AI & ML

TL;DR Do not create a VMware Cloud Foundation workload domain every time a tenant, business unit, application, or security team asks for separation. In VCF 9.1, the correct design is usually to choose the smallest boundary that satisfies the strongest verified requirement. A subnet or segment separates network attachment. An NSX VPC separates application networking, […]

TL;DR A rack of healthy GPU servers is not a production-ready VCF Private AI platform. Production acceptance should prove the complete service chain: supported hardware, aligned ESXi and NVIDIA software, a healthy VCF 9.1 workload domain, stable NSX and shared infrastructure services, a ready Supervisor and VKS layer, functioning tenant controls, trusted Harbor and model

TL;DR Deploying VMware Private AI Foundation with NVIDIA on VCF 9.1 is not a single-product installation. It is an integrated platform deployment spanning the VCF workload domain, GPU-enabled ESXi hosts, NVIDIA drivers and licensing, vSphere Supervisor, namespaces, Private AI Services, Harbor, identity, networking, certificates, and the AI consumption model. The most important design decision happens

TL;DR The NVIDIA RAG Blueprint is not one application pod. It is a coordinated retrieval platform that combines an ingestion service, a RAG server, NVIDIA NIM microservices, NV-Ingest, a vector database, object storage, model caches, and supporting Kubernetes operators. For the current 2.6.0 release, Elasticsearch is the default vector database and SeaweedFS is the default

TL;DR A coding agent should enter a CI/CD pipeline as an untrusted change producer, not as a privileged developer account. Give it an ephemeral sandbox, read-only repository access, a writable workspace, tightly controlled network egress, no deployment credentials, and no direct path to a protected branch. The agent should produce a patch and evidence bundle.

Educators and students find themselves on the front line as AI labs begin to muscle in, fears about cognitive atrophy surface, and AI cheating detection tools are revealed to be faulty. It is time to arm students with ‘eval’ powers, a professor argues

Anthropic and OpenAI are among groups providing free and cut-price tailored solutions for educators and students

Academic research on the phenomenon suggests AI could be behind both trends

Long-term effects of incorporating the technology into learning are still unknown, but inequalities are emerging

Some institutions are overhauling assessment and trying to move away from the emphasis on surveillance