Clipper for Github

31 Aug 2025 | in clips

Translations: en
Table of Contents

ppt2desc: Convert PowerPoint files into semantically rich text using vision language models

ALucek/ppt2desc: Convert PowerPoint files into semantically rich text using vision language models
Convert PowerPoint files into semantically rich text using vision language models - ALucek/ppt2desc
https://github.com/ALucek/ppt2desc

The text you've provided appears to be the README file from a GitHub repository for a project named "Convert PowerPoint files into semantically rich text using vision language models." Here's a breakdown of what the project does and how it works based on the information given:

Project Overview

This project aims to convert PowerPoint (.pptx) files into semantically rich text using Vision Language Models (VLLMs). The goal is to provide detailed descriptions of the content in each slide, including text, images, diagrams, and other elements.

How It Works

  1. LibreOffice Conversion: The project utilizes LibreOffice to convert PowerPoint (.pptx) files into a format that can be processed by the vision language models. This step is crucial for handling the complex structures and layouts found in presentation slides.

  2. Vision Language Models (VLLMs): After conversion, the project employs VLLMs to analyze the content of each slide. These models are capable of understanding both textual and visual elements within an image or document, generating detailed descriptions of what they "see."

  3. JSON Output: The output from the VLLM analysis is structured in JSON format. Each JSON file contains information about the presentation deck, including the model used for analysis, and a detailed description of each slide's content.

Usage

To use this project, one would need to:

  • Clone the repository.
  • Install any required dependencies (likely specified in a requirements.txt file).
  • Prepare their PowerPoint files for conversion.
  • Run the script with appropriate arguments, such as specifying the input directory for PowerPoint files, output directory for JSON files, and options for which VLLM to use.

The project supports various command-line arguments for customization, including:

  • --input_dir: Directory containing PowerPoint files to convert.
  • --output_dir: Directory where JSON output files will be saved.
  • --libreoffice_path or --libreoffice_url: Path to the local LibreOffice installation or URL of a LibreOffice service (if using Docker).
  • --client: Specifies which VLLM client to use (e.g., Vertex AI, Azure OpenAI Foundry, AWS Bedrock).
  • --model: Name of the specific model to use for analysis.
  • --instructions: Custom instructions that can be passed to the VLLM for tailored output.

Contributing and Future Work

The project is open to contributions. There are several areas marked for future development or improvement, including better support for AWS Llama Vision, dynamic font understanding, and unified SDK experiences for different providers like Google's GenAI.

Licensing and Acknowledgments

The project is licensed under the MIT License. It acknowledges and thanks LibreOffice for PPT/PPTX conversion capabilities and PyMuPDF for PDF processing.

Overall, this project offers a novel approach to extracting meaningful information from PowerPoint presentations using advanced AI models, potentially useful in education, research, accessibility, and content creation contexts.

lucide-icons/lucide: Beautiful & consistent icon toolkit made by the community. Open-source project and a fork of Feather Icons.

lucide-icons/lucide: Beautiful & consistent icon toolkit made by the community. Open-source project and a fork of Feather Icons.
Beautiful & consistent icon toolkit made by the community. Open-source project and a fork of Feather Icons. - lucide-icons/lucide
https://github.com/lucide-icons/lucide

This website is about Lucide, an open-source icon library that provides over 1000 vector icons for use in digital and non-digital projects. It's a community-driven project with various packages available for easy integration into different frameworks and libraries (e.g., React, Vue, Angular). The icons are free to use for both personal and commercial purposes under the ISC License. This resource could be useful for designers and developers looking for a consistent and high-quality icon set for their projects.

zauberzeug/nicegui: Create web-based user interfaces with Python. The nice way.

https://github.com/zauberzeug/nicegui

zauberzeug/nicegui: Create web-based user interfaces with Python. The nice way.
Create web-based user interfaces with Python. The nice way. - zauberzeug/nicegui
https://github.com/zauberzeug/nicegui

This website is about NiceGUI, a Python-based UI framework for creating web-based user interfaces. It allows you to create buttons, dialogs, plots, and more, and is useful for micro web apps, dashboards, robotics projects, and smart home solutions. You can use it to build interactive web applications with a simple and intuitive API, and it has features like automatic reloading, data binding, and customization options.

Zackriya-Solutions/meeting-minutes: A free and open source, self hosted Ai based live meeting note taker and minutes summary generator that can completely run in your Local device (Mac OS and windows OS Support added. Working on adding linux support soon) https://meetily.zackriya.com/

https://github.com/Zackriya-Solutions/meeting-minutes

Zackriya-Solutions/meeting-minutes: A free and open source, self hosted Ai based live meeting note taker and minutes summary generator that can completely run in your Local device (Mac OS and windows OS Support added. Working on adding linux support soon) https://meetily.zackriya.com/
A free and open source, self hosted Ai based live meeting note taker and minutes summary generator that can completely run in your Local device (Mac OS and windows OS Support added. Working on addi...
https://github.com/Zackriya-Solutions/meeting-minutes

The text you've provided appears to be a README file from a GitHub repository for an open-source project named "meeting-minutes." This project is designed to create a self-hosted, AI-based live meeting note taker and minutes summary generator. The tool can run locally on your device (currently supporting Mac OS and Windows, with Linux support planned) and utilizes Whisper for transcription and various other technologies like Rust, Python, and TypeScript for its backend and frontend components.

