We beat Gemini Embedding 2 by training only 16M params (open weights)
16M trained params beats Gemini Embedding 2 on audio-text retrieval.
Harmonic coherence as a universal relationship detection operator
Ancient geometry meets Fourier analysis—neat math, but application to LLMs and databases unproven.
ML researchers, database theorists, LLM architecture explorers
Positional encoding schemes (RoPE, ALiBi) · Fourier feature networks · Vector similarity search (FAISS, Pinecone)
16M trained params beats Gemini Embedding 2 on audio-text retrieval.
Replaces vector RAG with dependency graphs; 89% fewer tokens but benchmark setup is contrived.
Beautifully typeset quotes, but a static list lacks technical depth.
6M-token context window on one GPU when vLLM caps at 30K tokens.
Type a name and you can literally watch characters turn into IDs, 16‑dim embeddings get added with positional encodings, and causal attention matrices animate per head — all matched numerically to Karpathy's 244‑line microGPT. The implementation is pure TypeScript (no ML libs) and includes a helpful scrollable sidebar with the reference math, which makes this an excellent, low‑friction learning tool — more pedagogical deep dive than research innovation.
Semantic grep with word embeddings when traditional grep only does syntax.