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.
This article provides a hands-on coding guide to explore nanobot, a lightweight personal AI agent framework. It details recreating core subsystems like the agent loop, tool execution, memory persistence, skills loading, session management, subagent spawning, and cron scheduling. The tutorial uses OpenAI’s gpt-4o-mini and demonstrates building a multi-step research pipeline capable of file operations, long-term memory storage, and concurrent background tasks. The goal is to understand not just how to *use* nanobot, but how to *extend* it with custom tools and architectures.
This article details a tutorial on building cybersecurity AI agents using the CAI framework. It guides readers through setting up the environment with Colab, loading API keys, and creating base agents. The tutorial progresses to advanced capabilities, including custom function tools, multi-agent handoffs, agent orchestration, input guardrails, and dynamic tools.
It demonstrates how CAI transforms Python functions and agent definitions into flexible cybersecurity workflows capable of reasoning, delegating, validating, and responding in a structured way. The article also showcases CTF-style pipelines, multi-turn context handling, and streaming responses, offering a comprehensive overview of CAI's potential for security applications.
It demonstrates how CAI transforms Python functions and agent definitions into flexible cybersecurity workflows capable of reasoning, delegating, validating, and responding in a structured way. The article also showcases CTF-style pipelines, multi-turn context handling, and streaming responses, offering a comprehensive overview of CAI's potential for security applications.
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.
sudo for AI agents - allow, deny, or ask before any tool runs. AI agents run tools autonomously, but some calls are too risky to run unchecked. agentpriv gives you a permission layer to control what goes through.
This guide explains how to use tool calling with local LLMs, including examples with mathematical, story, Python code, and terminal functions, using llama.cpp, llama-server, and OpenAI endpoints.
LLM Council works together to answer your hardest questions. A local web app that uses OpenRouter to send queries to multiple LLMs, have them review/rank each other's work, and finally a Chairman LLM produces the final response.
This page details the topic namers available in Turftopic, allowing automated assignment of human-readable names to topics. It covers Large Language Models (local and OpenAI), N-gram patterns, and provides API references for the `TopicNamer`, `LLMTopicNamer`, `OpenAITopicNamer`, and `NgramTopicNamer` classes.
LLM 0.26 introduces tool support, allowing LLMs to access and utilize Python functions as tools. The article details how to install, configure, and use these tools with various LLMs like OpenAI, Anthropic, Gemini, and Ollama models, including examples with plugins and ad-hoc functions. It also discusses the implications for building 'agents' and future development plans.
PaperCoder is a multi-agent LLM system that transforms scientific papers into code repositories through a three-stage pipeline: planning, analysis, and code generation. It aims to create faithful, high-quality implementations.
