This App Makes $600k/Month… So I Built It in 24 Hours with AI

This App Makes $600k/Month… So I Built It in 24 Hours with AI

🎙 Astro K Joseph 👥 85K 📅 November 17, 2025 ⏱ 15 min 👁 6K 📄 tutorial 🧭 2026-09-17
Available in: English (current) Français

Keywords

DualiteCoin SnapOpenAI APISupabaseReact Native

Summary

The video showcases how to build a clone of the Coin Snap app, which allegedly earns $600k/month, using an AI-powered development platform called Dualite. The creator demonstrates prompting Dualite to generate a React Native mobile app that identifies coins via image upload and OpenAI vision API. He integrates Supabase for user authentication and adds a database to store scan history. The process involves creating a project on Dualite, pasting an OpenAI API key, adding Supabase authentication, changing the color theme, and implementing a history feature. The app is tested with sample coin images and works as expected. The creator emphasizes the simplicity of building such apps with AI and encourages viewers to try Dualite for similar projects, claiming that monetization is achievable with good marketing. The video is primarily a sponsored tutorial promoting Dualite, with unverified revenue data that adds little scientific value.

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Critical Evaluation

Value of the Information & Strength of the Argument

The video provides a step-by-step demonstration of using Dualite, a low-code AI platform, to create a functional mobile app. The technical steps are concise and easy to follow, showcasing real integration with OpenAI and Supabase. However, the primary value is promotional rather than educational: revenue claims are anecdotal and unverifiable, and the ‘clone’ is a simple prototype not a polished production app. The argumentation relies on the appeal to authority of the existing app’s success, but this link is not substantiated with data. Overall, the technical information is useful for beginners interested in no-code AI app development, but the presentation is skewed toward selling the tool.

Scientific Rigor, Source Quality, Title Accuracy

Scientific rigor is weak. The only external source named is ‘Sensor Tower,’ but no link or verifiable data is provided. The $600k/month figure is asserted without evidence. The video uses promotional links exclusively, with no independent sources. The title overstates the time frame (24 hours vs. actual 20-30 minutes) and implies a deeper analysis than the tutorial provides. While the tutorial itself is coherent, the unverified claims and lack of critical analysis reduce its reliability.

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Title / Content Match

The title suggests a 24-hour build, but the video shows a 20-30 minute clone. The core premise matches: leveraging AI to recreate a revenue-generating app.

Quality & Reliability

5/10

The video demos building an app with AI tools but relies on unverified revenue claims and promotional links. The technical walkthrough is clear, but financial figures are not substantiated.

Key Moments

Cited Sources

  • Dualite Alpha — The primary AI development platform demonstrated in the video
  • aiholiq Newsletter — Creator's newsletter, promoted in description
  • Astro K Joseph Website — Creator's personal website linked in description
  • Scrimba — Affiliate link for coding education, not covered in video
  • LinkedIn Profile — Creator's LinkedIn profile

Concurring Sources

  • Sensor Tower — Mentioned as source of revenue data, but no link provided

Contribution & Novelties

The video illustrates a practical workflow for non-programmers to build functional apps using generative AI, combining prompt-based code generation with backend services. Its novelty lies in demonstrating a fully integrated stack (app + AI + auth + database) in under an hour. However, the approach is derivative of existing low-code platforms and lacks comparison or critical evaluation.

Pour aller plus loin :

106 words

Radar Profile

The radar profile shows moderate information quantity and technical level, but low quality and reliability scores, reflecting the promotional nature and unverified claims.

Reliability 3/10