AI Infrastructure
10 articles exploring AI Infrastructure. Expert analysis and insights from our editorial team.
Latest in AI Infrastructure
The MCP Registry: GitHub's Play to Become the App Store for AI Tools
GitHub's MCP Registry centralizes discovery of Model Context Protocol servers, positioning GitHub as the primary distribution layer for AI agent tooling and addressing the fragmentation that emerged as MCP's ecosystem exploded past 5,000 servers in under a year.
MCP Is Everywhere: The Protocol That Connected AI to Everything
How the Model Context Protocol became the universal standard connecting AI assistants to data sources, tools, and enterprise systems—transforming isolated models into truly connected agents.
Nvidia's Deal With Meta Signals a New Era in AI Computing Power
Meta and Nvidia announced a multi-year strategic partnership in February 2026 that will see Meta deploy Nvidia's Vera Rubin platform across gigawatt-scale data centers, representing one of the largest single commitments of AI computing resources in history.
Alibaba's zvec: A Lightning-Fast Vector Database That Fits In-Process
Zvec is Alibaba's open-source, in-process vector database built on the battle-tested Proxima engine. It enables millisecond semantic search across billions of vectors without requiring external servers or infrastructure, making it ideal for edge AI and embedded applications.
Edge AI Deployment: Running Models Where the Data Lives
Edge AI deploys machine learning models directly on local devices, reducing latency to milliseconds while keeping sensitive data private. This comprehensive guide covers deployment strategies, optimization techniques, and key frameworks for running AI from smartphones to IoT sensors.
GitHub Agentic Workflows: AI That Commits Code For You
GitHub's agentic workflows bring autonomous AI agents directly into the developer workflow, enabling AI to write code, create pull requests, and respond to feedback—transforming the PR process from manual coding to AI-assisted systems thinking.
Vector Search at Scale: Architectures That Handle Billions of Embeddings
Vector search at scale requires distributed architectures, approximate nearest neighbor algorithms like HNSW and IVF, and intelligent sharding strategies. Leading implementations can query billions of embeddings in milliseconds with 95%+ recall.
Perplexity API: Adding Real-Time Search to Your Apps in Minutes
A comprehensive guide to implementing Perplexity's Search API, featuring pricing, code examples, use cases, and comparisons with alternatives.
RAG in Production: Retrieval Augmented Generation That Actually Works
RAG combines large language models with external knowledge retrieval to reduce hallucinations and ground AI outputs in factual data. While the concept is straightforward, production deployment reveals critical challenges around chunking strategies, latency optimization, and retrieval accuracy that separate working systems from prototypes.
The Complete Guide to Local LLMs in 2026
Why running AI on your own hardware is becoming the default choice for privacy-conscious developers and enterprises alike