RapGenerator – Turn lyrics/ideas into full rap tracks (no music skills)

Hacker News

RapGenerator – Turn lyrics/ideas into full rap tracks (no music skills)

Hey HN, I’ve been a casual rap fan for years but could never create my own tracks—beat making felt intimidating, and I’m no lyricist. So I spent a few weekends building RapGenerator ( https://rapgenerator.online/ ) to make it accessible for people like me. Here’s what it does, simply: Pick a style (trap, old school, boom-bap, drill—whatever you’re into) Drop in your own lyrics, keywords, or even just a vibe (e.g., “summer cookout with friends”) It spits out a complete rap track: lyrics with flow, matching beat, and even AI vocals No music production experience required—just hit generate I used Suno V5 under the hood for the audio generation, and tweaked the rhyme scheme logic to feel more authentic (less “AI-generated” stiffness). Everything’s royalty-free, so you can use the tracks for social media, demos, or just for fun. To make it easy to test, I’m giving HN users 10 free credits on sign-up—enough for one full track. No credit card needed, just a quick login. I’m still iterating on it—right now I’m working on adding more regional styles (UK drill, West Coast) and letting users adjust BPM/tempo. Would love HN’s take: Does the flow feel natural, or still too robotic? Any styles you’d want added next? Is there a feature missing that would make this useful for you? Check it out here: https://rapgenerator.online/Open to all feedback—good or bad. Thanks for taking the time to look!

Visit product View on Hacker NewsNov 26, 2025

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

90%90% 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: user, open · Missing: mac, agents, macos

80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

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

44%44% predicted probability of success on AppSumo, 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

28%28% 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

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: audio · 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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