Lemonade Server is a lightweight, open-source local LLM server for running and managing multiple AI applications on a local machine. It provides a simple CLI for managing applications and supports various LLMs, and offers endpoints compatible with OpenAI, Ollama, Anthropic and others, plus a local-first API and MCP gateway.
Chatbox
Chatbox provides conversational AI for questions, tasks, and general assistance.
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0Lemonade Server is a lightweight, open-source local LLM server for running and managing multiple AI applications on a local machine. It provides a simple CLI for managing applications and supports various LLMs, and offers endpoints compatible with OpenAI, Ollama, Anthropic and others, plus a local-first API and MCP gateway.
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0AnythingLLM is a free, private AI assistant that runs on the user's device, requiring no accounts or API keys. The page describes document knowledge and a meeting assistant, and states the app is available for macOS, Windows and Linux.
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0NVIDIA NIM is a catalog of models offered as inference microservices for deployment on the user's GPU infrastructure. The listings include downloadable models and free or partner endpoints, organized by use cases such as image generation, speech-to-text and drug discovery.
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0A collaboration platform where the machine learning community shares models, datasets and applications. It hosts public models, datasets and Spaces, and offers an open source stack including libraries such as Transformers, Datasets and Accelerate. It also provides paid Compute and Enterprise solutions, with an Inference Providers API giving access to models from multiple AI providers.
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0Arena.ai is an X account describing itself as a place where AI meets the real world. It states that it measures and advances the frontier of AI through community-driven evaluation.
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0VLFeat is an open-source library of computer vision algorithms focused on image understanding and local feature extraction and matching. Algorithms include SIFT, MSER, Fisher Vector, VLAD, k-means, SLIC superpixels, and SVM training. It is written in C with MATLAB interfaces and supports Windows, Mac OS X, and Linux.
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