From zero coding background to hardware hacker: How Cursor + a Raspberry Pi makes AI fun

What It Covers

Maddie Reese, a non-coder turned hardware hacker, demonstrates three AI-assisted physical computing projects built with Cursor and a Raspberry Pi: a web-to-thermal-printer messaging system, a Twitter-connected pager, and a personal API exposing her coffee order, pet names, and San Francisco restaurant preferences.

Key Questions Answered

• •Hardware purchasing validation: When buying physical components for AI-assisted projects, run at least three verification passes with your AI tool before purchasing. Ask "what if this fails" and "is this still correct" iteratively. Maddie caught multiple incorrect hardware recommendations — including wrong cable types — through this triple-check process before spending money.
• •Brainstorm-first prompting workflow: Start any hardware or software build by dumping the entire idea into Cursor and instructing it to ask clarifying questions until all unknowns are resolved. This interview-style session produces a concrete component list, architecture plan, and purchase recommendations before writing a single line of code or buying anything.
• •Raspberry Pi as universal hardware bridge: A Raspberry Pi connected via Bluetooth to a thermal receipt printer, paired with a Convex database backend, creates a persistent web-to-physical messaging pipeline. The database logs every print attempt independently, providing a fallback record even when the physical printer goes offline or fails after continuous operation.
• •Multi-hop API chaining for legacy hardware: Connecting modern web services to outdated hardware like pagers requires accepting impractical routing. Maddie's Twitter-to-pager pipeline traverses four hops: Twitter API to Cloudflare Worker, Worker to Resend, Resend to Gmail, Gmail to the pager's legacy email address. Accepting architectural messiness unlocks hardware that would otherwise be inaccessible.
• •Personal API as social friction reducer: Publishing a personal API endpoint with structured data — coffee order, dietary preferences, travel dates, favorite restaurants, pet names, time zone — removes the social friction of asking friends for preferences before doing something considerate. The same endpoint becomes machine-readable for AI agents handling agentic commerce or scheduling tasks autonomously.