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While writing this blog, I would like to be able to create a workflow that allows me to quickly create posts written in my own voice and post them to the site without a lot of effort. One of the things that causes a lot of effort to take place is having to type out every single word. These days, it’s pretty easy to use a microphone to record new spoken content and have it transcribed into something that can be posted online. I’ve recently been experimenting with a tool called AudioPen, which records my voice, transcribes it, and then submits it to an AI model to rewrite the content a bit. This is particularly interesting because if you’ve ever recorded yourself speaking live and listened back to it, you’ll note that you make a lot of mistakes, the grammar isn’t necessarily correct, and what you generally hear is pretty forgivable if you’re a listener, but not very forgivable if you are a reader of that transcribed text. I really wouldn’t want to publish directly transcribed text to the website without a significant amount of editing, and so it’s nice that it sends it through this AI model, which corrects some of the grammar and enhances some of the wording to really get at the meaning. Unfortunately, the prompt that AudioPen uses is a little more aggressive than I would like and removes a lot of what makes my voice hear, at least my written voice, sound like me. I tend to sound more like an AI model, so I’ve been playing with some other tools to hopefully make this as easy of a workflow, but with less loss of fidelity in the transcription.
I’ve been exploring a workflow that enables me to quickly generate posts in my own voice with minimal effort. One of the biggest challenges is the need to type out every single word. Nowadays, it’s fairly straightforward to record spoken content with a microphone and have it transcribed for online posting. I’ve been experimenting with a tool called AudioPen, which records my voice, transcribes the content, and then enhances it through an AI model. This approach is particularly fascinating because, as many of us might have noticed, live speech often includes numerous mistakes. The grammar might not be on point, and while these errors are generally forgivable to listeners, they are less so to readers of the transcribed text.
Last night, I modified the blog software running this website. Now, it can load different streams of posts into separate loops on the same page display, allowing a chronological listing in the main section and a random listing of posts in the sidebar that changes with every page load. Impressively, this doesn’t affect site performance – a key goal of mine is maintaining the speed of a static site with static HTML files.
While reviewing the site and ensuring it functioned as intended, I read through some older articles and noticed they generally fall into three categories. The first category dates back to before Twitter, featuring one or two sentence posts with links to other sites – many of which are now dead or defunct, rendering these posts mostly irrelevant.
I’ve completely rearranged the way I take notes over the last month, using Obsidian as my primary tool for note-taking.
I’m using a kind of modified PARA system (Projects, Areas, Resources, Archives), where the top-level layers of my notes are organized into buckets based on their volatility. So things at the Projects layer change more quickly than the Areas, which change more quickly than Resources, etc. The names are, in my case anyway, just guidelines for how the documents in each section should be used.
Over the holiday weekend, I put some time into some updates to Sn (the software that makes this blog go) that will hopefully shift it back in the direction I was originally hoping for.
When I initially wrote the app, I hand-rolled my own router function. It was pretty simple, breaking URLs apart at slashes and looking for replaceable variables. It worked very well, was small, and did exactly what I wanted. So why did I update it to the Gorilla mux library?
