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LLM App

build a realtime LLM app in 30 lines, with no vector database. A no-code RAG server that keeps answers in sync with your documents. Works with 8 vector databases. Built on Pathway. We’ve built a LLM microservice that answers questions about a corpus of documents, while automatically reacting to additions of new docs.

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    Text-To-Speech, RAG, and LLMs. All local!

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  2. LLMWare

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    Small Specialized Function Calling 1B LLMs for Multi-Step RAG

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  3. Open-source real time data framework for LLM applications

    getindexify.ai

    Sandboxes for AI agents. Securely run untrusted, LLM-generated code in isolated Firecracker microVM sandboxes that scale from one to thousands — with snapshot, suspend/resume, and durable orchestration.

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  4. A lightweight LLM proxy to get structured results from most LLMs

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    After struggling with complex prompt engineering and unreliable parsing, we built L1M, a simple API that lets you extract structured data from unstructured text and images.

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  5. SwiftAI

    github.com

    open-source library to easily build LLM features on iOS/macOS

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