Simon Willison’s Weblog – “A lot has happened in the world of Large Language Models over the course of 2024. Here’s a review of things we figured out about the field in the past twelve months, plus my attempt at identifying key themes and pivotal moments.” A lot has happened in the world of Large Language Models over the course of 2024. Here’s a review of things we figured out about the field in the past twelve months, plus my attempt at identifying key themes and pivotal moments. In this article:

• The GPT-4 barrier was comprehensively broken
• Some of those GPT-4 models run on my laptop
• LLM prices crashed, thanks to competition and increased efficiency
• Multimodal vision is common, audio and video are starting to emerge
• Voice and live camera mode are science fiction come to life
• Prompt driven app generation is a commodity already
• Universal access to the best models lasted for just a few short months
• “Agents” still haven’t really happened yet
• Evals really matter
• Apple Intelligence is bad, Apple’s MLX library is excellent
• The rise of inference-scaling “reasoning” models
• Was the best currently available LLM trained in China for less than $6m?
• The environmental impact got better
• The environmental impact got much, much worse
• The year of slop
• Synthetic training data works great
• LLMs somehow got even harder to use
• Knowledge is incredibly unevenly distributed
• LLMs need better criticism
• Everything tagged “llms” on my blog in 2024