 2:28:15Speaker 01

  And there's still a lack of, of some clear understanding. I go to the Python conference all the time and then there's only a few people in the PyPI who get it. And then others who are just massively trumpeting the power of PIP, but just do not understand the problem.

• 2:28:27Lex Fridman?

  Yeah. So one of the obvious things to me from a mom, from a non-programmer perspective is the cross. Operating system usability, that's much more natural. So this

• 2:28:37Speaker 01

  Yeah.

• 2:28:38Lex Fridman?

  People use Windows and just it seems much easier to uh to recommend Konda there. But then it you should also recommend it across the board. So I I'll definitely sort of.

• 2:28:48Speaker 01

  But what I recommend now is a hybrid. I do. I mean, I have no problem.

• 2:28:51Lex Fridman?

  Is it possible to use?

• 2:28:52Speaker 01

  Oh, it is. It is. What I like, build the environment with pip with Konda, build an environment with Konda. And then pip install on top of that. That's fine. Be careful about pip installing OpenCV or TensorFlow or because if somebody's allowed that, it's going to be most surely done in a way that can't be updated that easily. It's.

• 2:29:10Lex Fridman?

  So install like the big packages, the infrastructure with Konda and then the weirdos.

• 2:29:15Speaker 01

  Yeah, the.

• 2:29:16Lex Fridman?

  That like the weird like implementation for some uh head. Um, there's a cool uh library I used that based on your location and time of day and date tells you the exact position of the sun relative

• 2:29:31Speaker 01

  Oh, very cool.

• 2:29:32Lex Fridman?

  To the earth. And it's just like a simple library, but it's very precise. And I was like, all right, but that was that was uh and it's pip.

• 2:29:39Speaker 01

  Well, the thing they did really well is Python developers who want to get their stuff published, they you have to have a pip recipe.

• 2:29:46Lex Fridman?

  Yeah.

• 2:29:47Speaker 01

  Right? I mean, even if it's, you know, the challenge is, and there's a key thing that needs to be added to pip. Just simply add to pip the ability to defer to a system package manager. Like, because it's, you know, recognize you're not going to solve all the dependency problem. So let like, give up and allow the allow a system packager to to work. That way, Anaconda is installed and it has pip. It would default to Konda to install its stuff, but but Red Hat RPM would default RPM to install install more things. Like, that's the that's a key, not difficult, but somewhat work, some work. Feature needs to be added. That's an example of something like I've known we need to do it. I mean, it's where I I wish I had more money. I wish I was more successful in the in the business side, trying to get there, but I wish my, you know, my family, friends and fool community that I know.

• 2:30:32Lex Fridman?

  Was larger.

• 2:30:32Speaker 01

  Was larger and had more money because I know tons of things to do effectively with more resources. But you know, I have not yet been successful at channel tons of it. There are some, you know, I'm happy with what we've done. We we we created again at Quansight. What we created to get Anaconda started. We created Community Innovation to get Anaconda started. Done it again with Quantsite. Super excited by that.

• 2:30:55Lex Fridman?

  By the way.

• 2:30:55Speaker 01

  It took three years to do it.

• 2:30:57Lex Fridman?

  What is Quantsite? What is its mission?

• 2:30:59Speaker 01

  Yeah.

• 2:30:59Lex Fridman?

  We've talked a few times about different fascinating aspects of it. But it's like, big picture.

• 2:31:03Speaker 01

  Big picture, Quantsite. Quantsite is, uh, its mission is to connect data to an open economy. So it's basically consulting of the Python ecosystem, right? It's a consulting company. And what I've said when I started it was we're trying to create products, people and technology. So it's divided into two, two groups and a third one as well. The two groups are a consulting services company that just helps people do data science and data engineering and data management, uh, better and more efficiently.

• 2:31:30Lex Fridman?

  Full stack, like full thing.

• 2:31:32Speaker 01

  Full thing, but will help you build a infrastructure. If you're using Jupiter, we need, we do staff orientation, need more programmers, help you use DAS more effectively, help you use GPUs more effectively. Just basically a lot of people need help. So we do training as well to help people, you know, both, uh, immediate help and then get, get, learn from somebody. We've added a bunch of stuff too. We've kind of separated some of these other things into another company called Open Teams that I'm, that we currently started. One of the things I loved what we did at Anaconda was creating a community innovation team. And so I wanted to replicate that. This time we did a lot of innovation at Anaconda. I wanted to do innovation, but also contribute to the projects that existed, like create a place where maintainers, so that SciPy and NumPy and NumPy and all these projects we already started can pay people to work on them and keep them going.

• 2:32:17Speaker 01

  So that's Labs. Quantsite Labs is a separate organization. It's a nonprofit mission. The profits of Quantsite help fund it. And in fact, every project that we have at Quantsite, a portion of the money goes directly to Quantsite Labs to help keep it funded. So we've gotten several mechanisms. We keep Quantsite Labs funded and currently, so I'm really excited about Labs because it's been a mission for a long time.

• 2:32:38Lex Fridman?

  What kind of projects are within Labs?

• 2:32:40Speaker 01

  So Labs is working to make the software better, like make NumPy better, make SciPy better, make, it's, it's, it's only works on open source. So, you know, if somebody wants to, so, you know, companies do, we have a thing called a community work order, we call it. If a company says, I want to make Spider better. Okay, cool. You can pay for a month of a, of a developer of Spider, uh, or a developer of NumPy or developer of SciPy. You're not, you can't tell them what you want them to do. You can give them your priorities and things you wish, you wish existed and they'll work. On those priorities with the community to get what the community wants and what emerges what the community wants.

• 2:33:13Lex Fridman?

