AI companies spend record sums on Washington lobbying
Rising expenditure from OpenAI, Anthropic, Google and Microsoft reflects growing battle over federal policy
AI companies spend record sums on Washington lobbying Read More »
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Rising expenditure from OpenAI, Anthropic, Google and Microsoft reflects growing battle over federal policy
AI companies spend record sums on Washington lobbying Read More »
Some publishers think AI could not only help authors but replace them
The new premium product: books written by people Read More »
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
The Guardrail Paradox: Designing a Governed Forensic AI Platform for Cyber Defense Read More »
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
What is the risk of using Chinese open AI models like Kimi K3? Read More »
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.
Why Your GPU Is Idle: A Layer by Layer Troubleshooting Guide for Enterprise Inference Read More »
TL;DR An agent tool is not merely an API endpoint with a JSON wrapper. It is a contract between a nondeterministic decision-maker and a deterministic system. Reliable tools have distinct names, narrow responsibilities, constrained input schemas, useful descriptions, predictable output structures, retry-safe side effects, actionable errors, server-side validation, and evaluations built around realistic tasks. The
How to Design Tools That AI Agents Can Use Reliably Read More »
TL;DR A GPU-backed workload is not recoverable merely because its virtual disks were copied or its Kubernetes manifests were committed to Git. vGPU, MIG-backed vGPU, PCI passthrough, Enhanced DirectPath, RDMA, and bare-metal GPU configurations expose different snapshot, suspend, migration, and failover boundaries. Some support controlled mobility within a narrow compatibility matrix. Others deliberately trade those
How Do You Protect a GPU-Backed Workload When Snapshots and Mobility Have Limits? Read More »
Human approval is not useful just because a workflow pauses. It becomes useful when the system can prove what was proposed, why review was required, who reviewed it, what evidence they saw, what scope they approved, what executed, and whether the outcome matched the approved intent. That is the difference between an approval button and
From Approval Button to Control Plane: Implementing Human Review for AI Agents Read More »