Showing 2 open source projects for "xray-core"

View related business solutions
  • Vibes don’t ship, Retool does Icon
    Vibes don’t ship, Retool does

    Start from a prompt and build production-ready apps on your data—with security, permissions, and compliance built in.

    Vibe coding tools create cool demos, but Retool helps you build software your company can actually use. Generate internal apps that connect directly to your data—deployed in your cloud with enterprise security from day one. Build dashboards, admin panels, and workflows with granular permissions already in place. Stop prototyping and ship on a platform that actually passes security review.
    Build apps that ship
  • Find Hidden Risks in Windows Task Scheduler Icon
    Find Hidden Risks in Windows Task Scheduler

    Free diagnostic script reveals configuration issues, error patterns, and security risks. Instant HTML report.

    Windows Task Scheduler might be hiding critical failures. Download the free JAMS diagnostic tool to uncover problems before they impact production—get a color-coded risk report with clear remediation steps in minutes.
    Download Free Tool
  • 1
    X's Recommendation Algorithm

    X's Recommendation Algorithm

    Source code for the X Recommendation Algorithm

    The Algorithm is Twitter’s open source release of the core ranking system that powers the platform’s home timeline. It provides transparency into how tweets are selected, prioritized, and surfaced to users, reflecting Twitter’s move toward openness in recommendation algorithms. The repository contains the recommendation pipeline, which incorporates signals such as engagement, relevance, and content features, and demonstrates how they combine to form ranked outputs.
    Downloads: 2 This Week
    Last Update:
    See Project
  • 2
    TextTeaser

    TextTeaser

    TextTeaser is an automatic summarization algorithm

    textteaser is an automatic text summarization algorithm implemented in Python. It extracts the most important sentences from an article to generate concise summaries that retain the core meaning of the original text. The algorithm uses features such as sentence length, keyword frequency, and position within the document to determine which sentences are most relevant. By combining these features with a simple scoring mechanism, it produces summaries that are both readable and informative. Originally inspired by research and earlier implementations, textteaser provides a lightweight solution for summarization without requiring heavy machine learning models. ...
    Downloads: 5 This Week
    Last Update:
    See Project
  • Previous
  • You're on page 1
  • Next