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Ahhh my eyes the vibe coded readme
It’s so painful to read the LLM-compressed explanations. I can’t exactly identify what it is, but it’s an immediate tell and literally requires twice the effort to comprehend. For example: > Honest caveat, visible in the clip: the pxpipe arm answered the count first and needed one follow-up nudge to also print the ledger balance in the requested one-line format; the plain arm followed the format on the first try. Leg…
This seems like a pricing hack that burns resources, that when the loophole gets closed the price of OCR will have to rise?
It’s not a loophole, it just happens that encoding information as optical tokens is much more efficient than text.
Educate me: what is an "optical token" when dealing with LLMs?
Would that reduce the number of tokens used too?
Ahhh my eyes the vibe coded readme
It’s so painful to read the LLM-compressed explanations. I can’t exactly identify what it is, but it’s an immediate tell and literally requires twice the effort to comprehend. For example: > Honest caveat, visible in the clip: the pxpipe arm answered the count first and needed one follow-up nudge to also print the ledger balance in the requested one-line format; the plain arm followed the format on the first try. Leg…
To be fair, as you can see in the clip, the two models handled the prompt slightly differently. The pxpipe variant gave the right count initially but needed a quick follow-up to output the ledger balance in a single line. The standard model, on the other hand, nailed the formatting on its first try. We've completely solved readability here on Fable; our only real hurdle left is getting the models to follow formatting constraints perfectly on the very first reply.
Of course, this was just rewritten by another LLM.
This seems like a pricing hack that burns resources, that when the loophole gets closed the price of OCR will have to rise?
It’s not a loophole, it just happens that encoding information as optical tokens is much more efficient than text.
Earlier quoted context omitted.
It’s so painful to read the LLM-compressed explanations. I can’t exactly identify what it is, but it’s an immediate tell and literally requires twice the effort to comprehend. For example: > Honest caveat, visible in the clip: the pxpipe arm answered the count first and needed one follow-up nudge to also print the ledger balance in the requested one-line format; the plain arm followed the format on the first try. Leg…
Here's one rewrite that would have helped: To be fair, as you can see in the clip, the two models handled the prompt slightly differently. The pxpipe variant gave the right count initially but needed a quick follow-up to output the ledger balance in a single line. The standard model, on the other hand, nailed the formatting on its first try. We've completely solved readability here on Fable; our only real hurdle left…
Ahhh my eyes the vibe coded readme
It’s so painful to read the LLM-compressed explanations. I can’t exactly identify what it is, but it’s an immediate tell and literally requires twice the effort to comprehend. For example: > Honest caveat, visible in the clip: the pxpipe arm answered the count first and needed one follow-up nudge to also print the ledger balance in the requested one-line format; the plain arm followed the format on the first try. Leg…
My guess is that it's a known problem, which steered the frontier models into bullet point preference.