Hey, um I'm Sally. I work at Red Hat. I've been there for about 10 years and uh the first 7 years, awesome, totally cool. I was working on containers and uh Linux security stuff and Kubernetes. Um in OpenShift. That's what I did for the first 7 years. And then uh about 3 years ago, well about 5 years ago, I moved to the emerging tech org and that was awesome, too, because now I'm not totally tied to a product. I get to just work on what I want. I get to just try out new things. Awesome. And then about 3 years ago it was like all AI all the time, everything AI. I know not I knew there was a data science team at Red Hat. I had no idea what they did. Machine learning something something. Um so, I, you know, started doing AI and uh yeah, it was a lot of Python and markdown. Every single thing was like, "Okay, another chatbot, more python, more markdown." Um but uh here we are today and what a what a crazy awesome world we're in. Um so, the first time the first time I came across OpenClaw, I was home for a week on a like a staycation, took a few days off and uh a malt book happened and I was like, "What the What is this? I'm totally trying this." And so, I went and found it on GitHub. The first thing I do is I look at the license. Uh MIT, awesome. Uh OpenClaw, I'm like, "I'm so going to install this on OpenShift right now." And so, for the next few days I just kind of built the image, um ran it locally in a container, put it on OpenShift, just played around with it. Went back to work and like, "Guys, check out OpenClaw. This is so cool." And a couple people on Slack are like, "It's a security nightmare. Do not use OpenClaw. Don't put it on the work laptop." I'm like, "Guys, what have I What have I been doing the past 10 years? Is I'm We're We can take any application and run it securely. Like that's what rel is. Like if we can't take an application and run it securely, like come on. This is our golden opportunity to show everyone." And so, Red Hat's coming around to that.

Um but yeah, so uh this talk is about me running in containers. And so, I wanted to get a list of uh So, I wanted to get a list of why running in containers is the way to go. I run everything in containers. It's kind of foreign to me to take to just run something natively. It's messy. It just puts stuff on my computer that I have to clean up later. I don't like it. Um so, that's one one one thing. Uh and I asked her, you know, why should we run you in container? And uh she said all of that if you were reading, but it's reproducible, you can isolate your secrets, it's portable across infra, I can run on my laptop, I can run it on my X86, I can run it on my Mac, I can run it in Kubernetes. Um backed by volumes, which gives a really nice story for backup and recovery. Uh cuz I love my forever claw and I I back her up every night with uh with uh um like a system D service, whatever it's called on Mac. And um and and you just get that natural uh you just get that natural sandbox when you run something in a container. It's it's, you know, that's that's what it is and you have to be very explicit about what you um give access to, you know, from the host. And so, this Yes, I she loves running in a container. So, that's that's all you need to know. Um it gives her a clean, predictable environment, doesn't have to worry about the OS quirks, stale dependencies. This is literally the definition of why you should run everything in containers. Another another thing that containers do is you can set up a whole agent directory with maybe you run some tools, some skills, some some MCP servers. Uh you can keep those in a directory and mount that whole thing into your container uh and so, when at startup everything's just up and running. So, I do that as well.

Oh, no, let's talk about secrets. So, I run everything with Podman, not Docker. Um but in theory you can do anything with Podman and Docker. Except, Podman has this really cool feature called Podman secrets. And you can save your API keys. I'll show that I'll I'll show it off the sides later. You can save your API keys to a Podman secret. And then you mount that secret into the container. And so, it just gives the separation. Your your secrets, your API keys are then just a ref back to the secret. And with OpenClaw, what's really cool is there's like a double that because in OpenClaw there's a secret ref feature and I also use that. So, my API keys are pointer to a secret ref to the outside secret. And uh that's not perfect, but it gives me some peace of mind that I don't I'm not going to be showing my API keys in the logs and everything. And then very similarly, Kubernetes has Kubernetes secrets and same thing, instead of just a straight env var, you have a secret a secret ref to an env var.

I think we're heading to a world where these agents, these AI workloads, whatever, are going to be running everywhere. I hope we all can see that. And so, imagine my vision is for uh everybody's OpenClaws to be uh running everywhere and communicating with each other. And uh when and especially in for like business use cases, real real things, not astrology and and Bruins. Uh that opens up the need that the same need to run any application uh in that way is security and and how to do it at scale. And that's what Kubernetes gives you. And you can What I always do is develop something locally and then lift it to Kubernetes. And so, the same story holds for AI workloads or OpenClaw. And I was at PyTorch Con yesterday and um my friend from Nvidia said I could share this. They are running their model evals with OpenClaw. They have about 10 engineers. They each have their OpenClaws running in Kubernetes and uh periodically just checking in with the model evals. And it works so well for them. He said it was like, you know, doing the job of six engineers uh in with with himself. What that is enabling for his team is they get to do fun stuff, interesting stuff. They get to do creative things. And this is what AI is giving me and my team is we can focus on those like outside the box crazy things and you don't have to do the tedious code anymore. This this would be my vision of workplace setup for Open Claw where you maybe have your nice curated baseline Open Claw that as a new hire you you just you get your your base. And what does that have in it? It has your list of company approved MCP servers, your authentication that is approved through your company. It has all of your these skills that are very specific to your team, maybe access to your Google Drive. Like all these things uh that you use every day at work. You can take that and just fan it out across your whole team. And then you can personalize it as as the individual. And that's what this what this setup allows. And so yeah, team standards, portable environments, reproducible onboarding. That's my vision for like Open Claw in the workplace in the future.

So in order to run this local installer here which I think I have here. Yeah. It's just a NPM run dev. Now The one thing I don't like about this is when I'm on my Mac I can't run this in a container. Containers only run on Linux. So when you're running a container on your Mac, you are always running in a virtual machine. So it gets a little tricky when you want to take a container and spawn another container from it. But anyways, here we go. So if I wanted to run a local instance and I have a couple running now. Spin up Joe. All I all I do to set up my pod is I just give it a name. These Podman secret mappings I want to show you here. So you can see I have these set up already. They're just on my system. They're like envars, but they're not envars cuz they're contained. Um these are my API keys. And what happens with this installer is it takes If you're on Docker, this should work with Docker. It's got Podman written all over it, but I've designed it to work with Docker, too. For every current credential, create a secret ref. It creates that separation of uh running your secret within Open Claw or kind of just a pointer to it. And then uh your providers. So I'm going to start with Open Router cuz I have been playing with Gemma. And then as a fallback I'll use Anthropic. And then because I do observability at work uh I was like, "I'm going to give the option to set up an Open Telemetry collector with Jaeger." Another feature is SSH sandbox here we'll deploy. The SSH sandbox in Open Claw is super cool. You give it SSH keys and known hosts to to wherever you want and it it runs all of its commands in that workspace. So look, I just spun up a Podman container. People say it's hard to spin up Open Claw. It took 2 seconds and I was babbling through the whole way. It could have taken 1 second. Kubernetes. And you can do the same thing with Kubernetes just as easy. It just, uh, it it's connected right now to my kind cluster. And if I go over, I can access my Kubernetes claw very easily as well.