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Изходен канал @clockstackwheels · Post #970 · 4.04

Что ж, надеюсь, все оценили мою шутку на 1 апреля. Разумеется, в том тексте были "мысли", которые либо слишком примитивны для помещения в такую подборку (как, например, про деньги), либо откровенно ошибочны и деструктивны для общества (про автомобили и тиктокеров). Но настоящие мысли в марте мне тоже приходили. Снова про плохой UX, про свиней, про то, какая часть работы наиболее важна, и про непрямые решения проблем. Мне кажется, эта подборка получилась особенно интересной. #thoughts https://telegra.ph/Mysli-za-mart-04-04

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@githubtrending · Post #15283 · 09.11.2025 г., 14:30

#go#a2a#agents#agents_sdk#ai#aiagentframework#gemini#genai#go#llm#mcp#multi_agent_collaboration#multi_agent_systems#sdk#vertex_ai The Agent Development Kit (ADK) for Go is an open-source toolkit that makes it easy to build, test, and deploy smart AI agents using the Go programming language. It lets you create simple or complex agent workflows, use ready-made or custom tools, and run your agents anywhere, especially in cloud environments. With ADK, you get full control, flexibility, and the ability to scale your applications, making it faster and simpler to develop powerful AI solutions for real-world tasks. https://github.com/google/adk-go

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@githubtrending · Post #14693 · 10.05.2025 г., 12:00

#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