# Free resources - Maj Korent

Open-source repositories anyone can take: RoboCupJunior robotics, AI coding-agent tooling, machine-learning write-ups and small utilities.

## If you are on a RoboCupJunior team

The vision and simulation work from our national-team seasons, cleaned up and published. No datasets or competition models are included, so nobody gets an unfair run.

- [RoboCup Rescue Vision Model](https://github.com/korentmaj/RoboCup-Rescue-Model-2025): A TinyML victim-detection model for the OpenMV H7: letters and colored victims in real time, under 1 MB of memory, with GPIO and interrupt output for an STM32.
- [Rescue Maze Simulation (Erebus)](https://github.com/korentmaj/RcupSimulation2024SloveniaNational): A full Erebus simulation environment for the Rescue season, so you can test navigation and detection strategy before you commit any of it to hardware.

## If you run AI coding agents

Everything I use to keep several agents working at once. All of it local, dependency-light and free.

- [Agent Session Dashboard](https://github.com/korentmaj/simple-agent-session-manager): A dependency-free local dashboard and terminal UI for Claude Code and Codex sessions: which ones are working, which finished, and which are sitting there waiting for an answer.
- [Obsidian Tracehold](https://github.com/korentmaj/obsidian-tracehold): A structured Obsidian vault that doubles as persistent memory for coding agents: they orient themselves before they start and leave a trace of what they did, as plain Markdown you own.
- [MajBrief](https://github.com/korentmaj/majbrief): A morning briefing agent: it reads a vault, a task list and a calendar, pulls the tech news worth knowing, and emails one plan for the day.

## If you are learning machine learning

Reproductions and write-ups, with the experiments attached, so you can disagree with the numbers rather than take my word for them.

- [Bipropagation Study](https://github.com/korentmaj/bipropagation-study): An independent reproduction and component-level breakdown of bipropagation, benchmarked honestly against tuned modern backpropagation on MNIST and CIFAR-10.
- [Galaxy Classification Study](https://github.com/korentmaj/Classification-of-Galaxies-Using-Machine-Learning): A research pipeline and paper for classifying galaxies with machine learning, datasets and analysis included.
- [XOR by backpropagation](https://github.com/korentmaj/XOR_backpropagation): The smallest honest neural network exercise there is, in TensorFlow, for when you want to see the whole thing at once.

## Small tools that save an afternoon

Nothing clever. They exist because I needed them once and they work.

- [Weka Log File Analysis](https://github.com/korentmaj/WekaLogFileAnalysis): Turns Weka log files into learning curves you can actually read, for comparing training runs.
- [CSV Trimmer](https://github.com/korentmaj/CSVTrimmer): Pulls the columns you want out of a large CSV and writes a smaller one. Preprocessing, minus the notebook.
- [XRPL Bridge Monitor](https://github.com/korentmaj/XRPL_Bridge_Monitor): Watches XRP Ledger wallets: payment tracking, watchlists and enough caching to stay quick.

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