Hacker News with Generative AI: Embeddings

Embeddings are underrated (technicalwriting.dev)
Machine learning (ML) has the potential to advance the state of the art in technical writing. No, I’m not talking about text generation models like Claude, Gemini, LLaMa, GPT, etc. The ML technology that might end up having the biggest impact on technical writing is embeddings.
Go library for in-process vector search and embeddings with llama.cpp (github.com/kelindar)
This library was created to provide an easy and efficient solution for embedding and vector search, making it perfect for small to medium-scale projects that still need some serious semantic power.
Interpreting Clip with Sparse Linear Concept Embeddings (SpLiCE) (arxiv.org)
CLIP embeddings have demonstrated remarkable performance across a wide range of computer vision tasks. However, these high-dimensional, dense vector representations are not easily interpretable, restricting their usefulness in downstream applications that require transparency.
Show HN: AIQ – A no-frills CLI for embeddings and text classification (github.com/taylorai)
aiq is a no-frills CLI for embeddings and text classification, inspired by the power of jq. It does 4 things:
Knowledge graphs using Ollama and Embeddings to answer and visualizing queries (github.com/punnerud)
This application uses a local Llama model to answer queries, build embeddings, and create a knowledge graph for exploring related questions and answers.
Fine-Tune Embeddings in Google Colab (research.google.com)
Quaternion Knowledge Graph Embeddings (2019) (arxiv.org)