Text Me That iOS

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Text Me That iOS

Hey everybody, I have never posted this application anywhere before. But for some reason this morning, I am having the courage to show it. https://apps.apple.com/us/app/text-me-that/id1329223000 I built this application to suffice my needs for a reminder app. I’ve always enjoyed the reminders app from apple, but for some reason, I tend to lose some of the reminders after I dismiss it. And the reminders app is not on the top of my importance to open on a daily basis. What I do prioritize on a daily basis is my text messages app before all else on my phone. Text Me That simple purpose is to send reminders via text message. The reminders can sit in my unopened inbox until I’m ready to review it. I created the basic needs within this app. - location based reminders. - Frequency reminders. - group reminders - quick reminders. Remember, be gentle! I would love to know what you all think. I’ve enjoyed lurking on this community for over a year now, and have learned so much. There is a second reason I am posting this application. I am interested in collaborating with another developer for version 4.0. I’ve currently started it. Building in Swift UI. My end goal is to make it cross-patible with Mac OS. I’m also exploring the new widgets from apple. I think working with another developer would be awesome. Definitely during this time that most developers WFH. Community is necessary and needed! You can personally email me at ethanbonin@gmail.com Anyway, Love you all! If anything, don’t be afraid to check it out and leave me feedback. I’m all ears!

Visit product View on Hacker NewsNov 22, 2020

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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

94%94% 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: mac, apple, apps · Missing: agents, macos, agent

86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: ios, personal, apps · Missing: mobile apps, entrepreneurs, video

53%53% 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

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: 000, io · Missing: https docs, excited, just released

28%28% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

22%22% 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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