somehow I only recently learned that LLMs need the whole conversation fed back to them on each prompt so their i/o cost scales as O(n^2), something that would be considered completely unacceptable in almost any other production network-accessible software

they ran into this and then didn't figure out a way to externalise the state in some fixed-size form so the LLM can resume from there. you just have to restart the computer and redo everything

in any other program this would be considered a DoS vector and have CVEs issued

gosh I wonder why their costs are completely unmanageable

this also makes it hard to take seriously claims that the marginal cost of inference is not a serious contributor to the resource use of data centres. your program has polynomial running cost and people run it for hours on end

SAMA ET AL: why can't we make a profit with this revolutionary technology
ME: your operating cost scales super-linearly with the value it delivers to users
SAMA ET AL: better tell the government we have invented the angels from evangelion or something

they invented a technology business with super-linear marginal cost of production I am losing my *entire mind*

@jcoglan @buherator This also means you can “gaslight” the LLM by manipulating the conversation before it’s sent back to the LLM

@schrotthaufen @jcoglan And coding agents gaslight the user by injecting "system reminders" prefixes that are invisible to the user but the LLM sees them as user messages and regularly refer back to those as user requirements.