  Is there some aspect on the consulting side that is helping as we were talking about morphology and so on, is there specific application that are particularly like driving sort of inspiring the need for updates to

• 2:33:26Speaker 01

  Correct, absolutely, absolutely. GPUs are absolutely one of them. And new hardware beyond GPUs. I mean Tesla's Dojo chip, I'm hoping we'll have a chance to work on that perhaps. Things like that are definitely driving it. The other thing that's driving it is scalable, like speed and scale. How do I write NumPy code or NumPy Lite code if I want it to run across a cluster? You know, that's Dask or maybe it's Ray. I mean, there's sort of ways to do that now. Or there's Moden and there's so pandas code, NumPy code, SciPy code, scikit-learn code that I want to scale. So that's one big area.

• 2:33:59Lex Fridman?

  Have you gotten a chance to chat with Andre and Elon about because like

• 2:34:04Speaker 01

  No, I would love to, by the way. I have not, but I would love to. I just saw their Tesla AI days video. Super exciting.

• 2:34:11Lex Fridman?

  So this one of the, you know, I love great engineering, software engineering teams and engineering teams in general, and they're doing a lot of incredible stuff with Python. They're like

• 2:34:18Speaker 01

  They are.

• 2:34:18Lex Fridman?

  revolutionizing so many aspects of the machine learning pipeline.

• 2:34:23Speaker 01

  I agree.

• 2:34:24Lex Fridman?

  That's operating in the real world and so much of that is Python. Like you said, the guy running, you know, Andre Capati running Autopilot is tweeting about optimization of NumPy versus

• 2:34:35Speaker 01

  I would love to talk to him. In fact, we have at Quansight, we've been fortunate enough to work with Facebook on PyTorch directly. So we have about 13 developers at Quansight. Some of them are in labs working directly on PyTorch.

• 2:34:47Lex Fridman?

  On PyTorch.

• 2:34:48Speaker 01

  On PyTorch, right. So I basically when we started Quansight, I went to both TensorFlow and PyTorch and said, hey, I want to help connect what you're doing to the broader SciPy ecosystem because I see what you're doing. We have this bigger mission. We want to make sure we don't, you know, lose energy here. So and Facebook responded really positively and I didn't get the same reaction.

• 2:35:07Lex Fridman?

  Not yet. Not yet. I love the folks at TensorFlow.

• 2:35:10Speaker 01

  So I really love the folks at TensorFlow too. They're fantastic. I think it's the just How it integrates with their business, I mean, like I said, there's a lot of reasons, just the timing, the integration with their business, what they're looking for. They're probably looking for more users and I was looking to kind of cut some development effort and they couldn't receive that as easily, I think. So I'm hoping, I'm really hopeful and love, love the people there.

• 2:35:32Lex Fridman?

  What's the idea behind Open Teams?

• 2:35:34Speaker 01

  So Open Teams, I'm super excited by Open Teams because it's one of the, I mentioned my idea for investing directly in open source. So that's a concept called Pharoos S. But one of the things we, when we started Quansight, we knew we would do is we develop products and ideas and new companies might come out. At Anaconda, this was clear, right? Anaconda, we did so much innovation that like five or six companies could have come out of that. And we just didn't structure it so they could. But in fact, they have, you look at BASK, there's two companies coming out of BASK. You know, Bokeh could be a company. There's like lots of companies that could exist off the work we did there. And so I thought, oh, here's a recipe for an incubation, a concept that we could actually spawn new companies and new innovations.

• 2:36:15Speaker 01

  And then the idea has always been, well, money they earn should come back to fund the open source project. So, so labs is, you know, I think there should be a lot of things like Quansight Labs. I think this concept is one that scales. You could have a lot of open source research labs. Along the way, so in 2018, when the bigger idea came, how to make open source investable, I said, oh, I need to write, I need to create a venture fund. So we created a venture fund called Quansight Initiate at the same time. It's an angel fund, really. It's, you know, we started to learn that process. How do we actually do this? How do we get LPs? How do we actually go in this direction and build a fund? And I'm like, every venture fund should have an associated open source research lab. There's just no reason. Like our venture fund, the carried interest, a portion of it goes to the lab. It's, it directly will fund the lab.

• 2:36:58Lex Fridman?

  That's fascinating, brother. So you use the power of the organic formation of teams in the open source community and then like naturally that leads to a business that can make money.

• 2:37:08Speaker 01

  Yeah, correct.

• 2:37:09Lex Fridman?

  And then it always maintains and loops back to the open source.

• 2:37:12Speaker 01

  Loops back to open source, exactly. And to me, it's a natural fit. There's something, there's, there's absolutely a repeatable pattern there. And it's also beneficial because, oh, I have, I have natural connections to the open source if I have an open source research lab. Like they'll all, we, they'll all be out there talking to people. And so. We've had a chance to talk to a lot of early-stage companies and we and our fund focuses on the early stage. So Quansight has the services, the lab, the fund, right? In that process, a lot of stuff started to happen. They're like, oh, you know, we started to do recruiting and support and training. And I was starting to build a bigger sales team and marketing team and people besides just developers. And one of the challenges with that is you end up with different cultural aspects. You know, developers, you know, there's a, in any company you go to, you can kind of go, look, is this a business-led company? A developer-led company?

• 2:37:57Speaker 01

  Do they kind of coexist? Are they, what's the interface between them? There's always a bit of a tension there, like we were talking about before. You know, what is the tension there? With OpenTeams, I thought, wait a minute, we can actually just create, like this concept of Quansight plus labs. It's, well, we're, while it's specific to the PyData ecosystem, the concept is general for all open source. So OpenTeams emerged as, oh, we can create a business development company for many, many Quansites, like thousands of Quansites. And it can be a marketplace to connect, essentially be the enterprise software company of the future. If you look at what enterprise software wants from the customer side, and during this journey, I've had the chance to work and sell to lots of companies, Exxon and Shell and JPMorgan, Bank of America,

• 2:38:40Speaker 01

  like the Fortune 100, and talk to a lot of people in procurement and see what are they buying? And why are they buying? So, you know, I don't know everything, but I've learned a lot about, oh, what are they really looking for? And they're looking for solutions. They're constantly given products from the, from enterprise software. Here's open source, this enterprise software, now I buy it. And then they have to stitch it together into a solution. Open source is fantastic for gluing those solutions together. So whereas they keep getting new platforms they're trying to buy, what most open source, what most enterprises want is tools that they can customize that are as inexpensive as they can.

