The problem KudoSaurus solves

The project aims to offer alternative informal tools for fostering company culture, such as peer appreciation and performance reviews, while incorporating AI and NFT technology to bridge the gap between crypto and non-crypto communities.

The idea is to develop an interface that integrates with popular chat platforms like Discord and Slack. Through this interface, text-based kudos given by users are transformed into AI-generated NFT artwork. These tokens of appreciation can then be displayed in users' wallets, avatars, or any other NFT-supporting applications. Additionally, these Kudos NFTs can serve as a company benefit or bonus, as they may have an associated cryptocurrency value.

We plan to demonstrate that generative art can be tailored to align with a company's industry focus or desired social impact. For instance, game companies could have kudos stylized in a game or pixel art visual style, while architecture studios could adopt a specific art style such as art nouveau. Services companies can also customize kudos based on their specific topic focus, and so on.

We are confident that this tool can make a positive and inspiring impact on social interactions within companies and online communities. It has the potential to make new technologies more accessible and integrated into everyday life. Additionally, it can serve as a valuable tool for raising awareness about important topics and movements within diverse local and global communities.

Challenges we ran into

The most demanding aspect involves transforming text-based inputs from users into meaningful prompts for AI image generation. These kudos can often be either quite generic and abstract or very detailed, describing specific situations.

Our approach is to develop a foundational formula that simplifies, frames, and rephrases the text into a suitable format. To achieve this, we employ a two-layered approach using AI. The first layer focuses on adjusting and rephrasing the given text with the desired style and framing, while the second layer generates the actual image.

Moving forward, our next steps include gathering more data inputs and refining the formulas to encompass a wider range of cases, resulting in acceptable output.