Let's hack on llm!

If you have uv installed (and you should!), you can install llm globally in a uv-managed tool environment with:

uv tool install llm

If you want to use models other than OpenAI models, you'll need some extensions:

llm install llm-deepseek llm-gemini llm-openrouter

Additionally, you'll need to set API keys for the providers you want to use:

llm keys set openai
# Enter your API key when prompted

To avoid running up any bills during this hackathon, I recommend setting an openrouter key and then only using their free models, available here. There are lots of great free models, including Deepseek R1 and a "cloaked" pre-release model with 1,000,000-token context window called "cypher-alpha".

Once you've set API keys, you can get the full list of available models with:

llm models

Then you can use the -m flag to specify a model:

llm -m openrouter/openrouter/cypher-alpha:free "How many 'r's are in 'strawberrrrry'?"

You can also alias models to make them easier to use:

llm aliases set cypher-alpha openrouter/openrouter/cypher-alpha:free

Ways I'm using LLM in my daily work

1. Learning Linux with a prompt template

   LLM lets you define a prompt template with input variables. I've created one with the details of my Linux distribution to speed up the process of asking for help with writing shell commands.

   llm --system "You are a Linux command-line copilot running in WSL2 with the following Linux distribution:
   $(cat /etc/*-release)" --save linux

   Now I can use it with the --template option:

   llm "How could I split a PDF by page range?" --template linux

2. Whole-repository Q&A in combination with repomix

   For this one, you'll need repomix, which you can install globally with Node:

   npm install -g repomix

   The repomix command will pack the entire current directory (excluding hidden and untracked files) into a repomix-output.xml file designed for LLM consumption.

   repomix --include "*.sh,README.md"  # Optionally filter with --include and a glob
   repomix --remote https://github.com/boxabirds/awesome-hooks  # Or pack a remote repository

   You can then pass the contents of the repomix-output.xml file to llm with the -f flag to ask questions about the repository:

   llm -f repomix-output.xml "What awesome-hooks are currently available?"

   I often request implementation plans from multiple models and append them to a file for comparison:

   cat repomix-output.xml | llm -m deepseek-reasoner "Write a step-by-step implementation plan to add a user profile page." >> plans.md

   I then review the plans and implement the best one.

3. Progress reporting in combination with git log

   Say the last time you updated a client was June 1st, and you need to report on your progress since then. You can use git to get the list of commits since then, and then pipe the output to llm and have it generate a progress report:

   git log --since="2025-06-01" | llm "In visual markdown, prepare a timeline of development during this period, including stages of work and milestones."

   You can then provide this timeline to your client in a progress report.

4. Data extraction with structured outputs

   You can use the --schema option to specify a schema for JSON output. For instance, here's how we can extract structured information about a Github user from their profile page using curl and strip-tags:

   curl -o- https://github.com/chriscarrollsmith | strip-tags --minify | llm --schema "followers int, following int, tech_stack string"

Hackathon ideas

It's possible to do some pretty ambitious stuff with llm if you use it in a Bash script. Here's an example of how I used llm in a Bash loop to find underrated Github repositories.

But! We've only got an hour, so I suggest you focus on coming up with a proof of concept and a pitch for how to scale the idea.

Here are some suggestions:

1. Give llm a Python tool

   One of the cool things you can do with llm is give it Python functions that it can call. I haven't experimented with this yet myself, but there's all kinds of potential here.

2. Embed some personal files for semantic search

   You could try using llm's embed and similar commands to embed some personal files and make them fuzzy-searchable.

3. Chat with a local model

   The llm tool supports chatting with local models, such as those installed with llama.cpp or ollama. See more in the documentation here.

4. Add captions to your photos with a vision model

   If you select a vision model, you can pass a file attachment with the -a flag:

   llm "Generate a caption for this image" -a image1.jpg

   Try chaining this with other commands to actually add the caption as file metadata.

5. Combine llm with your favorite command-line tool

   Generate nix build scripts. Manipulate structured JSON outputs with jq. Open pull requests with gh. Think about stuff you already do from the command line, and see if you can use llm to do it better!