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Flurri – Build meaningful connections with your co-workers

Hacker News

Flurri – Build meaningful connections with your co-workers

Hi HN, we're Nikhil and Dhurv, founders of Flurri ( https://www.flurriapp.com/icycle ). We’re very excited to launch Flurri (www.flurriapp.com/icycle), a platform to help teams build meaningful connections and create an inclusive, engaging culture. From our own experiences and countless others, we know that starting in a new team can be isolating and remote work is lonely. However, the workplace is a great place to make new connections and build bonds, especially as other community institutions (sports clubs, volunteering, etc.) have declined in prominence. We wanted to make it easier and more comfortable to get to know your colleagues, focusing on what makes us all human! We capture a bit about your hobbies and interests, and provide a way for you to search against it, learning more about your co-workers in the process. Communities create profiles built on hobbies, interests, and short ice breakers and we create a searchable internal database of employees based on hobbies, interests, etc. Additionally, users can set up Icycle, our product to match co-workers based on mutual interests on a regular cadence, with meeting invites sent directly. We are completely free to use, so feel free to jump in - creating a profile takes 5 min! We believe work is a great place to find your community, and we want to make it simple, easy, and fun to do that. We appreciate your support and feedback and look forward to hearing your thoughts as we continue to build!

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Actual performance

3points
2comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, using · Missing: mac, agents, macos
89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist
54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users, way · Missing: mobile apps, ios, personal
39%39% 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
18%18% 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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