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The Model Context Protocol (MCP): connecting agents to real tools
Large language models are good at reasoning over text, but real-world software systems rarely live inside text alone. A useful AI agent needs to interact with calendars, databases, ticketing systems,
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Your first tool-using agent: function calling done right
A tool-using agent is not a language model that somehow reaches into your Python process and executes functions by itself.
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Hybrid search: combining BM25 keyword and dense vectors
Dense vector retrieval is excellent at matching meaning.
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Choosing a vector database in 2026: pgvector vs the specialists
Retrieval-augmented generation (RAG) systems have moved from prototypes into production software. In 2026, choosing a vector database is no longer just about asking, “Can this store embeddings?”
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Chunking strategies that actually matter for retrieval quality
If you’ve experimented with embedding models, vector databases, rerankers, or prompts, but your Retrieval-Augmented Generation (RAG) system still misses obvious answers, the bottleneck may be much sim