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Daily GitHub Project Recommendation: Stirling-PDF - Your Local All-in-One PDF Manager, Surprisingly Secure and Efficient!

PDF files are an indispensable part of daily work and study, but the security and functional limitations of online tools often cause headaches. Today, we bring you an open-source project that truly addresses these pain points—Stirling-PDF! This star project, with over 65,000 stars, is dedicated to providing a powerful, secure, and completely local PDF processing solution.

Project Highlights

Stirling-PDF’s core value lies in its unparalleled breadth of features and utmost respect for user privacy.

  • Full-featured PDF Processing Hub: It’s not just a simple PDF tool, but a PDF Swiss Army knife covering over 50 operations! From basic merging, splitting, rotating, and reorganizing pages, to advanced format conversions (PDF to/from images, Word, HTML), and then to encryption, watermarking, signing, text redaction, OCR recognition, and even PDF comparison, Stirling-PDF can meet almost all your PDF-related needs.
  • Local Deployment, Privacy First: Unlike many cloud-based PDF tools, Stirling-PDF is a local web application developed with Java and easily deployed via Docker. This means all your PDF files are only temporarily processed in the local client, server memory, or briefly stored during task execution, and immediately deleted upon task completion. Your sensitive information will never be uploaded to external servers, perfectly ensuring data security and privacy.
  • Enterprise-Grade Features, Benefiting Personal Users Too: In addition to powerful PDF capabilities, the project also offers parallel file processing, dark mode, custom download options, API integration, optional login authentication, and even supports custom “pipelines” to automate multiple operation workflows. Support for up to 40 languages also allows global users to use it without barriers.

Technical Details and Use Cases

With its excellent engineering design, Stirling-PDF encapsulates complex PDF operations into an easy-to-use web interface and API. Whether you are a tech enthusiast looking for a reliable local tool or an enterprise needing a controllable internal PDF processing solution, Stirling-PDF is perfectly capable. For developers, its provided API interface offers great convenience for integration into their own applications or automation scripts.

How to Get Started

Want to experience Stirling-PDF’s powerful features? Installation is very simple; you can quickly deploy it via Docker!

Visit the documentation to find detailed installation guides and feature introductions.

Call to Action

If you’re looking for a powerful, privacy-focused PDF processing tool, Stirling-PDF is definitely your best choice. Click the links to explore, try deploying it, or give this excellent project your Star! Developers interested in contributing are also welcome to join us in making it even better!

Daily GitHub Project Recommendation: Daft - Empowering Unified Multimodal Data Processing with Rust!

Hello, data enthusiasts! Today, we are proud to introduce Daft, a distributed query engine that has rapidly gained prominence on GitHub, boasting 3902 stars, and written in Rust. It’s more than just a data processing tool; it’s built specifically for modern data challenges, aiming to simplify and accelerate the entire workflow from data analysis and engineering to machine learning/AI.

Project Highlights

Daft’s core value lies in providing a simple and reliable processing solution for data of any modality and scale. It’s not a traditional tabular data processing tool; instead, it focuses on “any data,” especially complex, multimodal data types:

  • A Powerful Tool for Multimodal Data Processing: Daft fundamentally changes how we handle images, URLs, tensors, and even custom Python objects. Through its efficient Apache Arrow-based memory representation, these complex data types can be ingested and transformed easily and with high performance, offering unprecedented convenience for AI/ML workflows.
  • Balancing Efficiency and Experience: It provides a familiar Lazy Python Dataframe API for interactive iteration and also supports SQL for analytical queries. Internally equipped with a powerful query optimizer, it intelligently rewrites queries for maximum efficiency. Furthermore, thanks to its Rust-based underlying implementation, Daft excels in performance, setting records in I/O performance, especially when integrated with cloud storage like S3.
  • Built for Distribution: Daft seamlessly integrates with Ray, allowing your data processing workloads to easily scale to large clusters, harnessing thousands of CPUs/GPUs, and effortlessly handling massive datasets that local machines cannot.
  • Integration and Compatibility: It fully supports data catalogs like Apache Iceberg and is built upon the Apache Arrow memory format, ensuring seamless interoperability with other data ecosystems.

