Python Documentary Companion Pod  ## Metadata - Author: [[Changelog Master Feed]] - Full Title: Python Documentary Companion Pod - Category: #podcasts - URL: https://share.snipd.com/episode/16efe108-ee4c-475a-8687-06eee147bc7a ## Document Notes ## Highlights - **SIGs Fueled Diverse Python Ecosystems** - Python's growth came from special interest groups that let subcommunities form without dominating the whole project. - These SIGs seeded ecosystems like SciPy, Django and later pandas by enabling focused collaboration. Transcript: Travis Oliphant Yeah. Jerod Santo I was going to say, it's like so big, it can't be a community even. Travis Oliphant No, it can't be a single community. And actually that's one of the things for the major leadership of the Python, the Python Software Foundation or the ecosystem facilitators understand. I think that's one of the primary things I would, I could articulate this under is just, as you get so big, you're actually encompassing many nation states, respectively. How do you be the UN as opposed to an organization that's running one country, county, city, like the skills and the, what you have to do is different. And how do you do that without effectively making it feel like you're a bully or a totalitarian for other groups? Because what ends up happening is some people feel unwelcome. Not because they're intending to, but just because, well, you know, we're limited. Every human is limited. And if you have an organization, people have to do work. They have to do stuff. If the stuff they do just isn't feel inclusive enough for other people, then those people start to feel annoyed. So what are the patterns? And Python is definitely big enough for that. And you'll see issues on this over and over again, whether it be the documentary didn't cover this group enough. I'm sure we'll get that. I'm sure it's true. The documentary, I'm just like excited to see anything talked about that emphasize a couple of great points about Python, that it's been such a great group of people who have tried to Bring their best foot forward and try to do what's best for each other. Not perfectly, of course, because none of us can, but try and try to be inclusive, try to bring other people in, try to manage the growth and different mindsets that are there. I understood very, very well the difference between the, let's call it the developer mindset, the person who's using Python to build a website, and then from there, a system prompt Or a script and operating system or build an event programming system versus somebody who's building, using this to run a scientific experiment or to do math or to run physics systems And now evolving into data science and AI. Like the kinds of problems they have and the needs they have are different than somebody building a website or a database or a Raspberry Pi, right? Or a web app. All great, all wonderful. You know, there's not a, there's no judgment about which is more important. It's just simply different. And then as they come together, how do you have a language support all of these use cases? Great question, right? I think it's challenging. But the community is awesome. I think one thing that comes out in the documentary is very true, is people come for the language and stay for the community. And that has to do with kind of, you know, the original group, you know, Guido, his original personality and approach he took to engaging with people and the group of people that came Around him in the early days and then how that spawned multiple subgroups. Even from the very early days, they had these SIGs, special interest groups they created just to allow other communities to form instead of just all one mailing list in other places. Hey, you could talk about that problem over here and not be, and everybody didn't have to chime in on that problem if they weren't interested. And so people have these special interest groups. And that special interest group is the matrix SIG actually that ended up giving rise to numeric and then NumPy and then the SciPy ecosystem came out of that special interest group. Then you had other special interest groups that gave rise to Django and then FastAPI. And then there's packaging. It's its own story that we can go on about because there's still challenges there. But so I think that's one of the key things to understand. But each of those interest groups was infused with at least a general sense of, hey, here's how you treat people. Here's how you, you know, let's avoid kind of the, even though, you know, we want to make sure excellence is achieved and people do really good work and not just lowest common denominator Code gets in. It's still, how do you make it welcoming? You know, how do you make it so there's new people, they don't feel scared away because they're not capable enough to, or the people they feel they're not capable enough. How do you make it so that they feel welcome? But then also you respect the quality of the code and that people are attentive to that. You know, you sometimes, you know, in some areas I shouldn't be contributing code, right? The code I write should be tossed out. But I appreciate being listened to. At least if somebody tells me, you know, this is why it's always better than just being ignored. So I think some of those elements of the Python ecosystem, Python community have been helpful. Not perfect. You know, each