Coral-Board & More Obsidian

All right I’ve been working a bit more on Obsidian since the last log and decided to upload my Obsidian notes to GitHub so when I switch between my Mac and my desktop, I don’t have to store separate notes and whatnot. I also just didn’t want to have to pay for Obsidian Sync so it’s just a private repo, synced with the Obsidian Git plugin.

In addition to that I’ve been using Opus 5.5 a lot just because of how cheap and intelligent it is. I can run a bunch of research and just do a bunch of products and I literally don’t even come close to my rate limit, which is fucking awesome.

On top of that I started work on a Coral-Board project, which I was able to get from my good friend Justin. The Coral-Board has an NPU, which is like this chip that’s built to run neural networks using barely any power. It does this by storing numbers at lower precision: INT8 (meaning 8 bits, so 256 possible values) or INT4 (4 bits, 16 values) instead of the traditional 32-bit.

I decided that I’m going to start off with testing if memory can survive being stored that coarsely. I decided to use Hebbian memory, which was inspired by a paper I read last year called “The Dragon Hatchling” (BDH), which is an architecture that uses Hebbian learning. You might recognize it by saying, “Neurons that fire together wire together.” Every time two things show up together, the connection between them gets stronger and the old connections fade over time.

I’m currently running some simulations and then after that I am going to set up the Coral-Board and start running, depending on the results of that, which I sort of have a strong idea of what they’ll be, I think I’m gonna start on a more interesting project. You can follow along in the repo.

The idea is to have an image detection model that uses Hebbian style learning to remember certain shapes or objects, and gets better at it over time.