💡 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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🚀 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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🔍 Deep-diving into anthropics/claude-code — fresh off the trending list.

🔗 https://github.com/anthropics/claude-code
📝 Claude Code is an agentic coding tool that lives in your terminal, understands your codebase, and helps you code faster by executing routine tasks, explaining complex code, and handling git workflows - all through natural language commands.
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Claude Code is an innovative, agentic coding tool designed to live in your terminal, understand your codebase, and accelerate your coding process by automating routine tasks, explaining complex code, and managing git workflows - all through natural language commands.

You can use it in your terminal, IDE, or even tag Claude on GitHub for assistance. The installation process is straightforward, with options including curl scripts for MacOS and Linux, brew for MacOS, and PowerShell scripts or WinGet for Windows.

Key features include the ability to execute tasks, provide code explanations, and handle git workflows through natural language commands. The tool also comes with plugins that extend its functionality, allowing for custom commands and agents.

From a technical standpoint, Claude Code is built to be user-friendly and efficient, with a focus on data collection and usage policies that prioritize user privacy.

If you're a developer looking to boost your productivity, Claude Code is worth exploring - it's like having a coding assistant built right into your terminal. With its natural language commands and automated workflows, Claude Code is set to revolutionize the way you code: code smarter, not harder, with Claude.

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🔍 Deep-diving into ogulcancelik/herdr — fresh off the trending list.

🔗 https://github.com/ogulcancelik/herdr
📝 agent multiplexer that lives in your terminal.
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Herdr is a terminal-based tool that allows you to run multiple coding agents in a single terminal, with features like real-time state tracking, workspaces, tabs, and panes. It's designed to be lightweight, with a small binary size, and can run on Linux, macOS, and Windows. Key features include agent state awareness, persistent sessions, and a local socket API for custom integrations. herdr can be installed using a simple script, and agents can be integrated using the socket API or by installing official integrations. The project is open-source, dual-licensed under AGPL-3.0 and commercial licenses, and is actively maintained.

curl -fsSL https://herdr.dev/install.sh | sh

to get started. Herdr is the ultimate tool for managing multiple coding agents - try it and never go back to manual agent management again.

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🎯 obra/superpowers landed on trending. Worth a proper look.

🔗 https://github.com/obra/superpowers
📝 An agentic skills framework & software development methodology that works.
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Superpowers is a software development methodology that enables coding agents to work more efficiently. It's built on a set of composable skills and initial instructions, allowing agents to use them effectively. The system starts by having the agent ask questions to understand the project's requirements, then breaks down the work into manageable tasks with clear implementation plans.

Key features include test-driven development, systematic debugging, and collaboration tools like brainstorming, code review, and subagent-driven development. The methodology emphasizes simplicity, evidence-based decision-making, and process over ad-hoc approaches.

To get started, you can install Superpowers as a plugin for various coding agents like Claude Code, Antigravity, or Codex App, following the specific installation instructions for each.

Technical highlights include a skills library with testing, debugging, and collaboration tools, as well as a philosophy that prioritizes test-driven development, systematic approaches, and complexity reduction.

The target audience appears to be developers working with coding agents, looking to streamline their development process and improve productivity.

In summary, Superpowers is a powerful tool for coding agents - with it, your agent becomes a superpowered coding machine!

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

🔗 https://github.com/agentskills/agentskills
📝 Specification and documentation for Agent Skills
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Agent Skills is an innovative way to enhance AI agents with specialized knowledge and workflows. At its core, a skill is a folder containing a SKILL.md file with metadata and instructions that teach an agent to perform a specific task. These skills can also include scripts, reference materials, and other resources.

The key features of Agent Skills include:
- packaging procedural knowledge and context into portable folders
- enabling domain expertise and repeatable workflows
- supporting cross-product reuse

To use Agent Skills, agents load them through progressive disclosure in three stages: Discovery, Activation, and Execution. This approach allows agents to keep many skills on hand with minimal context footprint.

From a technical perspective, the Agent Skills format is open and supported by a growing number of AI tools and clients. The format details are available in the Specification, and example skills can be found in the Example Skills repository.

Agent Skills is perfect for developers and AI enthusiasts looking to unlock new capabilities in AI agents. The project is open to contributions, and the code is licensed under Apache 2.0.

In short, Agent Skills empowers AI agents with new capabilities - and that's a skill worth mastering!

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