Productionapps.ai – We fix vibe-coded apps for businesses

Hacker News

Hi HN! I'm Jeff, Founder from ProductionApps. We're all using AI to code faster, but it's no secret that vibe-coded apps are a new kind of technical debt. The prototype that looks great on the surface but is a tangled mess of security flaws, unmaintainable spaghetti code and partially functioning backends. ProductionApps.ai is a service built to solve this exact problem. We are a team of senior engineers and builders who implement a customized plan for your app - at a professional engineering-standard that can be integrated by your organization. Please let me know if you have any questions or feedback. Thanks!

Visit product View on Hacker NewsSep 30, 2025

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

Product HuntOn track for Day 1 leaderboard · Strong signals: apps, new, using · Missing: mac, agents, macos

87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps · 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: builder · Missing: plus, platform, intuitive

39%39% 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

32%32% 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

23%23% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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