One of the problems that I have with LLMs is knowing when they will be useful and how to apply them to any given problem. A lot of it just feels alien to me because with a background in computer programing I've been trained over 30 years that we frequently will get a deterministic set of results. I like to experiment and break new mental ground, so when I saw that Chat GPT had a code interpreter, I was was interested and yet had no clue what I would do with it.
I've been doing a lot of experimentation with Generative Machine Learning and one of the demo's that I've build is called "Ask Paul". You can ask me nearly any front-end web development question and the software will give you a direct answer if it can and links to further reading across the sites that I create content for (this blog, web.dev and developer.chrome.com) You can try it with a couple more queries:
I was honoured to be able to present at the "School of Computer Science and Electronic Engineering" last week with a talk called "Aiming for the Future" [pdf]. I had a lot of fun creating this talk where I could go from the earliest computing with the Difference Engine all the way to today and try and talk about the evolutions of computing and possibility at every transition (I tied the transitions to delivery of content/data).
This post wraps up the series of posts I created about applying ML to some developer tasks that are hard to do programatically. Specifically, I wanted to create a tool that would let me detect if an anchor on a page <a> was styled to look like a button or not (woot, it worked!) You can check out the previous posts here: Scraping images of links and buttons to train an ML model
After I trained a simple machine learning model that can detect if an image looks like a link or a button. I created a web app to help me test it using Deno, Fresh and TensorflowJS. My demo allows for dragging and dropping many images on a page and automatically classifying them.
A guide on how I trained an ML model that detects Buttons and Links in a web page.
After over 20 years I'm getting back in to ML. I looking at a simple (but practical) example that I can get back up to speed on
My world has been shook. I started writing this post in March 2021 and am revisiting it today. I discussed how watching Corridor Crew inspired me to look for ways ML can improve developer experience. After researching, I identified four challenges: inferring what developers meant for the DOM, aiding with accessibility, helping with performance, and creating layouts and images. Finally, I questioned how GPT-Chat has changed my job as a DevRel lead.