Video Trans Demo
- use Angular and NgRx to build the frontend
- use Express for backend
- use PostgreSQL for DB
- use AWS SDK and AWS S3 to store the video filters
- use ffmpeg to trans the video file formats
Steps by steps, I'm going to the future.
Used Nest.js and GraphQL to create and control API.
Used OpenAI API and PineCone (vector DB) to create a local DB natural language search function.
Used PostgreSQL as the current main DB.
Used Redis for caching the API return results.
Addressed the initial loading performance challenges of traditional Single Page Applications by implementing Next.js and TypeScript as a server-side rendering (SSR) solution.
Reduced CSS style redundancy and improved project maintainability and Responsive Design by employing Tailwind.
Simplified the deployment process and facilitated real-time collaboration among teammates by utilizing Vercel.
Solved the problem of external clicks on complex components and reduced code maintenance costs by customizing the useOutsideClick
hook. This contributed to fostering overall team progress.
Used React JS and Tailwind for front-end development.
Used Three.JS and Google Maps 3D View to import the 3D model in Google Map.
Used AWS Lambda, AWS DynamoDB, and AWS S3 to finish APP deployment.
Collaborate with the Brisbane Government.
Improved front-end development efficiency and enhanced code maintainability by utilizing React.js and Tailwind CSS.
Created and deployed stacks for AWS services to manage resources by utilizing AWS CDK and participating in the construction of a fully serverless project.
Developed TypeScript, C# and AWS Lambda function to encapsulate an API in accordance with the REST style for file (pictures) manipulation in the S3 Bucket and database operations in DynamoDB.
Developed the CI/CD pipeline using GitHub Actions, utilizing a YAML file to enhance the readability of the CI/CD process. The use of parallel tasks in GitHub Actions further improved the overall performance of the CI/CD workflow.
Implemented the researchers' models by utilizing Python and OpenCV2.
Implemented CV detection (road, face, gait, size ...) functions by utilizing Convolutional Neural Network (CNN).
Customized operators on filters in solving the noise of the images with a good effect in tasks of detecting sizes of components (screw, nut, pills, ...).
Worked with the researchers of Global Top 1 Research Institution.
Pursuing a Master's degree in Information Technology, focusing on developing a strong foundation in computer science and software engineering principles. Courses include advanced algorithms, data structures, machine learning, and software architecture.