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Speaker 1

00:00

The following is a conversation with Travis Olyphant, 1 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.

Speaker 1

00:27

Travis's life work across a large number of programming and entrepreneurial efforts has and will continue to have immeasurable impact on millions of lives by empowering scientists and engineers in big companies, small companies, 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. This is the Alex Friedman Podcast, and here is my conversation with Travis Oliphant.

Speaker 2

01:11

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

Speaker 3

01:14

Do you remember? Whoa, that's a good question. I think it was in fourth grade.

Speaker 3

01:18

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

Speaker 2

01:32

Did you use goto statements?

Speaker 3

01:34

Yes, yes, we used goto statements. I remember in

Speaker 2

01:38

the early days, that's when I first realized there's like principles to programming when I was told that don't use goto statements. Those are bad software engineering principles. Like it goes against what great, beautiful code is.

Speaker 2

01:51

I was like, oh, okay, there's rules to this game.

Speaker 3

01:54

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 low-level programming in TI, but finally when I took an AP Computer Science course in Pascal. Wow.

Speaker 3

02:06

Yeah, it was Pascal. That's when I, oh, there are these principles. Not C or C++? No, I didn't take C until the next year in college.

Speaker 3

02:14

I had a course in C, but I haven't done much in Pascal, just that AP Computer Science course.

Speaker 2

02:21

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

Speaker 3

02:26

Oh man, good question. I think actually when I was 10. You know, my dad got us a Timex Sinclair, and he was excited about the spreadsheet capability, and then, but I made him get the basic, the add-ons so we could actually program in basic.

Speaker 3

02:41

And just being able to write instructions and have the computer do something. And we got a TI-99, TI-99-4A when I was about 12. And I would just, it had sprites and graphics and music. You could actually program it to do music.

Speaker 3

02:55

That's when I really sort of fell in love with programming.

Speaker 2

02:58

So this is a full, like a real computer with like, with memory and storage and processors. So we're not, because you say TI,

Speaker 3

03:06

it's not. Yeah, the Timex Sinclair was 1 of the very first. It was a cheap, cheap, I think it was, well, it was still expensive, but it was 2K of memory.

Speaker 3

03:14

We got the 16K add-on pack. 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 modems would convert digital bits to audio's file, It was set on a tape drive.

Speaker 3

03:31

Still remember that sound, but that was the storage.

Speaker 2

03:34

And what was the programming language, do

Speaker 3

03:36

you remember? It was basic. It was basic.

Speaker 3

03:37

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

Speaker 2

03:42

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

Speaker 2

03:50

Games?

Speaker 3

03:51

Math. Mathy stuff? Yeah, 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, it's about memory.

Speaker 3

04:01

You know, when you're exposed to math early, you have a good short-term memory, you can remember those time tables. And I do have a reasonably, I mean, not perfect, but a reasonably long little short-term memory buffer. And so I did great at times tables. I said, oh, I'm good at math.

Speaker 3

04:15

But I started to really like math, just the problem solving aspect. And so computing was problem solving applied. And so that's always kind of been the draw, kind of coupled with the mathematics.

Speaker 2

04:30

Did you ever see the computer as like an extension of your mind, like something able to achieve? Not till later.

Speaker 3

04:37

Okay. Yeah, not then.

Speaker 2

04:39

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

Speaker 3

04:43

Yeah, 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 attracting me to Python is that that was more real, right?

Speaker 3

05:02

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. There's some languages that actually lead you to certain thought processes.

Speaker 2

05:23

Yeah, like, so I speak Russian fluently, and that's certainly a language that leads you down certain thought processes. Well, yeah, I mean, there's a history of the 2 world wars, of millions of people starving to death or near to death throughout its history of suffering, of injustice, like this promise sold to the people and then the carpet or whatever is swept from under them. It's like broken promises and all of that pain and melancholy is in the language.

Speaker 2

05:59

The sad songs, the sad hopeful songs, the over romanticized like, I love you, I hate you, the sort of the swings between all the various spectrums of emotion. So that's all within the language, the way it's twisted, There's a strong culture of rhyming poetry. So like the bards, like the sync, there's a musicality to the language too.

Speaker 3

06:24

Did Dostoevsky write in Russian? Yeah, so

Speaker 2

06:27

like Dostoevsky, Tolstoy, all the,

Speaker 3

06:32

all the- The ones that I know about, which are translated, and I'm curious how the translations.

Speaker 2

06:36

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 in this podcast with people who've translated Dostoevsky.

