🎯 donnemartin/system-design-primer landed on trending. Worth a proper look.

🔗 https://github.com/donnemartin/system-design-primer
📝 Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
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The System Design Primer is a comprehensive resource to help engineers learn how to design large-scale systems and prepare for system design interviews. It covers a wide range of topics, including scalability, performance, availability, and security. The repository provides an organized collection of resources, including system design topics, interview questions with solutions, and Anki flashcard decks.

Key features include a study guide to help you prepare based on your interview timeline and a section on how to approach a system design interview question. The repository is continually updated and open to contributions from the community.

Some technical highlights of the repository include system design interview questions with solutions, object-oriented design interview questions with solutions, and additional system design interview questions. The repository uses spaced repetition to help you retain key system design concepts.

The target audience for this repository includes engineers who want to improve their system design skills and those who are preparing for system design interviews. Overall, the System Design Primer is a valuable resource for any engineer looking to improve their system design skills.
The system design interview is not just about designing systems, it's about communicating your design effectively.

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🌟 firecrawl/pdf-inspector caught my eye on GitHub Trending today.

🔗 https://github.com/firecrawl/pdf-inspector
📝 Fast Rust library for PDF inspection, classification, and text extraction. Intelligently detects scanned vs text-based PDFs to enable smart routing decisions.
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Pdf-inspector is a fast Rust library for PDF classification and text extraction. It detects whether a PDF is text-based or scanned, extracts text with position awareness, and converts to clean Markdown — all without OCR. Key features include smart classification, text extraction, Markdown conversion, table detection, and CID font support.

The library is lightweight, with a single dependency on lopdf for PDF parsing. It has bindings for Python, Node.js, and browser WebAssembly, making it accessible to various users.

To get started, you can install the library using cargo add pdf-inspector or pip install pdf-inspector, and then use it in your project. For example, in Python, you can use import pdf_inspector and result = pdf_inspector.process_pdf("document.pdf") to classify and extract text from a PDF.

Pdf-inspector is suitable for users who need to process PDFs at scale, such as in document processing pipelines. It helps save cost and latency by routing text-based PDFs to local extraction and scanned PDFs to OCR services.

The library is well-documented, with a README that includes a quick start guide, benchmark results, and API references for each language binding.

In summary, pdf-inspector is a fast and lightweight library for PDF classification and text extraction that helps users process PDFs efficiently and effectively — process your PDFs smarter, not harder.

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⚡ esengine/DeepSeek-Reasonix is making waves. Here's the full picture.

🔗 https://github.com/esengine/DeepSeek-Reasonix
📝 DeepSeek-native AI coding agent for your terminal. Engineered around prefix-cache stability — leave it running.
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Reasonix is a DeepSeek-native AI coding agent designed for your terminal, engineered around prefix-cache stability to keep token costs low. The legacy TypeScript line is in maintenance mode, with active development moved to the Go rewrite in the main-v2 branch.

Key features include cache stability, prefix-cache mechanic, and DeepSeek API integration. To use Reasonix, simply install it globally with npm install -g reasonix and run reasonix code in your project directory.

The agent is suitable for developers and power users who want to leverage AI for coding tasks. With its cache-first loop and four mechanisms to keep cacheable bytes stable, Reasonix provides a cost-effective solution for coding tasks.

One notable example is a real user who achieved a 99.82% cache hit rate, resulting in significant cost savings.

The project has a bilingual Discord community for setup help, workflow showcases, and feature discussions.

To get started, grab a DeepSeek API key and install Reasonix globally.

In summary, Reasonix is a powerful AI coding agent that helps you code more efficiently with its prefix-cache stability and DeepSeek API integration - try it out and experience the power of AI-assisted coding.

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🧠 Channel: https://t.iss.one/GithubRe

📌 Spotted on GitHub Trending: addyosmani/agent-skills — let's break it down.

🔗 https://github.com/addyosmani/agent-skills
📝 Production-grade engineering skills for AI coding agents.
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The agent-skills GitHub repository provides a set of production-grade engineering skills for AI coding agents. These skills encode workflows, quality gates, and best practices used by senior engineers throughout the development process. The repository includes 24 skills that cover the entire development lifecycle, from defining what to build to shipping to production.

Key features include 8 slash commands that map to different stages of development, such as /spec, /plan, /build, and /ship. These commands activate the right skills automatically, ensuring that agents follow best practices consistently. The repository also includes a quick start guide that allows users to install the skills using the skills CLI or integrate them with various agents, such as Claude Code, Cursor, and Codex.

The skills are designed to be used by developers, engineers, and teams who want to improve the quality and efficiency of their development process. By using these skills, agents can automate tasks, reduce manual steps, and ensure that code meets high standards.

Technical highlights include the use of Markdown files to define the skills, which makes it easy to create, modify, and extend them. The skills also include verification gates and anti-rationalization tables to ensure that agents are following best practices and not introducing biases or errors.

In summary, the agent-skills repository provides a powerful set of tools for improving the development process with AI coding agents. With its production-grade engineering skills, 8 slash commands, and quick start guide, it's an essential resource for any team looking to streamline their development workflow. Automate your development process with agent-skills and take your team's productivity to the next level!

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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 cutting-edge software development methodology that empowers your coding agents with a set of composable skills and initial instructions. The purpose of Superpowers is to streamline the development process, from brainstorming to implementation, ensuring that your agent uses its skills effectively.

Key features include test-driven development, systematic debugging, and collaboration tools. To use Superpowers, simply install it as a plugin in your preferred coding agent, such as Claude Code, Antigravity, or Codex App.

