FlowiseAI

Pricing model
GitHub
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Flowise is a free open-source visual interface that enables users to create personalized language-based models (LLMs) with LangchainJS, developed in Node Typescript/Javascript. It is available for both business and personal use and can be easily installed with minimal commands. Additionally, it offers Docker support, and users can reach out to the Flowise team via Discord, Twitter, or email.

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Free
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Rose is a cloud-based data solution crafted to aid users in discovering, interacting with, visualizing, and sharing data. It facilitates the integration of both external and internal data, allowing for permission settings for internal teams or external partners. Additionally, it offers infrastructure tools for cleaning, analyzing, and visualizing data within their web application and includes a data marketplace for previewing, purchasing, and selling data.
Paid
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Hatch Canvas is an online creative platform that lets users craft interactive web content using easy-to-use drawing tools. Its main feature, Hatch Draw, empowers anyone to build websites, apps, games, and interactive narratives by drawing directly on a browser-based canvas, eliminating the need for coding. The platform converts hand-drawn elements into dynamic web objects complete with physics, animations, and interactive functionality. Creators can effortlessly publish their creations as live web pages, making it perfect for artists, designers, and individuals without technical expertise who wish to express their visual concepts online with a unique, creative flair beyond what traditional web development offers.
Price Unknown / Product Not Launched Yet
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Trag is an AI-driven code review tool aimed at helping engineering teams save time and enhance code quality. It enables users to establish custom rules using natural language, which allows Trag to automatically review pull requests, detect bugs, and propose corrections with AI-driven autofixes without directly altering the codebase. It supports multiple repositories and ensures adherence to best practices like memory management, DRY principles, and secure coding. Teams may choose to utilize Trag to optimize their review workflow, uphold coding standards, and decrease the time developers dedicate to reviewing code, allowing them to concentrate more on product development.