Speaker 2

07:01

And that's for people who worked in this field, know how difficult that is. Sometimes you can spend months thinking about a single sentence in context, because there's just a magic captured by that sentence. And how do you translate just in the right way? Because those words can be really powerful.

Speaker 2

07:22

There's a famous line, beauty will save the world from Dostoevsky. 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 to where over time, as you think in that language, you become a different human being.

Speaker 3

07:42

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

Speaker 2

07:49

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

Speaker 2

08:12

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 sad because I wonder how many geniuses we're missing because so much of the scientific world, so much of the technical world is in English, and so much of it might be lost because we don't have the common language.

Speaker 3

08:35

I completely agree. I'm very much in that vein of there's a lot of genius out there that we miss, and we're sort of fortunate when it bubbles up into something that we can understand or process, there's a lot we miss. So I tend to lean towards really loving democratization or things that empower people or, you know, very resistant to sort of authoritarian structures.

Speaker 3

09:00

Fundamentally for that reason, well, several reasons, but it just hurts us.

Speaker 2

09:04

We're worse off. So speaking of languages that empower you, so Python was the first language for me that I really enjoyed thinking in, as you said.

Speaker 3

09:16

Sounds like you've shared my experience too.

Speaker 2

09:18

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

Speaker 3

09:23

It's a good question. It was a process, it took about a year. I first encountered Python in

Speaker 1

09:27

1997.

Speaker 3

09:29

I was a graduate student studying biomedical engineering at the Mayo Clinic. I had previously been involved in 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 information out of it.

Speaker 3

09:46

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.

Speaker 3

09:57

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, it had 2 things that made me not filter it away. Because I was filtering a bunch of stuff.

Speaker 3

10:10

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

Speaker 1

10:20

95,

Speaker 3

10:22

not too much earlier, by an MIT alum, Jim Huganin. I went back and read the mailing list to see the history of how it grew, and it was very interesting. It's 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, about what that looks like.

Speaker 2

10:46

What gap did numeric fill? Numeric filled

Speaker 3

10:48

the gap of having an array object. So instead

Speaker 2

10:50

of- There was no array object.

Speaker 3

10:51

There was no array, there was a 1 dimensional byte concept, but there was no N dimensional, 234 dimensional tensor, they call it now. I'm still in the category that a tensor is another thing, and it's just an N-D-A-R-A, we should call it, but I kind of lost that battle.

Speaker 2

11:08

There's many battles in this world, some of which we win, some we

Speaker 3

11:11

lose. That's exactly right. But it had no math to it. So numeric had math and a basic way to think in a race.

Speaker 3

11:20

So I was looking for that and it had complex numbers, a lot of programming languages and you can see it because, you know, if you're just a computer scientist, you think, ah, complex numbers are just too, too float. So you can, people can build that on. But in practice, a complex number as 1 of the significant algebras that helps connect a lot of physical and mathematical ideas, particularly to FFT for an actual 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.

Speaker 3

11:54

1 of the common things in programming, 1 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 maybe left behind in producing an abstraction, which is at the heart of programming today and actually building around the programming world.

Speaker 3

12:24

I think it's a fascinating philosophical topic.

Speaker 2

12:26

Yeah, they will continue for many years, I think. They'll continue for many years. As we build

Speaker 3

12:29

more and more and more abstractions. Yes, I often think about, you know, we have a world that's built on these abstractions that, were they the only ones possible? Certainly not, but they led to, now it's very hard to do it differently.

Speaker 3

12:42

Like there's an inertia that's very hard to, you know, push out, push away from. There's, it has implications for things like, you know, the Julia language, which you have heard of, I'm sure. And 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.

Speaker 3

13:03

And there's strategies to approach that, but nonetheless, it's a phenomenon. And sometimes, so I love complex numbers, and I love to erase, so 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 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.

Speaker 3

13:39

I needed some tool to do that with. So I started using Python in 97. In

Speaker 1

13:44

98,

Speaker 3

13:45

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 Pearl in 95. I'd done the same thing, and then I looked back and I'd forgotten what I was even saying. Now, I'm not saying it, so hey, this may work.

Speaker 3

14:01

I like this. This is something I can retain without becoming an expert, per se. And so that led me to go, I'm gonna push more into this. And then that 98 was kind of when I started to fall in love with Python, I would say.

Speaker 2

14:18

A few peculiar things about Python, so maybe compare it to Perl, compare it to some of the other languages, so there's no braces. Yeah. So space is used, indentation, I should say, is used as part of the language.

