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Source channel @githubtrending · Post #15024 · Aug 2

#java#adversary_emulation#adversary_exposure_validation#aev#attack_simulation#breach_simulator#cybersecurity#purple_team OpenBAS is a free, open-source platform that helps you plan and run cyberattack simulations to find security weaknesses in your organization. It supports teamwork, real-time monitoring, and detailed feedback, letting you test defenses against real-world threats using up-to-date intelligence from OpenCTI. You can simulate attacks through emails, SMS, social media, and more, making your training realistic and comprehensive. OpenBAS offers both a Community Edition and a more advanced Enterprise Edition. It’s easy to install with Docker or manually, and you can try it online before using it. This helps you improve your cybersecurity by practicing and identifying gaps before real attacks happen. https://github.com/OpenBAS-Platform/openbas

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@githubtrending · Post #14693 · 05/10/2025, 12:00 PM

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#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