Here's a breakdown of the key points from the provided text:

  1. Project Overview:
  2. The project is an open-source, self-hosted AI solution for taking live meeting notes and generating summaries.

  3. It can run on local devices (Mac OS and Windows are supported, with Linux support in development).

  4. Technologies Used:

  5. Whisper.cpp for local transcription.
  6. Rust and Python for the backend.
  7. TypeScript for parts of the frontend.
  8. FastAPI for creating the API layer.

  9. SQLite for process tracking and metadata storage.

  10. Setup Instructions:

  11. Users can download packaged versions from the releases page or build it themselves by cloning the repository and following setup instructions provided in the README.

  12. Setup involves installing dependencies, setting up environment variables (for API keys), and building certain components like Whisper.cpp.

  13. Development Guidelines:

  14. The project encourages contributors to follow specific guidelines such as maintaining the project structure, writing tests for new features, documenting API changes, using type hints in Python, and adhering to ESLint configuration for JavaScript/TypeScript.

  15. Contributing and License:

  16. The project is open to contributions via pull requests.

  17. It's licensed under the MIT License, allowing users to freely use and modify the software for their purposes.

  18. Future Plans:

  19. There are plans to introduce a subscription option that would allow users to access the service without having to run the backend on their own server, facilitating better scalability and 24/7 operation.

  20. Languages and Topics:

  21. The project involves several programming languages (C++, TypeScript, Rust, Python) and topics related to AI, cross-platform development, live transcription, meeting notes, etc.

In summary, "meeting-minutes" is an innovative open-source project aiming to provide a privacy-focused solution for automatic meeting note-taking and summarization, leveraging the power of local processing and AI technologies.

GitHub - pyper-dev/pyper: Concurrent Python made simple

https://github.com/pyper-dev/pyper

GitHub - pyper-dev/pyper: Concurrent Python made simple
Concurrent Python made simple. Contribute to pyper-dev/pyper development by creating an account on GitHub.
https://github.com/pyper-dev/pyper

The website is about Pyper, a Python framework for concurrent and parallel data processing. It provides an intuitive API for building data pipelines, allowing you to write clean, reusable code using functional programming patterns. Pyper simplifies the execution of tasks, hiding underlying complexities such as thread management and resource cleanup.

You can use Pyper for:

  • Building ETL systems
  • Creating data microservices
  • Data collection and processing

Pyper's key features include lazy execution, safety, and efficiency, making it a useful tool for developers working with large datasets or complex data pipelines.

GitHub - zilliztech/deep-searcher: Open Source Deep Research Alternative to Reason and Search on Private Data. Written in Python.

https://github.com/zilliztech/deep-searcher

GitHub - zilliztech/deep-searcher: Open Source Deep Research Alternative to Reason and Search on Private Data. Written in Python.
Open Source Deep Research Alternative to Reason and Search on Private Data. Written in Python. - zilliztech/deep-searcher
https://github.com/zilliztech/deep-searcher

This is a README file for the DeepSearcher project, an open-source deep research alternative to reason and search on private data. Here's a summary of the content:

Overview

DeepSearcher is a Python-based project that allows users to search and reason on private data using various large language models (LLMs) and vector databases.

Modules

The project consists of several modules, including:

  1. Embedding Models: Supports open-source embedding models like OpenAI, VoyageAI, and Amazon Bedrock.
  2. LLM Support: Supports LLMs like OpenAI, DeepSeek, XAI Grok, Anthropic Claude, and others.
  3. Document Loader: Supports local file loading (PDF, txt, md) and web crawling using FireCrawl, Jina Reader, and Crawl4AI.
  4. Vector Database Support: Supports Milvus and Zilliz Cloud (fully managed Milvus).

Deployment

To deploy DeepSearcher, users need to configure the modules by modifying the config.yaml file and setting up environment variables for API keys. The project can be run as a FastAPI service on localhost:8000.

Q&A

The README includes answers to common questions, such as:

  1. Why parsing LLM output format fails?
  2. How to resolve connection issues with Hugging Face?
  3. Why DeepSearcher doesn't run in Jupyter notebook?

Evaluation

The project includes an evaluation directory for more details on performance and testing.

Future Plans

The developers plan to enhance web crawling functionality, support more vector databases, add large models, and provide a RESTful API interface.

Contributions

The project welcomes contributions and encourages users to star and fork the repository to help build a more powerful DeepSearcher.

