The following is a conversation with Travis Oliphant, 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 Konda,

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 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 Lex Friedman podcast, and here is my conversation with Travis Oliphant.

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

Do you remember?

Whoa, that's a good question.

I think it was in fourth grade, just a simple loop in basic.

Basic.

It was written on Atari 400, I think, or maybe it was Atari 800.

It was part of a class, and we just were just basic loops to print things out.

Did you use go to statements?

Yes.

Yes, we used go to statements.

I remember in the early days, that's when I first realized there's principles to programming

when I was told that don't use go to statements, those are bad software engineering.

It goes against what great, beautiful code is.

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

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 and TI, but finally, when I took an AP computer

science course in Pascal, that 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, I had a course in C, but I haven't

done much in Pascal, just that AP computer science course.

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

Oh, man, good question.

I think actually when I was 10, my dad got us a TI Timex in Claire, and he was excited

about the spreadsheet capability, but I made him get the basic, the add ons so we could

actually program in basic, and just being able to write instructions and have the computer

do something.

Then we got a TI 994A when I was about 12, and I would just, it had sprites and graphics

and music you could actually program to do music.

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

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

so we're not going to say TI.

Yeah, the Timex in Claire was one of the very first, it was a cheap, cheap, like, I think

it was, well, it was still expensive, but it was 2K of memory.

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 files, tape drive.

Still remember that sound, but that was the storage.

And what was the programming language, do you remember?

It was basic.

It was basic.

And then they had a VisiCalc.

And so a little bit of spreadsheet program in VisiCalc, but mostly just some basic.

Do you remember what kind of things drew you to programming?

Was it working with data?

Was it video games and video games?

Math.

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, they, it's about memory.

You know, when you're exposed to math early, you have a good short term memory, you remember

timetables.

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 timetables and said, oh, I get a math, 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, the draw kind of coupled with the mathematics.

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

to achieve?

Not till later.

Okay.

Yeah.

Not then.

You could play with it and you can, you could play with math puzzles and 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 attracting me to Python is that that

was more real, right?

I could think in Python, speaking a 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.

Yeah.

Like, so I speak Russian fluently and that's certainly a language that leads you down certain

thought processes.

Is that right?

There's a, there's a history of the two world wars of the millions of people starving to

death or near to death throughout his history of suffering, of injustice, like this promise

sold to the people and then the carpet or whatever swept from under them and it's like

broken promises and all of that pain and melancholy is in the language, 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, poetry, there's a strong culture

of rhyming poetry, so like the bards, like the, there's a musicality to the language

too.

Did Dostoevsky write in Russian?

Yeah.

Like Dostoevsky, Tostoevsky, all the, all the, all the, the ones that I know about which

are translated and I'm curious how the translations.

So Dostoevsky did not use the musicality of the language too much, so they actually

translate 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 have translated Dostoevsky and that's 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, right?

In context, like, 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, can be really powerful.

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 to where over time, as you think in that language, you become a different human

being.

Yes.

Yeah.

Yeah, that's a fascinating reality, I think.

I know people have explored that, but it's, I guess, rediscovered.

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 because I don't truly know the language, same with Japanese, I don't truly know Japanese

and Portuguese and 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 sad because I wonder how much, 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 they're, they just, we don't have the common language.

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 I tend to lean towards really loving

democratization or things that empower people or, you know, very resistant to sort of authoritarian

structures.

Fundamentally, for that reason, it, well, several reasons, but it just hurts us, we're

worse off.

So speaking of languages that empower you, so Python was the first language for me that

I could, I really enjoyed thinking in, as you said.

Sounds like you shared my experience too.

So when did you first, do you remember when you first kind of connected with Python, maybe

even fell in love with Python?

It's a good question.

It was a process that 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 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.

And I'd done that in MATLAB, I'd done that in Perl, I'd done that in, you know, 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 encounter 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 throughout there

at the time in 1997, but it had arrays, there's a library called Numeric that had just been

written in 95, like not very, not too much earlier.

By an MIT alum, Jim Hugenin, you know, and I went back and read the mailing list to see

the history of how it grew and there was a 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, but what that looks like.

What gap did Numeric fill?

Numeric filled the gap of having an array object.

There was no array object.

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 nv array, we

should call it, but kind of lost that battle.

There's many battles in this world, some which will win, some we lose.

That's exactly right.

So and, 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, you know, if you're just a computer scientist, you think, ah,

complex numbers just too, too float.

So you can, people can build that on, but in practice, a complex number as a, as one

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.

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?

It's powerful and 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 an 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.

Yeah, they will continue for many years, I think, as we build more and more and more

abstractions.

