I explored using ChatGPT's Code Interpreter to analyze browser compatibility data from the BCD project. My goal was to determine the latest released versions of different browsers. While the initial results weren't perfect, through a few iterations of feedback, the Code Interpreter generated a Python script that accurately extracted the desired information. I was impressed by the speed and efficiency of this process, as it accomplished in minutes what would have taken me much longer manually. The generated code also provided a starting point for further analysis, like visualizing browser release timelines. Despite minor imperfections, the Code Interpreter proved to be a powerful tool for quickly extracting and analyzing data.

I created a simple vector database called "Vector IDB" that runs directly in the browser using IndexedDB. It's designed to store and query JSON documents with vector embeddings, similar to Pinecone, but implemented locally. The API is basic with insert, update, delete, and query functions. While it lacks optimizations like pre-filtering and advanced indexing found in dedicated vector databases, it provides a starting point for experimenting with vector search in the browser without relying on external services. The project was a fun way to learn about vector databases and their use with embeddings from APIs like OpenAI.

This blog post introduces a bookmarklet utilizing the EyeDropper API for quickly grabbing color information in Chromium-based desktop browsers. The bookmarklet simplifies color selection by opening the eyedropper tool and returning the chosen color's sRGBHex value in an alert box. A link to a related blog post about creating a similar Chrome extension is also included.

I explored using LLMs for checking web API browser compatibility. Existing LLMs struggle with outdated data, so I experimented with MDN's Browser Compat Data (BCD). Initial trials using raw BCD JSON with GPT-4 had limitations. To improve this, I converted the BCD into English descriptions of API support and loaded it into a Polymath instance. This allows natural language queries about API compatibility across browsers, like "Is CSS Grid supported in Safari, Firefox, and Chrome?" or "When was CSS acos available in Chrome?". The results are promising, but further refinement is needed to ensure accuracy and reliability.

I built Ask Paul, a generative AI demo that answers front-end web dev questions using my content. It leverages Polymath-AI to index content, find related concepts, and generate summaries by creating embedding vectors, using cosine-similarity, and querying OpenAI. The implementation has a UI, a Polymath Client, and a Polymath Host. It's super cool how accessible this tech is now!

I presented "Aiming for the Future" at Bangor University, exploring computing's evolution from the Difference Engine to the modern era, focusing on content/data delivery shifts. I proposed that Machine Learning, especially Generative AI, is the next major computing wave, akin to the Web's rise in the early 2000s, potentially mechanizing mental labor. The Student Expo showcased many final-year projects incorporating AI, from creative tools to practical problem-solving, indicating the growing importance of AI in various fields.

I've added a new feature to time-to-stable that lists experimental APIs across browsers using BCD. This helps developers track experimental APIs, understand their stability, and consider the implications for website integration. It helps answer questions about cross-browser compatibility and potential deprecation, informing decisions about using these APIs.

In this post, I explore the potential of the File System Access API to create local-only web experiences. I discuss how this API, combined with tools like Logseq, allows for on-device data storage outside the browser sandbox. While exciting, I also acknowledge the current limitations, such as the need to re-grant file access on refresh, the lack of a visual indicator for local-only sites, and the difficulty of preventing data exfiltration entirely. Despite these challenges, I believe this area holds significant potential and deserves further exploration.

I'm offering support to those affected by recent layoffs, including those at Google and across the tech industry. I can help with networking, introductions, LinkedIn recommendations, resume reviews, interview preparation, and just being a listening ear. I've been running support calls for over a year and want to continue helping as much as possible. My calendar is open for bookings if you think I can be of assistance.

I created a Lighthouse audit that uses machine learning to detect if an anchor tag looks like a button. This involved training a TensorflowJS model, building a custom Lighthouse gatherer to capture high-resolution screenshots, and processing those screenshots to identify anchors styled as buttons. The audit highlights these anchors in the Lighthouse report. The code for the scraper, web app, and Lighthouse audit are available on GitHub. While there are edge cases, this project demonstrates the potential of using ML for visual inspection tasks in web development.