AI/ML / Agent framework
An open-source project for making AI research agents legible, auditable, and traceable through behavioral code and event logs.
The page clearly presents a real AI-related project with a defined problem, a proposed method, and visible implementation details. It explains the goal of making autonomous agent behavior readable and auditable, shows the behavioral-code approach, and notes runnable autoresearch loops plus an append-only events log. That is enough evidence for a useful public listing.
The snapshot does not show setup instructions, supported models, API surface, or a quickstart, so a visitor cannot tell how hard it is to run or adapt beyond the described demo. The actual breadth of the repo is also unclear from the visible page beyond the two showcased workflows.
Last evaluated 117 days ago. Current rank #1454. Down 1 spot in the rankings.
Market-derived RL environments for training agents on quant trading and long-horizon planning under adversarial noise.
A closed-loop controller for LLM training that adapts batch size, learning rate, checkpointing, and pruning to reduce OOM crashes on limited VRAM hardware.
An interactive visualizer showing how a tiny byte-level language model learns 2D token embeddings during training. It includes controls for stepping, pausing, resetting, and adjusting training behavior while plotting the learned vectors.