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?

Yeah.

There's a lot, but they led to, you know, it's very hard to do it differently.

Yeah.

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 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 to raise.

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

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

Pearl in 95, right?

I'd done the same thing.

And then I looked back and I, I'd forgotten what I was even saying.

Now, you know, I'm not saying it.

So I, that, that may, hey, this may work.

I like this.

This may be something I can retain without becoming an expert per se.

And so that led me to go, I'm going to push more into this.

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

A few peculiar things about Python.

So maybe compared to Pearl, compared to some of the other languages.

So there's no braces.

Yeah.

Yeah.

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

Yeah.

Right.

So did you, I mean, that's quite a leap.

Were you comfortable with that leap?

Or were you just very open minded?

It's a good question.

I was open minded.

So it, I was cognizant of the concern.

And it definitely has, it has specific challenges, you know, cut and pasting, for example, your

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 I Python and Jupyter notebooks handle it just fine.

So there's really no problem.

But in the past, it creates some challenges, formatting challenges.

Also mixed tabs and spaces, if your, 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.

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.

Yeah, there's a cleanness to it.

But I mean, the idea is supposed to be that Pearl 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.

Yeah.

So 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 Pearl, you write code that's basically unreadable.

Right.

There's a culture like.

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.

Yeah.

And it's hard in Pearl to understand it.

Whereas Python was allowed you not to have to be an expert, you don'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, different syntax.

Like what would computer programming look like in a, 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 program names does that, but that was a big deal.

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 bit of that.

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.

Um, 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 a, you remember APL, APL is actually

the predecessor of array based programming, which I think is really an underappreciated,

if I talked to people who are just steeped in computer programming and computer science,

like most of the people that Microsoft has hired in the past, for example, you know, Microsoft

is a company generally did not understand array based programming like culturally they

understand it.

So they kept missing the boat, kept missing the understanding of what this, what this was.

We've gotten better, but there's still a whole culture of folks that doesn't programming.

That's the, you know, that's, that's systems programming or web programming or lists and

maps and you know, what about an end dimensional array?

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 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 qwerty keyboard maybe wasn't as established

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 Pearl is 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 would have adjectives and

these things called adverbs, which are like methods, like reduction, it would be an adverb

on an ad operator, right?

But using these tools, you could construct and then you start to think at that level,

you think in end 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.

Yeah, I mean, outside of programming, if you really internalize linear algebra as a course,

I mean, it's philosophically allows you to think of the world differently.

It's almost like liberating.

You don't have to, you don't have to think about the individual numbers in the end 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.

No, no, no, they didn't still, even still, I would say, I mean, NumPy is a, as an 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 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 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.

So if we just sort of linger on the early days of what was your favorite feature of

Python?

Do you remember like what?

Yeah.

I think it's an interesting to linger on like the, what, what really makes you connect

with a language?

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

No, it isn't.

And I've thought about that.

It's just some length.

I'm not, I think definitely the fact that I could read it later, that I could use it

productively without becoming an expert.

And you, other languages I had to put more effort into.

Right.

That's like an empirical observation.

Like you're not analyzing any one aspect of language.

It just seems time after time, when you look back, it's somehow readable.

It's somehow readable.

And 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.

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

Yeah.

Right.

Now there are some, there are some warts there still.

It wasn't perfect.

Like there's some areas where I'm like, it'd be better if this were different or if this

were different.

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 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 Huguenin and David Asher and Paul Dubois

and Conrad Hinson.

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 a one J, right?

And the fact that they went the engineering route of J is interesting.

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.

I think that's actually why.

Probably.

I mean, there's a pragmatic aspect.

But the complex numbers were there.

I love that.

The fact that I could write in the array constructs and that reduction was there.

Very simple to write summations and broadcasting was there.

I could do addition of whole arrays.

So that was cool.

That was something that I loved about it.

I don't know what to start talking to you about because you've been, 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 two decades ago now.

Yes.

Right.

Yes.

I love to talk about SciPy.

SciPy was really my baby.

What is it?

Yeah.

What was its goal?

What is its goal?

How does it work?

Yeah.

Well, practically, 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 there's things that were missing.

Like I didn't have an ordinary differential equation solver I could just call.

I didn't have integration.

Yeah, 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 optimum value

is.

Those things like, well, why don't we just write a library that adds these tools?

And I started to post on the main 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

or David Asch would say this stuff 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 the PhD that I loved was

the fact that it's exploratory, but 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, but when I fell

in love with Python 98, I said, Oh, well, there's just a few things missing.

Like, Oh, I need a reader to read daikon files that was in medical imaging and daikon 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 IO package, right?

