The talk track I've been using is that LLMs are easy to take to market, but hard to keep in the market long-term. All the hard stuff comes when you move past the demo and get exposure to real users.
And that's where you find that all the nice little things you got neatly working fall apart. And you need to prompt differently, do different retrieval, consider fine-tuning, redesign interaction, etc. People will treat this stuff differently from "normal" products, creating unique challenges.
Recent articles
- LLM 0.22, the annotated release notes - 17th February 2025
- Run LLMs on macOS using llm-mlx and Apple's MLX framework - 15th February 2025
- URL-addressable Pyodide Python environments - 13th February 2025