#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
#go#blob_storage#cloud_drive#distributed_file_system#distributed_storage#distributed_systems#erasure_coding#fuse#hadoop_hdfs#hdfs#kubernetes#object_storage#posix#replication#s3#s3_storage#seaweedfs#tiered_file_system
SeaweedFS is a fast, simple, and highly scalable distributed file system designed to store billions of files and serve them quickly, especially small files. It uses a master server to manage volumes on volume servers, which handle file data and metadata, enabling very fast file access with minimal disk reads. It supports features like replication, erasure coding, cloud integration for elastic storage, and compatibility with many metadata stores and APIs including Amazon S3. This means you get efficient, cost-effective storage with fast access, easy scaling, and flexible deployment options for large-scale file storage needs.
https://github.com/seaweedfs/seaweedfs