of these communities in the Jupyter community, the notebooks, you know, the data frame community, pandas emerged from the kind of the SciPy ecosystem. ([Time 0:06:26](https://share.snipd.com/snip/c85ab2fe-21ca-452c-8429-c3d92787a6ce)) - **How Python Hooked A Scientist** - Travis Oliphant discovered Python in 1996–97 while needing fast, readable tools for scientific data at the Mayo Clinic. - He built SciPy modules, released tarballs, and attracted global contributors who sent patches and binaries. Transcript: Travis Oliphant Great question so i am a. I was a electrical engineering student who went to the Mayo Clinic to study medicine because I wanted to help diagnose cancer and diabetes and instead of make, make bombs. Right. I was, I knew I was, I loved applied math. I loved electromagneticism and I loved signal processing. And then I had this opportunity to go to the Mayo Clinic to study. And initially, when I was a master's student, I was measuring wind speed. I was sorry, I was measuring backscatter radiation from the planet in satellites. It's called satellite scatterometry. And then from the information from that signal from the satellite, you could infer wind speed. You could also infer other things like vegetation coverage, ice coverage. In the lab I was in, we were doing those kind of earth observation at the time. I was using, I was on a Vax VMS actually back in the day. We were using, it was programming C, Fortran, MATLAB, and then some Perl. So I just started to pull out Perl and my experience with Perl in the back, back 94, 96, I would write it and like, okay, this is pretty cool. I can do the scripting. I can write some code. But then I'd come back three months later, a year later, and I had no idea what I'd written. It was like, it was write once, read never. Right. And, you know, very interesting, like ideas were there and it was cool, but that was my experience. So as a domain expert who was using coding I couldn't use it because I couldn't understand what I'd written past so fast forward to 97 I'm at the Mayo Clinic doing work on magnetic resonance Elastography trying to measure I take a big MRI image and look at the phase that image to infer motion and from that motion you can see a kind of a propagating wave inside of tissue. You could then try to figure out the elasticity, like how stiff it is, and make a picture of elasticity. So that was the problem I was solving, and I needed to find derivatives and compute them on essentially four or five-dimensional data sets, so big data sets. And MATLAB was, I was using MATLAB a bit, but I ran out of space and I didn't have enough, I couldn't change the precision well enough, so I was looking around for other ways to do this. Now, I could just write C code, but man, when I write C, I'm spending a lot of time on pointer arithmetic and figuring out where memory references are, and I want to be thinking about my Problem, which is here's my volume of data, and I want to do derivative calculation. So if I have to write that, you know, if every line is like 10 lines of C, I lose track in my mind of what I'm doing very quickly. So that's what a high-level language did is give me a chance to be in my domain, writing some scripting or high-level conversations, but I needed to be fast. I needed to be efficient. So I was looking around for ways to do that in 1996, 1997. And I came across Python. Python was pretty new. It had been around for just a little bit. And it had an array library called Numeric that I learned later had been written by Jim Huguenin out of this matrix SIG, which was formed in 94, right? And the language itself was written 91. But I came to it about 97, right? And it was still early, still nascent, you know, kind of this thing on the net that a few people were doing and it wasn't being broadcast or promoted by anybody but the people who found It and then started to share their experiences. So I found it. I found, oh, this language actually feels familiar. It's like high level enough that I don't have to, the syntax doesn't get in my way. I'm not worried about chasing pointers, just a straightforward language that also can be extended. So if I had C code that did something fast, it wasn't like, good luck, run that in a different process. It was, that's fine. You can actually load that in a shared library into the main, into this, into the language. Next, you could extend it. And Numeric had already done that to make an array object. So if Numeric hadn't existed, I would not have, I would not have come out. I would not have start used it. I would have been using something else very likely. So I, you know, Jim Huguenin and the matrix sig, Conrad Hinson, Paul Dubois, they were hugely influential in making that happen. But they were the OGs of array computing in Python. Paul, yeah, and I'm leaving off names. David Asher. I mean, you look at the, there's a NumPy book. What I did early in 90, by about 98, is I made a chapter in the first, you know, numeric documentation that particularly covered how the C API worked. How, if you're a C developer, how do you use numeric and make an extension using that? So that's what I became really kind of known for. And what I really, my super skill was, was blending Python and low-level languages and releasing libraries that could do that. That's essentially what I learned to do as a grad student at the Mayo