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Source channel @githubtrending · Post #14693 · May 10

#jupyter_notebook#a2a#agentic_ai#dapr#dapr_pub_sub#dapr_service_invocation#dapr_sidecar#dapr_workflow#docker#kafka#kubernetes#langmem#mcp#openai#openai_agents_sdk#openai_api#postgresql_database#rabbitmq#rancher_desktop#redis#serverless_containers The Dapr Agentic Cloud Ascent (DACA) design pattern helps you build powerful, scalable AI systems that can handle millions of AI agents working together without crashing. It uses Dapr technology with Kubernetes to efficiently manage many AI agents as lightweight virtual actors, ensuring fast response, reliability, and easy scaling. You can start small using free or low-cost cloud tools and grow to planet-scale systems. The OpenAI Agents SDK is recommended for beginners because it is simple, flexible, and gives you good control to develop AI agents quickly. This approach saves costs, avoids vendor lock-in, and supports resilient, event-driven AI workflows, making it ideal for developers aiming to create advanced, cloud-native AI applications[1][2][3][4]. https://github.com/panaversity/learn-agentic-ai

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djangoproject

@djangoproject · Post #261 · 02/16/2017, 06:56 AM

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http://www.giantflyingsaucer.com/blog/?p=5557 In spring 2014 Python 3.4 shipped a provisional package (#asyncio) which according to the docs “provides infrastructure for writing single-threaded #concurrent code using #coroutines, #multiplexing I/O access over #sockets and other resources, running network clients and servers, and other related primitives“. I can’t possibly cover everything in this article but I can introduce some of the things you can do with it. As per my New’s Years resolution I’ll be building these #examples using Python 3.4.2 (Asyncio has been ported back to Python 3.3 now as well).

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#asyncio #concurrent #coroutines #multiplexing #sockets #examples