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Retrieval-augmented generation sounds straightforward when you describe it. Give an AI access to your documents so it can answer questions about them. In practice, building that system yourself involves choosing embedding models, configuring vector databases, writing chunking logic, managing document processing pipelines, and wiring everything together. Most solo builders who
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This site has spent several articles talking about workflows, triggers, and systems that run without being asked. At some point, the question becomes practical: what do you actually build them in? For solo builders who aren't primarily software developers, the answer increasingly is n8n. n8n is an open-source
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Running a model locally is the first step. Using it productively every day is a different problem. The command line works for testing. It doesn't work for the kind of sustained, document-heavy, multi-conversation workflows that solo builders actually need. At some point, you need an interface. Open WebUI
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When Meta released the first Llama models, the immediate reaction was surprise. A trillion-dollar company was giving away the kind of technology that others charged premium prices to access. The skeptics assumed there would be a catch. The catch never materialized. Instead, what materialized was an ecosystem. Llama is Meta&
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The assumption that AI requires an API key and a monthly bill is so widespread that most builders never question it. You sign up for a service, feed it tokens, and pay per request. It works. But for a growing category of tasks, there's a simpler option: running