An interactive 3D map visualizing over 900 agent skills sourced from the awesome-agent-skills repository. The project projects these skills into a latent space, allowing users to explore them through glowing points and a nearest-neighbor web, with options to color by topic cluster or authoring team.
Key features and technical details:
- Uses sentence-transformers/all-MiniLM-L6-v2 for embeddings.
- Employs UMAP for 3D dimensionality reduction.
- Utilizes KMeans clustering and Gemma 4 E2B for automated topic labeling.
- Interactive interface built with Three.js featuring search, tooltips, and info panels.
Key features and technical details:
- Uses sentence-transformers/all-MiniLM-L6-v2 for embeddings.
- Employs UMAP for 3D dimensionality reduction.
- Utilizes KMeans clustering and Gemma 4 E2B for automated topic labeling.
- Interactive interface built with Three.js featuring search, tooltips, and info panels.
Sparse Priming Representations (SPR) is a research project focused on developing and sharing techniques for efficiently representing complex ideas, memories, or concepts using a minimal set of keywords, phrases, or statements, enabling language models or subject matter experts to quickly reconstruct the original idea with minimal context.
Sparse autoencoders (SAEs) have been trained on Llama 3.3 70B, releasing an interpreted model accessible via API, enabling research and product development through feature space exploration and steering.
