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Translated:fire: This repository mainly contains introductory Spring Boot learning tutorials and some commonly used Spring Boot practical project tutorials, including various example code for using Spring Boot, as well as the source code and demos of some practical projects. The practical projects include basic web development and the online blog projects / large enterprise mall systems / front-end and back-end separation practice projects that are widely used today, freeing you from the constraints of all kinds of hello world introductory examples so you can truly master Spring Boot development.

ClosedXML is a .NET library for reading, manipulating and writing Excel 2007+ (.xlsx, .xlsm) files. It aims to provide an intuitive and user-friendly interface to dealing with the underlying OpenXML API.

TranslatedA security knowledge framework maintained by the ffffffff0x team, with content including but not limited to web security, industrial control security, forensics, incident response, blue team infrastructure deployment, post-exploitation, Linux security, and writeups for various vulnerable machines

  • markdown-article
  • linux-learning
  • pentest
  • study

Blendshape and kinematics calculator for Mediapipe/Tensorflow.js Face, Eyes, Pose, and Finger tracking models.

  • vtuber
  • mediapipe-facemesh
  • mediapipe-pose
  • mediapipe-hands

A Claude Code skill bundle for bug hunting and external red-team work - 82 skills, 15 slash commands, 681 disclosed-report patterns curated across 24 core vulnerability classes, plus enterprise identity + infrastructure attack matrices.

🕵️‍♂️ (2-in-1) Email & Username OSINT suite featuring native MCP support for deep data extraction just from a single Email/Username. Analyzes 2720+ actively maintained scan vectors (210+ email / 2510+ username) for security research, investigations, and digital footprinting.

  • osint
  • osint-tool
  • python
  • osint-tools

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.