A practical guide to building a fully local Retrieval-Augmented Generation pipeline: document ingestion, chunking strategies, embedding model selection, vector store options (ChromaDB, Qdrant, FAISS), reranking, and wiring it all together with LangChain and Ollama — no cloud APIs required.
Ai-Ml
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Building a Local RAG Pipeline: From Documents to Answers -
AI Code Review Bots: Building One Engineers Don't Mute What separates a review bot engineers actually act on from one they silence on day three: context selection, taste calibration, blocking vs. advisory mode, and the metrics that tell you which you've built.
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Computer Use and Browser Agents: How They Actually Work, What Breaks, and How to Run Them Safely The agent-drives-a-browser category explained: Anthropic Computer Use vs DOM-driven stacks, the real failure modes, and the sandbox patterns you need before putting any of this near production.
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The Local Coding Agent Stack: Aider, Continue, Cline, and OpenHands Pure-local and BYOK coding assistants compared head-to-head: architecture, model support, what works at the 32B–70B tier, and where each tool breaks down in practice.
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LiteLLM and Model Routing: The Proxy Pattern for Multi-Provider LLM Apps One OpenAI-compatible endpoint, many backends. How LiteLLM's proxy pattern works, how to configure routing, fallbacks, cost controls, and observability, and when a self-hosted gateway beats managed alternatives.
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vLLM vs SGLang vs TensorRT-LLM: Inference Engine Shootout 2026 A thorough comparison of the three dominant LLM inference engines: how each one works internally, where each wins on benchmarks, and which to reach for given your hardware, workload, and operational tolerance.