• 2:39:13Lex Fridman?

  Yeah, and so you almost want to maintain the connection to the open source because that's.

• 2:39:16Speaker 01

  Yes, so OpenTeams is about solving enterprise software problems.

• 2:39:21Lex Fridman?

  Brilliant, brilliant idea, by the way.

• 2:39:23Speaker 01

  With a connect, but we do it honoring the topology. We don't hire all the people. We are a network connecting the sales energy and the procurement energy. And we were on the business side, get the deals closed, and then have a network of partners like Quansight and others, who we hand the deals to, right? To actually do the work. And then we off, then we have to maintain, I feel like we have to maintain some level of quality control so that the client can rely on OpenTeams to ensure their delivery. It's not just, here's a lead, go figure that out. But no, we're going to make sure you get what you need.

• 2:39:51Lex Fridman?

  By the way, it's such a skill and I don't know if I have the patience. Everyone will have the patience to talk to the business people. Or more specific, I mean, there's all kinds of flavors of business people.

• 2:40:02Speaker 01

  There is, there are, yeah.

• 2:40:03Lex Fridman?

  Marketing people.

• 2:40:06Speaker 01

  There's a challenge. I hear what you're saying because I've had the same challenge.

• 2:40:09Lex Fridman?

  Yeah.

• 2:40:09Speaker 01

  And it's true. There's sometimes you think, okay, this is way overwrought.

• 2:40:13Lex Fridman?

  Yeah. So you have to become an adult and you have to, because the companies have needs. They have ways to make money and they, and they also want to learn and grow. And yet it's your job to kind of educate them in the best way, like the value of open source, for example.

• 2:40:25Speaker 01

  Right. And I'm really grateful for all my experiences over the past 14 years, understanding that side of it and still learning for sure. But not just understanding from companies, but also dealing with marketing professionals and sales professionals and people that make a career out of that and understanding what they're thinking about. And also understanding, well, let's make this better. Like, we can really make a place, like, open teams I see as the transmission layer between companies and open source communities, producing enterprise software solutions. Like, eventually, we want to, like, today, we're taking on SaaS and MATLAB and tools that we know we can replace for folks. Really, anytime you have a software tool at an organization where you have to do a lot of customization to make it work for you. Like, it's not you're just buying this thing off the shelf and it works. It's like, okay, you buy this system and you customize a lot,

• 2:41:07Speaker 01

  usually with expensive consultants, to actually make it work for you. All of those should be replaced by open source foundations with the same customization.

• 2:41:15Lex Fridman?

  You're doing such important work, such important work in these giant organizations that do exactly that, taking some proprietary software and hiring a huge team of consultants that customize it and then that's, the whole thing gets outdated quickly.

• 2:41:28Speaker 01

  Correct.

• 2:41:29Lex Fridman?

  And so, I mean, that's brilliant. So the one, one solution to that is kind of what Tesla's doing a little bit of, which is basically build up a software engineering team.

• 2:41:41Speaker 01

  Yeah.

• 2:41:41Lex Fridman?

  Like, build a team from scratch.

• 2:41:43Speaker 01

  Build a team from scratch. And companies are doing it well. That's what they're doing right now.

• 2:41:45Lex Fridman?

  Yeah.

• 2:41:46Speaker 01

  Right? And that's, that's okay.

• 2:41:47Lex Fridman?

  And you're creating a topology for some of that.

• 2:41:49Speaker 01

  Right. You just don't have to do, that's not the only answer, right? And so, other companies can access this, be more accessible. We literally, as we really say, open team is the future of enterprise software. It's, we're still early, like, this idea just percolated over the Past year as we've kind of grown quantsite and realized the extensibility of it. We just finished in our seed round that we're to help, you know, get more salespeople and then push the push the messaging correctly. And there's lots of tools we're building to make this easier. Like, we want to automate the processes. We feel like a lot of the power is the efficiency of the sales process. There's a lot of wasted energy in small teams and the sales energy to get into large companies and make a deal. There's a lot of money spent on that.

• 2:42:29Lex Fridman?

  Creating the tools and processes for the sales.

• 2:42:31Speaker 01

  Make that super seamless. So a single company can go, oh, I've got my contract with open teams. We've got a subscription they can get. They can make that procurement seamless. And then the fact they have access to the entire open source ecosystem. And we have a, you know, so we have a part of our work that's embracing open source ecosystems and making sure we're doing things useful for them. We're serving them and then companies making sure they're getting solutions they care about. And then figuring out which which targets we have, you know, we're we're not taking on all of open source, all of enterprise software yet, but but we're.

• 2:43:02Lex Fridman?

  Well, this feels like the future. The idea and the vision is brilliant. Can I ask you, why do you think Microsoft bought GitHub and what do you think is the future of GitHub?

• 2:43:11Speaker 01

  Great point. I thought it was a brilliant move. I think they did because Microsoft has always had a developer centric culture. Like they always have, like one of the things Microsoft's always done well is understand that their power is developers, right? It's been a balmor didn't necessarily make a make a good meme about how we approached that, but names are broadening that. I think that's why, because they recognize GitHub is where developers are at, right? And so.

• 2:43:33Lex Fridman?

  But do they have a vision like open teams type of situation, right?

• 2:43:36Speaker 01

  Think so yet.