Clinic. So fairly straightforward, very specific. I had the opposite experience from Pearl because I started in 97. I wrote some scripts to do some things. Fast forward a year in 98, I come back to the language. Oh, that script, say, oh yeah, I did that script to load those data and do this math. And I went, wait, I understand what I did. Like I can still read this. This still makes sense to me. And so then I was kind of hooked. I went, oh, this can work then. And so 98 was the start of my journey. And then 98, 99, Sci-Fi really was, was really created in 99. You can see on the mailing list, the early mailing list there posts from me, like probably every two months. I'm like, here's a new module. Here's a new library. Try this out. And just, you know, posting a tar ball. That's how we did it in those days here's the main list post a tarball on a web and you know you can go to my website my website was really dumb just had a bunch of links hyperlinks not pretty At all and you could download it and they'd download it and you'd unpack it and you and then i would get patches i get patch files people started to send me patch files i went oh this is cool Someone from anywhere in the world is sending me code. And, yeah, that's better. Yeah, that's cool. And then I decided to adapt or not adapt it. So I did that for about seven different modules between 98 and 99. And then a few other people started the same thing. One of the guys actually helped me. He saw what I was doing because the modules I was writing, it's things like order differential equation solving, integration, optimization. It's things I needed for my research. I'm like, oh, where's the library for that? Okay, well, here's some Fortran code that does it. Then I just write the connector to Python to make that work and then push that together in extension module, release it, and then tell people how to install it. But to install it, you had to literally compile it yourself on your code, right? So that's what I did. And then around me, people started to show up, right? It's sort of this, people in the community started to show up. One person showed up and said, oh, let me build a binary for Windows installers. And they did. They wrote a binary installer for the predecessor to SciPy called Multipack. And then somebody could just go to the website and click and an EXE, they can install in their windows box and they could start using it. Right. And that was huge. So all of a sudden usage went up about a hundred fold. Right. And that's actually why a guy like me ended up starting a company like Anaconda. Right. Which is all about getting the tools in the hands of people easier. Right. Because of that experience of like, ([Time 0:13:00](https://share.snipd.com/snip/2043dcc5-c561-407b-b4e6-84d79b6a9ab9)) - **NumPy Born From A Semester Off** - NumPy started in 2005 when Travis used spare time at BYU to unify array libraries and avoid costly copies between Numeric and Numarray. - The project solved fragmentation and became far more successful than he expected, even costing him his academic position. Transcript: Adam Stacoviak Well, friends, I'm here with Damian Shingleman, VP of R&D at Auth0, where he leads the team exploring the future of AI and identity. So cool. So Damian, everyone is building for the direction of Gen.AI, artificial intelligence, agents, agentic. What is Auth0 doing to make that feature possible? Speaker 4 So everyone's building Gen.AI apps, Gen.AI agents. That's a fact. It's not something that might happen. It's going to happen. And when it does happen, when you are building these things and you need to get them into you need security. You need the right guardrails. And identity, essentially authentication, authorization, is a big part of those guardrails. What we're doing at Otsido is using our 10 plus years of identity developer tooling to make it simple for developers, whether they're working at a Fortune 500 company or working just At a startup that right now came out like Combinator to build these things with SDKs, great documentation, API first types of products and our typical Auth0 DNA. Adam Stacoviak Building for this stuff, then you know, go to Auth0.com/AI. Get started and learn more about Auth4GenAI at Auth0.com/AI. Again, that's Auth0.com/AI. Travis Oliphant So that's the problem I set out to solve in writing NumPy. And in 2005, I basically was a professor at BYU at the time, and I said, ah, this is a problem, but who's going to solve it? Like, nobody's going to do this because, you know, it's complicated, and who knows enough to be able to do something like this. So there was a semester, a class fell through, and so I didn't have any teaching load. And so I said, well, I'll just do this. I'll take the next three months and do this. Why not? Okay. I've written a bunch of modules for Sci-Fi. I'll do this. That was in 2005. So that was NumPy. So that's where NumPy came from. That's where NumPy came from. Okay. And a big part of the strategy was actually, well, how do we get adoption? Well, let me go talk to Guido. Maybe we make NumPy and we put it into Python. So I to talk to Guido around that time, around 2005, about how do we make the Python community more aware of the NumPy ecosystem, and maybe by putting NumPy into Python itself. And I met with them about 2005. We talked about it, decided against it initially, but that is where the