Who it is forA developer who wants to change or check the prediction data or the UI before proposing a change.
The problemChanges to predictions live in a generated data file, and the site needs a local build to check them.
The outcomeA running local dev server and a production build that you can test against the predictions in data/app-data.json.
What you need first
- A web browser to use llmspeed.dev
- For contributing a benchmark: a machine with llama.cpp and a .gguf model file
- For local development: npm
Steps
- Clone https://github.com/nicorobinreal/llmspeed and open the folder.
- Run
npm install. - Run
npm run devto start the local dev server. - Review data/app-data.json to see the predictions the app uses.
- Run
npm run buildto create the production build with prerendered landing pages and sitemap.
Prompt for your agent
In the llmspeed repo (github.com/nicorobinreal/llmspeed), run `npm install`, then `npm run dev`, and report the local URL. Expected output: a running dev server and a successful `npm run build`. Verify that data/app-data.json loads in the app without errors.
Caveat. The README says predictions in data/app-data.json are generated by the benchmark pipeline, so manual edits may be overwritten on the next run.
Use this in your AI agent
Ask your agent
Using the Deduped MCP, get the setup steps for nicorobinreal/llmspeed and "Run Llmspeed locally to test prediction data"
Add Deduped to Claude Code once
claude mcp add --transport http deduped https://dedupedai.com/mcp
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