• 2:43:38Lex Fridman?

  Are they just basically throwing money at developers to show their support?

• 2:43:42Speaker 01

  I think so.

• 2:43:43Lex Fridman?

  Without a topology, like you put it like a way to leverage that, like to give developers actual money.

• 2:43:50Speaker 01

  Right. I don't I don't think so. I think there's still it's an enterprise software company and they make a bunch of money. They make a bunch of games. They have a they're a big company. They sell products. I think part of it is they know there's opportunity to make money from GitHub, right? There's definitely a business there, you know, to sell to developers or to sell to people using development. I think there's part of that. I think part of it is also there's they had definitely wanted to recognize that the that you need to value open source to get great developers, which is which is an important concept that was emerging over the past 10 years that, you know, pay it by data. We were able to convince JP Morgan to support PyData because of that fact, right? That was where the money for them putting a couple hundred thousand into supporting PyData for several conferences was they want developers and they realized that developers want to participate

• 2:44:34Speaker 01

  in open source. So enterprise software folks don't always understand how their software gets used. Having spent a lot of time on the floors at JP Morgan, at Inshell, at ExxonMobil, you see, oh, these companies have large development teams. And then they're kind of dealing with the what's being delivered to them. So I really feel kind of a privilege that I had a chance to learn some of these people and see what they're doing and even work alongside them, you know, as a consultant, using open source and trying to think, how do we make this work inside of our larger organization?

• 2:45:04Lex Fridman?

  Some of it is actually, for a larger organization, some of it is messaging to the world that you care about developers and you're the cool.

• 2:45:12Speaker 01

  Yep.

• 2:45:12Lex Fridman?

  You care. Like, for example, like if Ford, because I talked to them, the car companies, right? They want to attract, you know, you want to take on Tesla and autopilot, you want to take that, right? And so what do you do there? You show that you're cool, like you try to show off that you care about developers and they have a lot of trouble doing that. And like one way, I think like Ford should have bought GitHub. They just show off.

• 2:45:38Speaker 01

  Yeah, not a better idea. Yeah.

• 2:45:40Lex Fridman?

  Like these old school companies and it's in a lot.

• 2:45:42Speaker 01

  Good point.

• 2:45:43Lex Fridman?

  A lot of different industries. There's probably different ways. It's probably in art to show that you care to developers and the developers, it's exactly what you, like, for example, just spitballing here, but like Ford or somebody like that could give a hundred million dollars to the development of NumPy and like, like literally, look at like the top most popular projects in Python and just say, we're just going to give money.

• 2:46:11Speaker 01

  Right.

• 2:46:12Lex Fridman?

  Like, that's going to immediately make you cool.

• 2:46:15Speaker 01

  They could, actually. Yeah. And in fact, we set up NumFocus to make it easy.

• 2:46:18Lex Fridman?

  Yeah.

• 2:46:19Speaker 01

  But the challenge was, is also you have to have some business development. Like, it's a bit of a, it's a bit of a seeding problem, right? And you look at how, I've talked to the folks at Linux Foundation, I know how they're doing it. I know how, and starting NumFocus, because we had two babies in 2012. One was Anaconda, one was NumFocus, right? And they were both important efforts. They had distinct journeys and super grateful that both existed and still grateful both exist. But there's different energies in getting donations as there is getting, this is important to my business. Like I'm selling something that this is a, I'm gonna make money this way. If you can tie it, if you can tie the message to an ROI for the company.

• 2:46:58Lex Fridman?

  That's more effective.

• 2:47:00Speaker 01

  It's much more effective, right? So, and there are rational arguments to make. I've tried to have conversations with market, especially in marketing departments. Like very early on, it was clear to me that, oh, you could just take a fraction of your marketing budget and just spend it on open source development and you get better results from your marketing. Like because.

• 2:47:19Lex Fridman?

  How did those, can I, sorry, I'm gonna try not to go on rants here. What have you learned from the interaction with the marketing folks on that kind of, because you gave a great example of something that will obviously be much better investment in terms of marketing is supporting open source projects.

• 2:47:35Speaker 01

  The challenge is not dissimilar from the challenge you have in academia of the different colleges, right? Knowledge gets very specific and very channeled, right? And so people get, they get a lot of learning in the thing they know about. And it's hard then to bridge that and to get them to think differently enough to have a sense that you might have something to offer. Because it's different. It's like, well, how do I implement that? How do I, what do I do with that? Do I, which budget do I take from? Do I slow down my spend on Google Ads or my spend on Facebook Ads? Or do I not hire a content creator instead? There's an operational aspect to that, that you have to be the CMO, right? Or the CEO, you have to get the right level.

• 2:48:15Lex Fridman?

  So you have to hire at a high position level, people that care about this and these.

• 2:48:20Speaker 01

  Right, or they won't know how, right? And because you can also do it very clumsily, right? And I've seen, because you can, you absolutely have to honor and recognize the people you're going to and the fact that if you just throw money at them, it could actually create more problems.

• 2:48:34Lex Fridman?

  Can I just say, this is not you saying, can I just, because I just need, I need to say this. I've been very surprised.

• 2:48:42Lex Fridman?

  How often marketing people are terrible at marketing? I feel like the best marketing is doing something novel and unique that anticipates the future. It feels like so much of the marketing practice is like what they took in school, or maybe they're studying for what was the best thing that was done in the past decade, and they're just repeating that over and over, as opposed to innovating, like taking the risk. To me, marketing-

• 2:49:09Speaker 01

  That's a great point.

• 2:49:10Lex Fridman?

  Is taking the big risk.

• 2:49:11Speaker 01

  That's a great point.

• 2:49:12Lex Fridman?

  And being the first one to risk.

