what are the use cases for llm, the CLI tool? I keep finding tgpt or the bulletin AI features of iTerm2 sufficient for quick shell scripting. does llm have any special features that others don't? am I missing something?
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'
llm 'describe this photo' -a path/to/photo.jpg
llm install llm-cmd
llm cmd ffmpeg convert video.mov to mp4
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'
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-...
> had I used o3 to find and fix the original vulnerability I would have, in theory [...]
they ran a scenario that they thought could have lead to finding it, which is pretty much not what you said. We don't know how much their foreshadowing crept into their LLM context, and even the article says it was also sort of chance. Please be more precise and don't give into these false beliefs of productivity. Not yet at least.
- The official docs: https://llm.datasette.io/
- The workshop I gave at PyCon a few weeks ago: https://building-with-llms-pycon-2025.readthedocs.io/
- The "New releases of LLM" series on my blog: https://simonwillison.net/series/llm-releases/
- My "llm" tag, which has 195 posts now! https://simonwillison.net/tags/llm/
``` # AI cli (unstable.python3.withPackages ( ps: with ps; [ llm llm-gemini llm-cmd ] )) ```
looks like most of the plugins are models and most of the functionality you demo'd in the parent comment is baked into the tool itself.
Yea a live document might be cool -- part of the interesting bit was seeing "real" type of use cases you use it for .
Anyways will give it a spin.
Most recently I wanted a script that could produce word lists from a dictionary of 180k words given a query, like "is this an animal?" The script breaks the dictionary up into chunks of size N (asking "which of these words is an animal? respond with just the list of words that match, or NONE if none, and nothing else"), makes M parallel "think" queries, and aggregates the results in an output text file.
I had Claude Code do it, and even though I'm _already_ talking to an LLM, it's not a task that I trust an LLM to do without breaking the word list up into much smaller chunks and making loads of requests.