I see a lot of concerns about the lack of tangible results, high risk for the DAO, and the expectation of a timeline and milestones. To put you at ease, here’s an overview of the work done up until now and expectations for the following weeks:
Scope of the MVP:
We’re set on the scope of the MVP: It will be an NFT sniping tool with the unique proposition of estimating the pricing of tokens based on rarity rank and past sales after the reveal and helping traders take action on it quickly to sweep underpriced NFTs and to sell their own at an appropriate value while there’s enough volume and liquidity on the collection.
This is the concept mockup (Better UI over bellow, I’m not a designer ):
Price estimation:
The idea for the price estimation (purple line) is to do a regression based on the pricing of OpenSea sales of the collection and on a rarity score estimation of each token (we’ll be using the same methodology from rarity.tools, and will be able to estimate it before the official collection rarity reveal). This will allow traders to get a quick estimate of how much their tokens are worth, helping price them accordingly, and also to get an overview of existing listings and how much underpriced or overpriced they are.
Bulk orders
All the existing tools for NFT trading rely on interfaces that are not optimized to do a bunch of trades fast. A good deal of them provides a browser extension that layers some extra functionality on top of OpenSea’s website, which is optimized to buy a single NFT at a time, while the others don’t even have any feature to facilitate orders, just taking the user to OS.
We’ll have a bulk orders feature to streamline the buying, listing, and offering of NFTs without having to get out of our tool. The idea is to let the user pick a bunch of orders he wants to make using our price x rarity chart and then execute all those orders at once, signing the bunch of them at once.
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Buying will be easy as the user can find good deals by looking for NFTs that are underpriced for their estimated rarity score, so he can pick the ones he wants (peeking over them by hovering, if he’s interested in the aesthetics) and select a bunch to buy.
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Listing will also be optimal because the user can see the estimated price of each of his NFTs and quickly drag it to list for sale at an appropriate price, considering past sales and existing listings for NFTs of close rarity.
- Finally, there will be the option to make bulk offers to try and grab NFTs at a lower price than the estimated one (like: offer 20% under predicted price for the top 5% rarer ones) which can be really effective because the offer will probably be over the floor price, and people without tooling for rarity and pricing estimation will see it as a good deal.
Current development status:
We’ve made a bunch of Proof of Concepts for the many components of this solution and are now integrating all of them into a working prototype so we can figure out what works and what doesn’t (like exploring collections of different sizes and seeing how that fits on the chart, figuring out the regression methodology that gives the best fit with the data, and so on) and iterate on the design.
Stuff we already crossed off the list:
- OS listing and sales scraping
- price prediction with regression techniques
- metadata scraping
- rarity estimation
- performant charting lib
Here’s my current playground :
Also, we already have a designer working on making something beautiful out of my scribbles:
Yeah, our mascot is a dolphin
So we’re building this already, the release is planned for at most early December, and we’ve already hit the point of having most of the risks minimized (we know we can deliver this). Also, @DavidBankless, @Gecko007, and @yacht_tea_Ed are deeply involved with NFT trading communities and are active users of basically all of the existing tools, and can vouch for how our’s have a unique feature set and how it’s incredibly valuable in this niche.
That being said, IMHO the risks for this grant are pretty minimal for the DAO, and far outweighed by the upside: Investment returned in 3 months, and then ongoing returns for the lifetime of the project. Also, this is based on a pretty conservative estimation of secondary returns.