FindersKeepers.app VS Diffyn

FindersKeepers.app's answer

Two things eBay does not give you, in one place.

Alerts run on a fixed hourly schedule instead of eBay's own batching, so you hear about a price drop or a new match while it is still actionable. And every item you watch gets a price history, recorded every six hours from the day you add it, which eBay listing pages do not offer at all.

On top of that, listings are scored against similar active listings in the same search, so you see whether an asking price is actually good instead of just seeing a number. Twenty percent or more below the average is flagged as a hot deal.

All of it is free, with no paid tier, and you can search eBay through FindersKeepers without an account or an eBay login.

Diffyn's answer:

Addresses workflow and change management on LLM prompts, provide teams with traceability and visualization of tests across multiple models, provide deeper understading into efficiency of these prompts.

FindersKeepers.app's answer

Because it covers the whole job for free, and it tells you when another tool is the better pick.

Most eBay tools do one piece well. WatchCount reads real sold data. Madealert pushes alerts within seconds of a listing appearing. eBay's own saved searches are already built into the site you are shopping on. FindersKeepers puts hourly alerts, price history, deal scoring, auction countdowns, saved searches, seller watchlists, image search, and collection tracking together at no cost, on US and international eBay marketplaces.

We also publish comparison pages against nine other tools, written in-house, including the parts where the other tool wins. Madealert beats our hourly check on speed and the page says so. That is the same standard the blog holds: every factual claim about how eBay works links to the eBay help page it came from.

Diffyn's answer:

Diffyn is the platform that specializes on both change management and multi-model analysis.

FindersKeepers.app's answer

Next.js on the App Router, with React and Tailwind on the front end, deployed on Vercel.

Listing and pricing data comes from eBay's APIs. A scheduled job re-checks every saved search and watched item on a fixed hourly cadence, records watched-item prices every six hours, and computes deal scores against the comparable active listings in the same search. Alerts go out over email and web push.

Diffyn's answer:

React, Next.js, POSTGRESQL

FindersKeepers.app's answer

Collectors and bargain hunters who shop eBay regularly. Trading cards, watches, coins, video games, vinyl, retired LEGO sets, Funko Pops, vintage clothing, ex-corporate hardware.

These are buyers, not sellers. They already have a mental want list and a rough sense of what a fair price looks like, and they are tired of refreshing the same search, missing an auction by twenty minutes, or finding out later that the listing they passed on was actually underpriced.

The common thread is caring about paying the right price for one specific thing, rather than browsing deals in general.

Diffyn's answer:

Professionals incorporating LLMs or AI tools in their workflow and wants to keep track of changes and test their prompts.

Diffyn's answer:

I started working on Diffyn when I notice that prompting has become an essential part of work across many industries. While there are version control platofrms like github, they are not designed for just prompt management are can be overkill such applications, it is also not integrated natively with various LLMs and relevant tools for users to validate ideas and visualise results properly.