Showing 13 open source projects for "summarize"

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  • 1
    ChatPaper

    ChatPaper

    Use ChatGPT to summarize the arXiv papers

    ChatPaper is an AI-assisted research toolkit designed to accelerate academic reading and writing workflows. It uses language models to summarize papers and help researchers screen literature more efficiently. The project also supports full-paper translation, writing improvement, peer-review analysis, and responses to reviewer comments. It can retrieve recent arXiv papers based on keywords and time ranges for topic-focused research. Related tools can generate literature reviews, suggest paper titles, and turn papers into structured notes. ...
    Downloads: 3 This Week
    Last Update:
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  • 2
    WikiChat

    WikiChat

    WikiChat is an improved RAG

    WikiChat is a chatbot framework designed to interactively retrieve and summarize Wikipedia information, allowing users to ask questions and get context-aware responses?
    Downloads: 2 This Week
    Last Update:
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  • 3
    Agent Reach

    Agent Reach

    Give your AI agent eyes to see the entire internet

    ...Agent-Reach also supports modern agent environments by exposing capabilities that can be connected into larger AI workflows. Its main value is making scattered web platforms easier for agents to query, inspect, and summarize from the command line.
    Downloads: 63 This Week
    Last Update:
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  • 4
    Shapash

    Shapash

    Explainability and Interpretability to Develop Reliable ML models

    Shapash is a Python library dedicated to the interpretability of Data Science models. It provides several types of visualization that display explicit labels that everyone can understand. Data Scientists can more easily understand their models, share their results and easily document their projects in an HTML report. End users can understand the suggestion proposed by a model using a summary of the most influential criteria.
    Downloads: 6 This Week
    Last Update:
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    Paessler - Monitor Your Whole Network in Minutes

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  • 5
    VideoRAG

    VideoRAG

    "VideoRAG: Chat with Your Videos

    VideoRAG is a retrieval-augmented generation (RAG) framework tailored for video content that enables AI systems to answer questions, summarize, and reason over long videos by combining visual embeddings with contextual search. The system works by first breaking video into clips, extracting visual and audio-textual features, and indexing them into embeddings, then using an LLM with a retriever to pull relevant segments on demand. When a user query is received, VideoRAG locates semantically relevant moments in the video using the embedding index, retrieves associated clips or transcripts, and feeds them to a generative model to produce accurate, grounded answers or summaries. ...
    Downloads: 0 This Week
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  • 6
    how-to-optim-algorithm-in-cuda

    how-to-optim-algorithm-in-cuda

    How to optimize some algorithm in cuda

    ...These examples show how different optimization techniques influence performance on modern GPU hardware and allow readers to experiment with real implementations. The repository also contains extensive learning notes that summarize CUDA programming concepts, GPU architecture details, and performance engineering strategies.
    Downloads: 0 This Week
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  • 7
    myGPTReader

    myGPTReader

    AI Slack bot for reading, summarizing, and chatting with content

    myGPTReader is an AI-powered Slack bot designed to help users read, summarize, and interact with various types of digital content through conversational interfaces. It enables users to quickly understand web pages, documents, and even video content by transforming them into interactive discussions rather than static reading experiences. myGPTReader supports a wide range of file formats, including eBooks, PDFs, and text-based documents, making it flexible for both casual and professional use cases. ...
    Downloads: 0 This Week
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  • 8
    Pal

    Pal

    A personal context-agent that learns how you work

    ...Instead of acting as a simple chatbot, Pal continuously builds a structured database of a user’s knowledge and context so it can answer questions, recall information, and assist with future tasks more effectively. The agent can perform web research, summarize information, and store insights so that useful discoveries are not lost across conversations or sessions. Over time, the agent learns from interactions, remembers patterns that worked well, and applies those learnings to similar tasks in the future, allowing it to improve without requiring additional model training.
    Downloads: 0 This Week
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  • 9
    web-eval-agent MCP Server

    web-eval-agent MCP Server

    An MCP server that autonomously evaluates web applications

    web-eval-agent is a Model Context Protocol (MCP) server that spins up a browser-use–capable debugging agent to autonomously run and evaluate web apps straight from your editor. It’s positioned as a “let the coding agent debug itself” companion: the agent launches the app, navigates flows, captures evidence, and iterates on failures without manual copy-pasting of logs. The repository focuses on developer ergonomics, exposing typed MCP tools so clients like Claude Desktop can start sessions,...
    Downloads: 0 This Week
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  • 10
    cracking-the-data-science-interview

    cracking-the-data-science-interview

    A Collection of Cheatsheets, Books, Questions, and Portfolio

    ...The project organizes content across many fundamental areas of data science, including statistics, probability, SQL, machine learning, and deep learning. It includes cheat sheets that summarize important technical concepts commonly discussed during technical interviews. The repository also provides links to recommended books, tutorials, practice platforms, and blog posts that help learners strengthen their theoretical and practical skills. In addition to conceptual study materials, the project includes interview question banks and case study prompts that simulate real hiring scenarios. ...
    Downloads: 0 This Week
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  • 11
    ktrain

    ktrain

    ktrain is a Python library that makes deep learning AI more accessible

    ktrain is a Python library that makes deep learning and AI more accessible and easier to apply. ktrain is a lightweight wrapper for the deep learning library TensorFlow Keras (and other libraries) to help build, train, and deploy neural networks and other machine learning models. Inspired by ML framework extensions like fastai and ludwig, ktrain is designed to make deep learning and AI more accessible and easier to apply for both newcomers and experienced practitioners. With only a few lines...
    Downloads: 6 This Week
    Last Update:
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  • 12
    distribution-is-all-you-need

    distribution-is-all-you-need

    The basic distribution probability Tutorial for Deep Learning Research

    ...The material highlights relationships such as conjugate priors and special-case distributions. Examples connect probability functions with machine learning concepts including binary and multiclass cross-entropy. An overview image and presentation summarize the distribution families and their connections for quick reference.
    Downloads: 0 This Week
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  • 13
    TexLexAn is an open source text analyser for Linux, able to estimate the readability and reading time, to classify and summarize texts. It has some learning abilities and accepts html, doc, pdf, ppt, odt and txt documents. Written in C and Python.
    Downloads: 0 This Week
    Last Update:
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