Huh, you're right. It got the random string close, but not exact. I'm still blown away... what in its training set could predict that random string as base64 that closely?

https://chat.openai.com/share/02d616c2-5667-454d-9b72-43dfb1...

What about the 1-1 relation between the binary encodings of ASCII-characters and their base64 equivalents? I dunno, but isn't a base64-encoded character sequence still unambiguous / aren't the encoded character sequences formint a prefix-free grammar when concatenated?

Still very impressive.

What's even more interesting are the edge cases as presented above. Now that we consider almost-perfect natural translation a given, we're obsessed with marrying this to basic rule-based logic.

Me too, in every conversation I had with ChatGPT.

Because marrying the two seems almost magical and scary.

There isn't a 1-1 of ascii character to base64 character is the thing. 3 ascii character = 4 base64 characters, so changing one of the input changes 1 or 2 of the output.

Fair enough, what I meant by 1:1 wasn't the length, more the prefix-freeness thing.

Trivially, there is a 1:1-correspondence between the set of all possible ASCII strings and the image of that set under Base64 encoding, because otherwise it wouldn't be an encoding anymore :)

This doesn't guarantee that a Base64-encoded string can't be identical to a substring of a different Base64-encoded string. And for arbitrarily short substring length (bit), this doesn't make sense either.

So we rely on some definitions and minimal data structures such as bytes.

What I meant was that AFAIK, the ASCII character set might have an image under Base64 encoding that allows for prefix-free encoding given a minimum read length.