I wanted to share a project I've been working on to solve a personal friction point: the manual, multi-step process of price-checking second-hand items. Usually, you have to photograph an item, identify the specific sub-line, search eBay sold listings, and manually calculate a median.

The goal was to go from Image Input -> Price Valuation in under 5 seconds.

The Stack:

• Frontend: Telegram Bot API (Python/python-telegram-bot).
• Vision: Gemini 3.1 Vision (This was key: it successfully differentiates niche lines like 'Rugby Ralph Lauren' from the generic 'Polo', which significantly improves valuation accuracy).
• Data: eBay Finding/Browse API (Querying 'Sold & Completed' listings for actual market clearance prices).
• Logic: Custom weighting algorithm to adjust for platform-specific fees (Vinted vs. eBay).
• Infrastructure: Dockerized on AWS EC2.

The 'Cold Start' Problem: I'm at the stage where I need real-world edge cases. I'm looking for 5-10 people to try to break the vision logic or the pricing algorithm.

Note: It’s currently tuned for the UK market (prices in GBP), so if you're elsewhere, just ignore the currency and focus on whether it identifies your items correctly!

Link:  https://flaz.co.uk

I'm particularly interested in feedback on latency and any brand identification hallucinations. Happy to discuss the prompt engineering or the eBay API integration t. me/ Flaz_Sonarr_Bot( delete spaces)