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 128 days ago. Current rank #1602. Down 1 spot in the rankings.
A detailed vLLM blog post explaining speculative decoding on AMD GPUs, with mechanics, supported drafting methods, configuration guidance, tuning advice, and benchmark discussion.
A technical blog post showing a tiny diffusion transformer running on an RP2350 microcontroller to generate 128×128 face images, with details on model design, quantization, and on-device inference.
Market-derived RL environments for training agents on quant trading and long-horizon planning under adversarial noise.
