My

My MVP - InformationCollector.com

Hacker News

My MVP - InformationCollector.com

Could I please get some feedback on my MVP? https://InformationCollector.com It's a simplified version of Microsoft Access for Android users. Non-programmers can create a form online in about a minute. Android owners can then use my app to submit information to a central server (where it can be viewed in real time or downloaded as an Excel file). Possible use cases: Apartment hunting (submit pictures, sizes and rents), field research (no longer a need for typing up field notes / lugging around a laptop), recruiters evaluating candidates at networking events, etc. I have tons of use cases that I am fleshing out on my website. Even if you don't have an Android device, I'd still like your feedback! I have a "quick tour" button on the bottom of the front page, so there are screenshots of every step from creating an account to viewing submitted data.

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Actual performance

2points
1comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
72%72% 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: user, notes · Missing: mac, agents, macos
64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, io · Missing: https docs, excited, just released
48%48% 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: users · Missing: mobile apps, ios, personal
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
30%30% 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
11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real time · 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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