PH Deck

Users currently manage LLMS.txt files manually, often needing to switch between websites and text editors.

Website owners currently lack control over how AI crawlers access their site content, leading to unregulated scraping or improper use of data. Existing methods (like robots.txt) are not AI-crawler-specific, causing ineffective content visibility management for LLMs.

Users manually create LLMs.txt files to structure content for AI model training and RAG applications, which is time-consuming, error-prone, and lacks standardization.

Users struggle to find websites that provide LLM-friendly content and guidance due to the lack of a centralized directory.

Website owners struggle to provide information for LLMs (Large Language Models) to effectively use their websites at inference time.

Users manage files manually on Windows, leading to time-consuming and inefficient manual file management and lack of advanced search and organization features.

Users manually convert websites into structured formats for LLM training, which is time-consuming and error-prone.

Users may struggle with the default search engine not providing tailored results or relevant information.

Drawbacks: Limited personalized search results, lack of conversational and context-aware search capabilities.

Current Visual Studio Code users face challenges when trying to discover, search, and install useful extensions.

The old solution involves manually sifting through numerous extensions without effective filtering by category, popularity, and user ratings.

Users need to manage how AI crawlers access website content but lack a standard method. Current practices lead to inefficient content crawling and potential exposure of sensitive data.