Capturing the Ephemeral

A Quest to Log My Daily Reading Journey

As someone who spends a significant portion of their day reading articles, blog posts, and documentation on the web, I often find myself feeling a sense of frustration. Despite the wealth of knowledge I consume, much of it slips through the cracks, leaving me with a vague recollection of what I’ve read, but little tangible evidence to show for it.

It was during a conversation with my partner about the challenges of finding the energy to read in the evenings, especially non-fiction works, that the seed of an idea took root. My partner pointed out that I already did a substantial amount of reading throughout the day, but without a way to capture and organize that information, it was essentially lost to the ether.

That’s when it hit me: what if I could create a system that would automatically log and summarize the articles I read each day? A sort of digital reading journal, capturing the essence of the knowledge I consumed and preserving it for future reference and reflection.

Log and Summarize Firefox History into Obsidian

Thus, a small Python-based application that would integrate with my web browser’s history, filter out irrelevant pages, and leverage the power of large language models to summarize and tag the articles I found valuable was born. These summaries would then be neatly organized and stored in my Obsidian vault, creating a personal knowledge base that would grow with each passing day.

But this project isn’t just about personal convenience; it’s about reclaiming the time and effort we invest in our daily reading. How often have you found yourself revisiting a topic, only to realize you’ve already read about it, but can’t quite recall the details? With this application, that frustration could become a thing of the past.

The vision for this project extends beyond its current capabilities. Currently, the articles are sent to OpenAI for categorization and summarization. I plan to integrate support for various large language models, including Claude-3 and self-hosted models like Lama3. I’m sure on the technical side, it can also be greatly improved, since this is my first Python project. Additionally, I aim to improve the web page fetching process by implementing asynchronous fetching, ensuring that the application can handle a high volume of URLs efficiently.

Ultimately, this project is about finding a practical way to capture and organize the information we consume daily from the web. By leveraging technology, we can turn our ephemeral reading experiences into a permanent, searchable knowledge base.

Whether you’re a fellow web-developer or someone who simply values preserving what you’ve learned, this project might be worth checking out. It’s an exploration into making our online reading habits more fruitful and less fleeting.