Building the Vision Internet: Plainsight’s Kit Merker on Open Filter & the Future of Computer Vision

Hey everyone. Welcome back here to Techstrong tv. You know, I, as much as I like having first time guests on the show who tell us about companies we haven't heard of before, I actually really like having friends back on the show.

And this gentleman here has been my friend for, um, I don't know, kit, it's gotta be almost 10 years already, right? Eight years, something like that. That's right, yeah, For a long time.

Kit Meer Kid is the CEO of Plain Sight. Of course, I know Kit a lot longer than he's been CEO of Plain Sight Kit. It's so good to see you, man.

I hope all's well. How's everything? Great to see you too, Alan.

You know? Yeah, I think, I think we first met back in, I dunno, JFR days 2016 probably, right? So yeah, almost a decade.

Geez. We're uh, we're getting up there. Yeah.

Up there. You still look like you're 25, man. Oh, Thank you.

Great. It's the ma it's the magic. The magic of ai.

Yeah. Yeah. It's all the magic.

You ain't kidding it. So, kit, as I said, I knew you before. You mentioned Jay fraud.

Give people a sense, maybe of kinda your journey of how you wound up here as the CEO. Well, you know, I've, uh, been in this software game for about 25 years, believe it or not. You know, I spent 10 at Microsoft and then, um, got to be part of the Kubernetes project at Google, which was, uh, unbelievable experience.

You know, we launched it the same year as Google Glass, which I guess is back in the news. Um, yeah. And then, you know, after Google, uh, spent time at Jfr where I met you, but we've got to, uh, you know, that was an experience of taking a company, being part of an executive team from, you know, series C through IPO, and really doing something fantastic.

And then I, you know, I did another startup, uh, in the software reliability space, noble Nine, and I joined PlaySight about eight 18 months ago. Um, and, you know, it's been a really interesting journey. I think the, the unifying theme for me has been about, um, building big software systems that lots of people can use and help developers and help drive efficiency, reliability, scalability.

That's been my, my main, I guess, career focus. And I've gotten to do it in a lot of different roles from, you know, writing codes, uh, running teams to now being a CEO. And, uh, it's been every stage of company too.

So it's, yeah, it's been a great career so far. Hopefully just beginning. I hope so, too.

A long career. And it, it, I gotta tell you, as a friend, and you know, Pierre, I, it's, it's been a joy to watch that growth right at, at Jfr kinda we're running business development, corp development, stuff like that. And That's right.

Um, You know, and, and it's kind of where my background is as well. So it's always, it's always been great watching this ride. So you mentioned Plain Sight, you're there about a half a year, I don't think.

A year and A half. A year And a half. A year and a half.

I'm sorry. Yeah. I don't think you sell glasses, but I do see the eye chart behind you.

But tell us what, what's Plain Sight about Kit? Yeah. Plain Sight is a computer vision company, and what we really are focused on is vision, infrastructure, and what we're seeing in the world.

I mean, what's changing right now is that all the cameras in the world are generating all this data that instead of being consumed by people is gonna be, and is already being consumed by ai. And what we have in the world is all this data. And the question is like, how do we process that data?

So you kind of imagine like, I have all these cameras and I have all these AI systems, how do I marry the two together? And I can't just throw GPUs at the problem, right? It's not just a hardware problem, it's a software problem.

It's a infrastructure problem. So from, you know, my experience and my, my team's experience at Google and my CTO is at Amazon and PayPal and, and we, we both worked at Microsoft, we, we, um, we bring this kind of perspective of large software systems that we're taking to this traditionally data science problem of computer vision, right? An interesting thing is, you know, look, we know how computer vision works from a science perspective, and the software has existed for that for a long time.

OpenCV has been around for 20 years. Um, it's just kind of a solve slide, actually. Here's, you know, the book, the, the, the, the textbook on computer vision, right?

The GPUs are there, uh, the cameras are there. And so the question's like, well, what's stopping all these businesses and people and applications from getting the benefits of computer vision? And what we think the answer to that is, you know, plain Sight's technology focuses on really two, two, uh, uh, areas.

