Sebastian.run – Build mobile apps from prompts using AI

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Hi HN! I’ve been working on Sebastian.run — an AI tool that lets you build mobile apps by simply describing them in natural language. Think of it as “vibe coding” — you type: “Build me a recipe app with favorites, search, and pastel colors.” ...and in seconds, it generates the full app (frontend + backend + logic). My goal is to make mobile app creation as intuitive as talking to a designer — no code, no templates, just clear intent. Why I built it: I got tired of the friction in no-code tools. They made building faster, but not simpler. So I built an AI-native alternative. How it works: You type a prompt describing your idea The AI generates the full structure and UI You preview instantly in your browser It’s still in early beta, and I’d love your feedback — especially from developers, designers, and founders who’ve tried no-code tools before. Try it here: https://sebastian.run Also wrote a short piece about the concept of “vibe coding” here: https://medium.com/@tonprofil/why-i-stopped-coding-and-start... Would love to hear your thoughts — what feels promising, what feels missing? Thanks!

Visit product View on Hacker NewsOct 31, 2025

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

95%95% 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

89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: mobile apps, apps · Missing: ios, personal, entrepreneurs

55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, 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.

AppSumoMay struggle as an AppSumo deal · Strong signals: intuitive · Missing: plus, platform, reviews

31%31% 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

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

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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