Evaluating AI Code Review Tools: A Real-World Bug Detection Study
Signal65 conducted a hands-on assessment of five AI code review tools, each tested against bug-introducing pull requests across six open source repositories.
The NVIDIA DGX Spark Platform: Arm and NVIDIA Reinvent the Workstation
NVIDIA DGX Spark’s purpose is to bring data-center-class AI capability to the desk of every developer, researcher, and engineer who needs serious local compute without a cloud invoice.
Lenovo xIQ Agent Platform Enhances Retail in the AI Era with Lenovo Super Agent for Retail
The Lenovo AI Library is a curated catalogue of prebuilt agents and validated use cases built on hundreds of proven Hybrid AI solutions.
Digital Twins as the Control Plane for Physical AI
This paper presents a different way of thinking about what a digital twin is. A continuously learning operational mirror of a physical asset.
Improving AI Inference with AMD EPYC Host CPUs
This report explores the important role of the host CPU and evaluates the impact they have on AI performance.
Benchmarking Leadership in Open and Proprietary Models
This report evaluates agentic AI capability with the Kamiwaza Agentic Merit Index (KAMI) Benchmark and provides analysis of the agentic AI landscape as of Q1 2026.
Data Preparation for Enterprise AI: Compute-Ready Data with Dell AI Infrastructure
As enterprises accelerate AI adoption, the quality, structure, and accessibility of their data have become the primary determinant of success.
From Dense to Mixture of Experts: The New Economics of AI Inference
The AI infrastructure landscape is shifting again as model designs move from dense transformers to Mixture of Experts and as reasoning-heavy workloads become the norm.
Evaluating Agentic AI: Dell AI Solutions with Open Models
As agentic AI becomes the center of enterprise strategy, organizations increasingly recognize the need to run AI on premises to maintain data sovereignty, preserve operational control, and achieve predictable, cost-effective performance.
Dell PowerEdge XE7745 RAG Evolution
Retrieval-Augmented Generation (RAG) emerged as the foundation for trustworthy AI at scale, linking generative models to proprietary knowledge sources through search, embeddings, and vector databases.