GitHub - imanoop7/Ollama-OCR

https://github.com/imanoop7/Ollama-OCR

GitHub - imanoop7/Ollama-OCR
Contribute to imanoop7/Ollama-OCR development by creating an account on GitHub.
https://github.com/imanoop7/Ollama-OCR

This website is about Ollama-OCR, a powerful Optical Character Recognition (OCR) package that extracts text from images and PDFs using state-of-the-art vision language models. It's available as a Python package and a Streamlit web application, offering features like batch processing, custom prompts, and multiple output formats. You can use it to automate text extraction from documents, making it useful for tasks like data entry, document analysis, and information retrieval.

text-extract-api: Document (PDF, Word, PPTX ...) extraction and parse API using state of the art modern OCRs + Ollama supported models. Anonymize documents. Remove PII. Convert any document or picture to structured JSON or Markdown

https://github.com/CatchTheTornado/text-extract-api

GitHub - CatchTheTornado/text-extract-api: Document (PDF, Word, PPTX ...) extraction and parse API using state of the art modern OCRs + Ollama supported models. Anonymize documents. Remove PII. Convert any document or picture to structured JSON or Markdown
Document (PDF, Word, PPTX ...) extraction and parse API using state of the art modern OCRs + Ollama supported models. Anonymize documents. Remove PII. Convert any document or picture to structured ...
https://github.com/CatchTheTornado/text-extract-api

This is a documentation for a document extraction and parsing API called Doctractor. Here's a summary of the key points:

Overview
Doctractor is an API that uses state-of-the-art OCRs (Optical Character Recognition) and Ollama supported models to extract and parse documents, including PDFs, Word documents, and images. It can anonymize documents, remove PII (Personally Identifiable Information), and convert them into structured JSON or Markdown.

Features

  1. Document Extraction: Extract text and data from documents using OCRs.
  2. Anonymization: Remove PII from documents to protect sensitive information.
  3. Conversion: Convert documents into structured JSON or Markdown.
  4. Storage: Save extracted data using different storage strategies, including local file system, Google Drive, and Amazon S3.

API Endpoints
The API has several endpoints for different tasks:

  1. /storage/list: List files in a storage profile.
  2. /storage/load: Download a file from a storage profile.
  3. /storage/delete: Delete a file from a storage profile.
  4. /llm/pull: Pull an Ollama model.
  5. /llm/generate: Generate text using an Ollama model.
  6. /ocr/clear_cache: Clear the OCR cache.

Storage Profiles
Doctractor uses storage profiles to configure different storage strategies. The available storage profiles are:

  1. Local File System: Store files locally on the server.
  2. Google Drive: Store files in a Google Drive account using a service account.
  3. Amazon S3: Store files in an Amazon S3 bucket.

Requirements
To use Doctractor, you need to have the following:

  1. Python: Python 3.x installed on your system.
  2. OCR libraries: Tesseract-OCR and other OCR libraries installed.
  3. Ollama models: Ollama models downloaded and configured.
  4. Storage credentials: Credentials for your chosen storage profile (e.g., Google Drive service account, Amazon S3 access key).

License
Doctractor is licensed under the MIT License.

Contact
For questions or issues, contact the developers at info@catchthetornado.com.

superfile: Pretty fancy and modern terminal file manager

https://github.com/yorukot/superfile

GitHub - yorukot/superfile: Pretty fancy and modern terminal file manager
Pretty fancy and modern terminal file manager. Contribute to yorukot/superfile development by creating an account on GitHub.
https://github.com/yorukot/superfile

The website is about Superfile, a modern terminal file manager. It's an open-source project that provides a user-friendly interface for managing files and directories in the terminal. The website offers installation instructions, tutorials, and documentation for customizing and extending Superfile with plugins and themes. It could be useful to you if you're looking for a more efficient and visually appealing way to manage your files in the terminal.

ArchiveBox: 🗃 Open source self-hosted web archiving. Takes URLs

https://github.com/ArchiveBox/ArchiveBox

GitHub - ArchiveBox/ArchiveBox: 🗃 Open source self-hosted web archiving. Takes URLs/browser history/bookmarks/Pocket/Pinboard/etc., saves HTML, JS, PDFs, media, and more...
🗃 Open source self-hosted web archiving. Takes URLs/browser history/bookmarks/Pocket/Pinboard/etc., saves HTML, JS, PDFs, media, and more... - ArchiveBox/ArchiveBox
https://github.com/ArchiveBox/ArchiveBox

ArchiveBox is an open-source tool for self-hosted web archiving, allowing users to save HTML, JavaScript, PDFs, media, and more from websites.

If you're looking for instructions or information on how to use ArchiveBox, contribute to it, or understand its features, I can help summarize the key points:

Key Features of ArchiveBox

  • Web Archiving: Saves web pages in various formats.
  • Multi-Extractor Support: Uses multiple extractors (like SingleFile, YT-DLP) to archive different types of content.
  • Customizable: Allows for customization through configuration settings.
  • Self-Hosted: Can be run on your own server or local machine.

Contributing to ArchiveBox

If you're interested in contributing a new extractor or feature:
1. Open an Issue: Discuss your proposal first, including any dependencies.
2. Implement the Extractor: Create a Python script in archivebox/extractors/.
3. Add Config Settings: For enabling/disabling the extractor and its dependencies.
4. Modify Templates (if necessary): To display the output of your extractor in the UI.
5. Submit a Pull Request: Include tests for your changes.

Building and Releasing

For developers or maintainers, there are scripts to build the documentation, pip package, and Docker image manually:
- ./bin/build.sh for building everything.
- Specific scripts for docs (build_docs.sh), pip package (build_pip.sh), and Docker image (build_docker.sh).

