This is a submission for the Amazon Q Developer "Quack The Code" Challenge: Exploring the Possibilities

What I Built

What I was going for initially was an AWS Lambda-Based mini-clone of Cloudinary's cloud-based image processing API platform, but in the process I realized that Amazon's Q Developer cli produced code flexible enough to integrate with most external npm libraries out there.

Both API responsibilities and layers are separated so that external libraries such as pdf conversion utilities and 3d renders can be included in the future dynamically and with minimal effort.

So overall what was made, is a node.js v.17^ compliant image processing utility that currently supports the following:

• Several Image Transformation Operations (Resize, Blur, Grayscale conversion, Format Conversion, Quality Enhancement etc.)
• Image to Pdf Conversion Operations (Image to PDF conversion, PDF Metadata editing, Auto resize to fit-image-to PDF).
• Can be run on the cloud for automation via Lambda API Gateway.
• Includes built-in infrastructure templates to add WAF (Web Application Firewall) with relevant rate limitation for overusing AWS Lambda endpoints.
• Can be run by docker (included in the documentation).

Entire Architecture Presented below

What is not added (yet):

• STL file import /processing for 3d content.
• Handling multiple images in parallel.
• More usable and friendlier UI.
• Additional tests and infrastructure checks.

Demo

Standard use case displayed below:

and in animated capture below:

Code Repository

All code is strictly written by Amazon Q + VS Code (yes even README.md) and presented with the relevant documentation in the publicized repository in the link below:

PicProcessor/
├── src/                    # Source code for the Lambda function
│   ├── index.js            # Main Lambda handler with rate limiting
│   ├── imageProcessor.js   # Image processing logic
│   ├── pdfConverter.js     # PDF conversion functionality
│   └── local.js            # Local development server
├── tests/                  # Unit tests using Mocha and Chai
│   ├── index.test.js       # Tests for the Lambda handler
│   ├── imageProcessor.test.js # Tests for the image processor
│   └── pdfConverter.test.js # Tests for the PDF converter
├── localstack/             # LocalStack initialization scripts
│   └── init-s3.sh          # Script to initialize S3 bucket
├── Dockerfile.lambda       #

How I Used Amazon Q Developer

What I used, was VS Code + Amazon Q plugin.

Codewise, an empty folder and a .gitignore file with Amazon Q plugin + VS Code was created to get me started. (Note: The ignore file was overwritten by Amazon Q later in the process).

Using this little, Amazon Q handles all the workload for you and code modifications from within VS Code when adding or handling file changes.
Overall, no code was written by myself. Only git commands to deploy to remote repository.
AI-Guided Coding process was like so:

• Generated Code based on node version + packages required as dependencies.
• Generated the corresponding tests code parts to run alongside the parts of the code.
• Generated lambda-relevant code for usage of api gateway + corresponding deployment templates. This was also updated to include code for handling uploaded files on the s3 bucket.
• Added more features like WAF (Web Application Firewall) so that to handle rate limiting for the api etc.
• Generated the code files for docker and docker-compose usage.
• Updated when necessary and informed the AI of changes and noted to update the relevant files (Repeat until satisfied).

This was also to have a proof-of-concept of how capable a 2025 AI is in writing code. All infrastructure deployment files + Readme/Licenses added by A.I. assistant and some Amazon Q IDE plugin responses are shown below:

Importantly highlighted part of this is that Amazon Q added (and justified the license referenced in the readme). By default it selected ISC due to the nature of the solution (node.js with Npm packages in the json file).

OVERALL: A major benefit of using the IDE alongside the built-in plugin is that you get to modify your own context and thus, easily bulk-update the files included already in your solution.