You are listening to the HumAIn podcast. HumAIn is your first look at the startups and industry titans that are leading and disrupting artificial intelligence, data science, future of work and developer education. I am your host, David Yakobovitch, and you are listening to HumAIn. If you like this episode, remember to subscribe and leave a review. Now on to the show.
Welcome back to the HumAIn podcast, listeners. Today, I have a special guest on the show. Gabriele Columbro¹. I went to the FinTech Open Source Foundation forum known as the Finos² Open Source Strategy Forum a few months ago in New York City and got to see so much between FinTech, big banks, big tech, reg tech, privacy tech, and so many industries all around FinTech.
And what was so exciting about this forum is the mastermind behind it, Gab, as we’ll call him, has done a lot of great work on bridging the gap with the finance industry between New York, Europe, and now globally, and love to hear Gab’s story. Gab, Thanks so much for being with us on the show.
Well, thank you David, for having me. It’s exciting to be here.
One of my favorite things about the Open Source Strategy Forum is as a developer myself in data science and AI, I love to code. And what was so exciting about the forum is you didn’t only have banks. You didn’t only have FinTech companies. GitHub was there, a lot of code platforms were there about taking open source code. I thought that was so powerful. And we’ve seen it in so many industries before, but not traditionally in finance. Why do you see now is a great time for open source standards and open source collaboration in the finance industry?
Well, that’s a great question. And thank you so much for the kind words. The Open Source Strategy Forum, especially the one last year that you attended, certainly is probably the highest point of our community and our foundation.
You pointed out the code. That’s a really important angle. We have at the conference so many decision-makers and technical leaders from the industry, but pretty much an evenly split mix between again, technology decision makers and developers. And why I mentioned that it’s really that we see open source as really as a bridging factor.
Not only as you mentioned within financial institutions and technology, but even within financial services institutions between two worlds which have been historically separated. The world of decision-makers, the world of business and the world of financial developers, FinTech developers. We think open source has a huge potential and we’ve seen it over and over again over the last couple of years to bridge this gap in a way everyone wins with open source.
But to your original question, why are we seeing this surge of open sourcing financial services? I’d love to say that it’s because of the work that our foundation, the FinTech Open Source Foundation does, that’s the only reason, but that will be a little bit too presumptions. Of course, we have the health industry coming together as a non-profit providing a trusted umbrella for open source collaboration to happen in the industry.
But there are some major shifts happening in the industry and sort of all the drivers, all the arrows pointing to open source as a brand new way forward for this industry. Very much like, as you mentioned, several other industries have already realized over the last couple of decades, and I’ll tell you a couple of reasons.
There are systemic reasons why we’re seeing the rise of open source, same financial services margins. Revenues of nowhere nearly where they were 10 years ago in this industry, the cost of regulation keeps rising. And so ultimately, the margins are, again, nowhere nearly where they were just a few years ago.
So there is not an infinite amount of money to be thrown at every single technology problem in the industry. And open source certainly has had a history of reducing technology costs when using total TCO. And so certainly, oftentimes that’s one of the main driving reasons for financial institutions looking at open source collaboration.
If you pair that with every financial institution and beyond which wants to consider themselves a technology company nowadays in the realization that a large part of their stacks are non-differentiating, are really non unique selling points, then open source becomes a really good way to mutualize some of those costs.
And finally, again, just talking about systemic issues. If you look at the talent crunch, Wall Street is in another, even deeper talent crunch than the rest of the technology industry. And certainly, top talent oftentimes prefers working on a West Coast sort of Silicon Valley tech company, that ‘s a sort of the appeal that the financial services industry had maybe 10 or 20 years ago.
And so open source provides a way for this first access, a much larger, much broader talent pool, and certainly allows every individual to continue fostering its own portfolio. The moment you’re able to contribute back to GitHub to put your name out there that delivers value, not only to your employer, but delivers value to you as an individual.
And I wouldn’t be here hadn’t I started coding in the open about 20 years ago, and certainly I would not hire anyone without looking at their data profile nowadays. Those are the main three systemic issues that are driving this focus on open source and then a couple of more tactical reasons.
We were able to remove a lot of the barriers and misconceptions around open source. Huge acquisitions, like the Red Hat or the GitHub acquisition respectively from IBM and Microsoft last year really shone the light on the fact that open source doesn’t equal free, that there’s a lot to be saved, but also a lot of money to be made on open source.
