🎯 lobehub/lobehub landed on trending. Worth a proper look.

🔗 https://github.com/lobehub/lobehub
📝 The ultimate space for work and life — to find, build, and collaborate with agent teammates that grow with you. We are taking agent harness to the next level — enabling multi-agent collaboration, effortless agent team design, and introducing agents as the unit of work interaction.
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LobeHub is a revolutionary platform that enables humans and agents to co-evolve, transforming the way we work and interact with AI. The platform offers a range of key features, including the ability to create, collaborate, and evolve with agent teammates that grow with you.

To get started, users can join the LobeHub community and explore the various features and tools available. The platform provides a unified intelligence system, allowing users to access any model and modality, and a library of over 10,000+ skills and MCP-compatible plugins.

Some of the technical highlights of LobeHub include its Agent Builder, Agent Groups, and Personal Memory features, which enable users to build, collaborate, and learn with their agent teammates. The platform also supports local large language model (LLM) support, model visual recognition, and TTS & STT voice conversation.

LobeHub is designed for a wide range of users, from professional developers to enthusiastic users, and offers a user-friendly product ecosystem that is open, transparent, and constantly evolving.

In a nutshell, LobeHub is the future of human-AI collaboration - and it's here to revolutionize the way we work and interact with AI.

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🧠 Channel: https://t.iss.one/GithubRe

🚀 Meet datawhalechina/hello-agents: a gem from today's GitHub trending list.

🔗 https://github.com/datawhalechina/hello-agents
📝 📚 《从零开始构建智能体》——从零开始的智能体原理与实践教程
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Hello-Agents is a comprehensive open-source tutorial on building AI agents from scratch. The project aims to provide a systematic guide to constructing intelligent systems, from basic theories to practical applications.

Key features of the project include:
- A structured approach to learning, covering the fundamentals of AI agents, language models, and their applications
- hands-on exercises and projects to help learners gain practical experience
- A community-driven approach, with opportunities for contributors to share their knowledge and experiences

The project is suitable for AI developers, software engineers, students, and self-learners with basic Python programming skills and a conceptual understanding of large language models.

The project's technical highlights include:
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constructing AI agents using popular frameworks and tools

- Implementing memory and retrieval systems, context engineering, and communication protocols
- Training and evaluating AI models using Agentic-RL and other advanced techniques

By following the tutorial, learners can gain a deep understanding of AI agents and their applications, and develop the skills to construct their own intelligent systems.

In short, Hello-Agents is your one-stop-shop for learning AI agent development - so get building, and become an AI agent master!

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🧠 Channel: https://t.iss.one/GithubRe

🌟 flutter/skills caught my eye on GitHub Trending today.

🔗 https://github.com/flutter/skills
📝 No description.
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The Flutter Skills repository provides a collection of skills for happy path Flutter app development workflows. These skills offer tailored instructions to reduce mistakes and ensure agents complete tasks following best practices. You can install all skills into your project using the command npx skills add flutter/skills --skill '*' --agent universal. The available skills include flutter-add-integration-test, flutter-add-widget-preview, and flutter-apply-architecture-best-practices, among others. Each skill has a specific use case, such as adding integration tests or creating interactive widget previews. To update the skills, run the command npx skills update. The repository is maintained by the Flutter team and is suitable for developers looking to streamline their workflow. Take your Flutter development to the next level with these skills - automate repetitive tasks and focus on what matters.

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🧠 Channel: https://t.iss.one/GithubRe

🌟 Augani/openreel-video caught my eye on GitHub Trending today.

🔗 https://github.com/Augani/openreel-video
📝 OpenReel Video - Professional browser-based video editor. Open source CapCut alternative. 100% browser-based, no installation, no cloud uploads, no watermarks.
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OpenReel Video is a free, open-source, browser-based video editor that runs entirely client-side, offering professional-grade video editing without the need for expensive software or cloud processing. With key features like a multi-track timeline, keyframe animations, color grading, and audio effects, it's an alternative to popular video editing software. Try it live at openreel.video or run it locally by cloning the repository and installing dependencies.

The editor is built with React, TypeScript, WebCodecs, and WebGPU for smooth 4K editing and fast exports. It's suitable for anyone looking for a free, user-friendly, and feature-rich video editing solution.

Takeaway: OpenReel Video is revolutionizing video editing by making it free, open-source, and accessible to everyone, forever.

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🧠 Channel: https://t.iss.one/GithubRe

🔥 anthropics/financial-services is trending — and it deserves your attention.

🔗 https://github.com/anthropics/financial-services
📝 No description.
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The Claude for Financial Services GitHub repository provides a comprehensive set of tools and templates for financial services workflows, including investment banking, equity research, private equity, and wealth management. The repository offers reference agents, skills, and data connectors that can be installed as Claude Cowork plugins or deployed via the Claude Managed Agents API.

