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How to Run PicoClaw as an Ultra-Lightweight AI Assistant

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PicoClaw is a Go-based AI assistant that runs on $10 hardware with under 10MB of RAM PicoClaw is an ultra-lightweight personal AI assistant by Sipeed, rebuilt from the ground up in Go through a self-bootstrapping process where the AI Agent itself drove the architecture migration and code optimization. 95% of the core code was generated by an Agent and fine-tuned through human-in-the-loop review...

How to Run GLM-OCR for Local Multimodal OCR on Ollama

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Badge GLM-OCR is a multimodal OCR model designed for complex document understanding, and the maintainers provide an Ollama friendly workflow so you can run it locally with no API keys and no data leaving your machine. For browser-based document handling, pdfcraft offers a different approach to processing private PDFs entirely client-side. What it is GLM-OCR (zai-org) packages a multimodal OCR...

How to Run Hermes Agent Desktop as a Native Windows AI Agent

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Hermes Agent Desktop by RedWoodOG wraps the NousResearch Hermes Agent in a WinUI 3, .NET 10 desktop app. It gives you a fast, native Windows interface with multi-agent profiles, skill management, and messaging platform integrations. The project is still in active development but the repo is now functional beyond a shell. The native Windows app includes a soul identity system, 94 skills, eight...

How to Run Hermes Agent as a Persistent Terminal AI Assistant

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Hermes Agent is an open-source, terminal-first AI assistant from Nous Research that lives in your terminal or on a server. It connects to Telegram, Discord, WhatsApp, Signal, and email, remembers context across conversations, ships with 40+ tools for coding and research, and supports multiple LLM providers so you bring your own API key. It is intentionally lightweight and capable of running on a...

How to Run 1-Bit LLMs on CPU with BitNet.cpp

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BitNet.cpp is Microsoft’s official inference framework for 1-bit LLMs. It enables running very large quantized models on standard CPUs without GPU hardware. The framework provides optimized kernels for lossless inference at 1.58-bit precision using the BitNet b1.58 architecture. The first release focuses on CPU inference, with GPU and NPU support planned. This lets you run models that...

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