From a technical standpoint, Superpowers is built around a skills library that includes testing, debugging, and collaboration tools. The target audience for Superpowers is developers who want to improve their coding efficiency and quality.

Here's a sample installation command for Claude Code:

/plugin install superpowers@claude-plugins-official

If you're interested in contributing to Superpowers, you can fork the repository, switch to the 'dev' branch, and submit a pull request.

In a nutshell, Superpowers is a game-changer for coding agents - it's like having a super-smart, ultra-organized, and fiercely efficient coding sidekick!

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🔥 roboflow/supervision is trending — and it deserves your attention.

🔗 https://github.com/roboflow/supervision
📝 We write your reusable computer vision tools. 💜
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The roboflow/supervision GitHub repository offers a comprehensive toolkit for building computer vision applications. Its purpose is to provide a set of reusable tools for tasks such as loading datasets, drawing detections, and counting objects in zones. The repository includes key features like model-agnostic design, connectors for popular libraries, customizable annotators, and dataset utilities.

To get started, users can pip install supervision and explore the quickstart guide, which covers topics like loading models, using annotators, and working with datasets. The repository also provides technical highlights such as support for various model types, including classification, detection, and segmentation models, and integration with popular libraries like Ultralytics and Transformers.

The target audience for this repository includes data scientists, machine learning engineers, and developers working on computer vision projects. With its extensive documentation, tutorials, and community support, the roboflow/supervision repository is an excellent resource for anyone looking to build and deploy computer vision applications.

The repository is well-documented, with a comprehensive guide, tutorials, and a community-driven discussion forum. It is also actively maintained, with a strong focus on community engagement and contribution.

In summary, roboflow/supervision is a powerful toolkit for building computer vision applications, offering a wide range of features, tools, and resources to support developers and data scientists. With its flexible design, extensive documentation, and active community, it's an excellent choice for anyone working on computer vision projects: build computer vision applications faster and more reliably with roboflow/supervision.

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🎯 vercel/next.js landed on trending. Worth a proper look.

🔗 https://github.com/vercel/next.js
📝 The React Framework
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Next.js is a popular React framework for building server-rendered, statically generated, and performance-optimized web applications. Its main purpose is to simplify the development process, allowing developers to focus on creating amazing user experiences.

Key features include server-side rendering, static site generation, and internationalization. To get started, simply create a new project using npm init next-app or yarn create next-app, and you're ready to go.

From a technical perspective, Next.js provides a comprehensive set of tools and features, including built-in support for Webpack and Babel, as well as API routes for building custom server-side logic.

The framework is designed for frontend developers of all levels, from beginners to experienced professionals. Whether you're building a small blog or a complex e-commerce platform, Next.js has the tools and features you need to succeed.

In short: Next.js is the ultimate React framework for building fast, scalable, and performance-optimized web applications - so why wait, start building today!

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⚡ tailwindlabs/tailwindcss is making waves. Here's the full picture.

🔗 https://github.com/tailwindlabs/tailwindcss
📝 A utility-first CSS framework for rapid UI development.
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Tailwind CSS is a utility-first CSS framework that enables rapid building of custom user interfaces. Its key features include a configurable and highly customizable design system, making it easy to create unique and consistent designs.

To use Tailwind CSS, simply include it in your project and start writing classes. The framework is easy to learn and has a low barrier to entry, making it accessible to developers of all levels.

From a technical standpoint, Tailwind CSS is built using PostCSS and JavaScript, allowing for seamless integration with existing projects.

The target audience for Tailwind CSS includes frontend developers, designers, and anyone looking to streamline their CSS workflow.

In short, Tailwind CSS is the go-to framework for building custom and responsive user interfaces - write CSS faster, not more.

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⚡ uber/ADR is making waves. Here's the full picture.

🔗 https://github.com/uber/ADR
📝 ADR secures enterprise AI agents through observability, security benchmarking, and threat detection. Deployed at Uber.
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Meet ADR, an enterprise security system designed to secure AI agents. It's already being used in production at Uber and has been recognized in a paper accepted to MLSys 2026. ADR's key features include Observability, which captures agent activity, Benchmark, a testing system for agent security, Detection, which identifies risky behavior, and upcoming Prevention capabilities.

The repository contains the open-source ADR Sensor, ADR-Bench, and ADR Detector. To get started with ADR Detection, simply git clone the repository, navigate to the Detection directory, and run uv sync.

ADR is perfect for organizations looking to secure their AI agents, whether they're employee-facing or customer-facing. With ADR, you can ensure your AI systems are safe and reliable.
One-liner takeaway: ADR is revolutionizing AI security by providing a robust system to detect and respond to potential threats, giving you peace of mind in the ever-evolving world of AI.

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🎯 lyogavin/airllm landed on trending. Worth a proper look.

🔗 https://github.com/lyogavin/airllm
📝 AirLLM 70B inference with single 4GB GPU
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AirLLM is a game-changer for large language models, allowing massive models like 70B Llama 3.1 to run on a single 4GB GPU card without quantization, distillation, or pruning. This is achieved through sparse MoE models that stream one expert at a time.

Key Features:
- Support for various large language models, including Llama 3.x, DeepSeek V2/V3, and more
- Model compression for up to 3x inference speedup with minimal accuracy loss
- Easy usage with the AutoModel.from_pretrained(...) method

Technical Highlights:
- pip install airllm for easy installation
- from airllm import AutoModel for initializing models
- Supports compression for 4-bit or 8-bit block-wise quantization

Audience:
- Developers and researchers working with large language models
- Those looking to deploy models on limited hardware

Takeaway: With AirLLM, you can run massive language models on minimal hardware, making AI more accessible and efficient.

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