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Made First Android game using Codespaces and AI, now in AdMob purgatory

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Made First Android game using Codespaces and AI, now in AdMob purgatory

Two months ago, I had zero mobile development or publishing experience. I wanted to see if I could build and ship a complete game using only a browser-based workflow and AI assistance. The process: Built entirely in GitHub Codespaces using an AI Copilot. Wrote the core game for the web, then wrapped it into an Android APK/AAB. Pushed through 100+ versions to fix edge cases. Passed Google Play review with no policy violations. The game is a tough 2D platformer called Bionic Biome. To make the "impossible mode" bearable, I integrated a rewarded AdMob ad as a Continue/Revive mechanic. It’s an opt-in lifeline, not a forced pop-up. Here is the weird hurdle I’ve hit: Test ads worked perfectly. But in production, the AdMob dashboard just says "No data" and returns "Ad not ready." My core revive mechanic is effectively disabled because Google's algorithm won't serve real ads until it sees enough "organic" traffic to trust the account. It's a strange psychological shift. For weeks it was "build, fix, upload." Now, the AI code works, the wrapper works, the integration works, but the core mechanic is held hostage by a silent third-party algorithm waiting for strangers to play it. Has anyone else here launched a first app and hit this AdMob cold-start wall? How long did it take for your first live ad to actually fill? If you are curious to see what an AI-coded, web-wrapped game built entirely in a browser actually plays like on Android, the link is below. (No pressure to play, just sharing the technical journey). https://play.google.com/store/apps/details?id=com.pollitopro...

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
87%87% 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: google, apps, using · Missing: mac, agents, macos
86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, month, google · Missing: mobile apps, ios, personal
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host · Missing: plus, intuitive, reviews
48%48% 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
37%37% 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
26%26% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: reward · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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