• 2:49:13Speaker 01

  Yeah, there's an aspect of data observation from that risk, right? That's, that's, that's, I think, could share to what they're doing already. But absolutely, it's, it's about, I think it's content. Like, there's this whole world on content marketing that you could almost say, well, yeah, it's, it can get over, you can get, you can get inundated with stuff that's not relevant to you. Whereas what you're saying would be highly relevant and highly useful and highly, highly beneficial.

• 2:49:36Lex Fridman?

  Yeah, but it's, it's, it's risky. I mean, that's why sort of, uh, there's a lot of innovative ways of doing that. Tesla's an examples of people that basically don't do marketing. Uh, they do marketing in a very, like, it's like Elon hired a person who's just good at Twitter for running Tesla's Twitter account.

• 2:49:53Speaker 01

  No, right, right.

• 2:49:54Lex Fridman?

  I mean, that's exactly what you want to be doing. You want to be constantly innovating in, uh-

• 2:49:57Speaker 01

  Right, there's an aspect of telling, I mean, I've, I've definitely seen people doing great work where you're not talking about it. Like, I would say that's actually a problem I have right now with Quansight Labs. Quansight Labs been doing amazing work, really excited about it. We have not been talking about it enough. We haven't been-

• 2:50:11Lex Fridman?

  And there's different ways to talk about it. There's different ways to, there's different channels to which to communicate. There's also, like, I'll just throw some, um, shade at companies I love. Uh, so for example, iRobot, I just had a conversation with them. They, they make Roombas.

• 2:50:26Speaker 01

  Sure.

• 2:50:26Lex Fridman?

  And they, I think, I love, and they're incredible robots, but like, every time they do commer- like advertisement, it's just, not advertisement, but like, marketing type stuff, it just looks so corporate. And to me, the incredible, uh, maybe wrong in the case of iRobot, I don't know, but to me, when you're talking about engineering systems- It's really nice to show off the magic of the engineering and the software and the all the all the geniuses behind this product and the tinkering and like the raw authenticity of what it takes to build that system versus the marketing people who want to have like pretty people like standing there all pretty with the robots like moving perfectly. So

• 2:51:11Lex Fridman?

  to me, there's some aspect, it's like speaking to the hackers, you have to throw some bones, some some care towards the the engineers, the developers, because there's some aspect, one for the hiring, but two, there's an authenticity to that kind of communication that's really inspiring to the end user as well. Like if they know that brilliant people, the best in the world are working at your company, they start to believe that that product that you're creating is really good.

• 2:51:38Speaker 01

  It's interesting because your initial reaction would be, wait, there's different users here. Why would you do that? You know, my wife bought a Rumba, but she and she loves developers, she loves me, but she doesn't care about that.

• 2:51:50Lex Fridman?

  Yeah.

• 2:51:50Speaker 01

  But essentially, what you said is actually the authenticity, because everyone has a friend, everyone knows people, there's word of mouth. I mean, if you.

• 2:51:57Lex Fridman?

  Word of mouth is so, so.

• 2:51:58Speaker 01

  Yeah, exactly. That's interesting. Because I think it's the lack of that that realization. There's this halo effect.

• 2:52:03Lex Fridman?

  Right.

• 2:52:03Speaker 01

  That influences your general marketing.

• 2:52:05Lex Fridman?

  I.

• 2:52:06Speaker 01

  Interesting.

• 2:52:06Lex Fridman?

  For some stupid reason, I do have a platform and it seems that the reason I have a platform, many others like me, millions of others, is like the authenticity and like we get excited naturally about stuff.

• 2:52:18Speaker 01

  Yeah.

• 2:52:19Lex Fridman?

  And like, I don't want to get excited about that iRobot video because it's boring, it's marketing, it's corporate as opposed to I wanted to do some fun. This is this is me, like a shout out to iRobot, is they're not letting me get into the robot.

• 2:52:33Speaker 01

  Yeah. Well, there's an aspect of they could be benefiting from a from a culture of modularity, like add-ons and.

• 2:52:40Lex Fridman?

  Right.

• 2:52:41Speaker 01

  Like that could actually dramatically help. You've seen that over history. I mean, Apple is an example of a company like that, or or the like, I can see, I can see what your point is, is that you have something that needs to be, it needs to be adopted broadly. The concept needs to be adopted broadly. And if you want to go beyond this one device, you need to engage this community.

• 2:52:59Lex Fridman?

  Yeah. And connecting to the the open source, as you said, I got to ask you. You're a programmer, one of the most impactful programmers ever. You've led many programmers, you lead many programmers. What are some, from a programmer perspective? What makes a good programmer? What makes a productive programmer? Is there a device you can give to be a great programmer in this world?

• 2:53:23Speaker 01

  That's a great, great question. And there are times in my life I'd probably answer this even better than I hope maybe give an answer today. Cause I've thought about this numerous times. Like right now, I've spent on so much time recently hiring salespeople that.

• 2:53:35Lex Fridman?

  That your mind is a little bit on something else.

• 2:53:38Speaker 01

  On something else, but I reflected on the past and also, you know, I have some really, the only way I can do this. I have some really great programmers that I work with who lead the teams that they, they lead. And my goal is to inspire them and hopefully help them, encourage them and be, help them encourage with their teams. I would say there's a number of things, a couple of things. One is curiosity. Like I, I think a programmer without curiosity is mundane. Like you'll lose interest, you won't do your best work. So it's sort of, it's an affect. It's sort of, are you, have some curiosity about things. I think two, don't try to do everything at once. Recognize that you're, you're, we're limited as humans. You're limited as a human and each one of us are limited in different ways. You know, we all have our different strengths and skills.

