LLM infrastructure
-
AI Tools
ChatGPT Infrastructure Explained: GPUs, Memory, and Distributed Inference
When you ask ChatGPT a question, the hard part isn’t generating the answer. The hard part is moving enormous amounts of data fast enough that the response appears instantly. Modern AI systems process trillions of parameters across clusters of graphics processing units (GPUs) connected by specialized high-speed networks. Every word you type creates a chain reaction: Memory gets allocated. GPUs…
Read More » -
AI Tools
The Best MCP Servers in 2026
The Best MCP Servers in 2026: Why Most AI Agents Fail at the Coordination Layer Server Type Best Use Case Maturity Primary Transport Smithery Team Tool Management High SSE / Docker n8n MCP Human-in-the-Loop Ops High Webhook / SSE Postgres MCP Structured Data Queries Medium Stdio / SSE Filesystem/SQLite Local Coding Assistance High Stdio (Local) Custom SSE Proxy Private Auth…
Read More » -
Guides
How to Build a RAG System with pgvector and LangChain: The Production Architecture
How to Build a RAG System with pgvector and LangChain: The Production Architecture Most production AI failures are not model failures. They are retrieval failures. If you want to understand why your RAG system is hallucinating, stop looking at your prompt. A perfect prompt with the wrong data yields a confident hallucination. An average prompt with the correct data yields…
Read More » -
Blog
What Is Context Engineering?
What Is Context Engineering? Why Prompt Engineering Is No Longer Enough Most production AI failures are not model failures. They are retrieval failures. For the last two years, the internet was flooded with “Prompt Engineering Cheat Sheets,” as if knowing how to tell an LLM to “take a deep breath” was a technical moat. Typing instructions into a chat box…
Read More »