Generate, Clean, and Prepare LLM Training Data, All-in-One
Yet another LLM data prep tool competing with Label Studio and Scale AI.

Yet another LLM data prep tool competing with Label Studio and Scale AI.
Native ternary training beats post-training quantization for memory efficiency.
Beats GPT-5 on calibration via GRPO with auto-labeled news data.
Build a LLaMA-style model from scratch with zero ML prerequisites or math.
3.9s cold starts vs 45s+ for quantized models—real infra pain solved tangibly.
The repo openly rejects the 'frozen weights' assumption and tries to prototype an assistant that rewires online — you can see the scaffolding in files like autonomous_ai.py, view_graph.py, a configs folder, a streamlit_apps dir and chroma_data. That's an interesting, contrarian direction, but the project is clearly early-stage: the UI and repo layout are tidy, yet there’s little in-repo evidence of benchmarks, experiments, or reproducible results to back the big claim.