u/FactorHour7131

Hi everyone,

I'm reaching out from the CNCF TAG DevEx. We are currently mapping how AI is being integrated into the CNCF landscape, specifically focusing on the developer and maintainer experience.

We’ve just released a first look at the preliminary data on the CNCF blog: https://www.cncf.io/blog/2026/04/29/the-state-of-ai-in-cncf-projects-a-first-look-at-the-data/

While the initial findings are insightful, we realized we need a deeper perspective that only project maintainers and active contributors can provide.

The Goal: We want to understand the real friction points you face when integrating AI/LLMs into your projects—whether it's infra overhead, security concerns, or DevEx hurdles. This data will directly influence how the CNCF supports AI initiatives moving forward.

The Request: If you are a Maintainer or Contributor to a CNCF project, please take 5 minutes to share your experience in our technical survey:

👉 https://forms.gle/EapDLhjmVtd7gNpp9

Note: This survey is specifically designed for those building and maintaining the ecosystem.

Thanks for helping us shape the AI roadmap for the Cloud Native ecosystem!

Cheers.

Hi everyone,

I’ve spent the last few months analyzing how large orgs (mostly EU and US) handle Day 2 operations. While everyone is obsessed with "Golden Paths" for deployment, we found a massive gap in what happens after.

Key takeaway: 52% of orgs use a "Shared Ownership" model for optimization, which in practice means nobody does it. Developers want velocity, SREs want stability (overprovisioning), and FinOps want to cut costs.

I wrote a deep dive on why manual tuning is a "firefighting" mode we need to escape. Curious to hear: how do you resolve the conflict between SRE buffers and FinOps requests in your org?

Full article: https://akamas.io/resources/the-state-of-cloud-native-optimization-2026/