PH Deck

Users struggle to effectively compare large language models (LLMs), with existing methods often being time-consuming and complex. This leads to inefficiencies in selecting or optimizing AI models. The drawbacks include difficulty in swift benchmarking and complexity in performance analysis.

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

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.

Currently, content creators face a limitation in effectively capturing the attention of potential fans or followers online. Traditional methods like lengthy blog posts or videos don't necessarily guarantee engagement or interest. The main drawbacks are that it takes longer duration content to create impact and users often struggle to maintain audience engagement till the end, which dilutes the potential impact.

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.

Users struggle with nobody tracks it and slow signup flows leading to user drop-offs.

Users struggle with understanding complex LLM concepts through textbooks or technical articles, leading to difficulties in applying AI effectively in their projects.

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