@liquidai@mlech26l@saniwhya And we handle this at the data layer, so you get less redundancy => Better compute efficiency AND better model performance.
@GabeStengel@patrick_oshag We already built the intelligence layer for you that makes all your models better, self-improves over time, and vastly increases compute efficiency.
Check us out: https://t.co/BCkT4QDqDZ
@brian_armstrong My prediction: within 12-18 months people won’t have to make the tradeoff between capability and efficiency/cost.
The compositional intelligence wave will wash away the last vestiges of the scarcity mindset soon after.
@levie Hmm if only someone could invent a reasoning engine that drastically reduces LLM token burn (and improves performance)🤔
Oh wait, we did.👉🏽https://t.co/BCkT4QDqDZ
@biosemiote@hillbig Yeah buddy! Agents get compositional reasoning in a set-valued latent space. Our product is ready to go too. You can get a beta key on our website
@biosemiote@hillbig 🔥🔥We’ve already built a semantic lattice over high-dimensional point-wise embeddings for neurosymbolic search across text, tables, images, and graph data.
Check us out: https://t.co/BCkT4QDqDZ
@ebetica That’s a ton of data and compute! Let’s partner, I can give you memory-persistent reasoning at the data layer and orders of magnitude lower token burn.