Getting Help

  • Bug Tracker: GitHub Issues.
  • Discussions: GitHub Discussions or community forums like Zulip.

If you have a specific question about using ArchiveBox, contributing to it, or need further clarification on any of the points mentioned, feel free to ask!

monolith: ⬛️ CLI tool and library for saving complete web pages as a single HTML file

https://github.com/Y2Z/monolith

GitHub - Y2Z/monolith: ⬛️ CLI tool and library for saving complete web pages as a single HTML file
⬛️ CLI tool and library for saving complete web pages as a single HTML file - Y2Z/monolith
https://github.com/Y2Z/monolith

It looks like you've copied the entire README file of the Monolith project on GitHub!

To summarize, Monolith is a command-line tool and library written in Rust that saves complete web pages as a single HTML file. It's designed to help with tasks such as:

  • Saving web pages for offline access
  • Archiving web content
  • Avoiding link rot (broken links)
  • Reducing procrastination by saving interesting pages for later

The project has the following features:

  • Supports saving web pages with all assets (images, CSS, JavaScript, etc.)
  • Allows customizing the output file name and format
  • Supports authentication and proxies
  • Can be used as a library or a command-line tool
  • Has a simple and easy-to-use API

Monolith is open-source software released under the CC0-1.0 license, which means it's dedicated to the public domain.

If you're interested in using Monolith, you can install it from source or use pre-built binaries available on the project's GitHub page. There are also examples and documentation provided to help you get started.

Is there something specific you'd like to know about Monolith or how to use it?

OCRmyPDF: OCRmyPDF adds an OCR text layer to scanned PDF files, allowing them to be searched

GitHub - ocrmypdf/OCRmyPDF: OCRmyPDF adds an OCR text layer to scanned PDF files, allowing them to be searched
OCRmyPDF adds an OCR text layer to scanned PDF files, allowing them to be searched - ocrmypdf/OCRmyPDF
https://github.com/ocrmypdf/OCRmyPDF

It seems like you've provided the entire README file for the OCRmyPDF project on GitHub. I'll summarize the key points:

What is OCRmyPDF?
OCRmyPDF is a Python-based software that adds an Optical Character Recognition (OCR) text layer to scanned PDF files, making them searchable.

Key Features:

  • Converts scanned PDFs to searchable PDFs
  • Supports multiple languages using Tesseract OCR
  • Can deskew (straighten) crooked pages
  • Can convert images to single-page PDFs

Requirements:

  • Python 3.x
  • Ghostscript
  • Tesseract OCR

Installation:
The installation instructions vary depending on your operating system. You can find the detailed installation steps in the documentation.

Documentation and Support:
The project has extensive documentation on Read the Docs, and you can report issues on the GitHub issues page.

License:
OCRmyPDF is licensed under the Mozilla Public License 2.0 (MPL-2.0).

If you have any specific questions about using OCRmyPDF or would like to know more about a particular feature, I'd be happy to help!

docling: Get your documents ready for gen AI

GitHub - docling-project/docling: Get your documents ready for gen AI
Get your documents ready for gen AI. Contribute to docling-project/docling development by creating an account on GitHub.
https://github.com/docling-project/docling

Docling is a document processing tool that simplifies parsing of diverse formats, including PDF, DOCX, XLSX, HTML, and images. Key features include:

  • Advanced PDF understanding (page layout, reading order, table structure, etc.)
  • Unified document representation format
  • Export to various formats (Markdown, HTML, JSON)
  • Local execution for sensitive data
  • Integrations with AI frameworks (LangChain, LlamaIndex, Crew AI, Haystack)
  • OCR support for scanned documents
  • Support for Visual Language Models

It is an open-source tool under the MIT license, available for installation via pip, and works on macOS, Linux, and Windows environments.

crawl4ai: 🚀🤖 Crawl4AI: Open-source LLM Friendly Web Crawler & Scraper. Don't be shy, join here: https:

GitHub - unclecode/crawl4ai: 🚀🤖 Crawl4AI: Open-source LLM Friendly Web Crawler & Scraper. Don't be shy, join here: https://discord.gg/jP8KfhDhyN
🚀🤖 Crawl4AI: Open-source LLM Friendly Web Crawler & Scraper. Don't be shy, join here: https://discord.gg/jP8KfhDhyN - unclecode/crawl4ai
https://github.com/unclecode/crawl4ai

It appears you've provided an extensive overview of the Crawl4AI project, including its mission, features, licensing, and community engagement. To effectively engage with your request, I'll need to clarify what specific aspects of Crawl4AI or its documentation you would like me to focus on. However, based on the information given, here are some key points and potential discussions related to Crawl4AI:

Key Features of Crawl4AI

  1. Open-source LLM Friendly Web Crawler & Scraper: Designed to extract data from the web in a manner that is compatible with Large Language Models (LLMs), facilitating the use of real-world data for AI training.
  2. Data Extraction and Structuring: Enables users to transform unstructured digital footprints into structured, valuable assets.
  3. Ethical Data Sharing and Marketplace: Aims to create a fair data economy where data creators benefit directly from their contributions.

