How do you compare to Onyx? We've used it for some limited use cases, but one of the real challenges - and one I hope to see a lot of innovation on in the space - was permissioning.
I see in another comment that you encourage each user to build their own dataset with their own permissions, but often this breaks for founders. If I have a Super Secret Personnel Planning Google Doc at a founder level, how can I be the one to set up the system for our company, but ensure that only files that I've explicitly shared with the company are ingested? What if a file needs to be made anyone-with-link-can-access for sharing with a strategic partner, but that shouldn't be indexed for the entire company?
Far too much of the world relies on the security-by-obscurity of public-but-unindexed links, and communications that might look public from a metadata perspective but were carefully designed for a very specific group of people who have verbal/mental context about confidentiality expectations. Being able to categorize by likely confidentiality, and allowing an administrator to partition access on a project and sub-project basis based on that, might be crucial for growth.
My recollection is that Onyx had limited support for some security use cases, but very rudimentary. Hoping you can solve this in a thoughtful way!
1. How do you decide whether to cache the data into a vector database or fetch it on runtime using a tool call ?
2. Slowly all players like Open AI / Claude are trying to provide a somewhat equivalent offering of connecting your workspaces and then providing search on top of it either via direct integrations / mcp servers, how do you see that spanning out ?
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ameyamk
Looks good. Curious, how is auth handled? Lot of docs have permissions etc. Can you clarify how this is handled in both indexing side and searching side of things?
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suprnurd
Looks great! It's cool how you are able to unify multiple sources into a single searchable layer. I’m curious how you chose which connectors to support first (e.g. GitHub, Notion, Slack) and how you plan to scale connector coverage? Thanks!
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candiddevmike
Seems like Google Agentspace but without the UI. Do you folks keep a persistent copy of the data being ingested? How are you planning on solving RBAC? IMO, all of these "search anything" apps are going to be leaky by design unless you're indexing/gathering on the fly using passthrough credentials...
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janwilmake
Hey Lennert, congrats on the launch! Still open to chat about uithub
ripped_britches
Cool deal. How is this different from Glean?
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EGreg
"Give us access to any information on your computer."
And who is "us"?
"Well, our agents, of course. We'll send the information down to our servers, because -- surprise -- we have the GPU infrastructure to run it, and you don't. Don't worry, it's secure."
"Oops! Well don't worry, it's not like the company that bought us will do anything with your data, that we wouldn't have done."
Here's my question...
1) How much can we feasibly run on a consumer-grade GPU today, on-board the computer, either the latest macbook or latest mobile iphone? Does Apple Metal + Silicon ship with any models that are on board the latest iOS 26?
2) How can we extend the security boundary to GPU servers that are attested black boxes that store data encrypted at rest, guaranteed not to train on it and are not owned by some corporation that can peek at the data?
How do you compare to Onyx? We've used it for some limited use cases, but one of the real challenges - and one I hope to see a lot of innovation on in the space - was permissioning.
I see in another comment that you encourage each user to build their own dataset with their own permissions, but often this breaks for founders. If I have a Super Secret Personnel Planning Google Doc at a founder level, how can I be the one to set up the system for our company, but ensure that only files that I've explicitly shared with the company are ingested? What if a file needs to be made anyone-with-link-can-access for sharing with a strategic partner, but that shouldn't be indexed for the entire company?
Far too much of the world relies on the security-by-obscurity of public-but-unindexed links, and communications that might look public from a metadata perspective but were carefully designed for a very specific group of people who have verbal/mental context about confidentiality expectations. Being able to categorize by likely confidentiality, and allowing an administrator to partition access on a project and sub-project basis based on that, might be crucial for growth.
My recollection is that Onyx had limited support for some security use cases, but very rudimentary. Hoping you can solve this in a thoughtful way!
Onyx links for comparison:
https://www.onyx.app/
https://docs.onyx.app/developers/guides/chat_guide
https://docs.onyx.app/admin/connectors/official/
Great release,
1. How do you decide whether to cache the data into a vector database or fetch it on runtime using a tool call ? 2. Slowly all players like Open AI / Claude are trying to provide a somewhat equivalent offering of connecting your workspaces and then providing search on top of it either via direct integrations / mcp servers, how do you see that spanning out ?
Looks good. Curious, how is auth handled? Lot of docs have permissions etc. Can you clarify how this is handled in both indexing side and searching side of things?
Looks great! It's cool how you are able to unify multiple sources into a single searchable layer. I’m curious how you chose which connectors to support first (e.g. GitHub, Notion, Slack) and how you plan to scale connector coverage? Thanks!
Seems like Google Agentspace but without the UI. Do you folks keep a persistent copy of the data being ingested? How are you planning on solving RBAC? IMO, all of these "search anything" apps are going to be leaky by design unless you're indexing/gathering on the fly using passthrough credentials...
Hey Lennert, congrats on the launch! Still open to chat about uithub
Cool deal. How is this different from Glean?
"Give us access to any information on your computer."
And who is "us"?
"Well, our agents, of course. We'll send the information down to our servers, because -- surprise -- we have the GPU infrastructure to run it, and you don't. Don't worry, it's secure."
"Alright, well--"
https://www.wiz.io/blog/38-terabytes-of-private-data-acciden...
"Oops! Well don't worry, it's not like we're the first ones to sell your usage data..."
https://ferrumit.com/resources/it-s-now-legal-for-isps-to-se...
"You see! Well, just send us your DNA we'll analyze it -- with science! I mean with AI..."
"Alright, here is--"
https://www.nytimes.com/2025/05/19/business/regeneron-pharma...
"Oops! Well don't worry, it's not like the company that bought us will do anything with your data, that we wouldn't have done."
Here's my question...
1) How much can we feasibly run on a consumer-grade GPU today, on-board the computer, either the latest macbook or latest mobile iphone? Does Apple Metal + Silicon ship with any models that are on board the latest iOS 26?
2) How can we extend the security boundary to GPU servers that are attested black boxes that store data encrypted at rest, guaranteed not to train on it and are not owned by some corporation that can peek at the data?