idea of the buffer protocol and the memory view object came. So the memory view object and the extended buffer protocol, there was one already, we made a better one. That came because of those conversations. And I would say we got the data structure of numeric of NumPy into Python, if not the library. Right. So that was, that was the, and that's how I became like this deep embedded ambassador for a few years, probably about seven years. I did that for a while. Jerod Santo Did that solve the problem of this division and this competition? Travis Oliphant Yes. Yes. It solved the problem. It absolutely solved the problem. So NumPy was wildly successful, way more successful than I thought it would be. Honestly, it blew me away. It was work. ([Time 0:41:28](https://share.snipd.com/snip/99f61943-7cee-4f14-a91e-7eee14cf95fc)) - **NumPy Cost A Tenure Track Job** - Travis spent so much time building NumPy that he lost his tenure-track position. - He accepted the tradeoff because the project's community impact mattered more to him than academia. Transcript: Travis Oliphant Honestly, it blew me away. It was work. And I actually ended up losing my tenure track position at university because I spent too much time on NumPy. Jerod Santo Oh, really? Travis Oliphant Yeah. Now I know why you're an entrepreneur now. That's what I'm – yeah, exactly. There's a deeper story to that, but it depends on which version of the story you want to hear. It's all – the little sound bites, you never get to the full story. But it definitely had an impact on my academic career. So – but it was okay. It was okay. Jerod Santo It was something I really wanted to do. Now you're a Python martyr. You know you're a Python martyr. Python martyr, exactly. Exactly. Travis Oliphant Hey, if that helps people, if that helps people launch their careers, fine, right? Now what I do is I try to say, look, if I can be helpful to you to build a company, to build a Python organization, to build a project, I would love to help. Let me know because I've been so benefiting from other people as well. I want to help you do the same thing. Let me know if I can help. And that's what I do now. Right. So it's a long history of all kinds of stories. You can see there's all kinds of avenues and places where you can literally, you could have, you could have like a Netflix series. Yeah, totally. Jerod Santo There's lots of different ways to go with the right writers ([Time 0:44:32](https://share.snipd.com/snip/50c7771b-4d90-441a-9900-50b13c4f3d3e)) - **People Drive Corporate Language Adoption** - Industry adoption often follows people, not platforms; companies like Google used Python because their hires already loved it. - Corporate reliance then reshapes open source ecosystems and hiring pipelines. Transcript: Travis Oliphant And I know specifically what happened in the AI world because of TensorFlow and PyTorch. And then in 2018, when I left Anaconda, which is a company I founded to help make packaging work better and then data science better, actually, basically I saw, oh, my goodness, I wrote NumPy to bring the array communities together. And 2018, there were literally 20 others. But they're all around this AI story. There's MXNet, there's PyTorch, there's Torch, there's TensorFlow, there's so many of these other array libraries effectively. Now, fortunately, we'd fixed it twice in NumPy. Not only do I create NumPy, the array library, but also create a buffer protocol so that even if new array libraries existed and showed up, you could still share data. So there's a fix it twice concept. And then that's what we did get in there so that, OK, great. There are all these array libraries, but at least you can share data now. You don't have to copy data from one another. So that's that's at least the saving grace, but they're all over the place. Each one of these companies, they did it. You know, primarily they wrote like PyTorch started as a Lua library. There's a C++ Lua library and there's three versions of it. TensorFlow was a C++ library called Disbelief. You know, each of these have their own internal organizational C++ stories. They started to launch them in 2015 timeframe, 2014, 13, 14, 15, started to put these libraries out there. People started to use it. Well, at this time, there'd already been 13 years of the SciPy ecosystem and the SciPy conference. ([Time 0:51:23](https://share.snipd.com/snip/7aa66640-8e64-4e6d-9d36-889f818d666e)) - **Interoperability Prevents Fragmentation** - The NumPy buffer protocol fixed later fragmentation by enabling different array libraries to share memory without copying. - Designing interoperability early prevents costly ecosystem splits. Transcript: Travis Oliphant Now, fortunately, we'd fixed it twice in NumPy. Not only do I create NumPy, the array library, but also create a buffer protocol so that even if new array libraries existed and showed up, you could still share data. So there's a fix it twice concept. And then that's what we did get in there so that, OK, great. There are all these array libraries, but at least you can share data now. You don't have to copy data from one another. So that's that's at least the saving grace, but they're all over the place. ([Time 0:51:55](https://share.snipd.com/snip/61c4573f-53fb-4142-a85c-0595f0fbcad9)) - **Separate Internal And Community Workflows** - Successful corporate open-source projects often