I trained a machine learning model to differentiate between buttons and links on web pages. Using a dataset of ~3000 button images and ~4000 link images, I trained a convolutional neural network (CNN) with added noise for better generalization. Preprocessing included grayscale conversion, dataset diversification with multilingual sites, and image compression. The model performed well in initial tests, correctly classifying button-like and link-like elements. Next, I'll build a web app for easier testing and a Lighthouse audit for website analysis.
This blog post explores how machine learning (ML) can enhance the developer experience. Inspired by Corridor Crew's use of ML in VFX, I initially brainstormed ways ML could automate tedious developer tasks, like accessibility improvements and performance optimization. I also considered ML's potential for generating layouts and images. The emergence of tools like GitHub Copilot and DALL-E-2 significantly impacted my thinking, especially regarding the future of software development and my role as a DevRel lead. Ultimately, the transformative power of GPT-Chat, demonstrated through its ability to generate webpage layouts and populate them with images based on simple prompts, left me questioning the future of my profession and considering the role I might play in training the next generation of AI tools.