One is how do you define the unit of computer vision workload and our solution that is something we call filter, which I can talk more about, but it's an abstraction for computer vision applications that let you build these large, robust pipelines to process visual data into structured data. And the second part is in the machine learning and training, or what we call the data supply chain. So, you know, the software supply chain, I know you know that very well, the data supply chain question of how do I get my models which are powering all these AI apps, how do I make sure that the right data, that the data is improving over time, that it's sourced ethically, that I know exactly where it came from, the lineage and provenance of that data all the way from the camera to a trained AI model that I can use in applications and inference.

Those are the two sides of what we're doing. And the exciting part is now Plain Sight has decided to announce this open source project, open filter to take the first part and make it, uh, you know, hopefully the standard for how computer vision applications are defined and run. But plain sight, I think fundamentally what we're doing is we're democratizing the ability for computer vision to happen to be cost effective, scalable, secure, liable, and fit into these enterprise environments where traditionally they've struggled to get past the prototype phase of adopting vision and AI in their, uh, in their enterprise.

You know, plain sight's been around a while. I know it's gone through some, you know, reformation pivots or whatever, but, you know, for me it was always about, it's always been about computer Vision though. Yeah.

Give us an idea. Yeah. So the, the evolution, uh, with, with, uh, plain Sight really was originally focused on, I would say, services and professional services and solution building.

And there was some core IP that was developed in that. And I think the big change, um, that, you know, strategic change that I brought to it was really about focus. And the question was, well, what should we focus on?

A lot of the advice I got was focus on one industry vertical. And, you know, you get that advice enough times. And then I thought, well, maybe this is the obvious advice, so maybe I should ignore it, right?

And instead, what we ended up thinking about is how can we build a general purpose, vision capability so that we can serve many, many verticals. And then instead of us having to go learn an industry, which by the way, that's market risk. 'cause what if we get the wrong use case that people aren't willing to pay for, right?

I can't do everything. If I pick wrong, then that's existential. Instead I thought, well, what if we could let the community decide?

What if we could let the, the, the wisdom of the crowd? No. Correct.

Right. Crowd, crowd sort it. Mm-hmm.

Exactly. So we put out the general purpose vision capabilities that can be used for all kinds of different industries. So if you think about like being able to read text, uh, in a, in a scene OCR, right?

You think about object detection or other kinds of use cases. The the problem is not that the science of doing that is actually very hard. The problem is how do I manage and orchestrate and kind of build that into a solution?

This is really the insight. In fact, um, one of the interesting things that led us to, uh, a lot of these realizations was watching what happens in the community. So if you go to Reddit, for example, the computer vision, uh, subreddit has like 115,000 people talking about computer vision apps.

And when I look at that, what I see is a lot of frustration, frankly, with the state of the market for people who are trying to just get basic casts done that like they're all doing the same thing repetitively over and over again. So part of our big aha was what is the sort of software infrastructure data management solution to this computer vision problem? 'cause the issue is not computer vision per se.

The issue is how do I build the infrastructure to support vision workloads? And once you kind of think of it that way, it just led us to a really interesting invention, which is a new abstraction, which we call a filter. And the filter, you know, you're, everybody's seen the filter on Instagram or Snapchat.

It's an app, right? It's an app that takes a, a video, it gives you a, uh, AI power generally, uh, modification to that video and maybe has some action or data that comes off it. Now, if you take that front end concept of a Instagram filter and turn it into a backend concept, which is a filter as a, uh, workload we can do is take these apps which combine models plus code into a common a PIA common interface.

And now we've moved from monolith to microservice. We've moved from, you know, VM to container in a way we've got this new abstraction for describing computer vision. 'cause generally, if you talk to computer vision people, we'll spend a lot of time talking about how do I train the model?

We spend a lot of time talking about, oh, the application logic. But they don't really put those two things together. And once you do, as you know, look at what we see in cloud computing, look at the power and the scale of these kinds of systems.

It's because we can understand not only the work we're trying to do, but also how the work is constructed so we can manage and optimize and orchestrate that work. And that's really what Plain Sight is doing differently, is we're focused a lot on this workload concept, um, as well as, you know, helping people train really high quality models from, you know, their data supply chain. But that combination of the two really gives this powerful solution that I think can break through, um, you know, the, the impasse that a lot of people have.