Mission and Impact

  • Democratizing Data Access: By providing open-source tools for data extraction and structuring, Crawl4AI aims to make web data more accessible for research, development, and innovation.
  • Empowering Data Creators: The project seeks to ensure that individuals and organizations can capitalize on their digital assets, promoting a shared economy based on ethical data practices.

Community and Development

  • Open-source Community: Encourages collaboration and contributions from developers worldwide to improve the platform.
  • Documentation and Support: Offers comprehensive documentation and support channels for users and contributors, including GitHub issues, Discord, and social media platforms.
  • Licensing: Crawl4AI is licensed under Apache-2.0, which allows for free use, modification, and distribution.
  • Citation: Provides guidelines on how to cite Crawl4AI in academic and professional works, ensuring proper attribution.

If you have specific questions about using Crawl4AI, contributing to the project, or understanding its features and mission in more detail, please let me know. I'm here to provide guidance based on the information available up to my last update in 2023.

jan: Jan is an open source alternative to ChatGPT that runs 100% offline on your computer

GitHub - menloresearch/jan: Jan is an open source alternative to ChatGPT that runs 100% offline on your computer
Jan is an open source alternative to ChatGPT that runs 100% offline on your computer - menloresearch/jan
https://github.com/menloresearch/jan

Jan is an open-source alternative to ChatGPT that runs 100% offline on your computer. Key features include:

  • Model Library with popular LLMs like Llama, Gemma, Mistral, or Qwen
  • Connection to Remote AI APIs like Groq and OpenRouter
  • Local API Server with OpenAI-equivalent API
  • Extensions for customizing Jan

Jan is free and open-source, under the AGPLv3 license, and supports Windows, MacOS, and Linux. It requires specific system requirements to enable GPU support.

nicegui: Create web-based user interfaces with Python. The nice way.

17 May 2025

GitHub - zauberzeug/nicegui: Create web-based user interfaces with Python. The nice way.
Create web-based user interfaces with Python. The nice way. - zauberzeug/nicegui
https://github.com/zauberzeug/nicegui

NiceGUI is a Python-based UI framework designed to make it incredibly easy to create web-based user interfaces.

Key Features:

  • Ease of Use: It’s built for simplicity, allowing you to quickly build web apps with minimal code.
  • Versatile Applications: Suitable for a wide range of projects, including micro web apps, dashboards, robotics, smart home solutions, and even tweaking machine learning algorithms.
  • Rich UI Elements: Supports buttons, dialogs, Markdown, 3D scenes, plots, graphs, charts, and more.
  • Live Reloading: Automatically updates the UI when you modify the code.
  • Built-in Webserver: Functions as a webserver, accessible through your browser.
  • Modern Features: Includes features like data binding, notifications, and support for Tailwind CSS.
  • Community Support: Backed by a growing community and available on GitHub with extensive documentation and examples.

Essentially, NiceGUI is a great choice if you want a straightforward way to build interactive web applications in Python, particularly for projects where rapid development and ease of use are priorities.

Self-Hosting-Guide: Self-Hosting Guide. Learn all about locally hosting (on premises & private web servers) and managing software applications by yourself or your organization. Including Cloud, LLMs, WireGuard, Automation, Home Assistant, and Networking.

02 Jul 2025

GitHub - mikeroyal/Self-Hosting-Guide: Self-Hosting Guide. Learn all about locally hosting (on premises & private web servers) and managing software applications by yourself or your organization. Including Cloud, LLMs, WireGuard, Automation, Home Assistant, and Networking.
Self-Hosting Guide. Learn all about locally hosting (on premises & private web servers) and managing software applications by yourself or your organization. Including Cloud, LLMs, WireGuard, A...
https://github.com/mikeroyal/Self-Hosting-Guide

Okay, this is a massive and incredibly detailed response! You've provided a comprehensive overview of a huge range of technologies and tools, primarily focused on the Internet of Things (IoT), robotics, and self-hosting. Let's break down what you've given me and how we can use this information.

Here's a categorized summary of the key areas and technologies you've listed:

1. Operating Systems & Embedded Systems:

  • Raspberry Pi OS: The most popular OS for the Raspberry Pi.
  • Hass.io (Home Assistant OS): A popular open-source home automation platform.
  • Manjaro Linux ARM: A Linux distribution optimized for ARM processors.
  • Arch Linux ARM: Another popular ARM Linux distribution.
  • Ubuntu MATE for Raspberry Pi: A lightweight Ubuntu distribution.
  • Ubuntu Core on a Raspberry Pi: A minimal Ubuntu distribution.
  • Ubuntu Server for ARM: A server-focused Ubuntu distribution.
  • Debian: A stable and widely used Linux distribution.
  • Fedora ARM: A Linux distribution based on Red Hat Enterprise Linux.
  • openSUSE: A community-developed Linux distribution.
  • Kali Linux for the Raspberry Pi: A penetration testing distribution.
  • HeliOS: An embedded operating system.
  • Simba: A small OS for embedded programming platforms.
  • Trampoline: A static RTOS for small embedded systems.
  • DuinOS: A framework for FreeRTOS with Arduino.
  • VxWorks: A real-time operating system.
  • LynxOS: A hard real-time operating system.
  • Zephyr OS: A security-oriented RTOS.
  • FreeRTOS: A real-time operating system for microcontrollers.

