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PyTorch Call Stack Deep Dive: Tracing Tensor Operations from Python to C++ Kernels
Eliminating the ‘Rego tax’: How AI orchestrators automate Kubernetes compliance
Manually writing OPA Rego policies is a significant bottleneck for many platform teams, creating a 'Rego tax' that can slow down development and introduce risk. This article introduces a new approach: a Dynamic Kubernetes Policy Generator that uses a large language...
Zero trust AI agents on Kubernetes: What I learned deploying multi-agent systems on Kagenti
AI agent content focuses on prompt engineering and framework selection. But very little addresses what happens when those agents run in production: Who they are, what they're allowed to call, and whether anyone can tell what they did. I spent 2 weeks (January 2026)...
Zero Trust for autonomous agentic AI systems: Building more secure foundations
AI systems are no longer just single-purpose models. With the rise of agentic AI, software systems designed to carry out complex tasks and solve problems with limited human supervision. It's a step beyond generative AI, which creates content, to an AI that does...
From hand-tuned to generated: A reproducible Triton GPU kernel benchmark across different vendors
In the world of Large Language Models (LLMs), speed is very important. Much of this speed comes from highly specialized functions called GPU kernels which are small, focused routines that instruct the GPU how to perform calculations with the maximum efficiency....