Iris.ai – Search Engine for Email, Better Than Gmail

Hacker News

Hi HN, Today we're launching try-irisai.com, a search engine for your personal information. Iris automatically indexes all your emails and can answer any questions about them extremely fast. You can ask it things like: "get me the meeting link for my upcoming meeting", "when is my credit card bill due and how much is it", "was my CI run successful?, etc. You can even ask Iris to fetch your documents. For example, you can say "get me my boarding pass" and it will get you the PDF file from your email. In our daily use, we've found it way more ergonomic to use iris to find information instead of searching for keywords on Gmail search. Our demo video makes this clear: https://www.youtube.com/watch?v=g0mCf826l0U&ab_channel=Shafi... We'll be rolling out access over the next couple weeks so please join the waiting list on our landing page. We have a long way to go but we would love to hear your feedback and get your advice. You can also reach out to me personally via my twitter: https://twitter.com/_shafinsiddique . Cheers! Shafin

Visit product View on Hacker NewsFeb 16, 2024

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

84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

57%57% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, video, way · Missing: mobile apps, ios, entrepreneurs

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

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

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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