Kubernetes 1.37 Pod Certificates and Cluster Trust Bundles GA: Workload Identity Guide
Pod Certificates and ClusterTrustBundles reach GA in Kubernetes 1.37. How pod X.509 identity works vs SPIFFE and cert-manager, with migration steps.
Kubernetes 1.37 HPA Scale-to-Zero Beta vs KEDA and Knative
Kubernetes 1.37 moves HPA scale-to-zero to beta. How it works, its limits, and when KEDA or Knative still win for event-driven workloads.
Cognition SWE-2 Explained: Frontier Code Benchmark Results and Cost Trade-offs
Cognition SWE-2 reportedly scores 50 percent on Frontier Code at 70 percent lower cost. What is verified, how coding benchmarks mislead, and how to evaluate it.
OpenAI Agents API Public Beta: Managed Sandboxes vs Self-Hosted Agent Runtimes
OpenAI Agents API entered public beta with sandboxes near $0.03 per 20 minutes. Managed agent runtimes vs self-hosted sandboxes on cost and control.
Meta Muse and Muse Code: Personal Agent Architecture and Pricing Explained
Meta Muse is a 24/7 personal agent and Muse Code a coding tier. What is confirmed on tiers and quota, plus engineering trade-offs of always-on agents.
Grok 4.7 Explained: xAI Architecture, CursorBench Results and Pricing
Grok 4.7 from xAI explained: what is disclosed on architecture, the CursorBench 46 percent result, API pricing near $2 per million tokens, and how it compares.
Tencent Hy4 Explained: 770B MoE, Sherry Quantization and Open Weights
Tencent Hy4 preview explained: 770B total and 49B active MoE, Apache 2.0, Sherry quantization to about 214GB, and what it takes to run it locally.
DeepSeek V4.1 Flash Explained: MoE Architecture, KV Cache, Benchmarks
DeepSeek V4.1 Flash explained: 552B MoE with small active parameters, MIT license, KV-cache savings, benchmarks, and how to self-host or call it by API.
GPU Memory Physics: DRAM vs SRAM, Kernel Fusion and FlashAttention (Series Part 3)
GPU memory physics: DRAM vs SRAM latency and bandwidth, tiling, kernel fusion, and why FlashAttention made LLM inference faster. Series finale.
GPU Kernel Engineering: Why C = A + B Is Not Enough (Series Part 2)
GPU kernel engineering explained: thread indexing, launch configuration, coalescing, divergence, and why a naive C = A + B kernel starves on memory.