separate internal development from community-facing processes. - That separation lets outside contributions flourish while protecting internal business needs. Transcript: Travis Oliphant Yeah. And actually, great, great point, because I actually knew the person who was in charge of both TensorFlow and Kubernetes at the time, and they wanted to kind of figure out how to, you Know, get community involvement in those two. Kubernetes succeeded, TensorFlow did not in that regard. And I think you have a great point. And part of it was because they didn't really depend on Kubernetes internally. ([Time 0:57:19](https://share.snipd.com/snip/24fc14c7-f14b-45aa-9a82-555db1c0a1e9)) - **Join Open Source To Advance Your Career** - If you want to accelerate your developer career, join an open source community and contribute regularly. - Start with documentation or newbie guides because your beginner perspective is uniquely valuable. Transcript: Travis Oliphant They haven't really contributed open source communities. They're kind of let me just get that job. And, you know, my advice to a young developer is go go find a few open source communities you love. You get excited about and start participating in them. Just show up, you know, hear what the problems are. Contribute something to it. You know, if it's documentation, even if it's just, oh, read me for a newbie. If you're new, great. You have a power nobody else has in the community because you're new to it. Everybody else has been there a while. They don't know what the problems are for someone new adopting it. So great. Document your journey and write a story for a new person how to make this more accessible. There's a lot you can do, anybody can do to participate in open source and do that. Okay, maybe you can't do it 20 hours a week, but do it for five hours a week. Do it for an afternoon. Do it for a weekend. ([Time 1:00:21](https://share.snipd.com/snip/959b4abf-76d6-4d59-83ae-0a5dc794b988)) - **Use Python-Like Compilers For Speed** - Explore Python-to-native compilers (Numba, Cython, Mojo) to get high performance from Python-like syntax. - Standardize a statically-typed subset so many compilers can interoperate and compete. Transcript: Travis Oliphant At the same time, there was a language called Julia that was emerging. And Numba was a Python compiler that took a subset of the Python language and made machine code to make things fast. And we actually, by 2013, were targeting GPUs. So you could actually write Python code that ran faster than any C++. Like we were showing that in 2013 at the GTC conference at NVIDIA, because you could basically write Python code that would run on GPUs directly. So I love that whole space. Like this is something that it took me a while because I was, this is when I finally learned how to write compilers. Now I wrote the first version and then quickly found other people to make it better. But, but I took, I actually took a pilot class from my friend, David Beasley in Chicago to try to understand, at least have something besides just what I picked up from the internet, right? And that helped me see the vision for, oh, Python can actually be orchestrating compiler tools, and there's no reason you can't just write syntax that's Pythonic, looks Pythonic, And then have it compile the machine code. There's zero reason that can't happen. So there's no reason you have to use C++ or RUST to write machine code. You can totally write a subset of Python and have very fast code. And Numba proved that and proved it pretty well, actually. And then since then, there's been like 20 other versions of that. Like right now, if you go to lpython.org, lpython stands for LLVM Python. It's an example of a compiler for Python, lpython.org. And a table there shows a list of other similar projects. Some of them are a little bit more like Mutica, where they're translators from Python to C++. Others are like Pythran is another one. Kodon's another one that's out there. There are literally a dozen. Jerod Santo Yeah, I'm looking at the table. It's probably two dozen. Travis Oliphant Two dozen. Yeah. Now, you know, all of them are kind of this example of scratch your own itch, something that's, you know, that is built, you know, Numba has a team that kind of still working on it. And so it's actually received a lot of support. And so it's kept up to date with the latest releases. And there's, you know, there's a lot of, every time the bytecode changes, and Python, Numba's got to change because it goes from the bytecode on. And you can actually write. There's also a tool called Cython out there. Cython was in the same. It's not exactly the same, but it was like, oh, write it in this language, and then you can write extensions quickly. And even something like F2Pi might be considered in that category. But Cython, Numba. So I love this space, right? ([Time 1:04:55](https://share.snipd.com/snip/b161981e-27a7-4a63-a9c4-c242b9ed0982)) - **Funding Shapes Open Source Collaboration** - VC funding often promotes siloed, competing open-source projects instead of cooperative shared infrastructure. - Corporate sponsorship can help unify ecosystems, but motives and structures matter. Transcript: Travis Oliphant I think it's been promoting the silos primarily. I think where we've gotten the funding from the corporates have helped break them down, like the big funding. But VC funding has