MobileChords.com is an interactive chord platform that connects guitar and piano through a shared music-theory model.
Users can select a chord and immediately see its notes on both instruments, explore alternative guitar voicings, hear the result directly in the browser, or reverse the process by entering notes and letting the application identify the chord.
The platform was conceived, implemented and deployed within a single day as a practical experiment in AI-native software delivery. Claude supported product definition and the executable prototype, while Claude Code implemented the production milestones. Human responsibility remained focused on architecture, specification, review, user experience and release decisions.
Status
Live
Availability
www.mobilechords.com
Name
MobileChords
Genre
Music Education Web Application
Subsidiary
–
Time-to-Market Effort
One day from initial concept to production release
Maintenance
Continuous, usage-driven development
Project Highlights
- Guitar and Piano Side by Side: Every selected chord is visualized simultaneously on a guitar fretboard and piano keyboard, making the relationship between instrumental shapes and harmonic structure immediately visible.
- Reverse Chord Identification: Users can enter notes on either instrument and let the chord engine identify matching chords, including inversions such as
C/E. - Harmonic Role Visualization: Notes are color-coded according to their musical function, including root, third, fifth, seventh and extensions.
- Multiple Guitar Voicings: The application generates and ranks playable guitar voicings while prioritizing familiar, musician-friendly chord shapes where appropriate.
- Integrated Audio Playback: Guitar and piano voicings can be played directly in the browser through the Web Audio API.
- Extensive Chord Library: MobileChords supports twelve root notes and nineteen chord types, including major, minor, dominant, suspended, diminished, augmented and extended chords.
- Search-Optimized Chord Pages: The platform generates 228 dedicated chord pages with diagrams, chord tones, explanations, related chords and individual Open Graph images.
- Privacy by Architecture: The chord engine runs entirely in the browser. No account is required, and no entered note or chord data leaves the device.
- Responsive User Interface: The application is optimized for desktop and mobile use, including horizontally scrollable guitar-neck interaction and touch-friendly controls.
Technologies Used
- Frontend: Next.js, React and TypeScript
- Domain Engine: Reusable, dependency-free TypeScript package for chord construction, voicing generation, note-role classification and chord detection
- Project Structure: pnpm monorepo separating the reusable chord engine from the web application
- Testing: Vitest with automated tests covering chord construction, voicing behavior, inversions and detection edge cases
- Audio: Web Audio API for browser-based guitar and piano playback
- Content Generation: Next.js static generation for chord pages, metadata, sitemap entries and Open Graph images
- Deployment: Vercel with automatic production deployment from the main Git branch
- DNS and Edge Delivery: Cloudflare
- AI-Assisted Development: Claude for concept development, architecture and executable prototyping; Claude Code for milestone-based implementation
Architecture
The core music logic is implemented as an independent TypeScript package rather than being embedded directly in the user interface.
This separation allows the same chord engine to be reused by the interactive finder, the statically generated chord pages and potential future applications. It also keeps the musical domain model independent of React, Next.js and the browser user interface.
The production application is organized as a pnpm monorepo:
packages/chord-enginecontains the reusable music-theory and chord-processing logic.apps/webcontains the Next.js application and user interface.- Statically generated pages reuse the same tested engine as the interactive tools.
Architecture Decision Records document important technical and product decisions, while specification and handover files preserve context between AI development sessions.
Challenges and Solutions
Algorithmically correct but unfamiliar guitar shapes
The initial voicing algorithm strongly favored six-string voicings. Some results were musically correct but less useful than the familiar open chord shapes musicians expect.
A curated override layer was introduced for established chord shapes. The algorithm remains responsible for broad voicing generation, while conventional shapes receive explicit priority.
Interactive chord detection across two instruments
The reverse mode had to treat guitar and piano as different input surfaces for the same underlying harmonic information.
Both interfaces therefore operate on shared pitch-class data. Notes selected on one instrument are mirrored on the other, while the engine evaluates possible chord names and inversions independently of the input device.
Mobile interaction
Desktop browser testing did not reveal every usability issue. In particular, fretboard navigation and the positioning of the active input surface required additional review on narrow touch devices.
The mobile interface was refined through targeted device testing and responsive layout adjustments.
AI-generated implementation quality
Fast implementation alone was not accepted as a completion criterion. Each milestone was developed on its own Git branch, tested in the browser and merged only after review.
The workflow combined executable specifications, automated tests, architectural boundaries and explicit human approval.
AI-Native Delivery
MobileChords was built as a practical experiment in a development model where implementation capacity is no longer the primary bottleneck.
Claude helped shape the product, refine the scope and create an executable prototype. Claude Code then implemented the production system in separate milestones.
The human role shifted away from manually producing every line of code and toward:
- product definition,
- architecture,
- acceptance criteria,
- domain validation,
- code and browser review,
- UX testing,
- and release responsibility.
The project demonstrated that AI does not eliminate software engineering. It moves engineering effort toward decisions, verification and system ownership.
A detailed account of the development process is available in the article:
AI-Native Software Delivery in Practice: Building MobileChords in One Day
Future Directions
The next development phases will be guided by actual product usage and search data.
Potential extensions include:
- enharmonic note spelling,
- alternate guitar tunings,
- chord progressions,
- scale and chord relationships,
- additional voicing controls,
- improved music-theory explanations,
- and a mobile application based on the existing shared chord engine.
Conclusion
MobileChords combines music theory, interactive visualization and a lightweight technical architecture in a focused web application.
The project also serves as a concrete example of AI-native software delivery: AI generated substantial implementation capacity, while architecture, validation, product judgment and release responsibility remained under human control.
The result is a fast, private and accessible chord tool that helps musicians understand the relationship between guitar, piano and harmonic structure.
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