And that's the evolution of the company. It's been from kind of boutique computer vision to now building this dev ecosystem, separating the, uh, the core software from the, um, the, uh, the sort of solution development. And the next stage we wanna go to actually is quite ambitious, which is the fed the standard for how computer vision apps are created.

And that's why, you know, we've chosen to, uh, open source. Open source, The open filter. That's right.

Open filter is the, the new open source projects for defining computer visual workloads. You know, kit I, a couple weeks ago I was at a conference, a company called, uh, automation Anywhere. I don't know if you've heard of, they've been around 20 years.

Much like Plain Sight, they kind of, kind of invented the RPA right. Robotic process automation industry. But they, they're walking away from RPA and into their, what they're calling a PA agentic process automation.

Because as good as RPA was, it was always missing that little something. To me. It's like putting an STP gas additive to boost my enzyme, you know, to boost my, uh, octane in my gasoline right?

And make it a hot rod. To me, when I look at computer vision and the, the history and state of computer vision, you're at that same juncture kit where ai, I mean, and as usual, your timing's impeccable, right? Ai Yeah.

You know, ai, AI is the octane boost for computer vision because now everything we've always thought it was possible to do and could do, but you had to have resources and you didn't have, didn't have that intelligence, right? You, you either had to have a person and that gets quickly overwhelmed no matter how many people you have. And the scalability issues, right?

Of, of capturing all this to try to find the patterns and do these things well with ai, you know, as they say in the, in, on the street, s**t got real. Right? Right.

It's real now. That's right. You know, and, and so now, now we could do this right now, so many things that are possible, and as you say, you don't wanna be the T-shaped one that just goes into one vertical.

You wanna be the broom shape that we, we it scales across and let people decide, you know, I've got this great technology computer vision that I'm marrying to AI capability. The, the possibilities are endless. I, I couldn't agree with you more, but there's one big problem missing from the story you just said.

I'll tell you what that is. Good. All of this video data is running on infrastructure designed for humans.

Yes. All of this, this is the fundamental problem in the world that I am out to change. Okay?

If you take one thing away from like, the big picture is what I like to call the vision internet. And I know it's an insane thing to say that we like need a new internet, but we kind of do for this purpose because look, all of the systems connecting these cameras that are streaming video, they're all designed so that it's smooth and beautiful. So you can have a great experience as a human.

Well guess what? The robots, the agents, the AI systems, all these, they don't care about that. I mean, look at how much money OpenAI is losing because of please and thank you, by the way, I always thank Chad GBT, because now that I know it costs the money, you know, I always thank Chad GBT.

Well, you Do. I I just did it beforehand 'cause I thought it was cool. Yeah.

'cause You're a nice, you, you're a nice guy. I, no, no. I thank Alexa too.

'cause I like to hear what she says back to me. So look, I, I thank my toaster, I thank my coffee machine. But, but my point is this, my point is this, how much money is gonna be wasted processing frames that are not adding any new information or data.

So if you think about computer vision and this new vision, internet concept, but whole idea is we wanna take a video and have a human never watch it, right? So if you're running, you know, you know, your, uh, uh, your security systems, your, your smart glasses, your uh, lawnmower, your, all these different systems, right? That are having these cameras or industrial or power tracking or people tracking, whatever it is, the goal is not to have lots of people watching all this video, right?

There's just not gonna be enough people to watch it anyway. So it's all gonna go to robots, gonna go to ai. I mean robots in the sense of, you know, automated systems.

We need Netflix for robots. That's what we need to build. We need to think about this as a different kind of infrastructure.

And we have the core component to do that. If you wanna start processing this data, you start one bite at a time. The single bite is how do I take a frame and process it using filters, using computer vision applications filters to filter that data and also to narrow the data that's being consumed.

I take the raw video, I suit it into a chat TPT or an LLM to the most expensive way I could possibly do it. So when we move away from brute force, right? We really think about this as a data compression problem.