• 2:54:21Speaker 01

  So it's adapting the art of programming to your skills. One of the things that always works is to limit what you're trying to solve. Right? So if you're part of a team, usually maybe somebody else has put the architecture together and they've gotten, given a portion for you. If you're, if you're young, if you're not part of a team, it's sort of breaking down the problem into smaller parts. It's essential for you to make progress. It's very easy to take on a big project and try to do it all at once and you get lost and then you do it badly. And so thinking about, you know, very concretely what you're doing, define, you know, defining the inputs and outputs, defining what you want to get done. Even just talking about that and like writing down before you write code, just what are you trying to accomplish?

• 2:55:04Speaker 01

  I mean, very specific about it really, really helps. I think using other people's work, right? Don't, don't be afraid that somehow you're like, you should do it all, like. Nobody does.

• 2:55:18Lex Fridman?

  Standing on the shoulders of giants.

• 2:55:19Speaker 01

  Of giants.

• 2:55:20Lex Fridman?

  And copying and pasting from Stack Overflow.

• 2:55:21Speaker 01

  Copy and paste from Stack Overflow. It's like, but, but, but, don't just copy and paste. This is particularly relevant in the era of codecs and the, you know, the auto-generated code, which is essentially, I see as an indexing of Stack Overflow.

• 2:55:31Lex Fridman?

  Right, exactly.

• 2:55:32Speaker 01

  Exactly. It's like.

• 2:55:33Lex Fridman?

  It's a search engine.

• 2:55:34Speaker 01

  It's a search engine over Stack Overflow, basically. So it's not, I mean, we've had this for a while.

• 2:55:37Lex Fridman?

  Yeah.

• 2:55:38Speaker 01

  But really, you want to cut and paste, but, but, but not blindly. Like, absolutely, I've cut and pasted to understand, but then you understand, oh, this is what this means. Oh, this is what it's doing. And understand, you know, as much as you can. So it's critical. That's where the curiosity comes in. If you're just blindly cutting and pasting, you're not going to understand. And so, understand, and then, you know, be, be sensitive to hype cycles, right? Every few, every few often, there's always a, oh, test-driven development is the answer. Oh, object-oriented is the answer. Oh, like, there's always an answer, you know? Agile is the answer. Be cautious of jumping onto a hype cycle. Like, likely there's signal, like, there's a thing there that's, that's actually valuable you can learn from, but it's almost certainly not the answer to everything you need.

• 2:56:23Lex Fridman?

  What lessons do you draw from you having created NumPy and SciPy? Like, in, in service of sort of answering the question of what it takes to be a great programmer and giving advice to people?

• 2:56:35Speaker 01

  Yeah.

• 2:56:35Lex Fridman?

  How can you be the next person to create a SciPy?

• 2:56:37Speaker 01

  Yeah. So, one is listen.

• 2:56:40Lex Fridman?

  To?

• 2:56:40Speaker 01

  Listen.

• 2:56:41Lex Fridman?

  To who?

• 2:56:42Speaker 01

  To, to people that have a problem, right? Which is everybody, right? But, but listen and listen to many and, and try to, and then do. Like, don't, you're gonna have to do an experiment, you know? Do, fall down. Don't be afraid to fall down. Don't be afraid, you, you, the first thing you do is probably gonna suck, and that's okay, right? It's, it's honestly, I think, iteration is the key to innovation. And, and it's that, it's almost that psychological hesitation we have to just iterate. Like, yeah, we know, we know it's not great, but next someone will be better. I mean, just, just keep learning and keep improving and keep improving. So it's, it's, it's an attitude. And then, it doesn't take intense concentration, right?

• 2:57:27Speaker 01

  Good things don't happen just. It's not quite like TikTok or like Facebook, you know, you can't scroll your way to good programming, right? There are, you know, sincere like hours of of deep, don't be afraid of the deep problem. Like often people will run away from something because, oh, I can't solve this. And you might be right, but give it an hour, give it a couple of hours and see. And, you know, just five minutes, I'm not going to give you that.

• 2:57:51Lex Fridman?

  Was it lonely when you were building SciPy and NUPA?

• 2:57:55Speaker 01

  Hugely, yeah, absolutely lonely in the sense of you had to have an inner drive and that inner drive for me always comes from, I have to, I have to see that this is right in some angle. I have to believe it, that this is the right approach, the right thing to do. With SciPy, it was like, oh, yeah, the world needs libraries in Python. Clearly, Python is popular enough with enough influential people that to start and it needs more libraries. So that is a good in itself, so I'm going to go do that good. So find a good, find a thing that you know is good and just, just work on it. So that has to happen and it is, and you kind of have to have enough realization of your mission to be okay with the naysayer or the fact that not everybody joins you upfront. In fact, the one thing I've talked to people a lot, I've seen a lot of projects come and some fail.

• 2:58:40Speaker 01

  Like not everything I've done has actually worked perfectly. I've tried a bunch of stuff that, okay, that didn't really work or this isn't working and why. But you see the patterns and and one of the key things is you can't even know for six months, I say 18 months right now. If you're starting a new project, you got to give it a good 18 month run before you even know if the feedback's there. Like it's, you're not going to know in six months. You might have the perfect thing, but six months from now, it's still kind of still emerging. So give it time because you're dealing with humans and humans have an inertial inertia energy that just doesn't change that quickly. So.

• 2:59:13Lex Fridman?

  Let me ask a silly question, but you know, like you said, you're focused on the sales side of things currently, but. You know, back when you were actively programming, maybe in the 90s, you talked about IDEs. What's your setup that you have that brings you joy? Keyboard, number of screens?

• 2:59:33Speaker 01

  Yeah, I will.

• 2:59:33Lex Fridman?

  Linux?

• 2:59:34Speaker 01

  I do still like to program some, but it's not as much as I used to. I have two projects I'm super interested in, trying to find funding for them, trying to figure out some good teams for them, but I could talk about those. But what I, yeah, what, I'm an Emacs guy.

• 2:59:46Lex Fridman?

