Showing 3 open source projects for "mpeg-4"

View related business solutions
  • Top-Rated Free CRM Software Icon
    Top-Rated Free CRM Software

    216,000+ customers in over 135 countries grow their businesses with HubSpot

    HubSpot is an AI-powered customer platform with all the software, integrations, and resources you need to connect your marketing, sales, and customer service. HubSpot's connected platform enables you to grow your business faster by focusing on what matters most: your customers.
  • Red Hat Ansible Automation Platform on Microsoft Azure Icon
    Red Hat Ansible Automation Platform on Microsoft Azure

    Red Hat Ansible Automation Platform on Azure allows you to quickly deploy, automate, and manage resources securely and at scale.

    Deploy Red Hat Ansible Automation Platform on Microsoft Azure for a strategic automation solution that allows you to orchestrate, govern and operationalize your Azure environment.
  • 1
    CodiumAI PR-Agent

    CodiumAI PR-Agent

    AI-Powered tool for automated pull request analysis

    CodiumAI PR-Agent is an open-source tool aiming to help developers review pull requests faster and more efficiently. It automatically analyzes the pull request and can provide several types of commands. See the Usage Guide for instructions how to run the different tools from CLI, online usage, Or by automatically triggering them when a new PR is opened. You can try GPT-4 powered PR-Agent, on your public GitHub repository, instantly. Just mention @CodiumAI-Agent and add the desired command...
    Downloads: 2 This Week
    Last Update:
    See Project
  • 2
    Mentat

    Mentat

    Mentat - The AI Coding Assistant

    ... to context, so be careful not to exceed the GPT-4 token context limit.
    Downloads: 1 This Week
    Last Update:
    See Project
  • 3
    SWE-agent

    SWE-agent

    SWE-agent takes a GitHub issue and tries to automatically fix it

    SWE-agent turns LMs (e.g. GPT-4) into software engineering agents that can resolve issues in real GitHub repositories. On the SWE-bench, the SWE-agent resolves 12.47% of issues, achieving state-of-the-art performance on the full test set. We accomplish our results by designing simple LM-centric commands and feedback formats to make it easier for the LM to browse the repository, and view, edit, and execute code files. We call this an Agent-Computer Interface (ACI).
    Downloads: 0 This Week
    Last Update:
    See Project
  • Previous
  • You're on page 1
  • Next