2. IoT Middleware & Platforms:

  • IoTSyS: An integration middleware for IoT.
  • OpenIoT: An open-source IoT platform.
  • Thing System: A set of software components for IoT.
  • Home Assistant: (Again, listed here for its middleware aspects)
  • OpenHAB: A cross-platform home automation framework.
  • Eclipse SmartHome: A framework for building smart home solutions.
  • Thingful: A search engine for IoT data.

3. Robotics Software & Frameworks:

  • ROS (Robot Operating System) & ROS2: The dominant robotics middleware.
  • MoveIt: A motion planning framework for ROS.
  • RL (Robot Learning): A library for robot kinematics, motion planning, and control.

4. IoT Middleware & Tools:

  • AutoGluon: A tool for automating machine learning.

5. Networking & Security:

  • Docker Compose: A tool for defining and running multi-container Docker applications.
  • WireGuard: A modern VPN protocol.
  • OpenSSL: A cryptographic library.
  • SSH: Secure Shell for remote access.
  • OAuth: An authorization framework.

6. Data Visualization & Search:

  • Thingful: A search engine for IoT data.

7. Hardware & Devices:

  • Raspberry Pi: The ubiquitous single-board computer.
  • Arduino Ethernet Shield 2: Adds Ethernet connectivity to Arduino boards.
  • Hass.io (Home Assistant OS) devices: The hardware components used with Home Assistant.
  • OpenPicus FlyportPro: A system-on-module for IoT applications.
  • Arduino: A popular microcontroller board.

8. Tools & Utilities:

  • Open Source Robotics Foundation: A community supporting robotics development.
  • CMake: A cross-platform build system.
  • Git: Version control system.
  • GitHub: A platform for hosting and collaborating on code.
  • Awesome Lists: A curated list of open-source tools and resources.

Key Observations & Potential Uses of this Information:

  • Comprehensive Ecosystem: You've presented a massive ecosystem of tools and technologies. This is great for understanding the breadth of options available.
  • Self-Hosting Focus: A strong emphasis on self-hosting, which is a growing trend for privacy, control, and potentially cost savings.
  • IoT Dominance: The focus on IoT is very relevant given the increasing number of connected devices.
  • Robotics Community: The inclusion of ROS and robotics frameworks highlights the vibrant robotics community.

How can we use this information?

  1. Project Selection: This list can help you choose the right tools for a specific IoT or robotics project.
  2. Learning Path: It provides a starting point for learning about various technologies.
  3. Troubleshooting: If you're encountering issues, you can use this list to identify potential solutions.
  4. Community Exploration: It's a great resource for finding communities and resources related to each technology.

To help me provide even more targeted assistance, could you tell me:

  • What are you hoping to do with this information? (e.g., build a specific IoT device, learn about robotics, set up a self-hosted server, etc.)
  • What is your current level of technical expertise? (e.g., beginner, intermediate, advanced)

OpenCut: The open-source CapCut alternative

02 Jul 2025

GitHub - OpenCut-app/OpenCut: The open-source CapCut alternative
The open-source CapCut alternative. Contribute to OpenCut-app/OpenCut development by creating an account on GitHub.
https://github.com/OpenCut-app/OpenCut

OpenCut is an open-source video editor designed as an alternative to CapCut. Here’s what it offers:

  • Functionality: It’s a web, desktop, and mobile video editor.
  • Key Features: Includes timeline-based editing, multi-track support, real-time preview, and no watermarks or subscriptions. It also provides analytics through Databuddy (100% anonymized).
  • Technology: Built using Next.js, TypeScript, and Bun (recommended).
  • Open Source: MIT licensed.
  • Development Status: Currently in rapid development with potential for breaking changes, so contributions are advised to wait until stabilization.
  • Project Structure: The project is organized with a Next.js web application, UI components, custom React hooks, utility and API logic, state management (Zustand), and TypeScript types.

youtube-transcript-api

This is a python API which allows you to get the transcript/subtitles for a given YouTube video. It also works for automatically generated subtitles and it does not require an API key nor a headless browser, like other selenium based solutions do!

GitHub - jdepoix/youtube-transcript-api: This is a python API which allows you to get the transcript/subtitles for a given YouTube video. It also works for automatically generated subtitles and it does not require an API key nor a headless browser, like other selenium based solutions do!
This is a python API which allows you to get the transcript/subtitles for a given YouTube video. It also works for automatically generated subtitles and it does not require an API key nor a headles...
https://github.com/jdepoix/youtube-transcript-api

RapidOCR: 📄 Awesome OCR multiple programing languages toolkits based on ONNXRuntime, OpenVINO, PaddlePaddle and PyTorch.