Semantic data compression, live stream, a video to a little record in a database or an ERP system or A-J-S-O-N blob. That's what we're talking about. That's where the savings is gonna come.

That to me is what's gonna unblock it. So the developer experience is one part. There's a shortage of developers that know how to do computer vision.

We need to encapsulate that knowledge into simple packages that anyone can run. That's what open filter's gonna do. That's what filters enable.

And then the second part is the infrastructure problem. How do we make it that all these cameras have a smart and elegant and scalable and web, web scale, web friendly way to connect to the data systems, which include ai, LM, custom models, databases, rag systems, et cetera. Agen X systems.

We connect those together and plain site and this technology we're talking about, it's perfectly in the middle of those two things to enable all these new use cases that we can all imagine. And I really think, I mean, I I mean this sincerely. I think this is the missing piece.

'cause we have everything else, right? We have all the parts. Now the question is how do we block it?

And I, I really do believe this is the, this is the problem, is that we have not built vision infrastructure for robots. We built it for humans. I had, you know what?

I never considered it. And it, and it just like makes instant sense. It's like, you know, don't laugh at me, but I pay every month for a subscription to dog tv.

So that my, I won't laugh at that. I pay 11 bucks a month so that when we are not home, my dog gets to watch TV and not just any tv. 'cause dogs don't see in the colors we do.

They don't, you know, they have a, their vision of the world. I mean, they, they have great vision. They could see things that move or stuff like that.

But like for instance, they see a lot of greens and yellows, not so much red and blues. That's right. So dog TV is optimized for a dog's vision and it keeps them engaged.

It's the same thing. It's the same thing. It's a great analogy.

It's a great analogy. That's right. It, you want to optimize the data, the, the information that we're displaying here, and again, all we're talking about here, I mean, and I hate to be like, you know, anthropomorphize the robot, but like, it's literally just a grid of ones and zeros, right?

It's like RGB values. It's all doesn't say Yeah. It's, there's no motion, there's no blur.

It's just, it's just r It's Love that Yeah, that's right. So like why would we waste our time? You know, having all this extra redundant information, uh, for somebody who literally can't experience it.

Your dog TV is exactly the same thing. Why? You could, you, you could do it in a way that's for humans, but it's not gonna be a great product.

And the same thing here. And so as this proliferation and increase in cameras and data comes to, to the, you know, the reality, we're gonna need to consume more and more of it. And even when people do need to see it, we can reconstruct what the human wants for that narrow subset.

But the vast majority, 90% of it plus should all be processed through agents. In fact, the agents should be building computer vision apps on the fly to deal with tasks that they wanna accomplish for you. And guess what, by having these great abstractions, like filters will enable the cursors and you know, the, the other agent platforms, you know, coming out of great ERP companies and CRM companies like Salesforce and ServiceNow and SAP and they're all building agent platforms on top of their ERP systems and CRM systems.

So you're gonna have agents that can do tasks, but wouldn't it be great is instead of checking the database, you could check the warehouse. And the way that we're gonna do that is the agent is gonna be able to see filters will be built up into skills. Skills will be given to agents.

Agents will be able to use those skills based on prompts to construct programs on the fly that they can delegate to inexpensive hardware that run in the facility and report back events based on things happening in the real world. That's a completely different extension of the age agentic world that's gonna happen. And that's something that we're very, very excited about.

And again, it all comes down to starting from the simple core. 'cause we can't define the workload at its simplest level and we can't imagine these expansive use cases that are really gonna drive the impact. And that's, that's really to me, this is like when I look several years into the future, this is what we're driving toward is making it so that it's very straightforward and simple so that not only a human can do it, right?

Not only a developer, but literally an agent can do it on your behalf. And that's the way we're seeing programming happening. You can call it vibe coding if you want, but that idea that you're gonna basically do vibe vision, right?

You're gonna say, Hey, you know, you know, Hey Mr Agent, inventory agent, go check the warehouse if I have any boxes left, hey, let me know when their delivery arrives. Lemme know when this happens. Lemme know when that happens.

