Models have gotten good enough that they can (mostly) take on long-horizon, complex tasks. We believe the bottleneck now is that these smart-enough models often lack information about your company, which is scattered in people's heads, Slack threads, stale docs, and in back-and-forth convos with AI.
MCP is useful for getting some info in front of an agent, but there are problems: (1) Once the session dies, so does the insight, so instead of copy-pasting a whole doc each time you're telling the agent to dig through Drive each time - not much of a win; (2) Even when MCP works, what it gathers isn't comprehensive, because people decide things on a whiteboard, brainstorm out loud, post a little in Slack, and scribble the rest in a doc, which leaves the agent working from partial information; (3) And even if it had everything, it doesn't do the meta-reasoning required to do a great job. If you paste in a Notion doc and it won't learn your design taste or your writing style unless you tell it to, and it won't know why a decision was made or when.
As undergrads 5 years ago, we were into the tools-for-thought wave and became power users of Notion, Obsidian, Roam, Anki, real believers in building a second brain. After GPT-3.5 came out we started to realize how much more powerful that second brain could be if an AI could actually read it, because suddenly it would know our backstory, our taste, our preferences, and unlock genuinely new capabilities. That’s why we’re building Hyper.
We know it’s not for everybody! But for people who do want to be on the cutting edge, this is a force multiplier that makes agents faster and better. It increases the number of tasks they can do, and how effectively they do them.
Hyper works by ingesting everything you give it access to, Docs, Slack, Email, Calendar, Granola, and synthesizes it into a knowledge graph of facts and their relationships with embeddings for semantic search. The memory system we’ve built is hybrid, with two modalities. Episodes are the raw source items kept as the source of truth. Facts are the meaning pulled out of each episode, stored as subject-predicate-object records with a plain summary and timestamps for when the fact was introduced and when it was invalidated (subject=person, predicate=works_at, object=company). Facts form a graph with typed edges between them: X is in tension with Y, A is derived from B, J supersedes K. Every time a new fact comes in we update the facts in its neighborhood, so the graph stays current, and that's how we handle stale information. When "we'll ship Friday" is later contradicted by "we're shipping Monday," the new fact supersedes the old one instead of both looking equally true, and we never auto-discard the superseded version, so you can still ask how you landed on Monday.
Every fact carries provenance back to its source and access-control tags for who is allowed to see it. At retrieval we query-expand, then fuse semantic search over embeddings with Postgres full-text search using reciprocal rank fusion, and we only ever evaluate a query against the facts and episodes that person has access to, which means two people on the same team can ask the same question and get different answers. We keep information fresh with webhooks where they exist and polling where they don't, hashing contents to catch changes for sources that don’t handle native dedupe. Agents read and write through two paths: lifecycle hooks in tools like Claude Code, Cowork, Codex, and Cursor, where we inject relevant context on every prompt and pull interesting facts out of every response, and plain MCP tool calls for everything that doesn't expose hooks.
We love it! and so do our early users: one CEO uses Hyper to draft emails in his voice with full company context. What took hours/week now takes minutes and gets sharper each time Hyper learns more how he thinks and how his company is changing. Another YC founder one-shotted a launch video script because Hyper already knew their product, voice, positioning accumulated over months.
We have a 3-day free trial, explained more on our pricing page (https://heyhyper.ai/pricing) and there are more details in our FAQ (https://heyhyper.ai/faq), including things like privacy, compliance, and how we’re different from other “memory” companies..
Give it a spin! break it! and tell us where it falls short: https://heyhyper.ai/. We'd love to build you a 10-star experience :) Comments welcome!
Made me think this was for companies working on self-driving.
How are you handling cases where multiple sources of truth contradict each other?
Does Hyper assume best guess or is there any human in the loop verification?
Unlike many other memory systems, Hyper never actually deletes memories. It constantly reranks them based on confidence, which factors into how they're retrieved. So every statement has a full history and system of record for how it got there, and you can trace (with attribution) why Hyper gives the answers it does. If there's something that Hyper misses, we provide tools in-app and in-terminal-plugin that let a human explicitly correct what Hyper knows.
2. How do you deal with conflicting facts? In tech, the new is constantly replacing the old.
3. Is knowledge extraction real time? How fast is it in general?
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 :)
- as well as the Show HN guidelines, which apply when people are sharing their work:
"Be respectful. Anyone sharing work is making a contribution, however modest."
"When something isn't good, you needn't pretend that it is, but don't be gratuitously negative."
You're welcome to make your substantive points thoughtfully, but please don't post like this.