Kevin Takov

A smart paludarium is a hybrid terrarium-aquarium environment designed to sustain a self-regulating micro-ecosystem. This project focuses on building a fully automated and remotely managed system using distributed sensors and actuators. The platform continuously monitors environmental parameters such as temperature, humidity, pH levels, and soil moisture. It also enables remote control of key components including lighting systems, misting units, and ventilation modules. All system interactions are accessible through a web-based dashboard, providing real-time control, live data visualization, and continuous monitoring. Additionally, the system integrates fail-safe mechanisms and an alerting layer for anomaly detection.

• ESP32-based distributed sensor nodes for data acquisition.
• Asynchronous communication architecture (MQTT/WebSockets).
• Real-time data logging and historical analytics.

A script that periodically scrapes the Merateonline news website, detects newly published articles, and posts them to a dedicated Telegram channel. It can be customized with your municipality to filter news based on your interests.

• Duplicate protection with a local SQLite database to ensure each article is sent only once
• Resource friendly polling that runs periodically to minimize load
• Telegram notifications via the Bot API in Markdown

https://t.me/lomagna_news

Modern digital files often embed extensive metadata layers that are rarely visible to end users but can expose critical information about origin, structure, and history. This project provides a deep inspection and manipulation toolkit designed to analyze file internals beyond surface level properties. It enables precise control over metadata, allowing users to audit, sanitize, and modify embedded information while preserving file integrity and structure.

• Low level parsing of file structures and metadata segments.
• Detection of hidden or obfuscated metadata fields.
• Format aware analysis supporting multiple file types and encodings.

This project uses Python and hand tracking to control Windows through simple hand gestures. It is designed to translate real-time hand movements into system actions.

• Real-time gesture tracking using OpenCV-based computer vision.
• Gesture based controls for volume, media playback, and window navigation.
• Initial release focused on Windows support.