▸ Category · AI
Notes on AI.
6 posts in this category.
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AI7 mistakes you're making with your production RAG stack (and how to fix them)
Naive chunking, no reranker, embedding drift, latency blowups, vibe-checking — the seven structural mistakes that turn a slick RAG demo into a production nightmare, and the fixes that actually ship.
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AIClaude MCP: why I'm connecting my dev tools to LLMs for real-time context
The Model Context Protocol is the USB port for LLMs. One MCP server, any MCP-aware host — killing the M×N integration tax. How the host/client/server architecture works, the servers I run daily, and why the sandbox model makes it safe enough to plug into production.
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AIMCP and the future of tool-use: building context-aware agents
The Model Context Protocol kills the era of brittle one-off integrations. Tools, resources, prompts, and the three primitives that let one server talk to any MCP-aware client — with a working TypeScript example you can ship today.
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AIWhy your RAG implementation is failing in production (and how to fix it)
Vector-only retrieval is the silent killer of production RAG. Hybrid search with BM25, reciprocal rank fusion, smarter chunking, re-rankers, and an evaluation harness — the production checklist that turns a flaky demo into a reliable system.
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AIPicking the right RAG stack: vector databases for AI engineering
pgvector, Pinecone, Weaviate, Qdrant — a 2026 field guide. Which vector store to pick for your AI app, why hybrid search matters, and how to ship without painting yourself into a corner.
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AIVibe coding: why your next project needs more than just logic
Logic is the skeleton. Vibe is the soul. Why taste, intent, and feel are the new senior-engineer superpowers in the Cursor + Claude era — and how to keep the codebase from turning into a ball of mud while you chase it.
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