really great perspective. A lot of techniques from the past aren't conceptually wrong, we just have the tools today to make them efficient. The intuition behind them was always reasonable, if you could amortize the cost of making them work at scale. Appreciate the vote of confidence!

And re: the graph -- Postgres stays king here. There are a lot of fancy database mechanisms for building systems like this, but the convenience of a SQL data structure that can tie the graph into structured metadata is pretty unbeatable. This may evolve with time as well.

Appreciate the vote of confidence! And definitely hear the concerns re: data control. We're of the opinion that it's much easier to have a trusted vendor securely handle the system, keep it up-to-date, fix issues with some SLA, etc. and prioritize that part of the user experience. Your data is always yours to export, port, delete, whenever you want it. And should something catastrophic happen, we'd make sure that we give you enough resources/guidance to make sure you don't lose any of your hard-built intelligence.

In your specific example, we can use on external tool calls to supplement limited information. If the memory system isn't rich enough to get a good answer, we can always call back into the original data source for more information. Extraction is good when it's eager (extract more than you think you need) and rely on deduplication + graph orchestration mechanics to keep the graph size bounded.

There is also a capture problem. Imagine you hire an intern and you tell them "John Smith is the CEO of Foo". If they've never heard of Foo, it would be impossible to infer anything about the nature of Foo, unless they're allowed to look into the outside world. No system (even humans!) can capture 100% of information, but that doesn't mean the system is broken. The question is, can you organize and collect enough information to be able to (a) address most queries and (b) initiate deeper investigation if the information is incomplete? We believe the answer is yes.

Intent is very much the same way. Will hybrid search uncover your architecture notes at some point, for an unrelated reason? Almost certainly. Should there be enough surrounding context to indicate that this was written for a spike? Also yes (this is where Claude/markdown memories fail). It should be enough to still be (net) massively useful, and the error rate will go down over time.

Thanks for the congratulations :) There is no magic sauce when it comes to differentiation in this space. Ultimately it comes down to having a very deep understanding of the problem, and a lot of empathy and love for our users. We have certain threads we've discovered along the way that we're pulling on, but a lot of the differentiation in the long run will come from relentless iteration against user feedback. I am not sure what happened to any of the other companies that have attempted this challenge in the past, but we fully intend on capturing the market by not compromising on product/service as we iterate.

The current conflict resolution is fairly simple: always trust humans, and trust recent human info more than old human info. We're very aware that as the knowledge system gets more complex, we'll need more sophistication, including: - Human-in-the-loop verification - Role-based ranking, i.e. be more skeptical when an intern contradicts the CEO

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.

Thanks! We build as much in-house as possible. We've found that this is the only way to really build an excellent end-user experience without putting any of the burden on the user. Regarding sales, we've found that the most useful thing has just been putting the product in people's hands and demonstrating that it gives upfront value. The hard part with memory is that most of the value comes with compounding, so we've had to get creative with how we can show in 5 minutes how Hyper lets you do things you couldn't do before.

totally, honestly it's super inspiring to see how fast the field is moving. That being said though, I think there's a long way to go in this category. Efficient memory across tools, especially in a multiplayer/collaborative setting, is largely unsolved. And it's really hard to build something so elegant and simple that it appeals to all the people in the world that really have this problem, beyond the power-users in industries that already have high AI/engineering adoption.

Every new advancement from the model providers helps unlock new capabilities, but we are confident this "brain" idea is going to be core infrastructure for every company in the future. It extends beyond code and project management: we think about "what does the 'office of the future' look like? Ambient recording in every room? Smart whiteboards that turn drawings -> CAD -> kick off 3d printers?" and it's exciting to see how many unsolved challenges are on that road. Appreciate the support and excited to keep building :)

Update: turns out that when you run the app it installs a hook to run every time you start a session, submit a prompt, or agent ends a turn on all your coding agents / platforms. Zero notice was given, pretty shady. I get it, you want to ingest data, but installing things onto my system without being transparent isn't great.

Interestingly enough, this is how Hyper is structured under the hood. Rather than have one mega-agent whose job it is to go in and try to solve every problem simultaneously, we prefer a "production line" of narrowly scoped agents to make small decisions to keep the knowledge graph up to date. More broadly, agent orchestration is a problem tightly coupled to the "shared brain" problem. Definitely exploring what this means for Hyper as we grow.

Thanks for asking! Existing customers use Hyper consistently to power agents for email drafting, managing inbound, generating marketing materials, improving debugging workflows, and as a "backbone" for long-running parallel coding agents. Having relevant, narrow context at all times greatly improves performance.

Right now our measurements are primarily subjective; we have several customers tell us "Hyper let my agent draft outbound/do market research/run experiments overnight with no intervention or follow-ups, when I would have to constantly babysit it in the past." We have also run Hyper's algorithms on common benchmarks versus more traditional methods. I don't want to claim numbers before we've verified them, but Hyper performs significantly better.

We do not use RAG in the traditional sense (semantic similarity across chunked source documents). We use hybrid retrieval methods to fetch relevant information across our carefully designed knowledge graph, and then have shallow agents consolidate retrieved information into a format that the invoking agent can understand.

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 :)

I can answer that one. They're public domain as a result of a trademark lapse by Hanna Barbera many years ago. Widely available and downloadable on the Internet archive.

Good question. User data is the right of the user. We don’t have automations for everything yet (we’re super early!) but any user has total right to request deletion, updates, or deliverance of their data, which we seek to comply with fully. You can find more information on our privacy and compliance progress here: https://heyhyper.ai/faq

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?

It's a hot space right now! No one yet knows how this is going to shape up. We've realized (through tons of talking to users) that UX is a criminally underrated aspect of building this system effectively. The algorithms, infrastructure, performance, security have to be airtight, that much is true. But the reason that there is no winner yet in this space (even those these types of companies have been around for years) is because they often fail to deeply understand how users work, and how to build their products in a way that solves user problems comprehensively. It is a massive product design challenge; this is a core piece of infrastructure for how companies are going to be built in the future.

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

several of our customers have tried. Turns out the long tail is very long, and the amount of maintenance/edge cases/access control issues/quality issues explodes dramatically. It's a tremendous amount of work to get right, especially as a company grows. If you have been able to vibe code this and see great results, would love to learn what you've done!

Tried it, none of the integrations work, they do not connect, notion, slack, etc... I think you probably posted this a bit too soon, IMHO. :/

Thanks Nils!

Thank you! Very hot space right now and excited to be building in it

thank you !