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  • MongoDB Atlas runs apps anywhere Icon
    MongoDB Atlas runs apps anywhere

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

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    Demo Series - Small Business Backup By Veeam

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  • 1
    Reflex Dev

    Reflex Dev

    Web apps in pure Python

    Reflex is a Python framework for building full-stack web apps entirely in Python—without writing JavaScript for the frontend. It provides fast live reloads, built-in state management, deployment tooling, and optional AI-powered scaffolding to accelerate development.
    Downloads: 16 This Week
    Last Update:
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  • 2
    Django Ninja

    Django Ninja

    Fast, Async-ready, Openapi, type hints based framework

    Django Ninja is a web framework for building APIs with Django and Python 3.6+ type hints. Designed to be easy to use and intuitive. Very high performance thanks to Pydantic and async support. Type hints and automatic docs lets you focus only on business logic. Based on the open standards for APIs: OpenAPI (previously known as Swagger) and JSON Schema. Django friendly (obviously) has good integration with the Django core and ORM. Used by multiple companies on live projects.
    Downloads: 1 This Week
    Last Update:
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  • 3
    AI Chatbot Framework

    AI Chatbot Framework

    Python chatbot framework with Natural Language Understanding

    Building a chatbot can sound daunting, but it’s totally doable. AI Chatbot Framework is an AI powered conversational dialog interface built in Python. With this tool, it’s easy to create Natural Language conversational scenarios with no coding efforts whatsoever. The smooth UI makes it effortless to create and train conversations to the bot and it continuously gets smarter as it learns from conversations it has with people. AI Chatbot Framework can live on any channel of your choice (such as...
    Downloads: 1 This Week
    Last Update:
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  • 4
    SIA

    SIA

    AI framework to autonomously improve the performance of any AI system

    SIA is a self-improving AI framework designed to improve the performance of models or agents on benchmark tasks. It uses an iterative loop where a meta-agent creates or updates a task-specific target agent, while a feedback agent studies results and proposes improvements. The framework can refine both the harness around the task and the agent implementation itself. It is aimed at research and experimentation across tasks such as machine learning benchmarks, legal classification, code...
    Downloads: 0 This Week
    Last Update:
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