AI workloads push Postgres adoption, auto-configure Claude, RAG from scratch.
AI's voracious power demands are sparking local outrage. Town halls are erupting over data centers driving up electricity bills, a conflict that will directly shape future build-outs and cloud costs for everyone.
CES 2026 previews point to pervasive AI integration across consumer devices, along with new chip announcements from Intel (Panther Lake), AMD (Ryzen 9000G), and Qualcomm (Snapdragon X2 Elite). Expect a strong focus on AI performance for edge computing.
Read full story→Communities nationwide are increasingly opposing new data center proposals due to rising electricity bills, environmental impact, and loss of open space. This backlash is causing project delays for major tech firms like Microsoft and Amazon.
Andrej Karpathy's "Neural Networks: Zero to Hero" course teaches how to build neural networks from scratch, all the way to a GPT model. It covers backpropagation, PyTorch internals, and Transformer architecture through practical, code-based implementation.
A 55-minute video takes you inside the complex, capital-intensive process of manufacturing GPU wafers. This deep dive shows where the fundamental components powering all modern AI hardware originate.
AI workloads, with their bursty IOPS and data locality needs, are straining traditional managed databases. This drives a shift to "Bring Your Own Cloud" (BYOC) Postgres, offering direct control, NVMe performance, and cost efficiency.
The US and China are pursuing divergent AI strategies: China with a state-subsidized, top-down approach for practical applications, and the US through decentralized private enterprise targeting AGI. China's model faces challenges like US chip export curbs.
Claude Reflect is a self-learning system for Claude Code that automatically captures user corrections and positive feedback. It then syncs these learnings to config files like CLAUDE.md, making Claude remember your preferences across sessions.
LangChain AI published a practical, step-by-step guide for implementing Retrieval Augmented Generation (RAG). Presented in Jupyter Notebooks, it offers a direct way to learn and experiment with RAG using the LangChain framework.