And that’s not only financial institutions, but a lot of FinTech startups are more and more looking at open source as a viable way of going to market.
Gab, It’s so interesting that you talk about the talent crunch, because traditionally we look at talent from a startup perspective that we cannot hire enough technical people, whether they’re software engineers or data scientists, but one would not traditionally think that that would be the case with banking.
You look at banking in New York City, for example, as a financial hub in the United States. And traditionally, what would students do? They would go to undergraduate business or MBAs and go straight into banking, working for big firms like Goldman Sachs, RBC, Deutsche Bank, and even some of the big players like Bloomberg and Definitive in the alternative data space.
But we’ve seen that shift as you mentioned in the last 5 to 15 years towards FinTech startups and other firms. But the shift is going back to banking for many reasons. It’s that economies are driven by finance. And financial institutions help create stability, create security. And in this new age of needing data privacy and data security, a lot of startups have not met with those issues.
First, while banks are usually seen as those trusted custodians with your secure and private data, and they’re ones that look after you. So, maybe that talent crunch, we’re going to see that shift to be more friendly for financial institutions over time. One thing we’re seeing in New York, of course, was in 2016 and 2017, the launch of Cornell Tech.
Cornell tech is a partnership between Technion Institute and Cornell. And the focus has been a lot on product management. And initially, all the graduates who go from these masters and PhD programs the industry thought, Oh, we’ll all be the startups, but a lot of these graduates are going to big banks and helping improve production with product management.
I know for a fact I have friends in New York who worked for JP Morgan and they do product management today. So it’s no longer just finance and operations jobs, but there are tech jobs at the banks as well.
Oh, absolutely. And don’t get me wrong. That is a huge amount of talent in large corporations, in large financial institutions. I do think, though, that even from a generational standpoint, the new generation, which I often refer to as the GitHub generation, has grown up with, in terms of personal interaction, this generation has grown up with social tools and a really different way of even basically interacting with each other.
The new generation of developers that we see coming up really comes with being born and bred in github, where not only it’s sort of the social aspect of collaborating in the open, but really all the best of breed. Really easy to use tooling from bringing code, from ideation to actual release, the whole sort of word of pipeline, CICD, tools that are extremely integrated, the old style of waterfall development, where you need your dev and your ops, people with different responsibilities, has drastically changed of course, with the introduction of this hybrid figure of DevOps.
But the second element, so not only they have higher requirements in terms of development process and the way they interact with other developers and how a successful high velocity project has to be run. But there’s also an angle of one thing to give back. If you think about it, sometimes millennials that are made fun of, and maybe I am a millennial, depending on who you ask, I’m 38 years old.
So maybe I’m on the cusp, but I’ve heard from our board members, from the large bulge bracket firms, many times new talent coming in interview processes and asking, okay, what are you doing to give back to the community? I want to make an impact. And so you’ve seen over the last few years, unanswered services, nutrition is being more and more mindful of this aspect.
I want to be clear here. I don’t think open source is charity. I certainly think that there’s an element of conscience, of openness, or not reinventing the wheel for which open sources is good for the world. If we want to be grounded here, but to be clear.
Everyone, and most corporates participate in open source right now. And even to our foundation, they do it with a business goal. So it’s not necessarily per se charity. I don’t think open source should be considered charity, but it’s certainly a very powerful answer for an individual interview process to be able to say, yes, we do open source.
Yes. We don’t reinvent the wheel. Yes. You’ll be able to continue fostering your own personal profile if you work for us because you’re allowed to contribute to open source. And that’s where our foundation, over the last three years, really put a lot of effort into enabling the thousands of existing developers in the firms to be able to participate as first-class citizens in open source communities. So just to wrap up, it’s not just talent acquisition, it’s certainly a lot of talent retention as well.
It’s so incredible that you just went into that deep dive, Gab, because open source is, as we know it more than code to participate as that first-class citizen in your industry.
Well, there’s other things. There’s frameworks and framework development, and we know some of the companies like JP Morgan and Goldman Sachs have different frameworks that have been very much open-source in industry, but beyond that there are standards and there’s policies and there’s collaboration. So one of the big myths to dispel is that new users to github and open source thing, all of this is code, but it’s actually much more than that.
