3 ms·
This might potentially rejuvenate the writing by disrupting habitual patterns, which forces more original choices.
by card_zero 1mo ago
This might potentially
rejuvenate the writing
by disrupting habitual
patterns, which forces
more original choices.
- cottsak 1mo agoCould be a saving grace in the age where the robots come up with all the copy.
- scott_weber 1mo agoAnd here I at first thought the direction the original post was going was "here's how I got a LLM to do this automatically". Seems like it would not be so difficult. Might still be be a win over their innate dispositions.
- deleted 1mo ago[deleted]
- nvme0n1p1 1mo agoTom 7 did something like this. https://www.youtube.com/watch?v=Y65FRxE7uMc https://www.youtube.com/watch?v=Y65FRxE7uMc > BoVeX gives us a controlled tradeoff between these two states. By changing how much it costs for the text to be semantically wrong, we have a dial that allows us to smoothly interpolate between Lorem Epsom and Donald Knuth.
- sipjca 1mo agoit's unbelievable this was not pointed out earlier in this thread, long live tom 7
- someguyiguess 1mo agoRobots would excel at writing that sort of copy I’d assume. (I haven’t tested it but doubt an LLM couldn’t handle that)
- DoctorOetker 1mo ago"perhaps it would be best to let the browsers do it on screens, and typesetting software for printed materials, since they have access to the font, size, text region boundaries, it comes at the small cost that users don't actually get to read what an author wrote, but increasingly this is an LLM anyways, if its a website or legal contract alike"
- jobuildsstuff 1mo ago[flagged]
- chrisfosterelli 1mo agoI suspect LLMs would actually struggle significantly with doing it consistently if given purely as a prompt instruction, but you could always constrict the sampling to force words that create a legitimate chain or fine tune / RL in some signal that would assist with it.
- urams 1mo agoYou are right that this is hard from a prompt alone, but for a slightly stranger reason than the obvious one. The model never sees columns. It sees tokens, and a token can be one character or nine, so "make this line 80 wide" asks it to run a hidden tally over pieces it cannot count by looking at them. Any slip early in a line compounds, and there is no backspace key to reach for once it is committed. That said, the failure is not total. A model can lean on a learned feel for line length, pick shorter or longer synonyms to land close to the target, and rewrite a sentence when it overshoots. It will not be perfect every time, but it lands a lot more often than pure chance would suggest. The sampling trick you mention is the real fix: mask each token that would push a line past the limit, and force a newline the moment the count hits the mark. That converts a fuzzy instruction to a hard constraint with zero training. Fine tuning helps too, but mostly sharpens the same internal counter rather than replacing it. This reply is a small proof; if any line here is off by one, feel free to consider your point demonstrated...
- deleted 1mo ago
- boomlinde 1mo agoDeliberate distractions from the functional end goal benefit other arts just as well, promoting lateral thinking. Brian Eno made a card deck of "oblique strategies" to help you out of blocks. https://enoshop.co.uk/products/oblique-strategies https://enoshop.co.uk/products/oblique-strategies
- card_zero 1mo agoImagine a messageboard that required this writing style in order to make any posts!
- mjmas 1mo agoIncluding the making of the posts into nice rectangular portions of each thirty three characters wide ? Not including the double space at the beginning of each line or any of the punctuation character ends .