And that was my very first extension module, which is C. So I wrote C code to extend Python

so that the positive in 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 this

in C that easily for me, because I knew enough C.

And then Guido had written a link, I mean, the only, the hard part of extending Python

was something called the way memory management networks, 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.

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 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, 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 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 NetLib, which has hundreds of 4chan routines that people

are written in the 60s and the 70s and the 80s and 4chan 77, fortunately, it wasn't 4chan

60s, it had been imported to 4chan 77.

And 4chan 77 is actually a really great language.

4chan 90 probably is my favorite 4chan 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 4chan 90 or 4chan 77,

but it's totally fine to write a sub routine in, right?

And so, and then 4chan 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

infrastructure equation, here's integration, here's runge cut integration, 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 one and tested it.

And so, I sort of started this journey in 98, really, look back at the main list, there's

sort of this, this productive era of me writing an extension module to connect runge cut integration

to Python and making an ordinary additional equation solver and then releasing that as

a package, so we could call ODE pack, I think I called it then quad pack, you know, 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 tar

ball, just a GZip tar ball of source code, that was the release.

But okay, can we just stand that because that, the community aspect of creating the package

and sharing that, that's rare, that to have, to both have the, at that time, so like the

rust.

Yeah, it was pretty early, yeah.

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, and to also have the mind

that I'm going to make this usable for others, that's...

I would say I was inspired, I'd been inspired by Linux, been inspired by, you know, 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 into science because

I like the sharing notion, I like the idea of, hey, let's, if collectively we build

knowledge and share it, we can all be better off.

Okay, so you want to energize by that idea?

So I was energized by that idea already, right, and I can't deny that I was, I'm sort of 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 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.

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.

Compile this and then...

It's compiled, ready to install, you 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 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 running on scripts, I have to try to get

them, I have now the task of teaching them how to do Python enough to run the scripts,

and so I've been actually facing this, whether it's on the condor, 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, like, what is the minimal amount of information

I have to teach, what are the tools you use, the one you enjoy it, to your effect of it.

And they're related.

Those are two related questions.

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, 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 its effectiveness and its power.

And so you have to think, 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

the seed, have them catch the bug of programming so that they start on their journey.

That's a huge responsibility.

And ultimately it has to do with the Amazon one click buy, like how frictionless can you

make the early steps?

Frictionless is actually really key to go in any community is every, any friction point,

you're just going to lose, you're going to lose some people, right?

Now, sometimes you may want to intentionally do that, if you're early enough on that 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 a sci fi start 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 100 users by the end of that year, but the fact that I got 100 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 would say hello, but yeah, good job, but mostly it was, you're reviewed

with it's competitive, right?

You publish a paper and people were like, oh, it wasn't my paper, you know, it was starting

to see that sense of academic life where it was so much, I thought it was a cooperative

effort, but it sounds like we're here just to one up each other.

And you know, that'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 P.R.

Peterson in Estonia, right, was one of the first people, and he sent me back this make

file because, you know, the first thing it is, yeah, you're building stinks and here's

a better make file.

Now, it was a complex make file, I don't think I never understood that make file actually,

but it worked and it did a lot more, and so I said, 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 tar ball,

and I just found I really love that.

And the style back then was here's a main list is very, it wasn't as, there's certainly

more of 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 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, always

a high school student at the time, getting 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.

Yes.

Yes, it was on Linux.

I was a Linux developer doing it on a new box.

I mean, at the time, I was actually getting into, I had a new hard drive, just some kernel

programming to make the hard drive work, I mean, not programming, but modification to

the kernel so I could actually hard drive working.

I love that aspect of it.

I was also in, at school, I was building a cluster, I took Mac computers, and you put

Yellow Dog Linux on them.

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, and 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, I actually did that,

it doesn't happen 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, which helped in

the sense that I was using it for me, so at least I had one user.

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

know, 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, helping

people use the tools, thinking about where do I want to go from here.

There was a company, there was a guy actually, Eric Jones and Travis Vott, they were two

friends who founded a company called Enthought, it's here in Austin, still here.

And they, Eric contacted me at the time when 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'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 Piero Peterson had done

F2Py, 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.

How did you think of all SciPy in context of Python, in context of numeric, like what?

We saw SciPy as a way to make an R&D environment for Python, like use Python, dependent 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, replant, you know, the replant 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, but one, it's really hard to do massive scale projects with open source

collectives.

Actually, there's sort of an intrinsic cooperation limit as to which, you know, too many cooks

in the kitchen, you know, 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 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.

The 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, the more people use it, all of a sudden, oh, there's this scale at which

this doesn't work anymore, and we have to come up with different approaches.

