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Kubernetes v1.36 Promotes Stability, Compatibility & Reproducibility

Kubernetes v1.36 (Spring 2026) introduces 70 enhancements, including major security hardening for the Kubelet API and the debut of Workload-Aware Scheduling (WAS) for AI/ML. This release focuses on fine-grained resource health, stable ...

Adrian Bridgwater | April 22, 2026 | AI/ML Infrastructure, CI/CD, cloud native security, cloud-native applications, Cluster Hardening, container security, containers, CSI Token Redaction, developers, Distributed Training, DRA, Dynamic Resource Allocation, External Token Signing, Gang Scheduling, K8s v1.36, Kubelet API Authorization, kubernetes, Kubernetes Enhancements 2026., Kubernetes v1.36, microservices, Node Logs, open source, PodGroup API, Resource Health Status, storage, Volume Group Snapshots, WAS, workload-aware scheduling

SRE, autoscaling, Tailscale, Kubernetes, argo cd, Kubernetes v1.33, AI, Nelm, Kubernetes, architecture, , architecture, Rackspace, GPUs, Kubernetes, Solo.io Kubernetes cloud foundry keptn cloud-native automation

Kubernetes v1.35 Arrived, Right On Workload-Aware Schedule

Discover the latest enhancements in Kubernetes workload scheduling, including the Workload API and gang scheduling features aimed at optimizing application performance and management ...

Adrian Bridgwater | January 12, 2026 | autoscaling, cloud-native applications, Dynamic Resource Allocation, Gang Scheduling, kubernetes, Kubernetes v1.35, Multi-Node Scheduling, Opportunistic Batching, Performance Optimization, Pod Management, resource allocation, Scheduling Algorithms., Scheduling Improvements, Scheduling Latency, software engineering, Workload API, Workload Scheduling

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You Can’t Just Layer AI on Your Observability Platform: Why Context is King

19 May 2026

You Can’t Just Layer AI on Your Observability Platform: Why Context is King

AppSec in 2026: Security at Machine Speed — or Not at All

18 May 2026

AppSec in 2026: Security at Machine Speed — or Not at All

Multi-Million-Dollar Lessons: What FinOps Maturity Actually Looks Like

14 May 2026

Multi-Million-Dollar Lessons: What FinOps Maturity Actually Looks Like

From Prompt to Exploit: How LLMs Are Changing API Attacks

13 May 2026

From Prompt to Exploit: How LLMs Are Changing API Attacks

Data is the Differentiator for Exposure Management

6 May 2026

Data is the Differentiator for Exposure Management

The Context Engine: Why Consolidation is the Natural Future of AppSec

5 May 2026

The Context Engine: Why Consolidation is the Natural Future of AppSec

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ThreatHunter.ai Halts Hundreds of Attacks in the past 48 hours: Combating Ransomware and Nation-State Cyber Threats Head-On

ThreatHunter.ai Halts Hundreds of Attacks in the past 48 hours: Combating Ransomware and Nation-State Cyber Threats Head-On

Deloitte Partners with Memcyco to Combat ATO and Other Online Attacks with Real-Time Digital Impersonation Protection Solutions

Deloitte Partners with Memcyco to Combat ATO and Other Online Attacks with Real-Time Digital Impersonation Protection Solutions

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AI in CI/CD: Where Are You Really?

Step 1 of 7

14%

How would you describe your organization’s current level of AI adoption within your CI/CD pipeline?(Required)

No active use of AI in CI/CD

Experimenting with AI in isolated areas (e.g., code suggestions, testing)

Limited production use in specific pipeline stages

Broad adoption across multiple stages of the pipeline

AI is fully integrated and operational across the CI/CD lifecycle

Not sure / don’t know

How would you describe your current CI/CD environment?(Required)

Primarily legacy / Jenkins-based with custom integrations

Jenkins-centered with some modern tooling layered in

Hybrid environment with multiple CI/CD platforms and toolchains

Mostly modern, managed CI/CD platforms

Fully integrated, platform-driven architecture

Not sure / don’t know

In which areas of your software delivery pipeline are you currently using AI? (Select all that apply)(Required)

Code generation / developer assistance

Automated testing / test generation

Build and pipeline optimization

Deployment automation

Monitoring and incident response

Security / compliance automation

We are not currently using AI in our pipeline today

In which areas of your software delivery pipeline are you considering using AI? (Select all that apply)(Required)

Code generation / developer assistance

Automated testing / test generation

Build and pipeline optimization

Deployment automation

Monitoring and incident response

Security / compliance automation

Which AI use cases are delivering, or do you expect to deliver, measurable value in your CI/CD pipeline? (Select up to three)(Required)

Faster development cycles

Improved code quality

Reduced testing effort

Improved reliability / fewer incidents

Reduced operational costs

None of these

Not sure / cannot assess

What, if anything, is limiting your organization’s progress with AI in CI/CD? (Select up to three)(Required)

Fragmented toolchains

Lack of integration

Lack or unavailability of high-quality data

Data silos across pipeline stages

Security or governance concerns

Lack of internal expertise or skills

Unclear ROI or business case

Difficulty integrating AI into existing workflows

Performance or reliability concerns

Organizational resistance to change

Nothing is limiting my organization’s progress with AI in CI/CD

Not sure / don’t know

How do you expect your use of AI in CI/CD pipelines to change over the next 12–18 months?(Required)

No plans to adopt AI

Continuing experimentation only

Expanding use in selected areas

Scaling across multiple pipeline stages

Making AI core to delivery strategy

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