I made a tool to create "About Me" bios for people who struggle

Hacker News

I built https://aboutmepro.com — a simple tool to create profile bios. The idea came as a byproduct while I was experimenting with faster ways to build software using large language models (LLMs). My process focused on: Writing clear, simple rules to guide development Using structured prompts to communicate with AI models Creating lightweight product docs (PRDs) to outline features Breaking work down into small, focused tasks to move faster For AI support, I switched between two advanced models — Gemini 2.5 Pro and Claude 3.7 Sonnet — which helped me plan, write code, and even generate seeding data like categories and tools. The UI draws inspiration from V0 (for fast layout), Cursor (an AI-powered code editor I use daily), and Replit — where I host the app on their Core plan. Replit lets me build, test, and deploy quickly without worrying about infrastructure. The only real cost was buying the domain (I already had a Cursor subscription). I also applied SEO best practices like sitemap.xml, robots.txt, and static site generation to make pages fast and easy to find. You can try it here: https://aboutmepro.com Would love feedback. Any other tips/tricks that you use to speed up your dev work?

Visit product View on Hacker NewsMay 22, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: cursor, claude, model · Missing: mac, agents, macos

93%93% 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 · Strong signals: ios, gemini · Missing: supports, reddit linkedin, podcasting

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

AppSumoStrong fit for a featured deal · Strong signals: host · Missing: plus, platform, intuitive

58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: ios, way · Missing: mobile apps, personal, entrepreneurs

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: subscription · 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.

Incorrect prediction on native model

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