So SciFi 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

were bugs on how, you know, the Windows compiler handled complex numbers, and you were chasing

segmentation faults, and 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 at the time I was like, I was intrigued, but I was squaring a path, an academic

path, and I just got an offer to go and teach at my alma mater, so I took that tenure track

position, and SciFi, and kind of, then I started working on SciFi as a professor, too.

Okay.

So that's, I left, I've got the Mayo Clinic graduate, wrote my thesis using SciFi, wrote,

you know, there's images that were created, now the plotting tool I used was something

from Yorick, actually, it was a plotting PLT, kind of a plotting language that I used.

Yorick is a programming language.

It was a programming language, it had a plotting tool, Dislin, it had integration to Dislin,

I ended up using Dislin, plus some of the plotting from Yorick, linked to from Python.

But it was a, people don't plot that way now, but this was before, and SciFi was trying

to add plotting, 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 person just

trying to get stuff done, and kind of having more time than money, maybe, right?

And John Hunter created what?

Mapplotlib.

He's a creator of Mapplotlib.

Yeah, so John Hunter was, you know, he wasn't a student at the time, but he was working

in Quantfield, 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 Mapplotlib.

And he released in 2001, about the same time that SciFi came out.

And it was separate library, separate install, use numeric, SciFi, use numeric.

And so SciFi, you know, in 2001, we released SciFi, and then Enthoc created a conference

called SciFi, 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.

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.

And I remember being driven by, you know, I like MATLAB, but I didn't like the fact

that, like, 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.

Right.

And there's also culture around MATLAB as much, because I've talked to a few folks

at MathWorks, Greats MATLAB.

Yeah.

I mean, there's just a culture, they try really hard, but it's just this corporate IBM

style culture that's like, or whatever.

I don't want to say negative things about IBM or whatever, but there's a, 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 creating

profits?

Right.

And like right now today, you know, I'm still in the middle of that.

That's actually the early days of me exploring this question.

Because as 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.

I 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 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.

That's his take on, is that his dad?

That was the, 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.

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.

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 it really was, and I'm actually, I'm embarrassed for educational

system that they could let me, and I was valedictorian of 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 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.

You're an interesting example, because you've created tools that change 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 to build up a giant system is a problem.

Yeah, it's a problem.

And so, during my PhD, at the same time, this is back in 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 does it mean?

And I encountered a, basically what I, I encountered a set of writings from people that said they

were the inheritors of Adam Smith, but 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 of economics is also political, like, because economics, you know, people,

different parties running for office, they'll, they want their economic friends, they want

their economists to back them up, right, or 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?

I was looking at, as a scientist going, what is this space, what does this mean, how does

Paris get fed, how does, how does 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 Missas, 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 was 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 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 creates information flow that you just don't have if you try to top down

it.

Right.

Right.

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

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

space.

Yeah.

The prices are actually an important signaling mechanism, right?

And that money is just a bartering tool.

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, we're dangerously not learning

about this, not teaching our children about this.

So you had the three kids, they had to make some hard decisions.

They 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.

Yeah.

Right.

You need to eat.

So what, how did that resolve itself in terms of side by?

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.

Like I've heard other people be, you know, bitter about that when they write or they

talk, they go, 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 that actually creates more of the same.

Not to turn on your bitterness module, but there's some aspect, I wish there was mechanisms

for me to reward whoever created side pie and non pie because it brought so much joy

into my life.

I appreciate that.

And I, the tip dark notion was there.

I appreciate that.

And I think there should be a very, there should be a machine less 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 anything

that will work takes time to emerge.

Right.

And I 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.

Yeah.

Right.

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

the.

Right.

Exactly.

So we're sort of just one around taking credit for stuff.

Credit assignment has like a delay to it.

Yes.

That makes the credit assignment basically wrong more than right.

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 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 and I was there for six years, I was kind

of already out the door when I started and we can get into that.

Yeah, what can I just ask a quick question on, is there some design principles that were

in your mind or on side pie?

Like, is there some key ideas that were just like sticking to you that this is, this is

the fundamental ideas?

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 a 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 would have to be six, six letters early on,

right?

And that's way too, too much, too, too little, but I was like, I like to have names that

were informative, but short.

So even though Python was a different conversation, but documentation is doing so well.

There's 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.

Documentation is essential.

Yeah.

So that was actually, so we thought about several things.

One is we wanted plotting.

We wanted interactive environment.

We wanted good documentation.

These were 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 Enthought 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 then also promote

an open source project that's still fairly new.

Cypher was fairly niche.

