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@heyhyperai

self-driving company brain. @ycombinator p26

Bergabung Mayıs 2026
7 Mengikuti205 Pengikut
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Hyper
Hyper@heyhyperai·
🚨 HUGE ANNOUNCEMENT 🚨 hyper is now available to download directly from our website
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Hyper@heyhyperai·
12 days until YC Demo Day ⚡️
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Hyper@heyhyperai·
@benln one of these may or may not be sitting in the current batch 👀
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Ben Lang
Ben Lang@benln·
Every startup idea YC has shared in 2026:
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Hyper@heyhyperai·
@nikogrupen the interesting part isn't just building the knowledge base. it's making sure it stays alive as a company changes, learns, and makes decisions.
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Niko
Niko@nikogrupen·
The most powerful agent products will be the ones that effectively marry offline agents -- to build and maintain knowledge bases / indexes / datasets -- with online agents that complete work over those knowledge bases. Contract intelligence is one of our first explorations of what a production, enterprise-grade instantiation of this idea looks like.
Harvey@harvey

Introducing Contract Intelligence. Contract agents take first pass on inbound contracts, apply your playbooks, and generate redlines. See how clauses and negotiated positions are trending across your business. Escalate what needs legal judgment, while agents handle the rest.

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Hyper@heyhyperai·
@ashugarg the "why" is often more valuable than the final state. without it, every new employee and every new agent has to relearn the same lessons. that's where a lot of company knowledge gets lost.
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ashu garg
ashu garg@ashugarg·
A consideration for teams creating agent-based products. Being close to where agents are built is not the same as being in the execution path where decisions happen. To capture decision traces, you have to be present when the decision is being made. Once a decision lands as the final state in a system of record, the “why” is gone. This is the problem nearly every incumbent software company faces today. Salesforce is built on current-state storage. Snowflake and Databricks only receive the output of decisions via ETL. Systems-of-agents startups have the structural advantage because they sit in the write path by default. When an agent triages an escalation or decides on a discount, it captures rationale at the moment decisions become binding. That's a durable architectural edge. Essay from @JayaGup10 and me: foundationcapital.com/ideas/the-comp…
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Hyper@heyhyperai·
@aileenlee @CowboyVC MCP solves access. the harder problem we've seen is context. an agent can have access to every tool in a company and still struggle if it doesn't understand how the company actually operates.
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Hyper@heyhyperai·
@saranormous we've found that once an agent understands enough about a company, answering questions becomes the least interesting thing it can do. the real unlock isn't knowledge. it's action.
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sarah guo
sarah guo@saranormous·
no customers wants a chatbot that reads from help docs, they want a resolution (an agent able to take actions on your behalf to fix the issue, using internal systems as their tools)
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Noah Verrier
Noah Verrier@NoahVerrier·
My oil painting of an Uncrustable
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Hyper@heyhyperai·
for all linux lovers, give us a try at heyhyper.ai!
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Hyper@heyhyperai·
🔶🤝🐧
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Hyper@heyhyperai·
SF got a little more orange 🟠🌉
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shalin shah
shalin shah@_shalinshah_·
this is exactly why we built @heyhyperai, it specifically designed to solve this exact problem. it works by ingesting everything about your company (docs, slack, email, calendar, etc), synthesizing it into a knowledge graph of facts and their relationships (with embeddings for semantic search), then plugs into your AI skills & agents & automations. here's the difference it made for one of our early users.
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