You know what the biggest problem with pushing all-things-AI is? Wrong direction.
I want AI to do my laundry and dishes so that I can do art and writing, not for AI to do my art and writing so that I can do my laundry and dishes.
optimize time to value: make sure users reach the “aha moment” in as little time & with as little effort as possible
@pyq_ai achieves this with their interactive model zoo 🦙
Catch our co-founder and CTO, @emilycertified, who will be speaking at the #CerebralValley AI Summit tomorrow! More details here: https://t.co/LKFaSzv9i7
Stop recommending ski jackets in June! Rubber Ducky Labs (@ycombinator W23) builds better recommender systems with ML + human expertise, and it's bring-your-own-model.
🙏 Looking to meet other folks working on recommender systems - intros appreciated!
https://t.co/iKGCxITeKH
@elonmusk can we please have Twitter Blue even though we are less than 90 days old? PS: try out our product (https://t.co/v1QAvUMBFu) please and thank you!
Recently launched 🚀 @pyq_ai makes it easy for developers to build features powered by AI.
💻 MLOPs Made Easy
💰 Scale at a Reasonable Price
💪 Designed for big models
Founded by @araghuvanshi2 & Emily Dorsey + backed by @ycombinator (W23)
https://t.co/NhzwEbq4D3
More I use flan-t5, more I realize Google has given us something very powerful
Best to think of it as a smaller model that can be specialized, won't do everything out of the box
Training multiple flan-t5s and coordinating them for a larger task is the way