The model-agnostic point extends to tooling too. I've watched founders spend weeks evaluating vector databases or agent frameworks when any of the top three would've shipped the product. The bottleneck is almost never the tool, it's the decision paralysis before you start using one. What's your take on when it's actually worth switching versus just sticking with your first choice?
The switch is worth it once your vector database or agent framework has actually failed on a real query or task in production, not because a comparison post says another one benchmarks better. Founders who swap early are usually reacting to someone else's write-up, not their own failure. The ones who wait for that real failure tend to switch once and stick, because the decision came from an actual gap in their own stack, not a spec sheet.
You're the GOAT of AI....love your vibes and all your content is so legit. THANK YOU for everything you hook us up with. Its seriously a gift.
thanks Brynn! u in here already? https://www.skool.com/women-building-ai-2577
Wonderful Gems as usual! Sabrina.
Thank you.
The model-agnostic point extends to tooling too. I've watched founders spend weeks evaluating vector databases or agent frameworks when any of the top three would've shipped the product. The bottleneck is almost never the tool, it's the decision paralysis before you start using one. What's your take on when it's actually worth switching versus just sticking with your first choice?
The switch is worth it once your vector database or agent framework has actually failed on a real query or task in production, not because a comparison post says another one benchmarks better. Founders who swap early are usually reacting to someone else's write-up, not their own failure. The ones who wait for that real failure tend to switch once and stick, because the decision came from an actual gap in their own stack, not a spec sheet.
fantastic! nice one Sab
thanks!
Do you have any advice on the new Chinese AI?