OpenSource GitHub repository: https://github.com/Protofy-xyz/Protofy

Enhanced the Protofy platform to optimize development efficiency and reduce costs for companies and makers. By integrating advanced AI tools with a user-friendly low-code environment, the platform enables the rapid creation of diverse projects, from IoT systems, webs, apps to machine learning applications. This approach minimizes development time, simplifies system scalability, and promotes cross-functional team involvement.

Introduced low-code features to enable rapid development cycles while maintaining flexibility for customization.

Designed the platform to minimize redundant coding and automate routine processes with AI-driven tools.

Added modules for IoT device management and machine learning model deployment, ensuring compatibility for hybrid projects.

Streamlined workflows to allow users to combine IoT, ML, and web app components in a single environment.

Reduced development costs by automating repetitive tasks and enabling rapid MVP creation.

Simplified system understanding and extension, making it easier to onboard and involve non-technical personnel.

Designed collaborative tools that allow marketing, operations, and technical teams to contribute simultaneously.

Developed templates and AI suggestions to guide non-experts in building and iterating on project ideas.

Enabled straightforward integration with external APIs and systems to adapt to evolving project needs.

Ensured the platform supports seamless scaling, making it easy to add features or accommodate more users.

Created CI/CD pipelines using GitHub Actions.

Created unit and end-to-end tests using jest and play-wright (browserless to scrap websites and create UI end-to-end testing).

Automated repetitive tasks and introduced Dockerized environments to reduce setup time and development costs