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Source channel @githubtrending · Post #15271 · Nov 5

#cplusplus#arm#baidu#deep_learning#embedded#fpga#mali#mdl#mobile#mobile_deep_learning#neural_network Paddle Lite is a lightweight, high-performance deep learning inference framework designed to run AI models efficiently on mobile, embedded, and edge devices. It supports multiple platforms like Android, iOS, Linux, Windows, and macOS, and languages including C++, Java, and Python. You can easily convert models from other frameworks to PaddlePaddle format, optimize them for faster and smaller deployment, and run them with ready-made examples. This helps you deploy AI applications quickly on various devices with low memory use and fast speed, making it ideal for real-time, resource-limited environments. It also supports many hardware accelerators for better performance. https://github.com/PaddlePaddle/Paddle-Lite

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@githubtrending · Post #14904 · 07/03/2025, 12:00 PM

#go#ai_assistant#ai_generated_code#cloud_native#code_generation#custom_templates#developer_tools#development_framework#gin#go_sponge#golang#grpc#grpc_gateway#low_code#microservice#protobuf#restful_api#sponge#web Sponge is a powerful Go development framework that helps you quickly build backend services like RESTful APIs and microservices with minimal coding. It generates modular Go code automatically by parsing SQL, Protobuf, and JSON files, letting you create complete backend projects through a simple web interface without complex commands. Sponge supports custom templates and integrates AI assistants (like ChatGPT) to help write business logic, greatly speeding up development and reducing repetitive work. It also offers full support for testing, API docs, and deployment, making your project more stable, efficient, and easier to maintain. This saves you time and improves code quality. https://github.com/go-dev-frame/sponge

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@githubtrending · Post #14691 · 05/10/2025, 12:00 AM

#csharp#architecture#aspnetcore#clean_architecture#cqrs#ddd#dotnet#dotnetcore#event_driven_architecture#event_sourcing#kubernetes#masstransit#messaging#microservice#microservices#oauth2#opentelemetry#software_architecture#software_design#software_engineering#vertical_slice_architecture Migrating from a monolithic architecture to a cloud-native microservices architecture offers several benefits. It improves scalability, allowing different parts of the application to grow independently. This approach also enhances reliability by isolating faults, so if one service fails, others continue to work. Additionally, microservices enable faster deployment and updates, as each service can be developed and deployed separately. This flexibility allows teams to use the best technology for each service, making development more efficient and agile[2][3][5]. https://github.com/meysamhadeli/monolith-to-cloud-architecture