Oh, absolutely. One of the reasons why you’ve seen not just the rise of open source out there, but also if you look at the last few years, the rise of foundations like ours, of non-profit open source foundations. Think about the Linux Foundation. Think about the Cloud Native Computing Foundation. More recently, the Continuous Delivery Foundation, or even Hyperledger on the blockchain space.
It’s really because open source is not easy. Especially if you are a corporate, especially if you’re a large corporate who’s seeking to collaborate either with its competitors or with its customers and ecosystem at large through open source.
Code is certainly important then. And the quality of the code, we believe open source, is higher, given that there’s so many highs pointed on it. Everyone feels a bit more accountable for what they put out there than necessarily what you do behind the firewall. But that’s just the tip of the iceberg.
You mentioned policies. Several of the financial institutions that we started working with years ago, they didn’t even have an open source contribution policy that would not allow their developer service access GitHub.
And this little anecdote. I used to be a consultant 20 years ago, going and solving those problems for customers. And I remember walking into some of the customers and them telling me “no, sorry. We don’t allow access to Google in our infrastructure”. Basically be that as a consultant, as an open source consultant, without Google, I’d be useless. So I certainly feel for many of these developers who until a couple of years ago did not have access to github.
So certainly, there’s an element of internal policy. There’s an element of external policy, regulated industries are very understandably risk averse, and very much careful about what degree of collaboration they have with their competitors.
And so that’s why you see foundations like ours provide a very structured governance framework, conflict of interest policies, antitrust policies, really, again, making sure that it’s clear that through transparency, you can achieve a very productive level of collaboration without any compliance concerns, without driving your compliance folks crazy.
Policies are one element. You mentioned standards. The world of open standards and the world of open source had historically been very different, but they are more and more colliding because they reinforce each other.
I’ve been working in several standards in the past. So we’ve been in the same study when you add open source to the mix. When you add the open source reference implementation to an existing standard, that drastically speeds up the rate of adoption, and certainly the rate of compatibility, cross compatibility through the standard. As from the browser wars in the nineties and early two thousands, standard doesn’t always mean the same thing to every single vendor.
And finally, going back to the generational cultural aspects. There’s a lot to learn before you can be effective and productive in an open source community, the same way you do it in an internal project. You need to relinquish control in favor of influence. And that’s a big step for hierarchical organization, large corporate hierarchy organizations.
But there’s also an element of code of conduct and behavior. Open source communities that are driven by meritocracy, or even by the more contribution, the more sweat equity you put into, the more influence you have.
And that doesn’t always match with the idea of a large bank saying, Hey, I’m JP Morgan and Goldman Sachs. I should have more of this code decision through affairs. Foundations like cars put a lot of effort into making sure that, of course, we’re very thankful to our paying members as they drive our foundation and get certainly more value out of engaging in our community.
But, as any open source community, everyone is welcome to participate. And we got to balance this tension between wanting to give more to our members whilst making sure that again, our community is driven in a purely meritocratic fashion.
And so that’s sort of the other aspect. Again, policy standards and culture are really important elements of successful open-source collaboration where we’re still doing a lot of education.
Similar to what I’m hearing from you today, Gab, about the finance industry and open source and how education is meeting reality and how now there’s frameworks and collaboration and standards.
This is not just for the finance industry. This is the entire developer community. The Department of Defense after years of putting out proposals for what does it mean to be open source? What does it mean to have AI systems? Good data systems in place. Just in the beginning of 2020, they came out with their five pillars that they think are important for AI data science, open source, the whole gamut of the developer life cycle.
And what I heard from you is very similar. They said the five pillars are to be responsible, to be equitable, to be traceable, to be reliable and to be governable. And I feel like there’s so much in common with what you’re doing at the foundation.
Well, absolutely. Of the five pillars that you mentioned, and thanks for bringing that up, that’s really topical. Two comments. First a general comment on the fact that, of course, government is one of the models that we’re using for modeling the collaboration in our community, as you can hear from my accent. I live in San Francisco, but I’m from that Italian part, I guess, of San Francisco.
I actually come from Italy, and 15 years ago when I started my career, 20 years ago, open sourcing government was just a mirage, and then quickly in 10 years, you’ve seen the European Union mandating open sources, first choice for public tenders, the US here with code.gov, quickly following into having a set of found out that why the approach to open source.
