Jason Van Pham has released Niodoo, a local runtime designed to steer frozen large language models through their hidden states. The project aims to correct last-step errors by injecting noise or "physics forces" during inference to break token loops. This approach allows smaller models to improve performance without fine-tuning, targeting specific failure cases like the Llama strawberry prompt benchmark. The system generates its own telemetry tags and utilizes TDA analysis to monitor internal model states for looping behavior. Van Pham developed this tool organically through months of self-directed research and red-teaming, emphasizing reproducible results from pinned hashes. The code is available on GitHub under the repository Ruffian-L/niodoo-hidden-state-steering.