PH Deck

Users perform repetitive AI tasks manually (e.g., copying text, applying AI prompts), leading to inefficiency and time consumption.

Users manually manage copied text, losing previously copied content when copying new text. Losing previously copied content when copying new text and manual organization of clippings limit efficiency.

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 often lose important content copied to their clipboard

Users often rely on basic clipboard functions built into operating systems, which can be cumbersome and limited in functionality.

The drawbacks include the lack of efficient clipboard management, inability to sync clipboards in real-time across devices, and challenges in managing reusable content effectively.

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 save and organize copied text and images, leading to inefficiency in managing clipboard history and difficulty detecting duplicates.

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

Users previously managed interactions with different large language models (LLMs) across multiple platforms, leading to fragmented workflows, lack of centralized management, and inefficiency in real-time debugging.