Investigating Multimodal Large Language Models to Support Usability Evaluation

This project is a academic investigation (7 days old, 0 stars) into a high-value but easily commoditized use case. While it addresses a legitimate pain point—the high cost of expert usability audits—it relies entirely on the inherent reasoning capabilities of third-party MLLMs. There is no proprietary dataset, unique architectural breakthrough, or network effect visible here. Frontier labs (OpenAI, Google, Anthropic) are rapidly improving 'screen understanding' for autonomous agents, which directly overlaps with this project's core functionality. Furthermore, browser vendors (Google via Chrome DevTools/Lighthouse) or design platforms (Figma) are the logical owners of this feature. Without a unique 'data moat' of expert-labeled usability failures that frontier models haven't seen, this remains a thin wrapper around existing LLM capabilities that will likely be absorbed into standard QA/Design tools within the next two release cycles.