Inspiration

We researched AI-assisted human manufacturing workshops and their possible use of smart contracts

We built a reference implementation of part of this research that could possibly be economically viable today

This reference implementation was a decentralized marketplace for spare manufacturing capacity

WHITEPAPER

https://docs.google.com/document/d/1lRh0zNcuZgbo1QT5VpaLIroUJ7O6WbT9AguuSTQGv6g/edit

DEMO

https://www.loom.com/share/102c5f3fb81346d286f21ee9444ad67f?sid=a10ffde9-f548-4b04-a3fe-93d888f49fd6

Features

The Spare-Capacity-Marketplace offers the following functionalities:

""Matching Demand with Supply:"" Our marketplace provides a platform where suppliers can submit their spare capacity for a specific demand (task/part/work) and bid for contracts posted by the buyers.

""AI Integration:"" The marketplace utilizes AI to streamline the process of searching for and finding the right item. Our future roadmap involves expanding this AI integration to monitor supplier production and operations procedures for research purposes.

How it Works

EasyCraft.ai

Our project is essentially a simulation, with suppliers modelled as AI agent makerspaces. While the current version of the project simply models excess capacity, future iterations will involve simulating AI agents engaged in direct bidding and more nuanced market interaction.

Future Development Our research and development pipeline includes several exciting updates:

We plan to simulate a scenario where multiple AI agents engage in market interactions and monitor their output. We are considering the integration of AI agents into a game, akin to the concept proposed in the 2023 paper by Christoffersen, A. Haupt, and Hadfield-Menell. This would allow us to measure movement towards a socially optimal outcome. We will be introducing fees for API calls and a premium subscription model for B2B customers to ensure the economic viability of our platform. Potential Applications While our current focus is on manufacturing spare capacity, our implementation can be extended to manage the spare capacity of human resources as well.

What we learnt

This project was created during a Augment AI hackathon 2023, and we are grateful for the time and effort invested by the entire team in making this a reality in less than 48 hours. We are also thankful for the guidance from the sponsors including Gnosis and Bachalau. We learnt a lot in successfully deploying our contracts on the Gnosis test net and the ease of building with guidance from the Gnosis docs and speaking to their dev. team are excited for the next steps ahead. We welcome further contributions to enhance and expand this reference implementation.

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