I built an app to solve my Texting problem

Hacker News

Hi All, Backstory: I'm a real estate agent from Brooklyn, and I spend a good chunk of my day texting new clients, initially some standard messages like my contact info, document requirements to rent an apartment, link to fill out an online form, etc. To cut down on repetitive typing, I used to save this stuff in the Notes app so that I could copy instead of typing every time I have to send one of these standard text messages to a new client. The App: I ended up working with a friend of mine who's in tech to build an iOS app called "TapText" to solve my issue. The app lets me save a text message and send it with a single tap right from the Messages app. The app went live in the last week of December. 1,600+ downloads from 55+ countries so far. One unique feature of the app is that when user sends a tiny image such as a company logo, the app automatically pads it with extra white space to eliminate the appearance of the image being cropped with rounded corners. > App Store: https://itunes.apple.com/app/apple-store/id1444515096?pt=119... > Website: https://www.taptextapp.com I think people in sales professions who rely on texting may find this app particularly useful. I hope you guys can check it out and let me know your thoughts. Thanks! Remy

Visit product View on Hacker NewsFeb 26, 2019

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best fitHighest predicted score across all platforms for this description.

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

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

40%40% 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

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

36%36% 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

25%25% 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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