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Source channel @githubtrending · Post #14985 · Jul 22

#c_lang#cuda#cuda_driver_api#cuda_kernels#cuda_opengl You can use the CUDA Samples from NVIDIA to learn and test CUDA Toolkit 12.9 features by downloading them from GitHub or as a ZIP file. These samples show how to use CUDA for GPU programming, including utilities, concepts, libraries, and performance optimization. You build them with CMake on Linux, Windows, or Tegra devices, and can run tests automatically with a provided Python script. This helps you understand CUDA programming, debug GPU code, and optimize your applications for better performance on NVIDIA GPUs. It’s a practical way to develop and improve GPU-accelerated software efficiently. https://github.com/NVIDIA/cuda-samples

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GitHub Trends

@githubtrending · Post #14663 · 05/02/2025, 12:30 PM

#python#agents#ai#ai_agents#api#developer_tools#function_calling#integration#llm#mcp#oauth2#open_source#permissions#tools ACI.dev is an open-source platform that helps build AI agents by providing easy access to over 600 tools. It simplifies authentication and tool integration, allowing AI agents to work with many services like Google Calendar and Slack without needing separate setups. This platform offers multi-tenant authentication, flexible access methods, and natural language permissions, making it easier to manage and secure AI agent capabilities. It's open-source and works with any framework, which means you can build AI agents without worrying about vendor lock-in. https://github.com/aipotheosis-labs/aci

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@githubtrending · Post #15214 · 10/12/2025, 11:30 AM

#python#agents#ai#ai_agents#api#developer_tools#discord#function_calling#integration#llm#mcp#mcp_client#mcp_server#oauth2#open_source Klavis AI helps developers connect AI tools to other services like GitHub, Gmail, and Slack easily. It offers hosted servers that handle authentication and client code automatically, making it simpler to integrate AI with various platforms. This saves time and effort by eliminating the need for custom authentication management and client library maintenance. Users can quickly set up and scale their AI applications without worrying about complex integrations, making it easier to deploy AI-powered workflows securely and efficiently. https://github.com/Klavis-AI/klavis

GitHub Trends

@githubtrending · Post #15556 · 03/12/2026, 12:30 PM

#typescript#ai#ai_agents#coding#deno#embeddings#insforge#nextjs#oauth2#pgvector#postgresql#realtime#vectors#websockets InsForge is an open-source backend platform for AI coding agents, offering easy auth, Postgres database, S3 storage, edge functions, and model gateway via a simple semantic layer. Agents fetch context, configure services, and inspect state to build full-stack apps quickly. Set up locally with Docker or use cloud deploys. It boosts agent accuracy 1.7x, speed 1.6x, and cuts tokens 30% vs. rivals, letting you prototype and ship AI-driven apps faster with less hassle and cost. https://github.com/InsForge/InsForge

GitHub Trends

@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