Autopilot for Cursor IDE
I’ve always been looking for a solution to communicate larger technical systems faster. This includes a lot of known details and a technical de-risking plan. It is clear that LLMs need that structure and are capable of digesting it well. I’ve built MCP server for guiding the Cursor IDE through the plan that comes from the LLM assisted pipeline: PRD → TRD → Project files tree → Implementation Plan 1. The whole chain comes from one end-to-end solution 2. Codebase produced is easy to navigate by the automatically managed tracebility 3. Specification updates get reflected in tasks, so the flow…
What it does
In the maker’s words, at launch
I’ve always been looking for a solution to communicate larger technical systems faster. This includes a lot of known details and a technical de-risking plan. It is clear that LLMs need that structure and are capable of digesting it well. I’ve built MCP server for guiding the Cursor IDE through the plan that comes from the LLM assisted pipeline: PRD → TRD → Project files tree → Implementation Plan 1. The whole chain comes from one end-to-end solution 2. Codebase produced is easy to navigate by the automatically managed tracebility 3. Specification updates get reflected in tasks, so the flow is designed to support evolution I use gemini-2.5-pro for requirements capturing and tasks management. Cloud Code and Gemini CLI support is coming soon.
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Today marks ten years, 3653 consecutive days, of running at least one mile every day under the USRSA rules [1]. To celebrate, I built an interactive dashboard that turns a decade of GPX files into charts you can explore. Running has truly changed my life: I've made lifelong friends, explored beautiful places, and more importantly invested into my own health and fitness, which I'm starting to see the positive benefits as I get older. The stack is pretty simple: a NextJS app, with a Postgres database to keep all my running data, and all the stats are pre-computed and cached in Redis, so I…
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