Key features include:
- Agents: named, end-to-end workflow agents (e.g., Pitch Agent, Market Researcher) that can be installed and tuned to a firm's specific needs
- Vertical plugins: skill and command bundles for specific financial services verticals (e.g., investment banking, equity research)
- Data connectors: integrations with various data providers (e.g., Daloopa, Morningstar, S&P Global) via the Model Context Protocol (MCP)

To get started, users can install the repository as a Claude Cowork plugin or deploy it via the Claude Managed Agents API. The repository also includes a Claude for Microsoft 365 install tool for provisioning the Claude add-in in a customized Microsoft 365 environment.

The target audience for this repository includes financial services professionals, developers, and IT administrators looking to automate and streamline their workflows using AI-powered tools.

In short, the Claude for Financial Services repository is a powerful toolkit for financial services firms looking to leverage AI and automation to boost productivity and efficiency - and the best part is, it's all customizable to how your firm works.

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🧠 Channel: https://t.iss.one/GithubRe

🔍 Deep-diving into bytedance/UI-TARS-desktop — fresh off the trending list.

🔗 https://github.com/bytedance/UI-TARS-desktop
📝 The Open-Source Multimodal AI Agent Stack: Connecting Cutting-Edge AI Models and Agent Infra
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The UI-TARS-desktop GitHub repository offers a powerful desktop application that leverages the UI-TARS model to provide a native GUI Agent. This application is part of the TARS* multimodal AI Agent stack, which includes Agent TARS and UI-TARS-desktop. Agent TARS brings the power of GUI Agent and Vision into your terminal, computer, browser, and product, primarily shipping with a CLI and Web UI for usage.

Key features of Agent TARS include a one-click out-of-the-box CLI, hybrid browser agent, event stream, and MCP integration. The UI-TARS-desktop application provides a desktop GUI Agent based on the UI-TARS model, supporting local and remote computer operators, as well as browser operators.

To get started with Agent TARS, you can launch it with npx @agent-tars/cli@latest or install it globally with npm install @agent-tars/cli@latest -g. For more information, you can visit the comprehensive Quick Start guide.

UI-TARS-desktop is designed for users looking for a seamless and intelligent desktop experience, while Agent TARS is ideal for developers and power users who want to leverage the power of multimodal AI Agents.

In a nutshell, UI-TARS-desktop and Agent TARS are revolutionizing the way we interact with our computers and browsers, making it more human-like and efficient - experience the future of AI-powered desktop applications today!

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🧠 Channel: https://t.iss.one/GithubRe

🌟 rohitg00/agentmemory caught my eye on GitHub Trending today.

🔗 https://github.com/rohitg00/agentmemory
📝 #1 Persistent memory for AI coding agents based on real-world benchmarks
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The agentmemory GitHub repository provides a persistent memory solution for AI coding agents, allowing them to recall previous interactions and maintain context. The key features of agentmemory include auto-capture of interactions, retrieval accuracy of 95.2%, and support for multiple agents through MCP and REST API.

To use npx @agentmemory/agentmemory, simply run the command, and the agent will start capturing interactions and maintaining context. This eliminates the need for re-explaining previous interactions, making it a valuable tool for developers.

The technical highlights of agentmemory include its use of BM25, vector, and graph search algorithms, as well as its support for leases, signals, and SQLite storage. The repository also includes benchmarks and comparisons with other competitors, demonstrating the effectiveness of agentmemory.

agentmemory is designed for developers who work with AI coding agents, particularly those who use Claude Code, Cursor, Gemini CLI, Codex CLI, and other MCP clients. With its easy-to-use interface and robust features, agentmemory is an essential tool for anyone looking to improve their productivity and efficiency when working with AI coding agents.

In summary, agentmemory is a game-changer for AI coding agents, providing persistent memory and recall capabilities that make development faster and more efficient - with agentmemory, your coding agent remembers everything, so you don't have to.

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🧠 Channel: https://t.iss.one/GithubRe

💡 datawhalechina/hello-agents just hit the trending charts — here's why it matters.

🔗 https://github.com/datawhalechina/hello-agents
📝 📚 《从零开始构建智能体》——从零开始的智能体原理与实践教程
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Hello-Agents is a comprehensive open-source project that aims to provide a systematic guide to building intelligent agents from scratch. The project is designed for AI developers, software engineers, and students who want to gain a deep understanding of intelligent agents and their applications.

The project covers a wide range of topics, including the basics of intelligent agents, language models, and their applications. It provides a step-by-step guide on how to build intelligent agents, from the basics to advanced topics such as memory and retrieval, context engineering, and agent training.

One of the key features of Hello-Agents is its emphasis on practical implementation. The project provides a fully executable codebase that allows users to implement and experiment with intelligent agents. The codebase is written in Python and is designed to be easy to understand and modify.

The project also includes a range of real-world examples and case studies that demonstrate the application of intelligent agents in various domains. These examples include travel assistants, research agents, and social simulation agents.

Overall, Hello-Agents is a valuable resource for anyone interested in building intelligent agents and gaining a deep understanding of the underlying technologies. With its comprehensive coverage of topics, practical implementation, and real-world examples, it is an ideal project for researchers, developers, and students alike.

Start building your own intelligent agents today and join the Hello-Agents community to learn from and contribute to this exciting project!

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🧠 Channel: https://t.iss.one/GithubRe