encouraged silos. I've actually been an outspoken critic, not that outspoken, but I am a critic of the GitHub star funding model of VCs. For a while there, it was like, oh, I see your GitHub here has many stars. Here's $5 million. Right. And the trouble with that is there is not a one-to GitHub repository to company mapping. Jerod Santo You can't just trade those in for dollars? Yeah. Travis Oliphant I don't mind. In fact, we can have another conversation. I think I've got a way to actually create a market for that. But it's not as simple as because I have a VC fund, too. I've started a VC fund, right, for this purpose of helping entrepreneurs create. And we've got 14 companies invested. I've got an idea of how to actually create a marketplace where you can actually have GitHub stars be a market driving question. And investors are actually looking at, OK, this project is going to be used. Great. I'll invest there. Then have that investment dollar go to support the development of the project while also having to return some at some point from somewhere. Because that's the thing you've got to figure out. You've got to build an instrument that drives the return of the investor from the risk they took to invest in that project early. And a company is one way. ([Time 1:11:20](https://share.snipd.com/snip/70156c8c-727f-4901-9a96-07344d3a43ce)) - **A Capital Model For Open Source** - Fair OSS proposes treating open source projects like assets with cap-table-style allocations tied to dependency graphs. - That instrument would let companies and investors fund projects and route value back to contributors. Transcript: Travis Oliphant Effectively, you just have an organization that, on the one hand, works with open source communities to document their dependencies, both their dependencies on code and their dependencies On people. So essentially, I call it a it's their end dips, their millibips table. Right. And they basically have a basis point. And if you do a thousand of a basis point you basically have 10 million units to give out for every project and a project governance essentially gives out basis points for dependencies Like oh i'm numpy i depend on python a lot so we're going to give we're going to give you know 40 of our of our cap table to python right and then from the the rest of it we're going to give, you Know, award it to people and you have someone reserve. And as people come in and make contributions, the governance can actually go, oh, here you go. Here's, you know, here's ownership. Here's so you basically have one side helping people organize that because there's a specification, you stick in the GitHub table, then it's there and you can say, oh, this is you, the Project who decided a cap table, basically. You've constructed a capitalization table for the project, right? And it depends on the governance. It's not our decision. Now, what we can do is to drive that as Fair OSS could essentially have a default approach to doing that. So it says, well, this is what we'll use if you don't tell us what not to use. This is how we're going to do it. And then, of course, encourage them to do that. So we're not gated by whether they participate or not. So you build that. And then to the companies, you go to companies and say, hey, you're missing from your cap table, your employee option pool, your investor group, the warrants you're giving out to partners, Because this happens all the time. You're missing all the open source ecosystem from that group. And so let's get on your cap table. So we get on the ownership table of a company. And, of course, you know, we can't get a million open source contributors on the cap table. Okay, great. We put an entity on the cap table. And the entity on the cap table represents the open source you depend on. So you have an entry on the cap table and Ferrosys maintains your dependency, like their millibips table. Like who do, what do you depend on? Right. And it's, and then as equity returns happen, which is distant, usually it's like, as a venture capitalist, I know how this works. And you basically, over time, value will flow. Now, if you want quick value, it could be maybe you're not going to give up equity. You're a mature company. Equity is hard to come by, but we'll do a dividend agreement, like anacondas and dividend agreements with an agreement where we'll just allow some of this to come. Our venture fund, for example, we already do this with our carried interest. Like we have carried interest, which some of you know what that is. It's the, it's the motivation for the partners to find good deals to invest in for their LP's money. And then they get a percentage of that growth. And so we, our general partner, some of that carried interest already goes to fund open source. Right? And so it's the same thing. We can formalize it through that process. You got all these, now you have all these inputs. Right? You have equity deals. You have dividend deals. You have carried interest deals that are flowing through this instrument that becomes the funding source for all the open source dependencies. And then as opportunities arise, you're just streaming it out, distribution style, to the cap tables. And it flows down the cap table tree. Because, oh, you're dependent on Pandas. Well, Pandas looks like, oh, it's dependent on Python and NumPy. And then it flows down until you end up at the