Getting started in RL
An in depth guide on getting started with Reinforcement Learning by OpenAI.
Train RL agents to play Pokemon Red - GitHub
# Clone the repo git clone https://github.com/PWhiddy/PokemonRedExperiments.git # Install ffmpeg brew install ffmpeg # Copy ROM to git root path cd PokemonRedExperiments cp /path/to/pokemon-red.gb PokemonRed.gb # Validate rom is valid. Should produce ea9bcae617fdf159b045185467ae58b2e4a48b9a shasum ./PokemonRed.gb # Set up python environment cd baselines uv venv --python 3.10 uv pip install -r requirements.txt # Start the pre-trained RL agent uv run ./run_pretrained_interactive.py
MarkItDown is a lightweight Python utility for converting various files to Markdown for use with LLMs and related text analysis pipelines.
shot-scraper html https://learn.microsoft.com/en-us/azure/devops/boards/queries/wiql-syntax | markitdown > wiql.md
echo "<html>$(shot-scraper html https://learn.microsoft.com/en-us/azure/devops/boards/queries/wiql-syntax -s .content)</html>" | markitdown -o wiql.md
Wallbleed: A Memory Disclosure Vulnerability in the Great Firewall of China
Wallbleed exemplifies that the harm censorship middleboxes impose on Internet users is even beyond their obvious infringement of freedom of expression. When implemented poorly, it also imposes severe privacy and confidentiality risks to Internet users.