The problem Deano solves

Authenticity and Quality of Data used for training Machine Learning models is a major concern in the industry. We often have to blindly trust the data provided by centralized data providers, and there is no way to verify the authenticity of the data.

The existing data annotation platforms, such as 'Kaggle,' suffer from centralization, where the data provided is often not rigorously validated for accuracy, leading to potential data quality issues. Moreover, these platforms lack a systematic reward mechanism for annotators, making it less appealing for individuals looking to contribute.

In response to this challenge, Deano presents a groundbreaking solution in the realm of data annotation, designed to address critical issues in the current landscape. It endeavors to create a transparent and decentralized platform that fundamentally transforms the data annotation process.

Deano steps in as a transformative force to rectify these shortcomings. It establishes a decentralized ecosystem where vendors can post data annotation jobs with confidence. Annotators, part of the Deano community, are incentivized with DAN tokens for accurate data labeling, creating a win-win situation for both vendors and annotators. This reputation-based reward system ensures quality and trust in the data annotation process.

Challenges I ran into

Implementing onchain testing for Sismo proved to be a challenging task, as tracking onchain changes was intricate.
While working with XMTP Chat, we faced difficulties adapting to the latest features due to breaking changes in the latest versions. Fortunately, we received valuable support from the community in resolving these conflicts.
Configuring and setting up the workflow for Tableland initially presented challenges. However, with substantial community assistance, we successfully addressed these issues, ensuring its smooth operation.