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How to build a basic RAG pipeline for a local LLM

Wire up retrieval-augmented generation from scratch: chunk your documents, embed them, store the vectors, and feed the right context into a local model so it answers from your own data. No cloud required.

Ahmad J · Aug 10, 2026 · 4 min read

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Article · softwareDeep read

Vector Databases: How They Actually Work, and When You Don't Need One

A vector database does three plain things: store embeddings, index them, and answer "what's closest?" fast. Here's how that machinery really works (similarity metrics, HNSW and IVF indexes, quantization, filtering) and the many cases where Postgres already does the job.

Ahmad J · Aug 7, 2026 · 4 min read

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Article · securityDeep read

The Real Privacy Audit: What Data Your AI Coding Assistant Sends Home

Stop trusting privacy policies. Put your AI coding assistant behind a proxy and watch exactly what your code transmits: what fires on every keystroke, what "local mode" really hides, and how to shut the channel.

Ahmad J · Aug 5, 2026 · 11 min read

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Guide · aiDeep read

The best vector databases for RAG in 2026

Qdrant is still the safe default for new RAG projects in 2026, but the field moved on cost: Milvus 2.6 shipped 1-bit quantization, Pinecone launched Nexus to public preview, and object-storage-first newcomers Turbopuffer and LanceDB undercut the incumbents. An honest buyer's guide.

Ahmad J · Aug 1, 2026 · 12 min read

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Article · ai

What RAG actually is and is not

RAG does not teach a model new facts. It fetches relevant text and pastes it into the prompt, so the model answers from documents instead of memory.

Ahmad J · May 10, 2026 · 4 min read