shalinshah

it's fun!

Curious what you mean, why not? Business is just a group of people creating a product or service so valuable people are willing to pay more for it than it cost to build. In what way do you think Hyper does not fit that model?

Yes that's super fair feedback, we're changing the wording soon :)

Appreciate the thoughtful questions.

1. I'll address this in two parts.

(a) Memory vs. Enterprise Search. I consider search to address targeted, stateless retrieval whereas memory solves temporal, tacit, and derived problems. Glean can tell you why a ticket was filed or answer a specific question regarding a customer call. But in many companies, important questions are broader: "What went wrong the first time we went with this vendor?" "How has our brand shifted in tone over time?". These cannot be answered by a few documents, and it's not obvious whether this information would be in Slack or Notion or Drive. It requires an active, entropy-fighting system that is going to extract information and keep track of how it evolves over time.

(b) Benchmarks: absolutely. Don't want to claim anything before we've published results, but Hyper scores very well on LoCoMo and LongMemEval, and we are constantly trying to bolster our set of evals. We will publish results more openly in the coming weeks. I will caveat though: many SOTA memory providers are converging on the top end of these benchmarks, and yet we don't see mass adoption. We believe that UX affordances are underrated and critical to get "company brains" working in real, messy businesses. Many of our users have come to us from other providers purely because the competition was too difficult to use and maintain across the org.

2. Hyper maintains a graph of information where each node is an extracted "fact." This happens continuously, in the background, live from every connector or connected agent. At insertion-time, new information is compared against relevant information. Our system (a DAG of agentic nodes) determines the relationships between these facts and makes appropriate updates: X derives Y, A updates B. For now, we rely on recency as the primary indicator of conflict (i.e. we assume more recent information is generally more true than old information). We realize that this will need to become more sophisticated, and are iterating.

3. Knowledge extraction is real-time and asynchronous, and should add next to zero latency to any existing system. We continually update the graph in our backend, without relying on a nightly compaction/dreams cycle, so information from the world should be reflected in Hyper's responses in close to real time. Retrieval can be slightly more expensive, but the latency is negligible compared to the overhead of the calling agent. We recognize the importance of performance (we both worked on on-device robotics!) and are happy to publish numbers as we measure them :)

thank you !

Appreciate it! They're actually entirely vibe coded animations via claude code with (a lot of) handholding. Also that's a good question, we use hooks as much as possible which means on each chat turn hyper is being queried by claude asking if there's anything relevant, hyper's agents scour the knowledge-base to figure out if there is, and then return any relevant context back.

Is there a plan to add this for linux / windows? Either way, super cool project

Aside from challenges regarding per inference latency, any other unique challenges you guys faced when deploying nlp models to web? It's pretty cool to see ml being applied more actively in day-to-day web browsing.