You're gonna be typing those prompts in and what's gonna happen behind the scenes. You think it's gonna like just start streaming the video straight into the LLM? No, it's gonna generate a pipeline, right?

It's gonna generate a pipeline using industry standard technologies. They're gonna define vision workloads and also all those same vision workloads can be used for data collection into training for annotation, for uh, testing or ensuring that, you know, we don't have biases or ethical issues with the sourcing of data or identifying copyright infringements. All of those things can be expressed as these computer visual workloads.

It's not just about the inference, the final step. It's also about like, where does the data come from, right? If I have a camera that's watching something, how do I turn that into annotations that I can use for training?

Right? There's a whole bunch of data that needs to be processed and pre-processed. All of that can be described as vision workloads.

So this really is a comprehensive way of thinking about this lifecycle. And I'll think even one step further. 'cause we're just talking about vision.

There's the reason why we didn't call o you know, call it open filter and not open vision filter or something like that. This is also, uh, potentially going to be expanded into a multimodal, and we started with vision 'cause it's really the hardest one and one that we saw the most immediate business value. But our, my, my request to the community is like to build on top of it and let's add audio and geospatial and other kinds of data so that we can use filters as a way of thinking about taking real world data, raw, messy, rough, real world data and processing it into structured, usable data across all these different use cases, databases, age, agent systems, et cetera.

And now we'll have a way that we can all share together, and we're not all starting from scratch. We like the code is the community value, and that wisdom can be encoded into this, uh, this community asset. And then look, if you need help on the business side, we're here to help on a bunch of stuff and make it, you know, scalable and have somebody to call when you're, uh, you know, when it's not working and all that good stuff, right?

But the, the, to me, I'm a huge community believer, you know, that I wanna see mm-hmm. Software communities come together and build something really amazing. And software is the gift that keeps on giving.

You know, like it's just such a powerful, uh, innovation in the world. I just see it can be a huge, huge impact, Vibe, vibe, vision. It's the first thing.

We've had it here on Tech Direct tv. I love it, man. Five Vision.

Let me ask you a question, kid. I'm assuming Open Filter's available, like on GitHub, is there a particular website that the community's gathering around a Discord server? Something like that?

Yeah, all the above. io is probably the fastest handle. io, that's, the website has links to everything.

We have a Discord server, we've got, uh, community office hours. There's a bunch of cool people involved in it already. One of the, one of the really cool things with Open Filter by the way, is we didn't just start it from an idea, we actually took code that was battle tested inside Plain Site we were using with customers.

And, you know, some of our, uh, third party developers that were building stuff with it, um, basically told us we should open source data. We were like, well, it's, I guess it's ready. So it's really, uh, in a really good state.

There's lots to work of, work to do on it, of course, uh, as always. But, um, yeah, it's ready to go. io, it's on GitHub, discord and Office Community Hours.

I love it, man. That's fantastic. Yeah, kid, I feel obligated to mention though, if, if you're haven't, you know, if you've caught anyone here with this right by the, by the throat about, you know, the, the whole thing is just so fantastic.

We're doing a webinar on this on June 30th at 1:00 PM Eastern Time. com, you'll be able to register there for, we'll, we'll have it in the notes. Uh, you'll be able to register for the webinar.

Really, this is like, this is exciting stuff. I, I just feel like there's, so we're on the cusp of like just reinventing so many things and disrupting so many things, but also things like computer vision that really have been not in limbo, but are about to take escape velocity, you know what I mean? Because of, of, of AI and, and the, just the whole state of where we are technology wise.

And, and it's gonna be interesting. It's, it is gonna be a great webinar. June 30th, 1:00 PM Check it out.

I'll be there. Alright man. Yes you will, kid.

I know you're sick and you got bronchitis. I appreciate you wasting your voice on us here today. But go rest up man.

Take some lozenges. We need you healthy for June 30th. Absolutely.

Well, it's, uh, it's always a pleasure, Alan. I really appreciate spending the time with the US those great questions. And, uh, everybody out there, hope to see you in the open source community.

Absolutely. Kit Merker, CEO of Plain Sight here on Tech Drunk tv. We'll take a break.

We'll be back in just a moment. Remember June 30th.