Technical Details and Use Cases

Daft’s backend is written in Rust, which guarantees its excellent performance and memory safety, while its frontend provides a friendly API via Python or SQL, significantly lowering the learning curve. It is particularly suitable for:

  • Data Scientists and Machine Learning Engineers: For processing diverse data such as image datasets, text embeddings, audio files, and building complex feature engineering pipelines.
  • Data Engineers: Who need to process large-scale, heterogeneous data and build efficient, scalable data lakes or data warehouses.
  • Users Requiring Interactive Exploration of Large Datasets: Daft’s intelligent caching and query optimization make data exploration smoother.

How to Get Started

Installing Daft is very straightforward:

pip install daft

Want to learn more or experience Daft’s powerful features? Please visit:

Call to Action

With its innovative design and powerful features, Daft is leading a new wave in distributed data processing. If you’re looking for a high-performance, flexible data processing engine capable of handling various data types, then Daft is definitely worth checking out. Go explore it and join this vibrant community! If you have any insights or questions during use, feel free to join the discussion on GitHub, or even contribute your code!

Daily GitHub Project Recommendation: FIRST Tech Challenge Robot Controller - Kickstart Your Robotics Programming Journey!

Hello, GitHub explorers! Today, we are proud to introduce a project crucial for robotics enthusiasts, especially FIRST Tech Challenge (FTC) teams: FIRST-Tech-Challenge/FtcRobotController. It’s not just a codebase; it’s the starting point for FTC robot dreams, an official SDK that brings together cutting-edge technology and practical wisdom.

Project Highlights

As the official Android Studio workspace for FTC robotics competitions, FtcRobotController provides core tools for teams worldwide to build, program, and control their competition robots.

  • Technical Depth and Breadth:
    • Multi-Language Support: The project is based on Java, supporting professional-level development via Android Studio. Simultaneously, it offers perfect support for graphical Blocks programming and OnBot Java, significantly lowering the programming barrier and allowing members of different experience levels to participate.
    • Hardware Ecosystem Integration: It deeply integrates with REV Robotics’ Control Hub and Expansion Hub and supports various sensors (such as AndyMark ToF, IMU, color sensors, OctoQuad, etc.) and actuators. This means your robot can easily achieve precise control, state awareness, and complex movements.
    • Cutting-Edge Vision Capabilities: The project continuously updates with the latest vision processing features, such as the AprilTag library for the DECODE (2025-2026) season, advanced color processing software (like ColorBlobLocatorProcessor and PredominantColorProcessor), and support for professional vision sensors like Limelight 3A, providing robots with powerful environmental perception and target recognition capabilities.
  • Application Value and Community Impact:
    • Official Authority: As the official FTC SDK, it ensures compatibility and consistency of code with competition rules and hardware, making it the preferred choice for all FTC teams.
    • Rich Learning Resources: It provides numerous “Sample OpModes” (robot code examples) and detailed online documentation (Blocks tutorials, OnBot Java tutorials, Android Studio tutorials), guiding beginners from basic concepts to advanced applications, and helping advanced players enhance their skills.
    • Active Community: With over 1000 stars and an astonishing 6700+ forks, this fully demonstrates its broad user base and strong community support, ensuring continuous project updates and issue resolution. Its active development pace also indicates its ability to constantly adapt to new season challenges and introduce new features.

Applicable Scenarios

This project is an essential tool for all teams participating in the FIRST Tech Challenge robotics competition. Whether you are a high school student, a beginner, or an experienced programmer, you can find a suitable programming approach within it. It is not only a programming platform for implementing robot functions but also an excellent vehicle for students to learn robotics, software engineering, and teamwork.

How to Get Started

Want to delve deeper or start your FTC robot programming journey? It’s very simple! You can clone the entire repository via Git or directly download the ZIP archive. The project provides detailed getting-started guides, allowing even beginners with no prior experience to quickly get up to speed.

GitHub Repository Link: https://github.com/FIRST-Tech-Challenge/FtcRobotController

Call to Action

If you are an FTC participant or passionate about robotics programming, don’t hesitate—explore FtcRobotController now! It will be a powerful backer for you to build a smart, efficient competition robot. If you find this project useful, please don’t hesitate to give it a Star and share it with more robot enthusiasts!