Concept Prototype · Solo Project
Gear Radar
LIVE PROTOTYPE
The radar runs in your browser — real Canvas animation, full UI, mock data. Set a search, launch a sweep, click the blips.
LAUNCH GEAR RADAR →01
The problem
Finding vintage gear can feel like a full-time side hustle.
Finding vintage gear is a full-time side hustle. A 1972 Thinline Telecaster might show up on Reverb, eBay, Craigslist, Guitar Center's used section, a local shop's Facebook post, or a random classifieds board — all at once, or never at all. Serious collectors and flippers check all of them, manually, every day.
The opportunity window is narrow. A well-priced Jazzmaster might sit for two hours before it's gone. No single platform aggregates across all sources, and none of them score the opportunity — they just list it.
02
The insight
This isn't a search problem. It's a marketplace intelligence problem.
Traditional search returns results. What a gear hunter actually needs is a system that scans continuously, evaluates what it finds, and surfaces only the opportunities worth acting on — ranked by how good a deal they are, not just by when they were posted.
- 8+ sources to monitor simultaneously (Reverb, eBay, Craigslist, Guitar Center, Sweetwater, Facebook Marketplace, local shops, GC Used)
- Scores need to factor in price, condition, rarity, and source reliability
- Speed matters — high-score listings disappear fast
- The interface itself should create urgency, not just inform
03
Why a radar?
Honestly, part of it was just that I wanted it to be fun. A radar felt right — not just as a metaphor but as an experience. It sweeps, it pings, blips light up. There's something satisfying about watching it run even when you're not actively hunting for anything.
The CRT phosphor aesthetic — green glow, scanlines, monospace type, afterglow trail — leans into that. The data feels alive. High-score blips pulse. Brightness decays over time so older results fade naturally. The sweep speed gives it a heartbeat. The moment something pings onto the screen, you feel it.
It's functional, but it was also just genuinely fun to design and build. That came through in every decision.
04
The scoring system
The opportunity score (0–100) is the core of the product. A listing at fair market value scores in the 60s. A below-market listing in excellent condition from a reliable source breaks 80. A once-in-a-career find — original case, pre-CBS, verified condition — hits 90+.
- Price vs. market value — how far below typical asking is this?
- Condition — Near Mint through Fair, weighted by instrument type
- Source reliability — Reverb and Guitar Center score differently than anonymous Craigslist posts
- Rarity — vintage year, limited finish, discontinued model
- Recency — how long has it been listed? (New listings score higher)
Blip color maps to score tier: full phosphor green (80–100 HIGH), yellow-green (60–79 MEDIUM), dim green (below 60 LOW). High-score blips pulse. You can filter by minimum score before sweeping — so if you only want to see 80+ opportunities, the radar stays clean.
05
Proof of concept
I built an earlier version of this using a proprietary AI platform. It worked.
The system surfaced a 2005 Fender AVRI Jazzmaster in Ocean Turquoise Metallic — a finish that rarely comes up at fair price. The listing was on a secondary source that wouldn't have shown up in a standard Reverb search. I bought it. Sold it. $150 net profit.
That's not a huge number — but it validated every assumption behind the concept. The aggregation worked. The scoring worked. The speed advantage was real. A tool like this is most powerful for people who already know what they're looking for and just need to find it faster than everyone else.
This portfolio prototype demonstrates the full UI with mock data. The next step is connecting real APIs: Reverb has a public search API, eBay has the Browse API. Both are viable with a serverless backend to keep keys off the client.
06
What's next
The prototype is a functional UI demonstration. To ship it as a real product:
- Connect Reverb and eBay APIs via serverless functions (Netlify/Vercel)
- Build the real scoring algorithm on top of live listing data
- Add saved searches and push alerts — "ping me when an AVRI Jazzmaster under $2K appears"
- Expand source coverage via scraping or partner integrations
- Let users calibrate the scoring weights for their own preferences
I know I'm not the only one who would use this. Anyone who's spent an afternoon tabbing between Reverb, eBay, and three browser tabs of Craigslist searches would understand it immediately.
TRY THE PROTOTYPE
LAUNCH GEAR RADAR →Back to work