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Browse free open source AI Models and projects for Linux below. Use the toggles on the left to filter open source AI Models by OS, license, language, programming language, and project status.

  • Gen AI apps are built with MongoDB Atlas Icon
    Gen AI apps are built with MongoDB Atlas

    The database for AI-powered applications.

    MongoDB Atlas is the developer-friendly database used to build, scale, and run gen AI and LLM-powered apps—without needing a separate vector database. Atlas offers built-in vector search, global availability across 115+ regions, and flexible document modeling. Start building AI apps faster, all in one place.
    Start Free
  • MongoDB Atlas runs apps anywhere Icon
    MongoDB Atlas runs apps anywhere

    Deploy in 115+ regions with the modern database for every enterprise.

    MongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
    Start Free
  • 1
    Claude Code Security Review

    Claude Code Security Review

    An AI-powered security review GitHub Action using Claude

    The claude-code-security-review repository implements a GitHub Action that uses Claude (via the Anthropic API) to perform semantic security audits of code changes in pull requests. Rather than relying purely on pattern matching or static analysis, this action feeds diffs and surrounding context to Claude to reason about potential vulnerabilities (e.g. injection, misconfigurations, secrets exposure, etc). When a PR is opened, the action analyzes only the changed files (diff-aware scanning), generates findings (with explanations, severity, and remediation suggestions), filters false positives using custom prompt logic, and posts comments directly on the PR. It supports configuration inputs (which files/directories to skip, model timeout, whether to comment on the PR, etc). The tool is language-agnostic (it doesn’t need language-specific parsers), uses contextual understanding rather than simplistic rules, and aims to reduce noise with smarter filtering.
    Downloads: 2 This Week
    Last Update:
    See Project
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