PH Deck

Enterprise developers use generic AI coding tools that lack customization, security, and full-stack control, leading to potential vulnerabilities and inefficient workflows.

Enterprises struggle with maintaining app and API security due to the complexity of integrating security measures into their continuous integration/continuous deployment (CI/CD) pipeline, which leads to increased security risks and non-compliance issues. Complex security management and integration challenges.

Users with creative or business needs often struggle with the technical complexities of developing AI applications. These applications could significantly enhance their projects or operations, but the lack of coding skills and the high barrier to entry with traditional programming environments prevent many from leveraging the benefits of AI.

Users face challenges in coding, such as slow development processes, lack of innovative project ideas, and insufficient guidance during coding projects.

Developers currently face challenges in manually reviewing code for errors, optimizations, and security vulnerabilities. The drawbacks of this old situation include the inability to efficiently identify and fix all bugs due to human error, time consumption, and the difficulty in maintaining code quality across various programming languages.

Traditional QR codes are plain and not engaging, leading to a lack of interest and interaction from potential users. The plain and not engaging nature of traditional QR codes.

The current situation involves developers and project managers trying to code and manage projects while on-the-go. This often requires them to have access to a full-fledged desktop or laptop setup to run integrated development environments (IDEs), manage code via version control systems like Git, handle dependencies with tools such as npm, and test their code execution. The drawbacks of this old situation include the inability to efficiently work from a mobile device, which limits productivity and flexibility.

Learning to code can be challenging for beginners due to complex concepts and lack of personalized guidance. Traditional learning resources often fail to provide comprehensive explanations tailored to individual learning paces.

Developers and designers manually share and manage UI code across multiple platforms, leading to inefficient discovery and organization of AI-generated UI examples.

Developers using traditional cloud platforms face slow startup times and lack of stateful execution for AI-generated code, leading to inefficiency and limited flexibility in deploying AI agents.