AI & ML

Introduction Cloud repatriation has become another architecture debate that generates more heat than evidence. One side treats public cloud as the default destination for every application. The other treats every unexpected bill, provider outage, or jurisdictional concern as proof that workloads should return to privately owned infrastructure. Both positions fail for the same reason: they […]

TL;DR A reliable VMware NSX deployment on NVIDIA networking depends less on clicking through the NSX Manager wizard and more on getting the physical underlay right first. The NVIDIA Spectrum fabric must provide stable Layer 3 reachability between every ESXi and NSX Edge tunnel endpoint, consistent jumbo MTU, predictable uplink behavior, and resilient routing to

TL;DR Specification gaming becomes an operational security problem when an autonomous agent can pursue a valid evaluation objective through methods that violate authorization boundaries. A benchmark can measure the desired capability correctly while the surrounding infrastructure permits an unacceptable shortcut. The required response is not a longer system prompt. High-capability evaluations need independently enforced invariants

Rising expenditure from OpenAI, Anthropic, Google and Microsoft reflects growing battle over federal policy

Some publishers think AI could not only help authors but replace them

TL;DR Security teams need AI systems that can inspect the material most general-purpose assistants are designed to treat cautiously: exploit code, malware behavior, command-and-control traffic, exposed credentials, persistence mechanisms, and destructive commands. The answer is not to remove every safeguard or place an unrestricted model on an analyst workstation. The safer pattern is a governed

Introduction GPU sharing is easy to describe and difficult to govern. A platform team can expose one physical accelerator as several scheduler-visible resources, divide it into Multi-Instance GPU partitions, present virtual GPUs to virtual machines, or assign the entire device to one workload. Kubernetes can place those workloads into separate namespaces. An enterprise scheduler can

The real problem is not overseas open-source but lack of co-ordination to protect infrastructure in the face of cyber attacks

Introduction An enterprise inference service can look busy while its GPU remains nearly idle. The application may be accepting requests, retrieving documents, validating permissions, tokenizing prompts, waiting on storage, retrying dependencies, or building responses. None of those activities prove that enough executable work is reaching the accelerator. This is why GPU troubleshooting often goes wrong.

A look at how chatbot answer subjective questions.