Find similar content

Source channel @githubtrending · Post #14806 · Jun 8

#other Here’s a simple summary of the most important information and its benefit to you get enough good sleep, avoid smoking, move your body every day, and eat less sugar—doing just these four can make a big difference. The text also shares tips from neuroscience, like getting sunlight in the morning to help wake up and feel better, and avoiding bright lights at night to sleep well. Eating mostly plants and fermented foods helps your gut and immune system, while timing your meals (like eating in an 8-hour window) can boost your health and even help you live longer. The text also explains how your brain’s chemicals, like dopamine, affect your mood and motivation, and how you can use simple tricks—like taking breaks, trying new things, or doing light exercise—to stay focused and happy. The benefit is that you can feel better, think clearer, and stay healthier by making small, smart changes to your daily routine. https://github.com/zijie0/HumanSystemOptimization

Hashtags

#other

Results

2 similar posts found

Search: #a2a

当前筛选 #a2a清除筛选

GitHub Trends

@githubtrending · Post #15283 · 11/09/2025, 02:30 PM

Find similar View

#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

Hashtags

#go #a2a #agents #agents_sdk #ai #aiagentframework #gemini #genai #llm #mcp #multi_agent_collaboration #multi_agent_systems #sdk #vertex_ai

GitHub Trends

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

Find similar View

#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