I find it extremely useful as a research tool. It can talk to probably over 100 models at this point, providing a single interface to all of them and logging full details of prompts and responses to its SQLite database. This makes it fantastic for recording experiments with different models over time.

The ability to pipe files and other program outputs into an LLM is wildly useful. A few examples:

  llm -f code.py -s 'Add type hints' > code_typed.py
  git diff | llm -s 'write a commit message'
  llm -f https://raw.githubusercontent.com/BenjaminAster/CSS-Minecraft/refs/heads/main/main.css \
    -s 'explain all the tricks used by this CSS'

It can process images too! https://simonwillison.net/2024/Oct/29/llm-multi-modal/

  llm 'describe this photo' -a path/to/photo.jpg

LLM plugins can be a lot of fun. One of my favorites is llm-cmd which adds the ability to do things like this:

  llm install llm-cmd
  llm cmd ffmpeg convert video.mov to mp4

It proposes a command to run, you hit enter to run it. I use it for ffmpeg and similar tools all the time now. https://simonwillison.net/2024/Mar/26/llm-cmd/

I'm getting a whole lot of coding done with LLM now too. Here's how I wrote one of my recent plugins:

  llm -m openai/o3 \
    -f https://raw.githubusercontent.com/simonw/llm-hacker-news/refs/heads/main/llm_hacker_news.py \
    -f https://raw.githubusercontent.com/simonw/tools/refs/heads/main/github-issue-to-markdown.html \
    -s 'Write a new fragments plugin in Python that registers issue:org/repo/123 which fetches that issue
      number from the specified github repo and uses the same markdown logic as the HTML page to turn that into a fragment'

I wrote about that one here: https://simonwillison.net/2025/Apr/20/llm-fragments-github/

LLM was also used recently in that "How I used o3 to find CVE-2025-37899, a remote zeroday vulnerability in the Linux kernel’s SMB implementation" story - to help automate running 100s of prompts: https://sean.heelan.io/2025/05/22/how-i-used-o3-to-find-cve-...