conceptually it's always the same
1) know how your product differentiates itself
2) communicate this excellently
3) serve communication in a format that's easy for agents to understand
all 3 are really important, because otherwise you'll get a pretty washed up brand messaging.
3) is mostly technical, serving markdown, listening to the right headers, for UGC making transcripts available, etc
I find 1) and 2) much more interesting, because these determine how AI will understand your product now and in the future
wanted to see how fast AI would start recommending a new product
we launched Upstash Blob (file storage) 6 weeks ago and optimized everything for AI recommendations
the clankers are really starting to like it 👀
an update on our natural-writing ai model
it can take structural AI slop and turn it into nice, natural human output
the goal isn't to trick ai detectors or anyone into thinking it's human, it just reads so much better than ai slop
@gametheory747 we've tried so much before deciding that training was the best option
list of previous mistakes, humanizer skill, linking to "signs of ai tells" on wikipedia, so much. doesn't work for us
@jjacky it should be! there needs to be a (very) strong overlap between a potential hire and your product. this is naturally rare, but both sides "just know" when you've found a good fit. much harder than engineering imo