  Great, thank you. The superior editor, everybody. I've got, I don't often delete tweets, but one of the tweets I deleted when I said Emacs was better than Vim. And then the hate I got.

• 2:59:59Speaker 01

  It is.

• 3:00:00Lex Fridman?

  I was like, I'm walking away from this.

• 3:00:02Speaker 01

  I do too. I don't push it. I mean, I'm.

• 3:00:04Lex Fridman?

  I'm just joking, of course.

• 3:00:05Speaker 01

  Yeah, exactly. It's kind of like, but people do take the editor seriously, right?

• 3:00:09Lex Fridman?

  I did it as a joke.

• 3:00:10Speaker 01

  It's your life.

• 3:00:11Lex Fridman?

  It is, but there's something, there's something beautiful to me about Emacs, but there's, for people that love him, there's something beautiful to them about that.

• 3:00:18Speaker 01

  I mean, I do use Vim for quick editing, like command line. If I said quick editing, I will still sometimes use it, but not much. Like, it's simple, correct a single editor character.

• 3:00:27Lex Fridman?

  So when you were developing Scip, you were using Emacs?

• 3:00:30Speaker 01

  Yep. Scip and NumPy are all written in Emacs on a Linux box. And CVS and then SVN, version control. Git came later, like Git has, I love distributed branch stuff. I think Git is pretty complicated, but I love the concept. And also, of course, GitHub is, and then GitLab make Git definitely consumable. But that came later.

• 3:00:54Lex Fridman?

  Did you ever touch Lisp at all? Like, what were your emotional feelings about all the parentheses?

• 3:00:59Speaker 01

  Great question. So I find myself appreciating Lisp today much more than I did early. Because when I came to programming, I knew programming, but I was a domain expert, right? And to me, the parentheses were in the way. It's like, wow, there's just all this, like, it just gets in the way of my thinking about what I'm doing. So why would I have all these, right? That was my initial reaction to it. You know, now as I appreciate kind of the structure that kind of naturally maps to the, to a logical thinking about a program, I can appreciate them, right? And why it's actually, you could, you could create editors that make it not so. Problematic, right? Honestly.

• 3:01:35Lex Fridman?

  Yeah.

• 3:01:35Speaker 01

  So I actually have a much more appreciation of Lisp and things like Clojure and there's Hyvee, which is a Python, you know, a list that compiles the Python bytecode. I think it's challenging, like typically, these languages are, you know, I even saw the whole data science programming system in Lisp that somebody created, which is, you know, cool. But again, it's the, I think it's the lack of recognition of the fact that there exists what I call occasional programmers.

• 3:01:58Lex Fridman?

  Yes.

• 3:01:58Speaker 01

  People that are never going to be programmers for a living. They don't want to have all this the cuteness in their head. They want just, it's why BASIC, you know, Microsoft had the right idea with BASIC in terms of having that be the language of, uh, Visual Basic, the language of Excel and um, SQL Server. They should have converted that to Python 10 years ago, like world would be a better place if they had, but.

• 3:02:22Lex Fridman?

  There's also, uh, there's a beauty and a magic to the history behind a language in Lisp. You know, some of the most interesting people in, in the history of computer science and artificial intelligence have used Lisp, so.

• 3:02:33Speaker 01

  Yes.

• 3:02:33Lex Fridman?

  You feel.

• 3:02:34Speaker 01

  Well, it's back to that language, when you, when you have a language, you can think in it.

• 3:02:37Lex Fridman?

  Yeah.

• 3:02:38Speaker 01

  And it helps you think better.

• 3:02:39Lex Fridman?

  And it attracts a certain kinds of people that think in a certain kind of way, and then that's, that's there. Okay, so what about like small laptop with a tiny keyboard, or is there like?

• 3:02:48Speaker 01

  I'm a, you know, good question. I've never gotten into the big, the many screens, to be honest. I mean, and, and maybe it's because in my head, I kind of just, I just swap between windows.

• 3:02:57Lex Fridman?

  Right.

• 3:02:58Speaker 01

  Like, partly because I guess I, I really can't process three screens at once anyway. Like, I just am looking at one and I just flip, you know, I flip an application open.

• 3:03:09Lex Fridman?

  So what about?

• 3:03:10Speaker 01

  So where, where it's really helpful is actually when I'm trying to do, you know, here's data and I want to input it from here. Like, this is the only time I really need another screen.

• 3:03:17Lex Fridman?

  So now, because you're both developer, lead developers, but then there's also these businesses and there's salespeople, you're working with large companies.

• 3:03:25Speaker 01

  Operations people, hiring people.

• 3:03:27Lex Fridman?

  Yeah, the whole thing. Which operating system is your favorite though, at this, at this point? So Linux was the early days.

• 3:03:33Speaker 01

  So yeah, I love, love Linux as a, as a server side and it was early days I was, had my own Linux desktop. Um, I've been on Mac laptops for. Ten years now.

• 3:03:42Lex Fridman?

  Yeah, that this is what leadership looks like. You switch to Mac. Okay, great.

• 3:03:48Speaker 01

  Pretty much. I mean, just the fact that I had to do PowerPoints, I had to do presentations and, you know, plug in, I just couldn't mess with plugging in laptops that wouldn't project and.

• 3:03:59Lex Fridman?

  So you mentioned so Quansight Labs and things like that. Can you give advice on how to hire great programmers and great people?

• 3:04:09Speaker 01

  Yeah, I would say produce an open source project.

• 3:04:14Lex Fridman?

  Yeah.

• 3:04:14Speaker 01

  Get people contributing to it and hire those people.

• 3:04:16Lex Fridman?

  Yeah. I mean, you're doing it sort of, you may be perhaps a little biased, but that's probably a hundred percent really good advice.

