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Background
A startup based in Illinois sought to create a highly personalized WordPress plugin for bloggers. Their goal was to incorporate the capabilities of Google Search Console and natural language AI to optimize blog articles. One of the challenging aspects of the plugin was to generate a list of related posts for each blog post. The system was required to review thousands of posts and recommend the most suitable matches. This process had to be carried out on a daily basis to ensure that the related posts list for each blog post remained current.
Solution
To leverage the benefits of various technologies and break down the work into smaller, more manageable portions, we developed the project using a microservices architecture. For generating related posts, we utilized NLTK and scikit-learn. Additionally, we accelerated the generation process by utilizing an AWS Lambda function. We adhered to WordPress standards when creating the plugin updater module, ensuring that the WP admin receives a notification as soon as a new version of the plugin is available. To handle background tasks that do not require user interaction, we incorporated AWS SQS to manage the queue.
For deploying the API, we utilized GitHub Actions, while AWS Amplify was used for app deployment.
The deliverable of the project included a WordPress plugin, customer portal (for managing and configuring the plugin), and an admin app for reviewing and managing the system.
Tech Stack
- Angular
- AWS Amplify
- AWS EC2
- AWS Lambda
- AWS SES
- AWS SQS
- Figma
- GitHub
- GitLab
- Google Search Console
- Jenkins
- Laravel
- MongoDB
- MySQL
- NLTK
- Node JS
- Python
- scikit-learn
- Stripe
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