leaf nodes where there's individuals who are basically participating. So that's the basic idea. ([Time 1:15:19](https://share.snipd.com/snip/bfdd5990-77da-4f82-8087-7d18257fa6cf)) - **Publish A Project Cap-Table File** - Projects should publish a simple, auditable cap-table-like file listing contributors and dependency allocations. - Use that 'thanks-with-percentage' file to enable funding flows from companies and investors. Transcript: Travis Oliphant Could I get people to agree to that? And I thought I'd have to have a venture fund, actually. The whole reason I started a venture fund in 2019 is because I thought I would have to go to startups and say, yes, I'll invest in your company, but you have to do this. You have to put open source on your cap table, right? Because I knew they would say yes to that deal. What I found is I actually got people to say yes without the money. They understood the problem. Yeah. And so I went, oh, okay, that's good because, you know, it's like RegEx. You know, you use RegEx to solve string parsing. Now you have two problems. You have the problem I was trying to solve and then understanding RegEx. Same thing with starting a venture fund. Same thing with starting a company. I started Anaconda as a company to solve the problem. How do I fund NumPy? How do I make Cyp? How do I fund Cyp? How do I build this thing? So my whole life has been like, okay, here's a problem. How do I solve that problem? Here's the meta problem. Here's the next problem. How do I solve that problem? And they're just trying to learn along the way and just try to figure out ways that are learning from other people, trying to organize. Because what drives me is I want to create a world where open source is a meaningful way to live a life. You can be an open source contributor and still pay for your kids and still have it. That's a job and it's meaningful. It's not just a thing you do on the weekends and nights and when your day job is doing something else. So this is why I've been thinking about this. Adam Stacoviak And FAIR OSS is the epicenter of this? Travis Oliphant Yeah, it's the first version of the organization we've created to do this. And it's an idea of how it could be done. And we've tried it enough to know that I know it will work. But for it to work and to create the vision of where you can actually have investors, because eventually we'll have, you can have a ticker symbol for every open source project, literally. You can have a market where, oh, how's this open source project doing? That's going back to your stars. Your GitHub stars literally could translate to a value, a fair price, a fair market value for that open source project. And investors are looking and going, oh, yeah, I want to buy some of that. Right? Because they could. They could say, oh, I'll put in my portfolio that project because I think that's going to go somewhere. And how is it going to go? Because people are going to depend on it. And therefore, enough of these agreements are in place where value will accrue because of those agreements in place. So FeroSys puts the instrument together. ([Time 1:18:53](https://share.snipd.com/snip/9b26a540-f2e6-4a0c-b8fb-4bce7010b930)) - **Fund The Marketplace Before Scaling** - Start Fair OSS by securing multi-year seed funding (~$2–5M) to hire staff and build tooling before courting large investors. - Focus first on onboarding companies and pilot projects to prove the market mechanics. Transcript: Travis Oliphant So the highest is like 10 of their company they'll put on the cap table 10 that's a lot it is and it's it must not be worth very much it's it's startup land right because startup gets yeah Right and it's like and the argument really goes like hey here's a way to get and for that they're going to want more what they're going to what they want is connection to the open source Communities. They want for their hiring pipeline and for improving their, because a startup, your most important thing is the people you hire. So if Ferrocess can provide an opportunity to hire, then it's worth that, right? So it's worth, so that is high. Mostly it's like one to 2%. It's pretty easy. And as the company matures, you know, maybe a big company like Anaconda, it ends up being like 0.1% is all it is, right? Or 0.5%, right? But that's like, you add that up and that's a lot. Basis points on every company. And a lot of companies, they're throwing, I mean, they're putting more of that in their option pool for employees. And that's kind of where you're, that's where you're arguing. You're saying, okay, here's your option pool for employees. You need to recognize that there's a portion of this that you're not either taking advantage of and you're missing. And that's where I've had the best conversation with people. They go, oh, you're right. That could help. Now, what can, how, and then what they're looking for is, well, how do I, the company wants to know, well, can I connect that to feature requests? Can I figure out a way to kind of connect this to community connections or hiring? And that's where we can talk about that, right, because it could be a milestone-based vesting on that pool. There's ways to negotiate that, but that's what FerroAssess does is create the negotiation to increase the size of the instrument market. ([Time 1:29:21](https://share.snipd.com/snip/efc3a0c4-709f-4100-920e-c61949deaf10))