PH Deck

Users have difficulty efficiently training and running large language models (LLMs) on their own devices due to software that is typically too resource-intensive, requiring substantial hardware and often leading to slow processing times and high energy consumption. Running LLMs on their own devices often results in a need for expensive equipment and consumes significant computational resources.

Runners need to monitor and optimize their pace during workouts, relying on unreliable manual methods or basic fitness apps

Users struggle to showcase their app or product across different devices in a visually appealing way

The old solution involves manually adding device frames or bezels to screenshots or videos, which is time-consuming and requires design skills

Running language models often requires extensive technical knowledge or reliance on cloud services, which can be costly.

lack of privacy when using cloud-based AI services

Cost-intensive cloud API usage

Users are limited to traditional operating systems that are not easily accessible from any device or browser

Runners often face distractions during their workouts due to complicated tracking apps and devices, leading to a less focused and enjoyable running experience. The distractions from complicated tracking apps and devices hinder the overall exercise quality.

Users struggle to create personalized running training plans based on their workout data.

Drawbacks of the old situation: Lack of personalized feedback and training plans, manual generation of interval workouts, inefficient technique improvement, and difficulty in tracking progress.

Developers often face difficulties in running and testing their code snippets across different environments without going through the hassle of setting up the environment each time, which can be time-consuming and inefficient.

Users struggle to find suitable marathons that match their schedule and preferences

Drawbacks: Limited options lead to potential dissatisfaction and compromise in training goals