We stayed connected all while I was a student, sorry, a professor.

I went to BYU and started to teach, electrical engineering, all the applied math courses.

I loved teaching, single processing, probability theory, electrical magnetism.

I was the, if you look at right in my professor, which my kids loved to do, I wasn't, I got

some bad reviews because people.

What was the criticism?

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 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.

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.

And I agree, and that was kind of what was inspiring me, but, but you know, you also have to, I

cannot say I was an art, 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.

So transparencies, I'm writing my notes out, I go in, rooms half dark, I just blaring through

these transparencies.

Here it is.

Here it is.

And I did, gave a quiz after two weeks, nowhere knew anything, nothing I had taught, I had

gotten anywhere.

And I realized, okay, I'm not, this is not working.

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 their chalkboard, but I really loved that part of like the teaching.

So that, that entered sci pies world in terms of, we always understood that sci, there's

a didactic aspect of sci pie kind of, how do you take the knowledge and then produce

it?

The challenge we had was the scope.

Like ultimately sci pie 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,

the folks at Hubble has 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 to 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, broadcast 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 want a more structured data.

So he had called me even early on then and they said, yeah, would you want to work on

something to make this work?

And I said, yeah, I'm interested, but I'm going here and we'll see if I have time.

So in the meantime, while I was teaching and SIPI was emerging and I had a student, I was

constantly while I was teaching trying to figure a way to fund this stuff.

So I had a graduate student, my only graduate student, a Chinese fellow, Lu Hongze is his

name, great guy.

He wrote a bunch of stuff for iterative linear algebra, like got into writing some of the

iterative linear algebra tools that are currently there in SIPI and they've gotten better since,

but this is in 2005, kept working on SIPI.

But Perry has started working on a replacement to Numeric called Numeray and in 2004, a package

called and the image, it was an image processing library that was written for Numeray.

And it had in it a morphology tool.

I don't know if you know what morphology is.

It's open dilations.

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 SIPI, but just had never

gotten around to it.

So when it came out that it worked only on Numeray and SIPI needed Numeric and so we

effectively had the beginning of this split and Numeric and Numeray didn't share data.

They were just two, so you could have a gigabyte of Numeray data and gigabyte of Numeric data

and they wouldn't share it.

And so you had these, then you had these scientific libraries written on top.

I got really bugged by that.

I got really like, oh man, this is not good.

We're not cooperating now.

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, I was on a tenure

track position.

2004, I got reviewed.

They said, hey, things are going okay.

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 tie, the thinking that still goes on, still

goes on.

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

Maybe don't emphasize that so much in your tenure.

Right.

It illustrates some of the challenges.

It does.

And it's, people mean well, but we've gotten broken in a bunch of ways.

Certain things, a programming, understanding the role of software engineering, programming

in society is a little bit like, I guess.

Exactly.

Now I was in an electrical engineering position.

Right.

Yeah, of course.

There.

Yeah.

They were very focused.

And so, good people, and I had a great time.

I loved my time.

I loved my teaching.

I loved all the things I did there.

The problem was, this split was happening, this community I loved.

I saw people and I went, oh my gosh, this is going to be, this is not great.

And so, I happened, fate, I had a class I signed up for, I was trying to build an MRI system.

But I had a kind of a radio, a digital radio class, 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 numeric and nummery.

Like I'll basically take the numeric code base at the features nummery 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 CAPI documentation that

went in the first documentation for NumPy, for numeric, sorry.

This is Paul de Bois, David Asher, Conrad Henson and myself.

I got credit because I wrote this chapter, which is all the CAPI of numeric, all the

C stuff.

So I said, ah, probably the one to do it, and 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 my academic,

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

to do.

Can we just link on that moment because 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 you, it's like great leaders throughout history, like get what is the brave heart,

like get on a horse and, and rile the troops because I think that can have, make a big

difference.

We have TensorFlow versus PyTorch in the machine learning.

We have the same problem today.

Yeah.

I wonder, it's actually bigger.

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

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.

There's not a willingness to, you know, like I was literally walking into a field saying

I'm going to do this and you know, here I am, like, you know, I have five kids at home

now.

Pressure builds.

Sometimes my wife hears these stories and she's like, you did what?

I thought we were going to, I thought you were actually on a path to make sure we had

resources and money, but, but again, there's a, there's an aspect, I'm, I'm a very hopeful

person.

I'm an optimistic person.

My nature.

I love people.

I learned that about myself later on, uh, uh, part of my, uh, my religious beliefs actually

lead to that.

And it's why I hold them dear because it's actually how I feel about, it's what, 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 gain.

