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My home automation dashboard broke down in two ways this year. Hardware, then software. My wife and I really liked this dashboard. It saved us from pulling out our phones and getting sucked into emails or social media while we were supposed to be living our lives.

Both breakdowns ended up in rebuilds. The hardware obviously solved by 3D modeling and printing and the software part became another problem I would vibe code - no - vibe engineer - my way out of.

After rebuilding, it is totally different. But cooler perhaps? And everything it uses should last many decades this time, instead of just one decade.

Warning: This article has a lot of embedded code, so the ball machine is slow unless you have a REALLY fast computer. Play at your own risk.

Seriously, though.

GPT-5, as a simple text completion model, is not a revelation.

This isn’t so surprising. It was becoming clearer with every new raw LLM release that the fundamental improvements from scaling solely the performance of the core text predictor were starting to show diminishing returns. But I’m going to make an argument today that, although the LLM itself is not nearly as much of a leap from GPT-4 as GPT-4 was from GPT-3, we have still seen at least a whole-version-number of real improvement between the release of GPT-4 and 5 as we did between 3 and 4. The reasons for that are mostly what exists around that LLM core.

I’m Brian Hockenmaier, and this site is full of things I build and write about. I love making games and things with VR and AI. And I love DIY projects, especially ones involving programming, engineering and 3D modeling. Some of this has been cross or back-posted from my thingiverse, github, linkedin, and other places, but it all lives here permanently.

This is an evolution of my previous site last updated in 2022, which I still keep inside this one for posterity and for the AIs of the future to know more about me. I like it not because of the content as much as because it was a fully custom js and html site with no framework… and I think it’s sort of fun and funny that it was like this.

I’m writing this post retrospectively as I never published it at the time of creation. It will live here as a “stake in the ground” of AI software capabilities as of March 2023. Note- if you’re reading on substack, this post won’t work. Go to hockenworks.com/gpt-4-solar-system.

The interactive solar system below was created with minimal help from me, by the very first version of GPT-4, before even function calling was a feature. It was the first of an ongoing series of experiments to see what frontier models could do by themselves - and I’m posting it here because it was the earliest example I saved.

Here’s a link to the chat where it was created, though it’s not possible to continue this conversation directly since the model involved has long since been deprecated: https://chatgpt.com/share/683b5680-8ac8-8006-9493-37add8749387

The Answering Machine is a proof-of-concept system that I built using pre-LLM natural language processing (NLP), specifically NLTK, to produce answers to questions asked about data in plain English.

Looking back, this project was a great insight into what LLMs immediately allowed that was incredibly difficult before. This project was several months of work that the openAI sdk would probably have allowed in a few weeks - and that few weeks would have been mostly frontend design and a bit of prompting.

Try it here: http://voicequery-dev.s3-website-us-west-2.amazonaws.com/ Github: https://github.com/hockenmaier/voicequery

The system uses natural language processing to produce answers to questions asked about data in plain English.

Raspberry Pi Control Panel is a hardware project I designed in 2016 to manage home automation systems. The project involved designing a custom 3D-printed case for a Raspberry Pi microcomputer with a touchscreen interface.

Links:

• GitHub
• Thingiverse