Infrastructure & Runtime
Inference, serving, RAG, vector DBs, edge deployment, and hardware.
23 articles exploring Infrastructure & Runtime. Expert analysis and insights from our editorial team.
Latest in Infrastructure & Runtime
DNS-Persist-01: Let's Encrypt's New Model for Permanent Certificate Validation
DNS-Persist-01 is a proposed ACME challenge type that allows persistent DNS TXT records for certificate validation, eliminating the need for real-time DNS updates with each renewal as certificate lifetimes shrink to 47 days by March 2029 under CA/Browser Forum SC-081v3.
Tailscale Peer Relays: The Missing Piece for True P2P Networking
Tailscale Peer Relays became generally available on February 18, 2026, enabling high-throughput peer-to-peer relaying within your own infrastructure. This feature eliminates the performance bottleneck of DERP servers when NAT traversal fails, delivering true mesh networking even in restrictive network environments.
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