So actually, governments in 10, 15 years, which is not that much for government time, was able to realize and put a structure around this. Well, certainly this is one of the models that we’re using for creating an effective and transparent community in financial services.
But I think of the five pillars. I am particularly fond of, and certainly the foundation by extension, I am particularly fond of two of them. Governable is obviously a lot of what foundations do. It’s governance and code governance and corporate governance. All of our governance is public and transparent, which gets me to the second point, which is traceability very much.
Every decision that happens in that foundation is traceable and is auditable. Whether it happens on the mailing list, whether it happens on a meeting that we mean it and we put minutes out there. That’s been the beginning of the foundation. It has been one of the biggest hurdles to overcome in an industry that is generally again for good reason, risk averse and secretive, overcoming this idea that most, if not all the collaboration, should happen in the open, it’s been really hard.
But on the flip side, it is what has built an unprecedented level of trust amongst the players in our community. I had the luck to inherit when I started the board of the game, the most influential senior people at these large bulge bracket banks. Large vendors, like you mentioned, the definitive IHS market Standard & Poor’s.
We will not have gotten to the point that we are now with several or lively collaborative projects and really banks looking at us as the main outlet for that open sourcing, hadn’t we taken a pretty hard stance on the need for transparent governance and transparent collaboration. So I am really pleased to see that the Department of Defense is going in this direction as well.
Excellent. And speaking about directions that not just the DOD is going, but the foundation. And being in 2020, the last 10 years we’ve seen so much in all industries focused on data, show me the data. And myself as a data scientist and a professor and industry practitioner, I’ve recently launched my five steps for design thinking for data science and these five steps. A lot of it is what financial organizations do today.
So, the five steps are really simple. I give them very clean names. So the first one’s one data cleaning, two data refinement, three, data expansion four, data learning and five, data maintenance. So that’s my five steps. You clean your data, you refine it, you expand it, you learn from it and you maintain it. And you can give it many different names. Data cleaning could be pre-processing, data refinement could be feature engineering, data expansion could be APIs and integrations, data learning could be your machine learning, your modeling.
And data maintenance could be that deployment, the ethics and understanding about the processes. But I feel that the first three are where a lot of the work in finance for many years, cleaning, refining, expanding, but not that much attention has been put on the learning and the maintenance, or simply put the machine learning and AI. So I wanted to hear your take, any thoughts on where the industry and the work you’re doing with the foundation is going around machine learning and AI.
Well, it’s a really interesting question. And of course from our perspective, we look at everything we do in a collaborative manner. And so in a way it adds another layer of complexity, as a foundation, as a community, we don’t represent a single entity. And of course I can talk about notional terms, what single entities are doing. But from our standpoint we represent several firms wanting to collaborate with each other. And so before you can actually dive, we’ve had several conversations about starting programs or programs that are a cohesive set of areas where we collaborate on. There’ve been several conversations on should it start there and AIML focus program and really start building solutions or frameworks that make financial data shedding easier.
And certainly more importantly, build on it for advanced AI and ML features that clearly this industry is using and craving every day for more of. But when you look at, from the perspective of an entity that needs to work with several parties in my mind. We are still a little early, if you want. The first step is how to harmonize the data across these different institutions. Coming from a background from different industries while stub is, there are standards in the industry. And whilst obviously things like fix or swift of course we have had at several times attempts to harmonize the data.
There’s still a degree of bespoke. They did a presentation and data mapping and were very siloed approach to data within the different firms that we think the first step for us to be able to then build on top of a more harmonized set of data flows is really open standard, is really open standard on one end. And then providing, as I mentioned before, the open source reference implementation behind that standard to really accelerate the adoption. And so that’s what you see in our foundation. Efforts like the JP Morgan perspective contribution, that’s more of a data visualization library.
That’s sort of a little bit higher in the stack, but even more fundamentally. You were at the Open Source Strategy Forum. We had the big announcement from Goldman, announcing the contribution there. Alloy visual modeling framework, which is a really amazing basically web IDE for data modeling.
Think about it like a very advanced web based modeler for all data and data mapping with a whole underlying language called pure, which Goldman has been using. Through and through the organization for all sort of modeling, whether that’s regulatory reporting, modeling, whether that’s more internal pricing models, we are certainly really excited for that contribution. But your question shows how. There is an intention and a goal for the industry to better model the data in a collaborative way to be clear. The goal here is not only to collaborate on the code itself on the visual modeler and on the language, but it’s even more importantly to collaborate on the models themselves.