Speaker 3

14:34

Yeah, right.

Speaker 2

14:35

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

Speaker 3

14:42

It's a good question. I was open-minded, So I was cognizant of the concern. And it definitely has specific challenges.

Speaker 3

14:52

Cut and pasting, for example, you're cutting 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 iPython and Jupyter Notebooks handled it just fine, so there's really no problem, but in the past it created some challenges, formatting challenges. Also mixed tabs and spaces.

Speaker 3

15:12

If editors 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. Like it was occasional annoyances, but I really liked the fact that it didn't have all this extra characters, right?

Speaker 3

15:31

That these extra characters didn't show up in my visual field when I was just trying to process understanding a snippet of code.

Speaker 2

15:37

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 how many characters it takes to express a certain thing. So it's very compact.

Speaker 2

15:49

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

Speaker 3

16:09

Correct, and you're proud of it. Yeah, you're proud of it. Right, exactly, and it's like feels good, and It's really selective.

Speaker 3

16:16

It means you have to be an expert in Perl to understand it. Whereas Python 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.

Speaker 3

16:25

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. 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 syntax.

Speaker 3

16:46

Like, what would computer programming look like in that? I haven't looked at that at all, but it certainly doesn't, you know, leveraging your Chinese language center, I'm not sure Python or any programming language does that. But that was a big deal. The fact that it was accessible, I could be a scientist.

Speaker 3

17:00

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 nuance to that statement, but it certainly is more accessible. So more people could actually, as a scientist, as somebody who, or an engineer, who was trying to solve another problem besides point programming, I could still use this language and get things done and be happy about it.

Speaker 3

17:27

Now I was also comfortable in C

Speaker 2

17:28

at that time. And MATLAB you did a little bit.

Speaker 3

17:31

And MATLAB I did a lot before that, exactly. So I was comfortable in those 3 languages were really the tools I used during my studies and schooling. But to your point about language helping you think, 1 of the big things about MATLAB was it was, and APL before it, I don't know if you remember APL.

Speaker 3

17:47

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. Culturally, they didn't understand it.

Speaker 3

18:06

So they kept missing the boat, kept missing the understanding of what this was. They've gotten better, but there's still a whole culture of folks that doesn't. Programming, that's systems programming or web programming or lists and maps. And what about an n-dimensional array?

Speaker 3

18:22

Oh, yeah, that's just an implementation detail. 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 60s. 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 was back in the early days of computing, when the QWERTY keyboard maybe wasn't as established.

Speaker 3

18:48

Like, well, we can 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, as people would pride themselves on how much, could they write the game of life in 30 characters of APL? APL has characters that mean summation, and they have adverbs, you know, they have adjectives and these things called adverbs, which are like methods, like reduction, reduction would be an adverb on an ad operator, right? So, but doing, using these tools, you could construct, and then you start to think at that level.

Speaker 3

19:20

You think in n dimensions, is something I like to say, and you start to think differently about data at that point.

Speaker 2

19:25

Now you're, it really helps. 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. It's almost like liberating.

Speaker 2

19:39

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 the early, I don't know if many languages got that right ever.

Speaker 3

19:55

No, no, no they didn't.

Speaker 2

19:56

Even still.

Speaker 3

19:57

Even still, I would say.

Speaker 2

19:59

I

Speaker 3

19:59

mean, NumPy is an inheritor of the traditions, 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 was in Python, it's still in Python for the number of dimensions. That's different than say the rank of a matrix which people think of as well.

Speaker 3

20:31

So 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 current array, at least current as of

Speaker 1

20:44

2015, 2017,

Speaker 3

20:46

now there's a ton of them over the past 2 or 3 years. We can get into that too.

Speaker 2

20:50

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, what really makes you connect with a language.

Speaker 2

21:06

I'm not sure it's obvious to introspect that.

Speaker 3

21:09

No, it isn't, and I've thought about that to some length. I think definitely the fact that I could read it later, that I could use it productively without becoming an expert. Like other languages I had to put more effort into.

Speaker 3

21:21

Right,

Speaker 2

21:22

that's like an empirical observation. Like you're not analyzing any 1 aspect of the language, it just seems time after time, you look back, it's somehow readable.

Speaker 3

21:30

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

Speaker 3

21:43

And then the syntax wasn't that far behind it.

Speaker 2

21:45

Yeah.

Speaker 3

21:46

Right. Now there was some, there are some, there's some warts there still. It wasn't perfect. There were some areas where I'm like, ah, it would be better if this were different or if this were different.

