💡 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

⚡ datawhalechina/easy-vibe is making waves. Here's the full picture.

🔗 https://github.com/datawhalechina/easy-vibe
📝 💻 vibe coding 2026 | Your first modern programming course for beginners to master step by step.
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The datawhalechina/easy-vibe GitHub repository is a treasure trove for anyone looking to dive into the world of coding and AI development. Its primary purpose is to provide a beginner-friendly learning platform where users can turn their ideas into real products by conversing with AI. The key features include a step-by-step visual tutorial, immersive simulated coding, and visible AI principles.

To get started, users can simply describe what they want, and the platform will guide them through the process. The repository offers various learning paths tailored to different needs, including a fast first win, turning an idea into a product prototype, building full-stack products, and advancing Claude Code and agent workflows.

Technical highlights of the repository include interactive demos, cross-platform project tutorials, and a comprehensive knowledge base covering computer fundamentals, AI principles, and engineering practices.

The platform is designed for a wide range of audiences, from complete beginners to mid-level and senior developers, as well as product managers and founders.

In short, datawhalechina/easy-vibe is your one-stop-shop for learning AI-assisted coding, and with its interactive approach, you can turn your ideas into products in no time. So, what are you waiting for? Dive into the world of Easy-Vibe and start building your dream projects today - if you can talk, you can build apps!

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🧠 Channel: https://t.iss.one/GithubRe

🌟 rowboatlabs/rowboat caught my eye on GitHub Trending today.

🔗 https://github.com/rowboatlabs/rowboat
📝 Open-source AI coworker, with memory
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Rowboat is an open-source AI coworker that helps you get work done by turning your work into a knowledge graph and acting on it. It connects to your email and meeting notes, builds a long-lived knowledge graph, and uses that context to assist you in various tasks. You can use Rowboat to generate decks, prep for meetings, track people or topics, and visualize your knowledge graph.

The tool is local-first, meaning all your data is stored locally as plain Markdown, giving you full control over your information. Rowboat also supports integrations with various tools and services, including Gmail, Google Calendar, and Composio.

Whether you're looking to boost your productivity or simply want a more organized workflow, Rowboat is definitely worth checking out. Here's a glimpse of what you can do with Rowboat in Python:

import rowboat

# Connect to your email and meeting notes
rowboat.connect_to_email()
rowboat.connect_to_meeting_notes()

# Generate a deck about your next quarter roadmap
rowboat.generate_deck("Next Quarter Roadmap")

Rowboat is perfect for anyone looking for a powerful AI coworker to help them stay organized and productive. Get started with Rowboat today and experience the power of a local-first AI coworker - your new best work buddy that keeps all your secrets, and helps you get work done!

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🧠 Channel: https://t.iss.one/GithubRe

⚡ ChromeDevTools/chrome-devtools-mcp is making waves. Here's the full picture.

🔗 https://github.com/ChromeDevTools/chrome-devtools-mcp
📝 Chrome DevTools for coding agents
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The Chrome DevTools for Agents (chrome-devtools-mcp) repository provides a Model-Context-Protocol (MCP) server that enables coding agents to control and inspect a live Chrome browser. Key features include performance insights, advanced browser debugging, and reliable automation.

To use chrome-devtools-mcp, you'll need Node.js and Chrome installed. The repository provides a CLI for use without MCP. You can configure your MCP client to use the server by adding the provided config.

The server supports various MCP clients, including Gemini, Claude, Copilot, and Cursor. It also provides a slim mode for basic browser tasks.

Technical highlights include the use of puppeteer for automation and Chrome DevTools for performance insights. The server also sends usage statistics to Google by default, which can be disabled.

The target audience for this repository includes developers and users of coding agents who want to leverage the power of Chrome DevTools for automation, debugging, and performance analysis.

In short, chrome-devtools-mcp empowers your coding agent to supercharge your development workflow with the full power of Chrome DevTools - automate, debug, and optimize your way to better coding.

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🧠 Channel: https://t.iss.one/GithubRe

🔍 Deep-diving into masterking32/MasterDnsVPN — fresh off the trending list.

🔗 https://github.com/masterking32/MasterDnsVPN
📝 Advanced DNS tunneling VPN for censorship bypass, optimized beyond DNSTT and SlipStream with low-overhead ARQ, resolver load balancing, high packet-loss stability and speed.
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README not available for this repository.

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🧠 Channel: https://t.iss.one/GithubRe

🔥 playcanvas/supersplat is trending — and it deserves your attention.

🔗 https://github.com/playcanvas/supersplat
📝 3D Gaussian Splat Editor
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The SuperSplat Editor is a free, open-source tool for creating and editing 3D Gaussian Splats. This web-based application allows users to inspect, optimize, and publish their splats without any downloads or installations. Key features include a live version available at https://superspl.at/editor, a user guide for learning, and a local development environment for contributors. To get started with local development, clone the repository and run npm install followed by npm run develop.

git clone https://github.com/playcanvas/supersplat.git
cd supersplat
npm install
npm run develop

The editor supports localization with multiple languages and is made possible by its open-source community. Whether you're a developer or a 3D artist, the SuperSplat Editor is a powerful tool for working with Gaussian Splats. Dive into the world of 3D splatting with the SuperSplat Editor - create, edit, and optimize with ease.

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🧠 Channel: https://t.iss.one/GithubRe

🌟 Lordog/dive-into-llms caught my eye on GitHub Trending today.

🔗 https://github.com/Lordog/dive-into-llms
📝 《动手学大模型Dive into LLMs》系列编程实践教程
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The Lordog/dive-into-llms GitHub repository is a hands-on programming tutorial series for getting started with large language models (LLMs). The project, also known as "动手学大模型", aims to provide a free and open-source resource for students and researchers to quickly learn and deploy LLMs.

The tutorial series covers a range of topics, including 微调与部署 (model fine-tuning and deployment), 提示学习与思维链 (prompt learning and thought chain), 知识编辑 (knowledge editing), 数学推理 (mathematical reasoning), and more.

The repository also includes a new series of tutorials, 大模型开发全流程 (the full process of large model development), developed in collaboration with Huawei's Ascend community. This series provides a comprehensive guide to developing and deploying LLMs, covering topics such as model development, optimization, and safety.

The project welcomes contributions and issues discussions, and its contributors include researchers and students from Shanghai Jiao Tong University and the National University of Singapore.

Whether you're a beginner or an experienced researcher, this repository has something to offer. So, dive into LLMs and start exploring the world of large language models - your next AI project is just a git clone away!

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🧠 Channel: https://t.iss.one/GithubRe