GitHub - RapidAI/RapidOCR: 📄 Awesome OCR multiple programing languages toolkits based on ONNXRuntime, OpenVINO, PaddlePaddle and PyTorch.
📄 Awesome OCR multiple programing languages toolkits based on ONNXRuntime, OpenVINO, PaddlePaddle and PyTorch. - RapidAI/RapidOCR
https://github.com/RapidAI/RapidOCR

LocalRecall: 🔍 LocalRecall is a tool for searching your local files using LLMs.

https://github.com/mudler/LocalRecall

GitHub - mudler/LocalRecall: 🔍 LocalRecall is a tool for searching your local files using LLMs.
🔍 LocalRecall is a tool for searching your local files using LLMs. - mudler/LocalRecall
https://github.com/mudler/LocalRecall

Overview: LocalRecall is a powerful tool designed to help users search through their local files using Large Language Models (LLMs). This innovative approach leverages the capabilities of AI to enhance file searching, making it more intuitive and efficient by understanding the context and content of files rather than relying solely on filenames or metadata.

Key Features:
- AI-Powered Search: Utilizes LLMs to perform semantic searches across local files, allowing users to find documents based on content and meaning.
- Privacy Focused: Since the tool operates locally, it ensures that sensitive data remains on the user's device, addressing privacy concerns.
- Versatile File Support: Capable of indexing and searching through various file types, including text documents, PDFs, and more.
- Customizable: Users can potentially fine-tune the model or adjust settings to suit specific needs or file structures.

Usage: To use LocalRecall, users typically need to install the tool, configure it to index their local directories, and then interact with it via a command-line interface or a simple GUI to perform searches. The setup process and specific requirements would be detailed in the repository's documentation.

Development Status: As an open-source project, LocalRecall is likely under active development. Users and developers are encouraged to contribute by reporting bugs, suggesting features, or submitting pull requests to enhance functionality.

KeyCluCask: Simple and handy overview of applications shortcuts

18 Jul 2025

GitHub - Anze/KeyCluCask: Simple and handy overview of applications shortcuts
Simple and handy overview of applications shortcuts - Anze/KeyCluCask
https://github.com/Anze/KeyCluCask

KeyCluCask is a macOS application shortcut utility. It provides a simple and handy overview of applications shortcuts.

Key Features:

  • Displays a list of current applications shortcuts.
  • Allows for customization of the appearance to match the system (light or dark mode).
  • Supports bookmarking shortcuts.
  • Offers the ability to hide known shortcuts.
  • Provides a collapsible groups of shortcuts.
  • Exports App's shortcuts to markdown file.
  • Integrations with skhd to list shortcuts.
  • CLI params for more details.
  • Requires Accessibility API and Notification Center access for operation.

It’s a free, open-source project.

presenton: Open-Source AI Presentation Generator and API (Gamma, Beautiful AI, Decktopus Alternative)

03 Aug 2025

GitHub - presenton/presenton: Open-Source AI Presentation Generator and API (Gamma, Beautiful AI, Decktopus Alternative)
Open-Source AI Presentation Generator and API (Gamma, Beautiful AI, Decktopus Alternative) - presenton/presenton
https://github.com/presenton/presenton

Presenton is an open-source AI presentation generator and API, offering an alternative to tools like Gamma, Beautiful AI, and Decktopus. It allows you to create presentations using AI, giving you full control over your workflow and data privacy.

Here’s what it offers:

  • AI-Powered Generation: Create presentations from prompts, documents, or uploaded content using various AI models (OpenAI, Google Gemini, Anthropic Claude, Ollama, or custom models).
  • Customization: Design unlimited presentations with HTML and Tailwind CSS, choose from different themes, and control the number of slides.
  • Flexible Export: Save presentations as PowerPoint (PPTX) or PDF.
  • Local Processing: All processing happens on your device, ensuring data privacy and eliminating cloud dependencies.
  • API Access: Host the presentation generator as your own API service for your team.
  • Open-Source: Fully open-source under the Apache 2.0 license.

Essentially, Presenton provides a powerful, customizable, and privacy-focused way to generate presentations using AI, suitable for developers and anyone seeking control over their presentation creation process.

cleanmac: Clean your macOS with a script, not an expensive app

10 Aug 2025

GitHub - hkdobrev/cleanmac: Clean your macOS with a script, not an expensive app
Clean your macOS with a script, not an expensive app - hkdobrev/cleanmac
https://github.com/hkdobrev/cleanmac

This script, cleanmac.sh, is a shell script designed to clean up unnecessary files on your macOS system. It removes cache files, logs, temporary files, and more, freeing up disk space and improving system performance. Key features include: removing system and user cache files, cleaning application logs, clearing temporary files, emptying the Trash, cleaning Safari caches, cleaning Xcode derived data and archives, cleaning Node.js cache (npm, yarn), cleaning unused Docker images and containers, and supporting a dry-run mode to preview changes. It’s a free script available under the MIT license.

lux: 👾 Fast and simple video download library and CLI tool written in Go

10 Aug 2025

GitHub - iawia002/lux: 👾 Fast and simple video download library and CLI tool written in Go
👾 Fast and simple video download library and CLI tool written in Go - iawia002/lux
https://github.com/iawia002/lux

Okay, this is a comprehensive document outlining the Lux video downloader. Let's break down the key information and how to use it.

What is Lux?

Lux is a command-line tool (CLI) and a Go-based library for downloading videos from various websites. It's designed to be fast and simple.

