ryansworks

Great point. I regret not being more systematic about this. I have tried on most popular models since gpt 3.5 launched, but it’s all been very ad hoc with the same general approach of building up a language feature by feature.

It took about 10 hours. Arguably would have taken me 100 to do it manually but likely would have fewer bugs, there’s a few I’m aware of, but I bet there are many.

I started with a basic syntax for expressions and specified a lot up front such as it being a bytecode interpreter and using a recursive descent parser.

I found building it up feature by feature to be much more effective than one shotting an entire feature rich language. Still there was a lot of back and forth.

Testing the limits of vibe coding. Created a programming language 100% via prompting a o4-mini-high, but did carefully review the code. https://github.com/ryanmcdermott/spress

Thank you I had not heard of that! I’ll check it out.

I would tend to agree with you, though my tune might be different if I were an investment advisor!

Thank you! It was better than my second choice which was "Bird of Pair-a-Dice"

That's a very intriguing idea, I hadn't considered the possibilities of looking at public/private photos and sampling color values across to get some randomness. Private is certainly the key. With something like Birdseed, the randomness is totally public just as it is when getting randomness from atmospheric data. If someone figures out which wisps of clouds you are sampling or what search term you are using on Twitter, then the jig is up!

I've been working on a project to learn Go. It's called Spotifind. It basically connects two musical artists by a chain of related artists using Spotify's API. Like the Kevin Bacon game, but for musicians. Here's a link to the project, still learning a lot!

https://github.com/ryanmcdermott/spotifind