Med Karim Bchini ports jeffhub.ai use cases to Google's EmbeddingGemma-2 embedding model, replacing a 0.8B decider with dense embeddings and cosine similarity scoring. The Node.js implementation runs CPU-only using quantized ONNX weights (~314 MB), achieving ~170 ms latency per embedding and 100% accuracy across classification and retrieval tasks on small curated datasets.