PH Deck

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

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

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

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.

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

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 manually converting UNIX timestamps to ISO dates (and vice versa) face time-consuming calculations and errors due to timezone mismatches.

Users need to convert files between different formats but use multiple tools or manual methods, leading to time-consuming processes and potential quality loss

Users struggle with inconsistent text formatting across platforms and documents, leading to inefficiencies and a lack of professionalism.

Drawbacks include the time-consuming manual effort required to ensure consistent text format, potential for human error, and the inability to quickly switch between different text styles.