PH Deck

Users manually deploy and manage HuggingFace models, facing unreliable uptime, slow inference without custom GPU optimizations, and lack of integrated performance monitoring.

Architects, real estate developers, and urban planners manually create physical scale models for presentations, which is time-consuming, resource-intensive, and requires specialized craftsmanship.

Users have challenges with deploying fast and unmetered embedding inference in their own VPCs.

Drawbacks of the old situation: Limited access to dedicated embedding servers, slow inference speed, and metered usage leading to cost constraints.

Users struggle to track their intermittent fasting schedules effectively and monitor their weight loss progress.

Users struggle to stay consistent with intermittent fasting, and the drawbacks are not having a proper structure to track progress and set fasting plans.

Designers, brands, and e-commerce businesses struggle with creating lifelike digital fashion models for showcasing clothing and designs.

Users seeking detailed and realistic representations of their designs or ideas through physical models often rely on traditional model making techniques, which can be time-consuming, expensive, and limited in complexity and precision.

Users struggle to track their fasting periods manually by registering start and end times of their fasts.

Drawbacks: This manual tracking method is time-consuming, prone to errors, and lacks a convenient overview of fasting history.

Users need to manually create or purchase 3D models using traditional software/marketplaces, which is time-consuming, expensive, and lacks customization.

Users manually track fasting periods and weight using basic tools like notes or generic fitness apps, leading to inaccurate tracking and lack of motivation due to no specialized features for intermittent fasting.