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  • 1
    LLMSurvey

    LLMSurvey

    A Survey of Large Language Models

    ...By structuring the literature in a navigable format, LLMSurvey allows researchers and practitioners to quickly explore important publications in the field without manually searching through multiple databases.
    Downloads: 0 This Week
    Last Update:
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  • 2
    Google Workspace MCP Server

    Google Workspace MCP Server

    Control Gmail, Google Calendar, Docs, Sheets, Slides, Chat, Forms

    ...The project exposes a wide set of Google services including Gmail, Google Drive, Docs, Sheets, Slides, Calendar, Chat, and other Workspace components as structured tools that an AI system can call programmatically. By acting as a bridge between AI clients and the Google ecosystem, the server enables automated workflows such as searching emails, creating calendar events, retrieving documents, or editing files without leaving the AI environment. The system is designed to operate as a backend service that integrates with AI applications such as coding agents, automation tools, and conversational assistants. Authentication is handled through OAuth-based flows that allow both single-user and multi-user environments while maintaining access control over Workspace data.
    Downloads: 1 This Week
    Last Update:
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  • 3
    yt-fts

    yt-fts

    Search all of YouTube from the command line

    yt-fts, short for YouTube Full Text Search, is an open-source command-line tool that enables users to search the spoken content of YouTube videos by indexing their subtitles. The program automatically downloads subtitles from a specified YouTube channel using the yt-dlp utility and stores them in a local SQLite database. Once indexed, users can perform full-text searches across all transcripts to quickly locate keywords or phrases mentioned within the videos. The tool returns search results...
    Downloads: 0 This Week
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  • 4
    ReAct Prompting

    ReAct Prompting

    Synergizing Reasoning and Acting in Language Models

    ...The project implements the methodology described in the research paper “ReAct: Synergizing Reasoning and Acting in Language Models,” which combines reasoning traces with action-based interactions. Instead of generating answers in a single step, models using the ReAct approach produce intermediate reasoning steps and perform actions such as searching for information or interacting with external tools. This alternating sequence of reasoning, acting, and observing results allows the model to gather additional information and refine its decision-making process during task execution. The framework has been tested on several benchmarks including question answering, fact verification, and interactive decision-making tasks, demonstrating improved performance compared to methods that rely only on reasoning.
    Downloads: 1 This Week
    Last Update:
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