Now that I’ve spent some time with huggingface.co, specifically their NPL Course (natural language processing) I wanted to combine a couple of the learnings into a simple python script. What I ended up with was a script that could both categorize using a zero-shot-classification model, as well as get sentiment using a sentiment-analysis model. You can interact with this script in one of two ways, first by sending a string as input during execution:

With the current hype on artificial intelligence and platforms like OpenAI’s ChatGPT, I decided it’s about time I explore. I’m not going to lie, I’m not exactly excited about a company named OpenAI being a closed source, for profit platform; however, this has been one of the reason I decided to explore offline, open, and self hosted solutions (in the hopes of creating my own models). If you recall I’ve experimented with python and machine learning in the past, look no further than my 2016 Test your machine learning blog post.

After manually setting up a Lightweight Kubernetes cluster on a few of my Raspberry Pi’s I decided to tear it all down, and rebuild it from scratch using Ansible, and an infrastructure as code strategy. This gives me a chance to catchup a bit on ansible, and keep my documentation and notes for this project in a replay-able Github repository. The project at the moment is rough, and contains sensitive details; however, this is a local lab environment that will most likely be trashed later on, so I’m not worried.

I figured now the perfect time for me to explore the lightweight Kubernetes project k3s. The documentation was really solid, plus I found Alex Ortner’s Medium blog post very helpful. I dusted of a Raspberry Pi 4, and three Raspberry Pi 3’s for this setup. I’m still in the mists of my kubernetes journey, but I wanted to share some of the early primitives, and some of the notes I’ve written for myself.

I’ve heard of https://nostr.com for about a year, but not until recently have I experimenting with it. This post consists of my rough notes as I progressed to sending a nostr message. If you are interested in a bare minimum way to post events to nostr, read on. Generate a new private key $ openssl ecparam -name secp256k1 -genkey -out ec-priv.pem The output here as the file extension notes is PEM:

Was lucky enough to get my hands on a couple Raspberry Pi Pico W recently, and just finished up initial exploration. I started with a by flashing the CircuitPython .UT2 to the pico, like always Adafruit has a phenomenal write up on just that. Next I added copied in modules from the 8.x bundle, simply toss the following into your pico’s /lib/ directory: $ ls -1 /Volumes/CIRCUITPY boot_out.txt code.py lib $ ls -1 /Volumes/CIRCUITPY/lib/ adafruit_httpserver.

Well its been a minute since I’ve toyed around with electronics, but a $4 Raspberry Pi Pico board arriving in the mail changed that. This board packs a boat load of utility for $4, just check out the data sheet and check out all them I2C and SPI pins! I dusted off the last components I tinkered with, along with a handful of wires. After a couple days of tinkering, I ended up with a 433MHz radio packet receiver.

Well seems I forgot to share the most recent demo of my Unity project, I’m taking a break on it, but what I managed to accomplish makes me feel all warm and fuzzy.

This weekend felt like a good time to tackle something new. After successfully prototyping a 3D Disc Golf Game , and then a Game of Life-Simulation , I wanted to explore more of the user interface side. I decided to build an Inventory management system, also decided it best to create a new project, this way I can build the Inventory as a re-usable module for more than one project.

Took a little break from my Disc Golf Game to do a bit of AI programming in Unity . What I ended up with was a simple Game of Life with rabbits trying to survive. Each rabbit has a growing hunger and thirst which if not addressed will result in death. The population is also broken into male and female rabbits which can reproduce. Check out a couple minutes of the simulation running at realtime speed: