PH Deck

In the current situation, users looking to interact with large language models must rely on public, cloud-based platforms, which pose *privacy concerns* and sometimes *lack control over local data*. Users also face issues with *latency* and *internet dependency* with these solutions.

Users struggle to connect with people in their immediate physical environment (e.g., same hotel, office, gym) and break the ice organically due to generic social platforms lacking location-based, time-bound interactions.

Users struggle with impersonal interactions on traditional social platforms and face public nature of conversations that hinder genuine connections.

Users exploring the capabilities of Large Language Models (LLMs) often face difficulty in coming up with effective prompts to fully leverage these models' potential. This leads to suboptimal usage and experimentation, hindering learning and application in various contexts. The drawbacks include a lack of inspiration, inefficiencies in understanding the full capabilities of LLMs, and an inability to apply these models effectively across different domains.

Users struggle to build and maintain real-time chat functionality at scale due to the lack of purpose-built APIs and infrastructure.

Drawbacks: Limited chat room scalability, absence of typing indicators, message interactions, read receipts, and moderation tools.

Users interested in interacting with language models (LLMs) run into issues with current systems that rely on constant internet connections, are dependent on API calls, entail recurrent costs, and potentially jeopardize user privacy.

Dependent on API calls, entail recurrent costs, potentially jeopardize user privacy

Users previously relied on cloud-based AI models requiring internet, leading to dependency on connectivity, potential data privacy risks, and recurring costs

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 analyze CSV data manually or with basic tools, requiring time-consuming coding skills, privacy risks, and limited accuracy in insights

Users need to access and switch between multiple interfaces for different AI models (OpenAI, Google, Meta, etc.), causing fragmented workflows and reduced productivity.