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Re: Slack chat: Glean.com does it for the enterprise I work at: It consumes all of our knowledge sources including Slack, Google docs, wiki, source code and pr

Re: Slack chat: Glean.com does it for the enterprise I work at: It consumes all of our knowledge sources including Slack, Google docs, wiki, source code and provides answers to complex specific questions in a way that’s downright magical. I was converted into a believer when I described an issue to it, pointers to a source file in online git repo and it pointed me to another repository that my team did not own that controlled DNS configs that we were not aware about. These configs were the reason our code did not behave as we expected.

aurareturn 2y ago
Thanks. I didn't know that existed. But does it scale? Would it still work if large companies with many millions of Slack messages? I suppose one reason Slack doesn't have a solution yet is because they're having a hard time getting it to work for large companies.
- deleted 2y ago
  [deleted]
- hn_throwaway_99 2y ago
  Yeah, Glean does this and there are a bunch of other competitors that do it as well. I think you may be confused about the length of the context window. These tools don't pull all of your Slack history into the context window. They use a RAG approach to index all of your content into a vector DB, then when you make a query only the relevant document snippets are pulled into the context window. It's similar for example to how Cursor implements repository-wide AI queries.
  - aurareturn 2y ago
    I'm aware that one can't feed millions of messages into an LLM all at once. The only way to do this now is to use a RAG approach. But RAG approach has pros and cons and can miss crucial information. I think context window still matters a lot. The bigger the window, the more information you can feed in and the quality of answer should increase. The point I'm trying to make is that increase context window will require more compute. Hence, we could still just be in the beginning of the compute/AI boom.
    - reissbaker 2y ago
      We might be even earlier — the 90s was a famous boom with a fast bust, but to me this feels closer to the dawn of the personal computer in the late 70s and early 80s: we can automate things now that were impossible to automate before. We might have a long time before seeing diminishing returns.
_huayra_ 2y ago
This is the main "killer feature" I've personally experienced from GPT things: a much better contextual "search engine-ish" tool for combing through and correlating different internal data sources (slack, wiki, jira, github branches, etc). AI code assistants have been a net neutral for me (they get enough idioms in C++ slightly incorrect that I have to spend a lot of time just reading the generated code thoroughly), but being able to say "tell me what the timeline for feature X is" and have it comb through a bunch of internal docs / tickets / git commit messages, etc, and give me a coherent answer with links is amazing.
- aurareturn 2y ago
  This is partly why I believe OS makers, Apple, Microsoft, Google, have a huge advantage in the future when it comes to LLMs. They control the OS so they can combine and feed all your digital information to an LLM in a seamless way. However, in the very long term, I think their advantage will go away because at some point, LLMs could get so good that you don't need an OS like iOS anymore. An LLM could simply become standalone - and function without a traditional OS. Therefore, I think the advantage for iOS, Android, Windows will increase in the next few years, but less powerful after that.
  - thwarted 2y ago
    An LLM is an application that runs on an operating system like any other application. That the vendor of the operating system has tied it to the operating system is purely a marketing/force-it-onto-your-device/force-it-in-front-of-your-face play. It's forced bundling, just like Microsoft did with Internet Explorer 20 years ago.
    - aurareturn 2y ago
      I predict that OpenAI will try to circumvent iOS and Android by making their own device. I think it will be similar to Rabbit R1, but not a scam, and a lot more capable. They recently hired Jony Ive on a project - it could be this. I think it'll be a long term goal - maybe in 3-4 years, a device similar to the Rabbit R1 would be viable. It's far too early right now.
mvdtnz 2y ago
My workpalce uses Glean and since it was connected to Slack it has become significantly worse. It routinely gives incorrect or VERY incomplete information, misattributes work to developers who may have casually mentioned a project at some time and worst of all presents jokes or sarcastic responses as fact. Not only is it an extremely poor source of information, it has ruined the company's Slack culture as people are no longer willing to (for lack of a better term) shitpost knowing that their goofy sarcasm will now be presented to Glean users as fact.
- dcsan 2y ago
  Maybe have some off limits to glean shit posting channels?
- rendang 2y ago
  Interesting. I still find it to be a net positive, but it is amusing when I ask it about a project and the source cited is a Slack thread I wrote 2 days prior
sofixa 2y ago
> Glean.com does it for the enterprise I work at: It consumes all of our knowledge sources including Slack, Google docs, wiki, source code and provides answers to complex specific questions in a way that’s downright magical There are a few other companies in this space (and it's not something that complex to DIY either); the issue is data quality. If your Google Docs and wikis contain obsolete information (because nobody updated them), it's just going to be shit in, shit out. Curating the input data is the challenging part.

