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A startup came to me after Google Play rejected their app again and again. Then the developer account got suspended. Then a permanent ban notice arrived.
Nothing about it was magic. The store listing described the app wrong, a few policy issues had piled up, and every resubmission repeated the same mistakes. I went through it line by line, fixed each problem, and wrote a short, honest appeal.
About 10 days later the app was live again.
If your app is stuck in review, send me the rejection email and I'll tell you what I see. No charge for a first look.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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nice!
I built Idea Validator.
1. WHAT IS IT?
A validation tool for early-stage founders. Answer 6 simple questions, and it analyzes your competitors, pulls real-time market insights, and rates your idea: strong, medium, or unclear.
2. WHY I BUILT IT
Most early-stage founders start building on gut feeling. They skip research because it is slow and confusing.
3. WHAT PROBLEM DOES IT SOLVE?
Founders waste months on ideas nobody needs. Idea Validator replaces guesswork with real market insights and shows:
-> Problem and demand
-> Reason to choose your idea
-> What could stop it
-> Your next validation steps
-> You can download the full report in one click.
-> Know before you build.
Try it here: https://idea-validator-check.netlify.app/
What would you want this tool to tell you about your idea? Let me know in the comments.
#startups #saas #founders #productdesign #uiux #buildinpublic
does it ever come back unclear and basically tell you to kill the idea
I built an AI support-ticket router on Replit, then tested it against the real model. Three things broke that no mock would have caught.
A silent failure. The first run sent 33 of 40 tickets to "needs a person". The newer Claude model rejects the temperature setting, and my code swallowed the error, so every call quietly failed. Now every failed call is logged.
The model thinks before it answers. With a tight token limit, some tickets got no answer at all and some draft replies stopped mid-sentence. Both have room now, and a cut-off draft is never saved.
One incident looked like four bugs. Four Safari checkout failures each got a different issue key, so the "3+ tickets, same problem" alert never fired. Sorting tickets in arrival order and asking for keys that name the problem rather than the customer turned them into one flagged incident.
If you're adding an LLM feature to your product, test it on real inputs early. The failures are rarely where you expect.
Live demo: https://sorta--krishnanbhupath.replit.app