Like that's a thing, but it's not, you know, that's not the scorecard, uh, for me.

And so I just wanted to be helpful and I knew, and partly because these sci pi conferences

because of the main list 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, to the time said, yeah, we didn't think you'd be able to do it.

Yeah.

We thought it was crazy.

And also instructive, like practically speaking, that you had a cool feature that you were

chasing in the morphology, like the, like it's, it's not just like, it's not some visionary

thing.

I'm going to unite the community.

You were like, you were actually practically, this is what one person actually can do, uh,

and actually build.

Cause that is important cause you can get over your skis.

Yeah.

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 can, I can tell you all the flaws

with NumPy.

Right.

We want to go into it.

There's lots of stuff that I'm like, oh man, that's embarrassing.

That was wrong.

I wish I had somebody slot me with a wet fish there.

Yeah.

Like I needed, like what I'd wished I'd had was somebody with more experience and certainly

library writing and array library.

Like I wish I had me, I could go back in time and go, do this, do that.

There's an important thing.

Cause 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 nobody, and

I couldn't get the help.

Anyway.

So I wrote it.

It took about, it took four months to write the first version and 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.

That was, it was, it was definitely challenging.

And then the big thing I did was create a new type object called D type.

That was probably the sync, the, the contribution.

And then the fact that I added a broad, not just broadcasting, but advanced indexing.

So that you could do, um, masked indexing and indirect indexing instead of just slicing

in.

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.

Yes.

Exactly.

So you could do math on it easily.

Math on it easily.

Yeah.

So it had an associated library of math operations.

And effectively SciPy became an even larger operate set of math operations.

So the key for me was I was going to 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 the backward compatibility.

One of the big challenges of NumPy was I had to be backward compatible with both numeric

and numeric in order to allow both of those communities to come together.

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

to 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's a remnant, it's an artifact of its historical existence.

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.

I think in order to unite people, you have to kind of put your ego aside and truly listen

to others.

You do.

What do you love about Numeric?

What do you love about numeric?

Actually get a sense.

I was talking about languages earlier, sort of empathize to the culture of the people

that love something about this particular API, the naming style, or the usage patterns,

and truly understand them so that you can create that same draw in the united thing.

I completely agree.

You have to also have enough passion that you'll do it.

It can't be just like a perfunctory, oh yes, I'm really illicit 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 filia, there's a love of esteeming

of others that's actually at the heart of what is sort of a life philosophy for me,

right, that I'm constantly pursuing, and that helped, absolutely helped.

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 the story.

Well, in some aspects, maybe.

Some aspects, right, right, exactly.

Well, it's a good question, how do we teach this, how do we encourage it, how do we lift

it?

Because so much of the software world, it's giant communities, right, but it seems like

so much is moved by little individuals.

You talk about Linus Tarvald, it's like, could you have had Linux without him?

Could you?

Yeah, Guido and Python.

Guido and Python.

Guido and Python.

I mean, in the scipy community in particular, 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 Map.lib, we had Fernando Perez who created iPython, and so we sort of inspired

each other, but in the credit, there's sort of a culture of this selfless, giving the

stewardship mentality, as opposed to ownership mentality, but stewardship and community focused,

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.

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 West McKinney, like West McKinney was critical to the success

of Python because of his creation of pandas, which is the roots of that, we're all the

way back in American num array and NumPy, where NumPy created an array of records, West 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 the heart

of a data frame.

And we thought about that back in the memory days, but West 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, so NumPy ultimately took a little while, about six months in, people started joining

me, Francesc Alted, Robert Kern, Charles Harris, and these people are many of the unsung

heroes, I would say.

People who are, they sometimes don't get the credit they deserve, because they were critical

both to support, it's hard and you need some support, people need support, and I needed

just encouragement, and they were helping, 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, you know, between numeric and nummery, 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 of Matplotlib.

As soon as he did that, and I remember specifically when he did that, I said, okay, we've done

it.

Like, that was when I knew we had to see success.

Before then, it was still, you know, doing sure, but that kind of started a roller coaster

and then 2006 to 2009, and then I've been floored by what it's done.

Like, I knew it would help, I had no idea how much it would help, right.

And it has to do with, again, the language thing, it just, people started to think in

terms of NumPy.

And that opened up a whole new way of thinking.

And part of the story that you kind of mentioned, but maybe you can elaborate, 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.

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

took over.

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, 2006.

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 this way, and they didn't say no, they said, come back

in two years and do it again.

And I went, 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?

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 still have some libertarian trends, but I'm more of a, I'm more of a collectivist

libertarian.

So you value broadly, philosophically, freedom?

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 got to go out and explore this entrepreneurial world.

