TitleTuner – Generate YouTube titles, scripts and tags from one idea

Hacker News

Hi HN, I built TitleTuner after noticing that new YouTubers struggle more with what to make and how to title it than with editing. TitleTuner starts from a single idea and helps with: – Video idea angles – Script outlines – YouTube titles – Relevant tags It’s free and mainly built for beginners who want clarity before hitting record. I’d really appreciate honest feedback on whether this is useful and what feels missing or unnecessary. Thanks!

Visit product View on Hacker NewsDec 28, 2025

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

56%56% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

36%36% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

31%31% 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 · Missing: https docs, excited, just released

23%23% 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

20%20% 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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