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FitnessQuest is a CRUD-based web app developed utilizing FastAPI for the backend and React for the frontend. It aims to provide users the ability to organize and track their workouts and exercises. The app implements RESTful API endpoints with pydantic data validation, incorporates user authentication through JWT, supports PostgreSQL for data storage, and ensures error handling.
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JeissonWee / API - LS
OtherAPI LS is a secure and efficient API for delivering resources, such as images or videos. It uses a user-based encryption and token model to protect the privacy of your data. Each token is a key to unlock a file, but this key changes its key every 5 uses. This guarantees that only the authorized user can access the resources.
API LS is built on a microservices architecture that makes it fault-tolerant. In case of failure, the service sends an email or Telegram message to the administrator.
API LS is also prepared to optimize response times. It uses AI to predict peak traffic times and prepare the service accordingly.
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digitalvital / backend
MIT LicenseBackend for the Seniorenportal https://portal.digital-vital.eu/ and its variants
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REST Countries challenge from FrontendMentor.io
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Rest API server for WLP/_DevTask app
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Open source Windesheim API by HBO-ICT students
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API using the Go programming language, following the Clean Architecture
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The project is a real estate and car property management system. The core idea is to facilitate property buying and selling, eliminating the need for real estate offices. i used Ml Kit(Object detection and tracking)to prevent fake data ,and lottiefiles for animations
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The project is a real estate and car property management system. The core idea is to facilitate property buying and selling, eliminating the need for real estate offices.
The system incorporates the following key features:
Users can't access the system without confirming their identity and account, ensuring the credibility of users by obtaining essential data. The system is divided to ease usability, with dedicated sections for property (houses and lands) and cars, facilitating user search. To counter fake data, we employed Artificial Intelligence and Machine Learning; any images not depicting real estate or cars are automatically removed. We utilized Google Maps for precise user location. For data browsing in the property and car sections, we implemented pagination to retrieve data from the server in batches, enhancing user experience. Advanced filtering and search capabilities are available. Real-time communication is facilitated through in-system chat or direct calls using mobile numbers. Data retrieval isn't random; it returns favorites and similar items for the user, along with the highest-rated properties. Users can rate properties, add them to favorites, and view the number of likes for each post. In-app notifications for messages and likes are present. Each user has a personal profile to manage favorites and posts; sellers can delete their posts after selling. Users can modify their data. Password recovery is enabled via Gmail verification code. The system supports both Arabic and English languages. Account deletion is possible.Updated -
Geoff Simmons / blinkbeern
BSD 2-Clause "Simplified" Licenseblinkbeern is an application for the Raspberry Pi PicoW microcontroller that drives SK6812 LED strips, controlled by a REST API and web interface.
blinkbeern is "Platt für Blinkenlights".
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This Project contain Contact Person App
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This project contain contact person app
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A blogging site backend using C++, Oat++ and POCO, a RealWorld implementation.
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This project contain Mobile App for Distribution Company
Figma Link https://www.figma.com/proto/s8cUK2iFfaPeibF6vPvwih/CSI---Chat?page-id=25%3A113&node-id=72-433&viewport=283%2C48%2C0.15&scaling=scale-down&starting-point-node-id=72%3A433 https://xd.adobe.com/view/208b3413-4399-427b-a652-8a7cecd67e75-75d8/grid
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