The surprising ease and effectiveness of AI in a loop (via) Matt Webb on the langchain Python library and the ReAct design pattern, where you plug additional tools into a language model by teaching it to work in a “Thought... Act... Observation” loop where the Act specifies an action it wishes to take (like searching Wikipedia) and an extra layer of software than carries out that action and feeds back the result as the Observation. Matt points out that the ChatGPT 1/10th price drop makes this kind of model usage enormously more cost effective than it was before.
- Weeknotes: the Datasette Cloud API, a podcast appearance and more - 1st October 2023
- Things I've learned about building CLI tools in Python - 30th September 2023
- Talking Large Language Models with Rooftop Ruby - 29th September 2023
- Weeknotes: Embeddings, more embeddings and Datasette Cloud - 17th September 2023
- Build an image search engine with llm-clip, chat with models with llm chat - 12th September 2023
- LLM now provides tools for working with embeddings - 4th September 2023