Web Development

Building a Privacy-First RAG Pipeline with LangChain and Local LLMs

A code-heavy tutorial on building a ‘Chat with your PDF’ app that never touches the internet. Uses widely available open-source tools. Key Sections: 1. **Architecture:** Ingestion -> Embedding -> Vector Store -> Retrieval -> Generation. 2. **The Stack:** LangChain, Ollama (Llama 3), ChromaDB or pgvector, Nomad/local embeddings. 3. **Code Implementation:** Python implementation steps. Handling document […]

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The $1,500 Local AI Server: DeepSeek-R1 on Consumer Hardware

A hardware-focused tutorial on building a dedicated AI inference server using consumer components. Focus on the sweet spot of dual used RTX 3090s or a single RTX 4090. Key Sections: 1. **Component Selection:** Why VRAM is king. The concept of ‘VRAM per dollar’. 2. **The Build:** Physical assembly notes, cooling requirements for continuous load. 3.

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Local AI Coding Assistant: Cursor vs VS Code + Ollama + Continue

A comparative guide for developers seeking a private, free alternative to GitHub Copilot. Contrasts the polished experience of Cursor with the DIY flexibility of VS Code + Continue. Key Sections: 1. **The Privacy Imperative:** Why send code to the cloud if you don’t have to? 2. **Setup Guide:** Configuring Ollama with DeepSeek-Coder-V2. 3. **Integration:** Setting

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Ollama vs vLLM: A Migration Guide for Scaling Teams

A technical migration guide for teams outgrowing Ollama’s developer-friendly experience and needing vLLM’s production throughput. Key Sections: 1. **When to Migrate:** Identifying bottlenecks (concurrency, latency spikes). 2. **Architecture Comparison:** Ollama’s monolithic approach vs vLLM’s PagedAttention and decoupled architecture. 3. **Migration Steps:** Converting Modelfiles to Docker-compose setups, handling quantization format changes (GGUF to AWQ/GPTQ). 4. **API

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The 2026 Definitive Guide to Running Local LLMs in Production

A comprehensive pillar guide on architecting, deploying, and managing local Large Language Models (LLMs) for enterprise and production use cases in 2026. This article must move beyond ‘how to install Ollama’ and cover the full stack: hardware selection (H100 vs A100 vs RTX 4090 clusters), inference engine selection (vLLM vs TGI vs TensorRT-LLM), and observability

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