• 3:04:25Speaker 01

  I find it hard to hire. I still find it hard to hire. Like, in terms of, I don't think, like, it's not hard to hire if I've worked with somebody for a couple of weeks. But a cup, an hour or two of interviews, I have no idea.

• 3:04:38Lex Fridman?

  So that instinct, that radar of knowing if you're good or not, that you've you found that you're still not able to.

• 3:04:45Speaker 01

  It's really hard. I mean, the resume can help, but again, the resume is like a presentation of the things they want you to see, not the reality of, of, and there's also, you know, you have to understand what you're hiring for. There are different stages and different kinds of skills. And so it isn't just a, um, one of the things I talk a lot about internally at my company is just that the whole idea of measuring ourselves against a unit, a single axis is flawed. Cause we're not, it's a multi dimensional space. And how do you order a multi dimensional space? There isn't one ordering. So this whole idea, you, you immediately have projected into a thing. When you're talking about hiring or best or worst or better or not better. So what is the thing you're actually needing?

• 3:05:28Speaker 01

  And you can even hire for that. There is such a thing. Generally, I really value people who have the affect. That care about open source. Like so in some cases, their their affinity to open source is simply a kind of a filter of an affect. However, I have found this interesting dichotomy between open source contributors and product creation.

• 3:05:54Speaker 01

  I don't know if it's fully true, but there does seem to be the more, the more experienced, the more affect somebody has in open source community, the less ability to actually produce product that they have. And the opposite is kind of true, too. The more product focused are, I find a lot of people, I've talked to a lot of people who produce really great products and they they have a, they're looking over the open source communities, kind of wanting to participate and play, but they played here and they do a great job here and then they don't necessarily have some of the same. And I don't think that, I don't think that's entirely necessary. I think part of it is cultural, how that's how they've emerged. Because one of the things that open source communities often lack is great product management, like some product management energy.

• 3:06:35Lex Fridman?

  That's brilliant, but you want both of those energies in a same place together.

• 3:06:39Speaker 01

  Yes, you really do. And so it's a lot of it's creating these teams of people that have these needed skills and attributes that are hard. And so, so one of the big things I look for is somebody that fundamentally recognizes their need to learn, to learn. Like one of the values that we we have in all of the things we do is learning. If somebody thinks they know it all, they're going to struggle.

• 3:07:01Lex Fridman?

  And some of that is just, there's more basic things like humility, just being humble in the face of all the things you don't know. And that's like step one of learning.

• 3:07:10Speaker 01

  That's step one of learning, right? And, and you know, I've spent a lot of time learning, right? Other people spend a lot more time, but I've spent a lot of time learning. I went, I mean, my whole goal was to get a PhD because I love school and I wanted to be a scientist and then what I found is what's been written about elsewhere as well is the more I learned, the more I didn't know. The more I realized, man, I, I, I know about this, but this is such a tiny thing in the global scope of what I might want to know about. So I need to be listening a whole lot better than than I am just talking. That's changed a little bit, actually. My wife says that I used to be a better listener. Now that I'm so full of all these ideas I want to do, she kind of says, you've got to give people time to talk.

• 3:07:50Lex Fridman?

  So you've succeeded on multiple dimensions. So one is the 10-year track faculty. The other is just creating all these products and building up the businesses, then working with businesses. Do you have advice for young people today in high school and college of how to live a life as nonlinear and as successful as yours? A life that they could be proud of?

• 3:08:16Speaker 01

  Well, Lex, that's a super compliment. I'm humbled by that, actually. I would say, a life they can be proud of, honestly, one thing I've said to people is, first, find people you love and care about them. Like, family matters to me a lot. And family means people you love and have committed to, right? So it can be whatever you mean by that, but you need to have a foundation. So find people you love and want to commit to and do that. Because it anchors you in a way that nothing else can, right? And then you find other things. And then from out there, you find other kinds of things you can commit to, whether it's ideas, or people, or groups of people. So, you know, especially in high school, I would

• 3:09:01Speaker 01

  say, don't settle on what you think you know. Like, give yourself 10 years to think about the world. Like, I see a lot of high school students who seem to know everything already. I think I did, too. I think it's maybe natural. But recognize that the things you care about, you might change your perspective over time. I certainly have, over time. You know, I was really passionate about one specific thing, and I was kind of softened. You know, I was a big, I didn't like the Federal Reserve, right? And there's still, we could have a longer conversation about monetary policy and finances, but...

• 3:09:37Speaker 01

  I'm a little more nuanced in my, in my perspective at this point. But you know, that's, that's one area where you learn about something, go, ah, I want to attack it. You know, build, don't destroy.

• 3:09:50Speaker 01

  Build, like so much, so often the tendency is to not like something, they want to go attack it. Build something, build something to replace it.

• 3:09:57Lex Fridman?

  Yeah.

• 3:09:57Speaker 01

  Build up, you know, attract people to your new thing. You'll get, you'll be far more, far better, right? You don't need to destroy something to build something else. So that's, I guess, generally. And then, you know, definitely like curiosity, you know, follow your curiosity and, and let it, don't just follow the money.

• 3:10:19Lex Fridman?

  And all of that, like you said, is grounded in family friendship and ultimately love.

• 3:10:25Speaker 01

  Yes.

• 3:10:26Lex Fridman?

  Which is a, a great way to end it. Travis, you're one of the most impactful people in the engineering, the computer science in the human world. So I truly appreciate everything you've done. And I really appreciate that you would spend your valuable time with me. It was an honor.

• 3:10:42Speaker 01

  It was a real pleasure for me. I appreciate that.

• 3:10:45Lex Fridman?

  Thanks for listening to this conversation with Travis Olafant. To support this podcast, please check out our sponsors in the description. And now let me leave you with something that in the programming world is called Hodgson's Law. Every sufficiently advanced Lisp application will eventually be re-implemented in Python. Thank you for listening and hope to see you next time.