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  • 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
    MCP Hub

    MCP Hub

    An MCP client for Neovim that seamlessly integrates MCP servers

    mcphub.nvim is an MCP (Model Context Protocol) client plugin for Neovim that seamlessly integrates MCP servers into your editing workflow with an intuitive interface for managing, testing, and using MCP servers with your favorite chat plugins. Create your first MCP capable agent you need only 6 lines of code. Works with any langchain-supported LLM that supports tool calling (OpenAI, Anthropic, Groq, LLama etc.) Explore MCP capabilities and generate starter code with the interactive code builder. An MCP client for Neovim that seamlessly integrates MCP servers into your editing workflow with an intuitive interface for managing, testing, and using MCP servers with your favorite chat plugins.
    Downloads: 0 This Week
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  • 2
    CycleGAN

    CycleGAN

    Software that can generate photos from paintings

    ...Rather than requiring matching image pairs between source and target domains (which are often hard or impossible to obtain), CycleGAN learns two mappings — one from domain A to B, and another back from B to A — along with a cycle-consistency loss that encourages the round-trip to reconstruct the original image. This innovation lets the model learn domain-to-domain translations like turning horses into zebras, changing seasons, or transforming photos into paintings, using only collections of images from each domain. The original implementation (in Torch) has since been complemented by other re-implementations (including in PyTorch), but the core idea remains: unpaired image-to-image translation. Because of its flexibility, CycleGAN has become one of the most widely adopted generative models for domain translation tasks.
    Downloads: 0 This Week
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