When I left academia, I said, no thanks, called my friend, Eric, here, who had, his company

was going, I said, hey, could I join you and start this trend?

And he, at that time, they were usually inspired by 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 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?

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, and there's people I talk to, like researchers, faculty,

in those conversations and the white board and just the conversation, that's magic there.

All the other stuff, the administration, all that kind of stuff, seems to, you don't want

to say too harshly criticized sort of bureaucracies, but there's a lag that seems to get in the

way of the magic.

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 the industry.

So like we need that, and we need that flame going, and it's the same thing as exactly

as you said, it has the same kind of elements like the open source community does.

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 want to build one robot, I'll stay at MIT, but if I want to

build millions and make money enough to work and explore the magic of that, then you can't.

And I think that dance is...

The translational dance has been lost a bit, and there's a lot of reasons for that.

I'm certainly not an expert on this stuff, I can opine like anybody else, but I realized

that I wanted to explore entrepreneurship, 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, because

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 in an emergent way,

that respects everybody's opinions.

So this is actually powerful.

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 and voluntarily people

are asking us to do something that they like, and that's a huge deal.

And so I really liked that energy, so that's what I came to do and to learn and to try

to figure out.

I've been kind of stumbling through for the past 14 years.

And that's 2007.

2007, yeah.

And so you were still working on an opine.

So no pie was just emerging, right?

Just emerging.

One of the things I'd done, it's worth mentioning because it emphasized the exploratory nature

of my thinking at the time.

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 fundraising from 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 going to write a book, and I'm going to 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 writing that book, and it was not a page turner, Guide to NumPy is not a book you pick

up and go, oh, this is great, over the fire.

But it's where you could find the details, like how did all this work?

And a lot of people love that book.

And so a lot of people ended up, but I said, look, I need to, so I'm going to charge for

it.

And I got some flak for that, not that much.

Just probably five angry messages, people yelling at me saying I was a bad guy for charging

for this book.

It was one of them which is dumb.

I'm just kidding.

No, I haven't really had any interaction with him personally, like I said.

But there were a few, but I'm just surprisingly not, there were actually a lot of people

like, no, it's fine.

You can charge for a book.

That's no big deal.

That's the way you can try to make money around open source.

So what I did, what I did in an interesting way, I said, well, you know, kind of my idea

is 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 the software is a little more complicated than that because creation is actually a continuous

thing.

You know, it's not like you build a widget that's done, it's sort of a process of emerging

and continuing 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, it'll, 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 forever.

Cool.

I didn't know this story.

Yeah.

So 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.

It's 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 mean, 90,000.

So sold 30,000 copies by myself.

I just put it up on, you know, used PayPal and sold it.

And those are my first tastes of kind of, okay, this can work to some degree.

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 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 sci fi emerging and getting company usage.

I ended up not letting it go the 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 as an 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 sci fi as a collective.

And they were essentially needing the stuff in the book.

Right.

And so they kind of asked, hey, can 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.

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.

It's probably true.

Because of the tooling and the community building.

Yeah, I agree.

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

I agree.

There'd be things to do.

I've thought about that.

I really had thought about a couple of books or a couple of things that could be done there.

And I just haven't, right?

I even, I tried to hire a ghostwriter this year too to speak if I could help, but it

didn't.

Part of my problem is this, I've been so excited by a number of things that stemmed

in from that.

Like, so I came here, worked at and thought for four years, graciously, you know, Eric

made me president and we started to work closely together.

We actually helped him buy out his partner.

It didn't end great.

Like unfortunately Eric and I aren't friends now.

I still respect him.

I have a lot, you know, I wish we were, but he didn't like the fact that Peter and I

started Anaconda.

Right?

That was not, I mean, so there's two sides of that story, so I'm not going to go into

it, right?

Sure.

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

I do.

I do.

It saddens me.

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

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

And I hold him in esteem.

I'm grateful for him.

I think they're doing, you know, their thoughts still exist.

They're doing great work helping scientists.

They still run the SciPy conference.

They're in the, they have an R&D platform they're selling now that's a tool that you

can go get today, right?

So they've been, Enthought has played a role in the SciPy, 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 try.

I mean, it was not just because, it was just because the business aspect.

So, and then I wanted to build a company that could do, that could get venture funding,

right?

Better for worse.

I mean, that's a longer story.

We could talk a lot about that, but.

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

That's where Anaconda came to be.

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

the knowledge that you're leaving NumPy and some of the things you've listed and 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?

Good questions.