Key Features:

  • Multi-Site Support: It supports downloading from a wide range of websites, including:
    • YouTube
    • Bilibili
    • QQ
    • iQiyi
    • Youku
    • Tumblr
    • and many more (the list is extensive).
  • Command-Line Interface (CLI): You interact with Lux through the command line.
  • Go-Based: Built using the Go programming language.
  • Open Source: It's an open-source project, encouraging community contributions.

How to Use Lux (Basic Commands)

The core command structure is:

lux [options] <URL>
  • <URL>: The URL of the video you want to download.

Common Options:

  • -i or --information only: Displays information about the video without downloading it.
  • -F or --file <file_path>: Specifies the output file path.
  • -d or --debug: Enables debug mode for more detailed output.
  • -j or --json: Prints the extracted data in JSON format.
  • -s or --minimum outputs: Specifies the minimum number of output files to create (useful for multi-part downloads).
  • -v or --version: Displays the version number.
  • -f or --select stream: Allows you to select a specific stream to download.
  • -p or --playlist: Downloads a playlist.
  • -n <int> or --threads <int>: Sets the number of download threads (default is 10).
  • -c <string> or --cookie <string>: Provides a cookie for authentication.
  • -r <string> or --referer <string>: Sets the Referer header (useful for some sites).
  • -cs <int> or --chunk-size <int>: Sets the HTTP chunk size (default is 1 MB).
  • -aria2 or --aria2: Uses Aria2 RPC for downloading.
  • -aria2addr <string>: Aria2 RPC address (default: localhost:6800).
  • -aria2method <string>: Aria2 method (default: http).
  • -aria2token <string>: Aria2 RPC token.

Example Commands:

  1. Download a YouTube video:

    bash lux -i https://www.youtube.com/watch?v=dQw4w9WgXcQ

  2. Download a Bilibili video to a specific file:

    bash lux -f /path/to/save/video.mp4 https://www.bilibili.com/video/BV13441177826

  3. Download with debug mode:

    bash lux -d https://www.youtube.com/watch?v=dQw4w9WgXcQ

Important Notes & Considerations:

  • Cookies: Many sites require authentication. You'll often need to provide a cookie. The exact cookie format varies by site.
  • CCode (Youku): For Youku, you'll need to use the ccode option. The default ccode value (0502) might not always work; you may need to find a valid ccode.
  • Aria2: Lux can use Aria2 for downloading, which can be faster and more reliable.
  • Multi-Part Downloads: Some videos are split into multiple parts. Use the -s option to create multiple output files.
  • Updates: The Lux project is actively maintained. Keep an eye on the GitHub repository for updates and changes.

Resources:

To get started, clone the Lux repository from GitHub:

git clone https://github.com/iawia002/lux

Then, navigate to the lux directory and you'll find the executable.

This comprehensive breakdown should give you a solid understanding of how to use the Lux video downloader. Remember to consult the GitHub repository for the most up-to-date information and examples.

handsontable: JavaScript Data Grid

31 Aug 2025

GitHub - handsontable/handsontable: JavaScript Data Grid / Data Table with a Spreadsheet Look & Feel. Works with React, Angular, and Vue. Supported by the Handsontable team ⚡
JavaScript Data Grid / Data Table with a Spreadsheet Look & Feel. Works with React, Angular, and Vue. Supported by the Handsontable team ⚡ - handsontable/handsontable
https://github.com/handsontable/handsontable

Handsontable is a JavaScript Data Grid component with a spreadsheet-like interface, designed for building data-rich internal applications.

Here’s what it offers:

  • Key Features: It includes features like built-in themes, a flexible API, virtualization for performance, IME support, internationalization, RTL support, accessibility, keyboard shortcuts, sorting, filtering, 400 spreadsheet formulas (via HyperFormula), merged cells, frozen rows/columns, hiding rows/columns, a right-click context menu, and data validation.
  • Installation: It can be installed using npm, CDN, or other frameworks like React, Angular, and Vue.
  • Use Cases: It’s suitable for resource planning software (ERP), inventory management systems, digital platforms, and data modeling applications.
  • Technical Details: It’s built in JavaScript and TypeScript, supports various languages, and offers a commercial license option for production use. It’s maintained by the Handsontable team and has a strong community.

rowfill: Open-source unstructured data (PDFs, Images, Audiofiles) processing platform built for knowledge workers

31 Aug 2025

GitHub - harishdeivanayagam/rowfill: Open-source unstructured data (PDFs, Images, Audiofiles) processing platform built for knowledge workers
Open-source unstructured data (PDFs, Images, Audiofiles) processing platform built for knowledge workers - harishdeivanayagam/rowfill
https://github.com/harishdeivanayagam/rowfill

Rowfill is an open-source platform designed for knowledge workers to process unstructured data from documents, images, and audio files using AI. It offers features like advanced OCR and document parsing to extract text, tables, and handwriting. Key capabilities include auto-schema generation, custom workflows with automated task processing, and support for local LLMs (like Llama and Mistral) alongside OpenAI vision models. It prioritizes privacy with local LLM support and data syncing. It’s a relatively new project (alpha version) and is built using TypeScript and Next.js.

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