2 projects for "reference" with 2 filters applied:

  • AI-generated apps that pass security review Icon
    AI-generated apps that pass security review

    Stop waiting on engineering. Build production-ready internal tools with AI—on your company data, in your cloud.

    Retool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
    Try Retool free
  • Outgrown Windows Task Scheduler? Icon
    Outgrown Windows Task Scheduler?

    Free diagnostic identifies where your workflow is breaking down—with instant analysis of your scheduling environment.

    Windows Task Scheduler wasn't built for complex, cross-platform automation. Get a free diagnostic that shows exactly where things are failing and provides remediation recommendations. Interactive HTML report delivered in minutes.
    Download Free Tool
  • 1
    VibeSDK

    VibeSDK

    Open source full-stack AI vibe coding platform & web app generator

    VibeSDK is an open source “vibe coding” platform. VibeSDK is a project built by Cloudflare. It provides a full-stack reference implementation of an AI-driven system. Users describe the application they want in natural language, and the system generates, previews, and deploys the resulting web app. It uses Cloudflare’s infrastructure (Workers, Containers, sandboxes). It can run untrusted code safely, provide live previews, and deploy apps at scale. VibeSDK gives you the exact methodology, tools, and confidence to turn your ideas into revenue-generating products, faster than you thought possible. ...
    Downloads: 5 This Week
    Last Update:
    See Project
  • 2
    Sec-Context

    Sec-Context

    AI Code Security Anti-Patterns distilled from 150+ sources

    Sec-Context is a curated security research project that distills common code anti-patterns and vulnerabilities that generative AI tends to produce, presenting them as a comprehensive set of examples and secure alternatives that can be used to train or guide AI assistants and reviewers toward safer code generation. It compiles insights from over 150 industry and academic sources into structured reference documents that outline real-world security problems such as hardcoded secrets, SQL injection, cross-site scripting, command injection, weak password storage, and other frequent issues that occur when code is auto-generated without context of best practices. Each anti-pattern is paired with a secure coding alternative and explanation, offering educational value for both humans and automated review agents designed to flag or correct unsafe patterns.
    Downloads: 1 This Week
    Last Update:
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