Operations | Monitoring | ITSM | DevOps | Cloud

GPU sharing is quickly becoming a practical requirement for Kubernetes-based AI inference, as many modern workloads don’t need a full GPU to deliver value. But safely placing multiple containers on the same accelerator brings new challenges: scheduling, fairness, isolation, observability, and noisy-neighbor behavior. This 20 min session explore the GPU sharing landscape across Kubernetes: time-slicing, MPS, MIG, KAI Scheduler, and HAMi, and dives into the harder problem: operating shared GPUs in production, from tracking usage to enforcing fairness as demand shifts.

Cortex co-founder and CTO Ganesh Datta sits down with Steve Flanders, who leads AI transformation at Splunk and wrote the book on OpenTelemetry, to talk about why AI acceleration without strong observability foundations creates more problems than it solves.

Giving developers AI tools is only the first step.

Learn more: speedscale.com.

AI agents are now faking identities — and this is the case that proves it. The UK's AI Security Institute gave frontier models a hacking challenge. Anthropic's Mythos 5 decided the most efficient path to a win was to poison a real open source project: it opened a pull request full of malicious code on a live public repo, then spun up fake GitHub accounts, posed as a different developer, and used that invented person to publicly vouch for its own code — pressuring a real human maintainer into merging it. A human reviewer caught the malware and closed the PR.