Chris Aniszczyk

Serving an open-weight model is only the start. The real work is closing the learning loop: run agents, learn from results, evaluate, redeploy. How Kubernetes can bring it together, via @thenewstack thenewstack.io/kubernetes-ai… #Kubernetes #AI

Give agents autonomy without losing control.

The Cloud Native Computing Foundation and Buoyant are pleased to present the premiere of The Linkerd Story: The People Behind the Service Mesh, at KubeCon + CloudNativeCon North America. See how the inaugural service mesh was built for production. Monday, Nov 9 at 6:30 PM Salt Palace Convention Center, Level 1, Room 151 Read more: bit.ly/4sHUEeX #KubeCon

Today we launched Kinference, a new open source project I've been working on for building scalable inference platforms on top of @vllm_project. Self-hosted inference is such a fascinating and crazy space and I did not really expect to ship a project here, but this is probably the most excited I've felt about anything since the early days of @Linkerd! Kinference came about when we saw how hard it was (for our customers and ourselves) to run your own inference. Sure you can serve a model, but... what then? What if you have a bunch of models? And you want to change them without interfering with your clients? What if you have multiple users with different requirements and you need to juggle them all? Make sure no single user can consume everything? Make behavior under saturation or failure explicit? What if you need to burst to the cloud? And on and on... Once we took more than 3 steps down this path it was clear that serving inference was a *platform-shaped problem*. Yes shockingly no one had built a platform-shaped solution. Which is crazy because platforms are central to how to build scalable reliable software in the modern world! So we ended up building Kinference. We design it to be a platform layer for inference. It runs on top of vLLM and provides all the features necessary for treating it as a platform problem, including token rate limiting, tenant usage and cost tracking, intelligent load balancing, and inference policies. The policy stuff is especially powerful IMO and I'm pretty happy with how it turned out. It's an early project, but we've been using it internally at Buoyant to juggle a bunch of AI tasks from a bunch of clients across a bunch of models, and it has been awesome. So: if you are serving inference to an ever-changing set of clients with varied and competing demands, from a ever-changing set of LLMs running on a heterogeneous, evolving pool of GPUs, and somehow have to keep everyone happy... please take a look and I'd love your feedback! kinference.ai/

@Linkerd turned 10, and our team, community members, and the @CloudNativeFdn sat down to chat about how we got here. Add to your schedule hubs.ly/Q04D9Cn40

Guess what's common between the projects on this slide? All of them are open source! A bunch of them you use without realizing it with @kubernetesio #kubecon #nvidia #OpenSource

Wolf Creek Ski Area has a Treat this Halloween for Early Season Skiing Aficionados!! Wolf Creek is Opening Saturday, October 31st, 2026!! 📣📣 Important Details about Wolf Creek’s 2026-2027 Opening Day at WolfCreekSki.com. 📸: Scott DW Smith ⛷️: Dave Sugnet