Explores using VMware Cloud Foundation as a shared private cloud for multiple enterprise AI domains, balancing common infrastructure with domain-specific governance.
An exploration of AI adoption in software development, comparing purists who avoid AI with companies integrating it deeply, using a root system metaphor.
AI infrastructure capacity now depends on power, cooling, and community approval, not just GPU counts. CEOs/CIOs must treat it as a chain of constrained gates.
An analysis of technology concentration risk for CEOs and CIOs, covering AI, cloud, chips, and vendor dependency, with strategies for resilience.
Explores why neoclouds need vendor-neutral AI infrastructure orchestration to differentiate beyond GPU capacity, focusing on lifecycle management and platform services.
Analyzes AI infrastructure upgrade risks, emphasizing compatibility chains from server firmware to model runtime for reliable GPU deployments.
Explores VMware Cloud Foundation 9.1 as a private AI operating model, integrating AI workloads into existing VMware infrastructure for governed, enterprise-ready AI deployment.
Analysis of the enterprise AI ecosystem as a food chain, examining which vendors control key layers and capture value.
Analysis of true private GPU costs vs. public cloud for enterprise AI FinOps, including depreciation, power, and utilization.
Explains how to design AI storage architecture using PowerScale, PowerFlex, vSAN, object storage, and local NVMe based on data lifecycle.
A daily curated list of tech articles covering AI mandates, TypeScript 7.0, AI agents, cloud storage, and more.
Explores the future of personal AI, arguing we're moving toward a single digital assistant with full context about our lives.
A guide to building eval loops for LLM-based apps, focusing on measuring prompt performance across model updates.
Explores GPU consumption models as a foundational architectural decision for AI production platforms, focusing on workload usage.
A guide to customizing the spinner text in Claude Code AI with personal, meaningful verbs from books, movies, and life.
AWS and Microsoft push sovereign AI infrastructure, OpenAI seeks Amazon funding, and Google launches Ironwood TPU for AI inference.
AI transitioned from experimental tech to critical infrastructure in 2025, bringing massive commercial growth and severe, systemic security risks.
Explains the multi-layered architecture of production generative AI systems, covering hardware, models, orchestration, and tooling.
Anyscale transfers the Ray distributed computing framework to the PyTorch Foundation, creating a unified, vendor-neutral AI stack with PyTorch and vLLM.
Explores the critical bottleneck of power grid capacity for AI data centers, highlighting transmission constraints and costly workarounds.
