ShellGPT is a powerful command-line productivity tool driven by large language models like GPT-4. It is designed to streamline the development workflow by generating shell commands, code snippets, and documentation directly within the terminal, reducing the need for external searches. The tool supports multiple operating systems including Linux, macOS, and Windows, and is compatible with various shells such as Bash, Zsh, and PowerShell. Beyond simple queries, it offers advanced features like shell integration for automated command execution, a REPL mode for interactive chatting, and the ability to implement custom function calls. Users can also leverage local LLM backends like Ollama for a free, privacy-focused alternative to OpenAI's API.
Project N.O.M.A.D. is a self-contained, offline-first knowledge and education server designed to provide critical tools, knowledge, and AI capabilities regardless of internet connectivity. It's installable on Debian-based systems and accessible through a browser interface. The project includes features like an AI chat powered by Ollama, an offline information library via Kiwix, an education platform using Khan Academy and Kolibri, and data tools like CyberChef.
It aims to be a comprehensive resource for learning, data analysis, and offline access to vital information.
It aims to be a comprehensive resource for learning, data analysis, and offline access to vital information.
This article details how to set up a local AI assistant within a Linux terminal using Ollama and Llama 3.2. It explains the installation process, necessary shell configurations, and practical applications for troubleshooting and understanding system logs and processes. The author demonstrates how to use the AI to explain command outputs, interpret journal logs, and gain insights into disk usage and running processes, improving efficiency and understanding for both beginners and advanced Linux users. It also discusses the benefits and limitations of this approach.
This article details how to use Ollama to run large language models locally, protecting sensitive data by keeping it on your machine. It covers installation, usage with Python, LangChain, and LangGraph, and provides a practical example with FinanceGPT, while also discussing the tradeoffs of using local LLMs.
This article details the process of running a personal AI assistant on a low-cost microcontroller. It covers the use of Ollama for running large language models (LLMs) locally and MimicLaw for optimizing the model for resource-constrained devices. The author shares their experience with porting and running the models, along with the challenges and solutions encountered.
This article discusses how to effectively prompt local Large Language Models (LLMs) like those run with LM Studio or Ollama. It explains that local LLMs behave differently than cloud-based models and require more explicit and structured prompts for optimal results. The article provides guidance on how to craft better prompts, including using clear language, breaking down tasks into steps, and providing examples.
This post reviews two LLM options in Emacs - Ellama and gptel - and how to set them up, including adding models from OpenRouter and Ollama.
A "Clawdbot" in every row with 400 lines of Postgres SQL. An open-source Postgres extension that introduces a claw data type to instantiate an AI agent - either a simple LLM or an "OpenClaw" agent - as a Postgres column.
This article details the setup and initial testing of Goose, an open-source agent framework, paired with Ollama and the Qwen3-coder model, as a free alternative to Claude Code. It covers the installation process, initial performance observations, and a comparison to cloud-based solutions.
This article provides a comprehensive guide on implementing the Model Context Protocol (MCP) with Ollama and Llama 3, covering practical implementation steps and use cases.