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

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, it's important to be able to understand that I'm grateful for the opportunity at the

role I had, I could play and grateful that things I did had an impact, but they only

had the impact they had because the other people that came to the, 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 or actual Python objects so that there's for every, for a 32 bit float

or a 16 bit float or a 16 bit integer, Python doesn't have a natural, it's just a one integer

has 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 an improved

the type system and like made the type system actually a Python type system, as opposed

to currently it's a Python one level type system.

I don't know if you know the difference between Python one, Python two, it's kind of technical

kind of depth, but Python two, one of its big things that Guido did, it was really brilliant.

It was he actually Python one, all classes, new objects were, were one.

So he was a user wrote a class.

It was an instance of a single Python type called the, called the class type, right?

In Python two, 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 Python and NumPy created a type system that was Python one 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 efficiencies or usability?

It's usability primarily.

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 to quaternion type

or you want to add a new, you know, posit type or you want to.

So it's hard.

Now in the, and now if we had done that well, when NumPy 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 NumPy plus better typing, could actually be

a, you'd smooth out a lot of rough edges.

But there's already, there's like, but are you talking about from the perspective of

developers within NumPy or users of NumPy?

Developers of new, not really users of NumPy so much.

It's the development of NumPy.

So you're thinking about like how to design NumPy so that it's contributors.

Yeah.

The contributors, it's easier.

It's easier.

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.

The fact that we have to, you'd have to download a whole other object called Koopi to have arrays

on GPUs is just an artifact of history.

Like there's no, there's no fundamental reason for it.

Well, that's really interesting if we could sort of go on that tangent briefly is you have

PyTorch and other library like TensorFlow that basically tried to mimic NumPy.

Yeah.

Like you've created a sort of platonic form of multi dimension.

Yeah.

Yeah.

Exactly.

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

There were a lot of edges.

They were like, well, we should cut those out before we present it.

So I mean, I wonder if you can comment, is there like a difference between their implementations?

Do you wish that they were all using NumPy over, like in this abstraction on GPU?

And sorry to interrupt it, that 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.

Yeah.

Yeah.

There's several comments there.

We are working on something now called data dash apis.org, data dash api.org.

You can go there today.

And it's our answer.

It's my answer.

You know, it's not just me.

It's me and Rolf and Athen and Aaron and a lot of companies are helping us at Quonsite

Labs.

It's not unifying all the arrays, it's creating an API that is unified.

So we do care about this and we're trying to work through it.

I actually 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, the split

in the array community that exists today makes what I was concerned about in 2005 pretty

parochial.

It's a lot worse.

Right?

It's a lot worse than 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, I've also learned to appreciate, it's okay to have some competitions, okay to have

different implementations, but it's better if you can at least refactor some parts.

I mean, you're going to have more efficient if you can refactor parts.

It's nice to have competition over things, over what it's nice to have competition.

They're innovative.

Yeah, innovative.

And then maybe on the infrastructure, whatever, however you define infrastructure, maybe it's

nice to have competition together.

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, that 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 the users were like, hey, where's the NumPy interface?

Oh, and 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'm usually, I mean, one of the challenges I have is I don't criticize

enough because in the sense that I don't give people input enough.

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

But I went to it.

It was a talk given at a Py...

My Orca in Spain and a guy, great guy, came and gave a talk and I said, you should never

show that IPI again at a PyData conference.

That was terrible.

You're taking this beautiful system we've created and you're corrupting all these poor

Python people, forcing them to write code like that or thinking they should.

Fortunately, they adopted Keras as their, and Keras is better.

And so Keras, TensorFlow is fine, is reasonable, but they bolted it on.

Facebook did too.

Facebook had their own C++ library for doing inference and they also had the same reaction.

They had to do this.

One big difference is Facebook, maybe because of the way it's situated in part of FAIR, part

of their research library, TensorFlow is definitely used and they couldn't just open it up and

let the community change what that 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 community users.

People use it and build the infrastructure, but it's much more walled.

It's harder to become a contributor to TensorFlow itself.

And it's also, this is a very difficult question to answer and 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?

Correct.

Correct.

Do you tend to the millions of people that are giving you almost no money, or do you tend

to the few that are giving you a ton of money?

I tend to stand with the people.

I feel like if you nurture the hackers, you will make the right decisions in the long

term that will make the companies happy.

I lean that way too.

I totally agree.

But then you have to find the right dance.

But it's a balance, because you can lean to the hackers and run out of money.

Yeah, exactly.

Exactly.

Which has been some of the challenge I've faced in the sense that 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, right?

I mean, it didn't succeed in the early days of getting enough financial contribution to

NumPy, so they didn't really 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 like that.

I've wanted to be able to do something about that for a long time and try to figure out