Extracting Plaintext from Pixelated Screenshots: Technical Validation of Hidden Content Recovery | Open Source Daily No.800

Honghao Wang

— 2 min read

spipm/Depixelization_poc

GitHub Repository Stars: 2.8k License: NOASSERTION

Depixelization_poc is a proof-of-concept project for restoring plain text from pixelated screenshots.

Key Features

  • Targeted Processing for images pixelated with a linear box filter.
  • Search Image Matching: Compares input images with prepared search images (including De Bruijn sequences and character combinations) to restore obscured text.
  • Helper Tools:
  • `tool_show_boxes.py` — detects and displays pixel block positions.
  • `tool_gen_pixelated.py` — generates simulated pixelated images for debugging and testing.
  • Multiple Averaging Modes: Supports gamma-encoded averaging, linear sRGB averaging, and background color filtering for flexible application scenarios.
  • Block-by-Block Matching Algorithm: Matches simulated filtering patterns in the search image to find unique restored results.

---

suitenumerique/docs

GitHub Repository Stars: 14.8k License: MIT

docs is an extensible collaborative note-taking, wiki, and documentation platform built on Django and React.

Key Features

  • User-Friendly Online Editor with rich formatting and Markdown support.
  • Flexible Content Blocks with keyboard shortcuts for quick page layout design.
  • Offline Editing with automatic synchronization.
  • Integrated AI Assistance: rewrite, summarize, correct, and translate text.
  • Real-Time Multi-User Collaboration for team knowledge sharing.
  • Granular Access Control to ensure information security and restricted access.
  • Multi-Format Export: `.odt`, `.docx`, `.pdf`, with customizable templates.
  • Easy Deployment via Kubernetes or Docker Compose for flexible self-hosting.

---

GeeeekExplorer/nano-vllm

GitHub Repository Stars: 9.1k License: MIT

nano-vLLM is a lightweight vLLM implementation built entirely from scratch.

Key Features

  • Offline Inference Performance comparable to original vLLM.
  • Minimal Codebase (~1200 lines of Python), easy to read and extend.
  • Optimizations Included: prefix caching, tensor parallelism, Torch compilation, CUDA graphs.
  • API Design similar to vLLM for ease of adoption.
  • Benchmark Results: On RTX 4070, achieves high throughput and faster inference with Qwen3 model.

---

btjawa/BiliTools

GitHub Repository Stars: 4.0k License: GPL-3.0

BiliTools is a cross-platform toolbox for Bilibili, built on Tauri v2.

Key Features

  • Comprehensive Download Support: videos, audio, music, historical and real-time danmaku, with 4K/8K HD and Dolby Vision.
  • Cover & Subtitle Downloads for series, courses, and movies, supporting ASS and SRT formats.
  • AI Summarization to generate Markdown content using Bilibili’s large model technology.
  • Multiple Login Methods: QR code, password, SMS, with auto session refresh for security.
  • Multilingual UI: Simplified Chinese, Traditional Chinese, Japanese, English.
  • Tech Stack: Rust + Node.js, local SQLite storage, compatible with Windows 8.1+ & macOS 11.0+.

---

megadose/toutatis

GitHub Repository Stars: 3.5k License: GPL-3.0

toutatis is a Python 3 tool for extracting information from Instagram accounts.

Key Features

  • Extracts public and partially hidden details (email addresses, phone numbers).
  • Lookup via username or Instagram ID.
  • Retrieves full name, verification status, business flag, follower/following counts.
  • Displays bio, public phone numbers, masked emails, profile links, IGTV post count.
  • Available via PyPI for easy installation and integration.

---

Bonus: AI-Assisted Content Publishing

For creators exploring AI-powered workflows, these tools can pair with cross-platform publishing solutions for maximum reach.

AiToEarn Highlights

  • Open-Source AI Content Monetization Platform.
  • AI-powered content generation, multi-platform publishing, analytics, and model ranking in one ecosystem.
  • Supports simultaneous publishing to Douyin, Kwai, WeChat, Bilibili, Rednote, Facebook, Instagram, LinkedIn, Threads, YouTube, Pinterest, and X.
  • Efficient monetization of AI-powered creativity.

Resources

Read more

Translate the following blog post title into English, concise and natural. Return plain text only without quotes. 哈佛大学 R 编程课程介绍

Harvard CS50: Introduction to Programming with R Harvard University offers exceptional beginner-friendly computer science courses. We’re excited to announce the release of Harvard CS50’s Introduction to Programming in R, a powerful language widely used for statistical computing, data science, and graphics. This course was developed by Carter Zenke.