This is getting personal… 🎧 From Idea to Working Product in 7 Minutes: MP3Tagger AI in Action
How I used what I've learned in the last month to create a simple app that helped me cleanup my mp3 library and complete their metadata in less than 10 minutes
So… last week I decided to venture into the DJ scene.
Well… not exactly.
I decided I wanted to learn how to DJ for my wife’s upcoming birthday party (won’t say her age!). And as I usually do when I want to know something, I started by buying equipment.
That’s why I own two guitars, two bass guitars, and way more books than I’ll ever read. It’s not that I want to learn to play or read them. I just wish I already had — and I’d love to say I know how to play fluently.
Anyway…
Friday night, I dove into the amateur-DJ rabbit hole. After a few hours of YouTube tutorials — and many more (legal!) downloads from DJ music sites — I hit the first big hurdle:
🧨 The Challenge: Real Files, Real Chaos
Every DJ knows the pain. You download a fresh batch of tracks, and your folder turns into metadata hell:
01 - Rolling Stones - Satisfaction
Adele - Someone Like You!
track_003.mp3
Bryan Adams - 05
BACK IN BLACK - ACDC.MP3Some filenames have artist and title reversed. Others are ALL CAPS. Some are missing genre, year, or even file extensions.
So this morning, I decided to fix it.
And by “I,” I mean: me + three Claude Code agents + one external ChatGPT advisor.
Seven minutes later, it was done.
No mockups. No fluff. Just a working CLI tool with multi-layered AI tagging, batch processing, and production-grade error handling.
⏱️ The Timeline: Git Doesn’t Lie
10:51:27 - git commit -m "feat: implement MP3Tagger AI core functionality"
10:58:43 - git commit -m "feat: complete MP3Tagger CLI with all features"Total build time: 7 minutes and 16 seconds.
Normally, this would be a 2–3 week project. That’s a 180x acceleration.
Of course, I also spent ~30 minutes planning (with help from my AI agents), plus another ~20 minutes testing and tweaking. Let’s call it a 1-hour Sunday morning sprint. Not bad.
🔧 The Solution: MVP but Mighty
In those 7 minutes, we shipped:
✅ Multi-Tier AI Processing: Mistral (local) → Claude (Anthropic) → GPT (OpenAI)
✅ Batch Processing: Async, scalable to thousands of files
✅ Checkpoint System: Resume even after a crash
✅ Privacy-First: Audio never leaves your machine
✅ Confidence Scoring: Escalate model quality only when needed
✅ Rich CLI Output: Progress bars, dry-run mode, summaries
✅ Audit Log: CSV export of every rename, tag, and fix
📁 From Chaos to Clean
Before:
track 105.mp3
Sugra - Marroon 5.MP3
daddy yankee song.mp3
Pharrell Williams - Happy!.mp3
After:
Dua Lipa - New Rules [Pop] (2017).mp3
Maroon 5 - Sugar [Pop] (2015.mp3
Daddy Yankee - Gasolina [Reggaeton] (2004).mp3
Pharrell Williams - Happy [Pop] (2013).mp3What started as a junk folder is now a clean, searchable, performance-ready music library.
The tool even added missing metadata like artist, album, genre, and year — all using a local (free!) LLM running directly on my laptop.
Here’s a screenshot of five original MP3s (top), and how they look after cleanup (bottom).
Note that fields like BPM, key, and length are still empty — I haven’t added audio processing yet… but that’s coming next.
🔮 What’s Next?
This was a quick prototype — a fun test that solved a real pain point. But even during the planning stage, ideas for a much bigger product started to emerge:
🎛️ Duplicate Detection & Storage Optimization
Clean up alternate versions, bitrates, or duplicate tags.
🧠 AI-Powered Playlist Builder
“Give me a 90s Latin dance playlist, 120–130 BPM, in minor key.”
🎚️ Track Intelligence Layer
Extract BPM, key, intro/outro length, vocal presence, etc.
💾 Rekordbox/Serato Export
Add cue points, color codes, and export straight into your DJ software.
🖼️ Cover Art & Lyrics Matching
Automatically pull visuals and lyrics from Discogs, Genius, or Spotify.
🌐 Web App + Cloud Backup
Upload your ZIP, clean it in the cloud, download a polished package.
🪄 Smart Setlist Suggestions
Get recommendations based on previous gigs, tempo, or mood.
These are just starting points — but it’s clear this could evolve into a real, revenue-generating product for DJs, collectors, and music nerds like me.
The solo-preneurship era is here. Are you ready?