And so, that’s where FINOS is providing for now a pilot instance for our members to start collaborating on some specific instrument that uses some specific data mode, and the goal will then be to continue using this platform.
Once it’s fully open sourced, we expect that to happen within Q2 and Q3 to really then potentially involve the regulators and really create this common modeling tool and common set of data models in the hope that then with common data models, we can start building on top of it common tools and common ideally AI and ML and intelligence around it.
Back to your question, when you externalize yourself from a senior firm and you look at the industry at large, there’s still a lot of work to be done on how basic common representation of data that has to be the starting point, otherwise whatever we build, we still need this and by N need of mapping data into your own firm’s model. I’ve seen, kind of quoting Blade Runner here, I’ve seen things which in terms of data modeling and for you like a data scientist would probably make you itch, in terms of that data is modeled. So definitely looking at standardization as a first step. Of course, there are other initiatives that are ongoing beyond this, but as far as our foundation is concerned, we’re still set. We have several initiatives around data standardization first.
it’s incredible to see I’ve got to play around with at least the JP Morgan perspective package and to see how that was a collaboration internally. And now so many financial institutions are creating fantastic visualizations from this open source code. It sounds that the same will soon be possible with Pure Alloy from Goldman Sachs for modeling, and that’s going to help with machine intelligence.
But just, as you mentioned, Gab, one of the biggest challenges we always see with financial institutions, is that it feels like it’s been around since the dawn of time is data security, data privacy and how do you manipulate and use and secure data? And that’s maybe been one of the things we’ve not solved yet. But how do you think we can better address data privacy or what are banks doing today with data privacy?
Well, it was really interesting, just to mention the example here, there was a really interesting initiative that I participated too from the World Economic Forum who was really focusing on, okay, what are the top issues?
Could we enable this data shedding again, going back to the conservative nature of this industry? It is absolutely understandable how institutions who have not only such a regulatory nature, but obviously they have very sensitive information about their customers would think twice before sharing that information with some of their competitors, whether that’s because it’s a unique differentiator or even just because of fear of breaking some regulation.
And as sometimes regulations are not as clear cut as we’d want them to be. And so it was interesting to sit in this tiny industry initiative. Which brought in both community banks, retail banks, bulge bracket, investment banks, regulators were in the mix. Vendors really look at pros and cons and there has been a really interesting report published by the World Economic Forum, and look not to sort of sound biased there.
But one of the key issues that was identified was again, lack of data standardization, and that’s where we’re pushing hard on our side and certainly identifying more technical solutions to enable shedding in a safe way. So as I was saying, this report was really interesting because it really ranged from regulatory issues to data standards, to really technology advancements that AI and ML are driving.
Those firms should hone in and double down on. Interestingly, this report was published mid last year and Google quickly followed up in September last year with open sourcing their differential privacy framework, an extension to TensorFlow and again, differential privacy, like homomorphic encryption, like zero knowledge proofs were really some of the key areas that the economic forum report had identified for financial services to double down on to better collaborate in the open. So kind of interesting to see how, in a way, big tech through open sourcing is enabling some of these better collaborations happening also in financial services.
That’s so important because as data scientists and practitioners in the industry, and for listeners of the show, we know today that everyday things are being hacked, we have deep fakes as we’re continuing to accelerate into election 2020 where pretty much every candidate has been deepfake dubbed, redubbed, reaudioed.
And so it’s not just there, but even with machine learning models, it’s a very known secret in the data science world that you can reverse engineer models and anonymized data. It can often get de-anonymize. So it sounds like this TensorFlow privacy or this differential privacy extension could maybe help mitigate that and be used with all industries.
What you’re sharing there really goes more than just at privacy and more than just regulation, but it really goes back to open source. TensorFlow is open source, even though it’s an initiative sponsored by Google and they have a team that’s helping build and strengthen the whole community that uses open source and open standards.
They’re really a means to an end. They’re really about building an interoperable world. And if we were in a perfect world today, would we need open source? it’s really here to help us get to a better steady state.
I might be biased, but I couldn’t agree more with that statement, David.
