I always see these threads and think I'm not working on anything, but I just realised it's a lie, I'm exploring a couple of things right now, both heavily AI supported:

Simracing trainer.

I love simracing, I'm moderately competitive and want to improve, and I like to be efficient with my practice. So having access to and using a lot of telemetry, I noticed that the "turn a few laps, load telemetry, compare against reference lap, try again" is not as efficient as it could be.

Also a lot of my telemetry analysis is very rote and "rules based": Look at the biggest laptime delta jump against reference, try to determine the cause among a few usual suspects".

So I have started experimenting with a system that reads the iRacing telemetry in real time, and compares against the reference telemetry live, finding the biggest delta jumps, and trying to find the root cause of the time loss using an increasingly sophisticated GOFAI rule and pattern matching system. Then this report is fed to a cheap LLM call to be condensed into clear advice, and the result goes to the free Microsoft TTS API. So I get instant feedback of where I'm slow and maybe even why.

So far I fear it's mostly making me faster from all the test laps involved more than the advice itself, but when it clicks it does feel magical and really help.

But sometimes I feel like I'm just speedrunning the collapse of 70s AI, as it feels a bit too brittle and situational.

I also have added additional tools for tracking improvement across sessions, finding statistically problematic corners (where am I plain bad?, where am I inconsistent?) or even training my muscle memory by tracing fast driver brake traces using my pedal.

Yay compiler: The other ongoing thing is a clean room reimplementation of Jon Blow's Jai. I've been curious about the language for years, but it's a closed beta and for some reason I've never felt about asking Jon to get into it. I'm not really a game dev so I wouldn't even know what to put in the request.

So now I have 100k+ lines of Rust that can compile a very significant subset of the publicly available Jai source code. I just used various LLMs to condense the public information about the language and come up with a dev plan and started chipping at it. Once I had something in a kind of working state I started with the Way to Jai big tutorial and make sure every example there compiles and works as intended, fixing errors or missing features one by one.

I mostly use Claude Code or Codex, but sometimes what I do is having them guide me into the new feature and doing the edits myself while they explain, so I get to know how things really work under the hood.

It's a silly pointless project, but for some reason I find very satisfying watching it compile the examples.