AI & ML

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.

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

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

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

London-based company shows that while the continent is industrialising, it still has manufacturing clout

Kanishka Narayan tells FT that new prime minister views technology as way for Britain ‘to get its mojo back’

Metals boom is reliant on high-altitude, declining mines that are exposed to increasing weather volatility

Many white-collar professionals worry about the tech inhibiting creativity and making errors

Introduction Enterprise GPU design becomes confused when several different decisions are compressed into one question: “How should we share the GPU?” That question mixes hardware assignment, virtualization, Kubernetes scheduling, tenant isolation, business priority, and service-level commitments. The result is often a platform that advertises many GPU “slices” but cannot explain what each slice guarantees. A