Geminee – Transfer files between colocated devices

Hacker News

Hi HN, I noticed I often have the need to send files or urls between devices (of myself and others) which are within arms reach, so I decided to build something which allows me to do just that: geminee. Of course I hope this isn't of use to just myself but to others as well, so I welcome you to try geminee at https://gemin.ee . If you want to read more about it first you can also have a look at https://about.gemin.ee . I'm hoping to receive feedback and am happy to answer any questions! Thanks! // Also, a previous discussion ( https://news.ycombinator.com/item?id=10733534 ) was around the time of the Geminids meteor shower, but unfortunately it went somewhat unnoticed.

Visit product View on Hacker NewsDec 21, 2015

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1points

Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: gemini · 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: ide, io · Missing: https docs, excited, just released

65%65% 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 · Missing: mobile apps, ios, personal

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: new, gemini · Missing: mac, agents, macos

31%31% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

12%12% 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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