#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
Lookonchain | ꘜ
Whales are accumulating $BGB recently.
0x8900 withdrew 192,668 $BGB($936K) from #Bitget over the past 2 months.
0x171D withdrew 30,607 $BGB($134K) from #Bitget 2 days ago.
0x7C9C withdrew 20,980 $BGB($102K) from #Bitget over the past 3 months.
Notably, #Bitget has burned a total of 860M $BGB($5.25B) over the past 8 months, reducing the total supply by 43%.
https://intel.arkm.com/explorer/address/0x89006C3aADfF87c5113b835660E3459C6Ad61F16
https://intel.arkm.com/explorer/address/0x171D1285a9a8De3f16d4c45706d4E2F4A5C9e175
https://intel.arkm.com/explorer/address/0x7C9C4f9046ba2173fae539FE62eEFAb1aBAD1523