Kit is an open source #MLOps project that packages your model, datasets, code, and configuration so data scientists and developers can use their preferred tools
Explore a complete MLOps workflow using
→ KitOps for model packaging
→ Cog for local testing
→ KAITO for scalable Kubernetes deployment
read full article!
https://t.co/Ie2Oiil2ve
Jozu just launched Agent Guard → a zero-trust runtime for AI coding agents and MCP (Claude Code, etc).
Most agent guardrails today run in user-space.
So if the agent can access the same host processes/filesystem, a bypass is always on the table.
@Jozu_AI Agent Guard 👉 hypervisor isolation + policy enforcement at runtime.
What that means in practice:
→ agent runs in a hypervisor-isolated container
→ it only sees the workspace you explicitly share
→ host FS, processes, SSH keys, creds stay inaccessible
→ only supply-chain-verified artifacts get in
→ policies are tamper-evident + enforced on every action
→ policy server runs as PID 1 (agent can’t kill it)
If your agent touches real credentials or prod-adjacent workflows, this is worth a look.
I'll explore it in more details soon!
We just ran @opencode with @Jozu_AI Rapid Inference Containers.
Opencode supports local models, and we used the 4-bit quantized GGUF version of Qwen3.5-9B.
Jozu RIC is even faster than @nvidia NIM in benchmarks.
In one test, NIM took 239.2 seconds, while RIC took only 42.8 seconds.
With Jozu secure packaging, speed, and deployment, anyone can run critical workloads on their infra more easily.
Jozu also enables 7x faster model deployment with a proper audit trail.
Full tutorial coming soon!
When working with DevOps projects, I can't see myself using anything but @warpdotdev. It works like a charm at fixing annoying kubectl bugs.
POV: Running a demo showing how to integrate @Kit_Ops with KServe
The KitOps Project wrap is here! 🎉
Huge shoutout to @SaiyamPathak and @kubesimplify
for building such an awesome project.
Thanks to everyone who supported KitOps along the way!
Learn how to package an LLM into a ModelKit, deploy it using KServe inference endpoints, and run it on Kubeflow with Jozu orchestration
→ all without needing dedicated GPUs. Practical LLM serving at scale.
It's another @CloudNativeFdn@KubeCon_ week
If you are around, do come and say hi!
Sharing my sessions for the week, do add them to your schedule! Happy to discuss anything about @mesheryio, @Kit_Ops, @kubeflow, @Docker or anything MCP / cloud native AI!
Learn how to use Flux CD and KitOps together!
If your aim is to build scalable, shareable ML deployment workflows
using GitOps principles for production-ready AI/ML apps.
read our latest blog👇
https://t.co/x85w1W72LG
It's Preptember, who's up for some cool stickers? 👀
Don't forget to check out @Kit_Ops during #Hacktoberfest
Lots of good quality issues will be available for contributors!
Join KitOps Discord community to explore and ask questions while you prepare.
repo link👇
Stop wrestling with ML deployment chaos. Your model works perfectly on your laptop, but breaks everywhere else?
There's finally a solution: @Kit_Ops + OCI artifacts.
Here's why this changes everything - https://t.co/myH2e4QQJI
#Mlops
It's Preptember, who's up for some cool stickers? 👀
Don't forget to check out @Kit_Ops during #Hacktoberfest
Lots of good quality issues will be available for contributors!
Join KitOps Discord community to explore and ask questions while you prepare.
repo link👇