I built haystack – Google for workplace knowledge

Hacker News

tl;dr: haystack is a workplace search engine for devs that runs in the browser. Iv'e started working on haystack recently because I feel modern workplaces are in dire need of a good workplace search product that is free to use just like google. Information is scattered between too many communication channels and platforms, we communicate with our peers through slack and email, share docs and specs on confluence, work with tickets on jira, commit code and have discussions on github, not to mention all the .docx, .ppt and .pdf that fly around the organization. Existing search featuers plain suck, if you tried using confluence search you know what I mean, keyword search is terrible. Even when you find relevant looking results, they require you to commit to entering the page, and scroll through to get to the relevant paragraph. What does haystack do? - Enable you to search all your workplace applications from one place. (slack, confluence, notion, jira, github, outlook, gmail, etc...). - Natural language queries ("How to do X”, "Do we support Y", "How do I connect to Z"). - No download, all the magic happens in the browser. - Local browser storage option. - Code references embedded in search results. Example, "How to connect to integ2 machine" on haystack could give you: "I've finished setting up the second integration server ... ssh connection details" ssh -i private.pem ubuntu@ec2-integration2.eu-west-1.compute.amazonwes.com aggregated from a slack communication you had a while ago. Next Steps Fine-tuning haystack for lower-end laptops with no dedicated GPU, public release date - Feb/March 23. If you would like to get early access + you have dedicated graphics, there's a button in our landing page, and my email address. I'll be here in the comment section!

Visit product View on Hacker NewsDec 28, 2022

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, slack, google · Missing: agents, macos, agent

95%95% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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Indie HackersFits the IH revenue-focused audience · Strong signals: started, para · Missing: supports, reddit linkedin, podcasting

69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, io · Missing: https docs, excited, just released

69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: google, para · Missing: mobile apps, ios, personal

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

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BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

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