# Introduction-SCREAM AI ! **Scream-AI.-Turn Attention Into Income-Your Mindshare, Monetized** In an era where content is abundant but true engagement is rare, we’re redefining how value is created and captured online. Our platform is centered around what would be classified as yap to earn: a system where your voice, opinions and conversations aren’t just heard, they’re rewarded. Built for the attention economy, we convert genuine digital interaction into measurable economic value using advanced AI based engagement scoring models. We are building a multimodal system that is not just text based but will reward videos and images too. 45% of the crypto edutainment that happens in videos or images is currently neglected by existing yap to earn systems. We’re starting where the world yaps the most i.e. Twitter. Through seamless integration, we track, evaluate, and reward users for meaningful interactions such as originality, replies, value threads, and mentions. But we don’t just count likes and retweets. Our system measures signal over noise, analyzing depth, impact, and resonance to ensure that smart, authentic content gets recognized and compensated. Our long-term vision goes far beyond one platform. The same Scream AI scoring engine powering Twitter-based yapping we will be extending to YouTube, Reddit, and even other communities and forums. Whether you're a creator, community builder, meme curator, or researcher, we’re building the rails for a future where every yap you make contributes to your economic graph. This is more than engagement farming, it’s a **proof-of-attention** protocol for the next generation of online credibility. Scream loud. Scream smart. Get rewarded. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://markers.gitbook.io/noah-docs/readme.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.