#jupyter_notebook
Retrieval Augmented Generation (RAG) helps large language models (LLMs) answer questions using up-to-date or private information by connecting them to external data sources, unlike fine-tuning which retrains the model on specific data. RAG is useful when you need current, dynamic information without costly retraining, making it ideal for tasks like customer support or knowledge management. Fine-tuning is better for deep expertise in a specialized field but requires more data and effort. Using RAG lets you get accurate, relevant answers quickly by combining the model’s language skills with fresh, specific data, improving usefulness and reliability.
https://github.com/langchain-ai/rag-from-scratch
LinkWarden
Self-hosted, open-source #bookmark + archive manager to collect, and save websites for offline use.
The objective is to have a self-hosted place to keep useful links in one place, and since useful links can go away (see the inevitability of Link Rot), LinkWarden also saves a copy of the link as screenshot and PDF.
https://github.com/Daniel31x13/link-warden
📖 Inside the First Art Gallery for Blind Artists and Audiences #bookmark#raindrop
https://www.thrillist.com/travel/nation/envision-arts-gallery-wichita-kansas