Experimenting with new ways of publishing technical papers as NFT on Mirror
I’ve recently come across mirror.xyz and discovered new ways of sharing ideas and funding projects through writing and publications. Mirror is essentially a web3 toolkit for crowdfunding. You can write about your ideas — either a blueprint of a project or a very in-depth technical analysis or models that might become someone’s investment tool or any ideas you have, Mirror provides writers with tools that can be used to receive funding for the project.
Writers have the option to launch a crow fund through the article; or let the readers mint the article as NFT, which can be used as a ticket to gain access to future premium material; or drop tokens to grant readers special stakes in the writer’s community. Whatever it is, it creates a platform for specialised content to be supported and funded on the Ethereum network.
I find the idea very interesting and different from the traditional newsletter subscription structure. It is a much better structure for in-depth contents that might contain code, models or any technical driven analysis that require detailed explanations.
I very often receive messages on Medium or Twitter from readers asking me how I generate certain graphs, or how I calculate a specific number or simply how to get the data. Even though I share the links to the graphs or code that produces the content, it is very hard for someone to understand completely the data, the code, or the calculations in a piece of deep quantitative driven analysis. I find it hard to respond to questions on Medium within the word limits and even harder on Twitter with all the scam DMs.
So, I decide to give it a try on Mirror. The idea is to provide my readers with access to all my quantitative work (data, code, model, predictions etc.) and the chance to ask questions or discuss solutions to a specific problem as a community.
In the first article, I published a model I built in January 2022 to predict the NFT floor price. It shows you how to acquire historical NFT data to build a floor price prediction model for Bored Ape Yacht Club (BAYC) collection in Python. The model can be also used to predict other NFT collections by simply changing the NFT contract address.