aurareturn 2y ago

Thanks. I didn't know that existed. But does it scale? Would it still work if large companies with many millions of Slack messages? I suppose one reason Slack doesn't have a solution yet is because they're having a hard time getting it to work for large companies.

deleted 2y ago
[deleted]
hn_throwaway_99 2y ago
Yeah, Glean does this and there are a bunch of other competitors that do it as well. I think you may be confused about the length of the context window. These tools don't pull all of your Slack history into the context window. They use a RAG approach to index all of your content into a vector DB, then when you make a query only the relevant document snippets are pulled into the context window. It's similar for example to how Cursor implements repository-wide AI queries.
- aurareturn 2y ago
  I'm aware that one can't feed millions of messages into an LLM all at once. The only way to do this now is to use a RAG approach. But RAG approach has pros and cons and can miss crucial information. I think context window still matters a lot. The bigger the window, the more information you can feed in and the quality of answer should increase. The point I'm trying to make is that increase context window will require more compute. Hence, we could still just be in the beginning of the compute/AI boom.
  - reissbaker 2y ago
    We might be even earlier — the 90s was a famous boom with a fast bust, but to me this feels closer to the dawn of the personal computer in the late 70s and early 80s: we can automate things now that were impossible to automate before. We might have a long time before seeing diminishing returns.

_huayra_ 2y ago

This is the main "killer feature" I've personally experienced from GPT things: a much better contextual "search engine-ish" tool for combing through and correlating different internal data sources (slack, wiki, jira, github branches, etc). AI code assistants have been a net neutral for me (they get enough idioms in C++ slightly incorrect that I have to spend a lot of time just reading the generated code thoroughly), but being able to say "tell me what the timeline for feature X is" and have it comb through a bunch of internal docs / tickets / git commit messages, etc, and give me a coherent answer with links is amazing.

aurareturn 2y ago
This is partly why I believe OS makers, Apple, Microsoft, Google, have a huge advantage in the future when it comes to LLMs. They control the OS so they can combine and feed all your digital information to an LLM in a seamless way. However, in the very long term, I think their advantage will go away because at some point, LLMs could get so good that you don't need an OS like iOS anymore. An LLM could simply become standalone - and function without a traditional OS. Therefore, I think the advantage for iOS, Android, Windows will increase in the next few years, but less powerful after that.
- thwarted 2y ago
  An LLM is an application that runs on an operating system like any other application. That the vendor of the operating system has tied it to the operating system is purely a marketing/force-it-onto-your-device/force-it-in-front-of-your-face play. It's forced bundling, just like Microsoft did with Internet Explorer 20 years ago.
  - aurareturn 2y ago
    I predict that OpenAI will try to circumvent iOS and Android by making their own device. I think it will be similar to Rabbit R1, but not a scam, and a lot more capable. They recently hired Jony Ive on a project - it could be this. I think it'll be a long term goal - maybe in 3-4 years, a device similar to the Rabbit R1 would be viable. It's far too early right now.

mvdtnz 2y ago

My workpalce uses Glean and since it was connected to Slack it has become significantly worse. It routinely gives incorrect or VERY incomplete information, misattributes work to developers who may have casually mentioned a project at some time and worst of all presents jokes or sarcastic responses as fact. Not only is it an extremely poor source of information, it has ruined the company's Slack culture as people are no longer willing to (for lack of a better term) shitpost knowing that their goofy sarcasm will now be presented to Glean users as fact.

dcsan 2y ago
Maybe have some off limits to glean shit posting channels?
rendang 2y ago
Interesting. I still find it to be a net positive, but it is amusing when I ask it about a project and the source cited is a Slack thread I wrote 2 days prior

sofixa 2y ago

> Glean.com does it for the enterprise I work at: It consumes all of our knowledge sources including Slack, Google docs, wiki, source code and provides answers to complex specific questions in a way that’s downright magical There are a few other companies in this space (and it's not something that complex to DIY either); the issue is data quality. If your Google Docs and wikis contain obsolete information (because nobody updated them), it's just going to be shit in, shit out. Curating the input data is the challenging part.