Speaker 3

21:54

Some of those things got out of the language too. I was really grateful for some of the early pioneers in the Python ecosystem back, Because Python got written in 91, that's when the first version came out. But Guido was very open to users. And 1 of the sets of users were people like Jim Huganin and David Asher and Paul Dubois and Conrad Hinson.

Speaker 3

22:13

These were people that were on the main 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 A1J, right? And the fact they went the engineering route of J is interesting. I don't think that's entirely favoring engineers.

Speaker 3

22:28

I think it's because I is so often used as the index of a for loop. I think that's actually why. Probably.

Speaker 2

22:35

I mean there's a pragmatic aspect.

Speaker 3

22:36

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 broadcasting was there.

Speaker 3

22:46

I could do addition of whole arrays. So that was cool. Those are some things I loved about it.

Speaker 2

22:52

I don't know what to start talking to you about because you've created so many incredible projects that basically change the whole landscape of programming. But okay, let's start with, let's go chronologically with SciPy. You created SciPy over 2 decades ago now,

Speaker 3

23:09

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

Speaker 2

23:12

What is it? What was its goal? What is its goal?

Speaker 2

23:16

How does it work?

Speaker 3

23:17

Yeah, fantastic. So SciPy was effectively, here I'm 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.

Speaker 3

23:28

But 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. I wanted to integrate this function, okay, well, I don't have just a function I can call to do that.

Speaker 3

23:40

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 optimum value is. Those are things like, well, why don't we just write a library that adds these tools? And I started to post to the mailing list and there had previously been, you know, people have discussed, I remember Conrad Hinson saying, wouldn't it be great if we had this optimizer library?

Speaker 2

24:01

Or

Speaker 3

24:01

David Asch would say this stuff. And I'm, you know, I'm an ambitious, ambitious is the wrong word, an eager and probably more time than sense. I was a poor graduate student.

Speaker 3

24:13

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 taking orders, fulfilling a list of things to do, you're trying to figure out what to do. And so I thought, well, 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.

Speaker 3

24:36

But really 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. 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?

Speaker 3

24:48

So I wrote something called, it was an IO package, right? And that was my very first extension module, which is C. So I wrote C code to extend Python so that in Python I could write things more easily. That combination kind of hooked me.

Speaker 3

25:02

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 link.

Speaker 3

25:15

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 a tracking of reference counting you have to do manually. And if you don't, you have memory leaks. And so that's hard.

Speaker 3

25:29

Plus then C, you know, it's much more, you have to put more effort into it. It's not just I have to now think about pointers and I 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.

Speaker 3

25:43

And there are distinct modules you end up having to think about. So it's harder. When I was just in Python, I could just think about MRI and high-level writing. But I could do that, and I liked it.

Speaker 3

25:53

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 NetLib, which has hundreds of 4chan routines that people have written in the 60s and the 70s and the 80s. And 4chan 77, fortunately, it wasn't 4chan 60s, it had been ported to 4chan

Speaker 1

26:16

77.

Speaker 3

26:18

And 4chan 77 is actually a really great language. Fortran 90 probably is my favorite Fortran because 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 Fortran 90 or Fortran 77, But it's totally fine to write a subroutine in.

Speaker 3

26:34

Right, and so, and then Fortran 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 order of difference equation. Here's integration, here's run cut integration.

Speaker 3

26:46

Already done, I don't have to think about that algorithm. I mean, you could, but it's nice to have somebody who's already done 1 and tested it. And so I sort of started this journey in 98, really, if you look back at the main list, there's sort of this productive era of me writing an extension module to connect Runge-Kutta integration to Python and making an ordinary additional equation solver. And then releasing that as a package, so we call it ODE pack, I think I called it then, quad pack, and then I just made these packages.

Speaker 3

27:14

Eventually that became multi-pack because they were 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, 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.

Speaker 3

27:35

That was the release.

Speaker 2

27:37

But, okay, can we just end that? Because the community aspect of creating the package and sharing that, that's rare. To have, to both have the, at that time, so like the

Speaker 3

27:51

raw stuff. Yeah, it

Speaker 2

27:51

was pretty early, yeah. So, oh, well, not rare. Maybe you can 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, the data that you need.

Speaker 2

28:08

And to also have the mind that I'm going to make this usable for others, that's...

Speaker 3

28:15

I would say I was inspired. I'd been inspired by Linux. I'd been inspired by Linus and him making his code available and I was starting to use Linux at the time.

