In part 1 of the hackathon I participated in at work, I set up the GPT4-x-Alpaca LLM with Oobabooga in an AWS EC2 instance. Next up in my hackathon journey was an attempt to make the LLM do something useful and fun. I’ve been casually interested in creating a Multi-User Dungeon or MUD for short. So for part 2 of the hackathon, I dug into the documentation for Evennia, a Python-based MUD game engine.

When I attended AWS re:Invent at the end of 2019, I attended a workshop for using machine learning via Amazon SageMaker to teach an AI how to play blackjack. Seeing as re:Invent was held in Vegas, I decided to take the spirit of Vegas home with me and create my own text-based blackjack game in Go. I added a simple interface so it would be easy to create different AI opponents.

We had yet another hackathon at work. This time around, I wanted to do something with Python. Since we have a gap in test data at work, I decided to create a script to generate oodles of fake test data using a Python library called Faker. It has a number of default providers for generating different types of data. It can generate fake addresses, names, dates, phone numbers, etc. This simple code block:

A couple weeks ago, we had another hackathon at work. This time, I wanted to create something useful that could potentially be adapted by one of our teams. My initial thought was to convert some of our old C++ code to Go and remove a Windows dependency, but after talking with one of my coworkers, we decided to instead create a DNN service for executing a Caffe2 model in Go. We had a third volunteer join us and we set out to achieve our goal.