Helm 4 ditches the old plugin model for a sharper, plugin-first architecture powered by WebAssembly. That means isolation/control, and deeper customization - if you're ready to adapt! Post-renderers are now plugins. That breaks compatibility with earlier exec-based setups, so expect some rewiring. ..

Broadcom underwent significant changes post-VMware acquisition, with emphasis on subscription-based pricing and portfolio simplification. Prashant Shenoy claims VCF lowered prices by 50%, challenging industry norms about AI workloads on bare metal versus virtualized environments. Integration pointed..

OCI artifacts quietly leveled up. Over the last 18 months, they’ve gone from a niche hack to production muscle for AI/ML workloads on Kubernetes. The signs? Clear enough:KitOpsandModelPacklanded in the CNCF Sandbox. Kubernetes 1.31 got native support forImage Volume Source. Docker pushedModel Runner..

Amazon just droppedEC2 P6e-GB200 UltraServers, packingNVIDIA GB200 Grace Blackwellchips. Built for running trillion-parameter AI models onAmazon EKSwithout losing sleep over scaling. Under the hood:NVLink 5.0,IMEX, andEFAv4stitch up to 72 Blackwell GPUs into one memory-coherent cluster per UltraServ..

MIT and McKinsey found a gap the size of the Grand Canyon: 80% of companies claim they’re using generative AI, but fewer than 1 in 10 use cases actually ship. Blame it on scattered data, fuzzy goals, and governance that's still MIA. A new stack is stepping in:product → agent → data → model. It flips..

A serverless AWS pipeline runs image moderation on autopilot - withS3,Lambda,Rekognition,SNS, andEventBridgeall wired up throughTerraform. When a photo gets flagged, it’s tagged, maybe quarantined, and triggers an email alert. Daily scan? Handled...

Grokipedia just dropped - a Wikipedia remix built from LLM output, pitched as an escape from "woke" bias. The pitch? Bold. The execution? Rough. Entries run long. Facts bend. Citations wander. And the tone? Cold, context-free, and unmistakably machine-made. The usual LLM suspects are here: hallucina..

Senior engineers are starting to spin upparallel AI coding agents- think Claude Code, Cursor, and the like - to run tasks side by side. One agent sketches boilerplate. Another tackles tests. A third refactors old junk. All at once. Is it "multitasking on steroids"? Not just this as it messes with ho..

Modern AI eats GPUs for breakfast - training, inference, all of it. Matrix ops? Parallel everything. Models like LLaMA don’t blink without a gang of H100s working overtime...

Agentic LLM apps come with a glaring security flaw: they can't tell the difference between data and code. That blind spot opens the door to prompt injection and similar attacks. The fix? Treat them like they're radioactive. Run sensitive tasks in containers. Break up agent workflows so they never ju..