Like we say in Italy, you’re breaking an open door here. Yes. We’re learning more as we go through this journey of something that even 10 years ago would probably be flagged as a DLP audit violation.
We’re learning more that it’s not just about banks collaborating with each other. We started with that. That was our early focus, getting individual firms to be able to collaborate in the open and then get them to trust each other that this methodology can deliver value for the whole industry. As a 501 C(6) nonprofit foundation, we are in charge to do the good of the whole industry.
We are a trade association, a business league that is supposed to promote advancement in the whole financial services industry. But we have seen that as a very powerful means to your point to also enable a better collaboration between financial services institutions, and the up and coming FinTech firms.
We have several fintechs participating in our community. And I want to say that so much more than even fintechs can do with open source in this industry, there is a very high potential for disruption of a very locked in industry in many ways. Vendor locked in, in this and in many ways. But again, open source can be a means for financial institutions to really have an alternative to the usual maker by decision.
Do I make it myself? Do I buy or invest in an up and coming startup? We’ve seen the acquisition of Plaid over the last couple of weeks. There’s actually a third way that open source provides, which is you can collaborate with several fintechs at the same time, whether that’s on an open standard, whether that’s on a reference core, reference implementation for a key use case that then one or more startups can bring to market.
Open source really provides a powerful way to influence rather than control the evolution of the whole FinTech ecosystem, because otherwise, my guess is that we’ll move from a centralized mess to a completely decentralized mess.
And that’s not going to be good for ultimately us, the average end users of the whole financial services complex. So finance to finance, finance to FinTech, and certainly, more and more we’re seeing, finance to big tech, and is now a long-standing hate-love relationship within the East Coast and the West Coast.
We are seeing some really successful inroads in our foundation where not only we have several financial institutions, but we have the githubs, the red hats, the gitlabs, the CloudBees of the world, participating with us. Certainly we expect more of these large vendors coming and really playing ball in the foundation. That is really the only one that is solely focused on financial services.
Last but not least, and when I was sneaking in there, we have seen an increase in interest from regulators in what we are doing and that goes back also to the conversation on data privacy. Obviously there’s a lot of regulatory concern that there’s a lot of privacy concern there, but that’s just one element.
We think that open source, we’re this transparent nature, which is a talent pool expanding nature which is going back to traceability. What’s better traceable than a piece of open source code that you can look at paired with a transparent CICD, immutable pipeline that brings that code to production without anyone being able to touch it and just fully automated?
We think that that’s almost an ideal value proposition for regulators that are not only right now having to learn how every single firm complies to the requirements, but also how do they bring it to production and who has access to it?
We think that open source and again, paired with the whole pipeline, immutable pipeline concept is really valuable for regulators to be able to say “write the policy once and I’m forced everywhere”.
If we were all to do it the same way, considering that it’s not a competitive differentiator for anyone, it’s an item on the bottom line for every financial institution. That’s really a good driver for financial institutions and fintechs to collaborate in the open and we could bring in regulators to make sure that what we’re building once is actually satisfying against the regulatory requirements.
I’ll throw one more thing in there, which is, we talked about talent crunch before. Well, if financial institutions have seen a talent crunch, let’s not even start talking about regulators because of course the regulator in general cannot offer a competitive package, but financial institutions.
So oftentimes it becomes really hard for regulators to keep up with latest technology advancements. And so that’s what open source can come to the rescue. Rather than having to train and specialize people in every single system that you’re going to have to go and regulate, you can build a broader talent pool if the implementation and the process is dealt in the open.
So we expect for us in 2020, certainly a lot of focus on what we call compliance as code compliance and open-source code again, because it’s good for financial institutions, is good for tech companies. It’s good for the regulators.
Whether we call a compliance as code, whether we think of ethical AI, responsible systems, traceable systems, governable systems, one thing’s for certain, between the big tech, between the FinTech, between the finance and all the players in the industry, we have to work together to build better systems, because it’s about everyone.
It’s about all of us humans. It’s all about all of us customers as consumers and enterprises. And we have to have safe, secure data and have a world where if we’re private first with good systems, then anything’s possible.
We can build and scale systems. We can have open source leading that direction. And I love the work that you’re doing at Finos and Gab, thanks so much for being with us today on the HumAIn podcast.
Thank you, David. Thank you for your time and appreciate you having us here.
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