I made an app for coffee shops to go to work and network with others

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Hey there! I realized I was compiling a list of coffee shops with highly curated reviews in my personal notes: chair comfort, Wi-Fi networks and passwords, internet speed, noise level, bathroom cleanliness, etc... It worked fine for me, but when I wanted to share my list to some friends I realized the underlying problem. So I created Workffee for two reasons: 1. To allow me to save my reviews in a more convenient way and share them. 2. To connect with others who are interested in this information, and who knows, maybe share a coffee and a work session together eventually. I hope you like my app, and if you find it useful, I invite you to upload your coffee shops and reviews and join to our Telegram group ( https://t.me/workffee ) Would love to hear what you think about it! NOTE: I could scrape Google Maps and load everything automatically, but that's not the idea. My goal is for the content to be organic and created by a large community of people who enjoy working in coffee shops. Ata

Visit product View on Hacker NewsMay 29, 2024

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

77%77% 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: google, notes · Missing: mac, agents, macos

61%61% 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

58%58% 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: personal, google, way · Missing: mobile apps, ios, entrepreneurs

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

40%40% 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

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.

Correct prediction on native model

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