AI

In this tutorial, we’ll explore how to leverage the power of GPT-4 and Langchain to analyze the historical prices of Bitcoin from custom CSV data Link

Great, we can see that the first thing the agent did was default to the “Lex Fridman DB” tool. The input to that tool was generated by the LLM, and is “What did Lex Fridman say about the future of AI?”. Link

A simple web application for a OpenAI-enabled document search. This repo uses Azure OpenAI Service for creating embeddings vectors from documents. For answering the question of a user, it retrieves the most relevant document and then uses GPT-3 to extract the matching answer for the question. Link

Your Personalized AI Tools Catalog Link

Language serves as an interface for LLMs to connect numerous AI models for solving complicated AI tasks! Link

The simplest, fastest repository for training/finetuning medium-sized GPTs. Link

The AI search engine for developers. Link

Build websites with AI. Link

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To enable the functionality described here, select and install the Wolfram plugin from within ChatGPT. Note that this capability is so far available only to some ChatGPT Plus users; for more information, see OpenAI’s announcement. Link

Building a text and image-based journey game powered by, and with, GPT 3.5 Link

Ever since ChatGPT came out, people have been building a personalized ChatGPT for their data. We even wrote a tutorial on this, and then ran a competition about this a few months ago. The desire and demand for this highlights an important limitation of ChatGPT - it doesn’t know about YOUR data, and most people would find it more useful if it did. So how do you go about building a chatbot that knows about your data?

I built an app for question-answering over the full history of Lex Fridman podcasts. It uses Whisper for audio-to-text followed by Langchain for dataset processing and embedding. It uses Pinecone to store embeddings and Langchain vectorDB search to find relevant podcast clips given a user question. It uses UI elements inspired by Mckay Wrigley’s work. Code is here. Link

Prompt Engineering, also known as In-Context Prompting, refers to methods for how to communicate with LLM to steer its behavior for desired outcomes without updating the model weights. It is an empirical science and the effect of prompt engineering methods can vary a lot among models, thus requiring heavy experimentation and heuristics. Link