Speaker 3

28:23

I went, this is cool. So I'd kind of been previously primed that way. And generally 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.

Speaker 2

28:35

Okay, so you were energized by that idea.

Speaker 3

28:37

So I was energized by that idea already, right? And I can't deny that, I was. I'm sort of, I had this very, I liked that part of science, that part of sharing.

Speaker 3

28:45

And then all of a sudden, oh wait, here's something, and here's something I could do. And then I slowly over years learned how to share better so that you could actually engage more people faster. 1 of the key things was actually giving people a binary they could install, right? So that it wasn't just, here's source code, good luck.

Speaker 3

29:01

Compile this and then. 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, you know, it's really the key as to why a scientist with dreams of doing MRI research ended up starting a software company that

Speaker 2

29:19

installs software. 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 run on scripts, I have to try to get them, I'm having now the task of teaching them how to do Python enough to run the scripts.

Speaker 2

29:39

And so I've been actually facing this, whether it's on the condo, 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. It's a to-do item for me to figure out, what is the minimal amount of information I have to teach? What are the tools you use?

Speaker 2

29:57

That's 1, you enjoy it. 2, you're effective at it.

Speaker 3

30:00

And they're related. Those are 2 related questions.

Speaker 2

30:02

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. So do you compile it, do you distribute, like how do you distribute that code to them? And it's interesting because I think it's exactly what you're talking about.

Speaker 2

30:20

If you increase the circle of empathy, the circle of people that are able to use your programs, you increase its 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 medical engineers, by all kinds of people that don't know programming and actually maybe plant a seed, have them catch the bug of programming so that they start on their journey? That's a huge responsibility and ultimately has to do with the Amazon 1 click buy.

Speaker 2

30:55

Like how frictionless can you make

Speaker 3

30:57

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

Speaker 3

31:05

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 many users as opposed to contributors 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 I think I got 100 users, right, by the end of that year.

Speaker 3

31:32

But the fact that I got 100 users and more than that, people started to email me with fixes. And that was actually intoxicating. That was the, here I'm writing papers, I'm giving conferences, and I get people to say hello, but yeah, good job. But mostly it was you're viewed with, it's competitive.

Speaker 3

31:51

You publish a paper and people are like, oh, it wasn't my paper. I was starting to see that sense of academic life where it was so much, I thought there was this cooperative effort, but it sounds like we're here just to one-up each other. Right. And, you know, that's not true across the board, but a lot of that's there.

Speaker 3

32:08

But here in this world, I was getting responses from people all over the world. You know, I remember Piaro Peterson in Estonia, right, was 1 of the first people. And he sent me back this make file because, you know, the first thing it is, yeah, your build thing stinks and here's a better make file. Now, it was a complex make file.

Speaker 3

32:24

I think I never understood that make file actually, but it worked and it did a lot more. And so I was like, thanks, this is cool. 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 tarball.

Speaker 3

32:39

And I just found I really love that. And the style back then was here's a mailing list is very, it wasn't as there's 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 think I did about 7 packages that year, right? And then by the end of the year, I collected them into a thing called Multipack.

Speaker 2

32:57

So in

Speaker 1

32:57

99,

Speaker 3

32:58

there was this thing called Multipack, 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. And then of course a massive increase of usage.

Speaker 2

33:12

So by the way, most of this development was under Linux.

Speaker 3

33:15

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, did some kernel programming to to make the hard drive work.

Speaker 3

33:26

I mean, not programming, but modification to the kernel so I could actually get a hard drive working. I love that aspect of it. I was also, at school, I was building a cluster. I took Mac computers and you put Yellow Dog Linux on them.

Speaker 3

33:40

At the Mayo Clinic, they were just, 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++ program to do MRI simulation. That was what I was doing at the same time for my day job, so to speak. So I was loving the whole process.

Speaker 3

34:03

At the same time, I was, oh, I need an ordinary differential equation. That's why ordinary differential equations were key, was because that's the heart of a block equation for simulating MRI, is an ODE solver. And so that's, but I actually did that, those happened at the same time. That's why, you know, kind of what you're working on and what you're interested in, they're coinciding.

Speaker 3

34:20

I was definitely scratching my own itch

Speaker 2

34:22

in

Speaker 3

34:22

terms of building stuff, and which helped in the sense that I was using it for me, so at least I had 1 user. I had 1 person who was like, well, no, this is better. I like this interface better.

Speaker 3

34:31

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 with the Windows installer, it was the first time I realized, oh, yeah, the binary installer really helps people.