Explores user interfaces for LLMs that minimize text chat, using clicks and user context for more intuitive interactions.
The article distinguishes between interactive and transactional prompting, arguing that prompt engineering is most valuable for transactional, objective tasks with LLMs.
A developer explores running LLMs on a Raspberry Pi Pico with memory constraints, creating a witty e-ink display that generates content from news feeds.
A software engineer compares the hype around cryptocurrencies and LLMs, arguing that LLMs provide tangible value while crypto is plagued by scams.
A developer shares experiments building LLM-powered tools for research, reflection, and planning, including URL summarizers, SQL agents, and advisory boards.
Explores autoregressive models, their relationship to joint distributions, and how they handle out-of-distribution prompts, with insights relevant to LLMs.
Explores using GPT-3 text embeddings and a simple classifier to predict the winner of a headline A/B test, potentially replacing traditional testing.
Explores the Reflexion technique where LLMs like GPT-4 can critique and self-correct their own outputs, a potential new tool in prompt engineering.
A reflection on past skepticism of deep learning and why similar dismissal of Large Language Models (LLMs) might be a mistake.
An experiment comparing how different large language models (GPT-4, Claude, Cohere) write a biography, analyzing their accuracy and training data.
An overview of prompt engineering techniques for large language models, including zero-shot and few-shot learning methods.
A tutorial on coding self-attention, multi-head attention, causal attention, and cross-attention in LLMs using Python and PyTorch.
An overview of four different methods for detecting AI-generated text, including OpenAI's AI Classifier, DetectGPT, GPTZero, and watermarking.
