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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
Search: #xmlrpc
@djangoproject · Post #408 · 08/10/2017, 10:42 AM
https://docs.python.org/3/library/xmlrpc.html #XML-#RPC is a #Remote_Procedure_Call method that uses XML passed via #HTTP as a transport. With it, a #client can call methods with parameters on a remote server (the #server is named by a URI) and get back structured data. #xmlrpc is a package that collects server and client modules implementing XML-RPC. The modules are: xmlrpc.client xmlrpc.server