We build breakthroughs in long-context for infinite memory + advanced inference solving model scale, context depth & lost in the middle.hypernym.ai 1 AUJoined August 2024
Persistent memory has been one of AI’s longest-standing challenges, but practical breakthroughs in context compression and inference are making reliable recall achievable.
Persistent memory recall at incredibly fast speeds has already been achieved at Hypernym. I think 4-8 weeks before an actual system that largely solves long-running memory is production ready.
Total recall remains the ultimate endgame @HypernymAI
@Altimor Pulled the trigger today and switched 100% of Lindy traffic to @HypernymAI , churning from vanilla models. Saves us millions of $ and we're actually seeing an *increase* in context performance on many core use cases. Transformative for the business.
The common belief today is that once models exceed 256k tokens, context performance falls off a cliff.
At Hypernym, we’ve internally proven this widely accepted “fact” is false.
It’s not a technical limitation — it’s simply the result of an industry moving too fast.
No matter which model we apply our infrastructure to, LITM drastically improves — among many other things.
More to come. 🚀
@josephweinberg@elonmusk Wait until they see Modulum, the next evolution of inference and long context persistent memory enhancements. It would put @SpaceX on steroids.
The explosion of agentic AI and compute shortages are pushing up prices: Average LLM token costs are now $2.12/mil tokens,+12% this week alone and +65% since end of Feb.
> youʼre OpenAI
> hire a small army of ex-Meta ad and monetization people
> a Slack channel just for ex-Facebook staff
> brings in the full “targeted ads” playbook
> launch a browser
> users install it, and OpenAI collects personalized, granular data at scale
> it’s a
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