Technical Deep Dive into TabCrunch
We were excited to get our hands on something that probably thousands of other small teams like ours want to do: build something cool that pushes the capabilities of LLMs. We had an idea that actually solves a problem we’ve often encountered over the years - going through hundreds of different browser tabs and bookmarks when doing research. While we think we got the job done, it turned out that this project would, in fact, push the boundaries of the current LLMs more than we anticipated. Here is what we learned.
In short, we built TabCrunch - a browser extension that uses Large Language Models (LLMs) to provide bite-size summaries of browser tabs, and to categorise them into groups according to their content. Although this may seem like a straightforward task, it involves a complex process with numerous steps. In this post, we will focus on the steps related to Natural Language Processing (NLP), as well as the pitfalls we encountered and some alternative approaches we tried, as we believe this may be beneficial to other builders.
Here's an overview of all the steps performed for tab summarisation and categorisation. Detailed explanations for each step, including the technologies we chose and our reasoning for selecting them, will follow.
