Generate unique LogoMarks with generative AI

Hacker News

Every time I started a side project in the past, I struggled to create a good-looking, unique logo. Although there are many Logo Generators out there, they either use premade icons or are too expensive. So, I decided to create the MakeBrand LogoMaker, the perfect Logo Maker for small businesses and side projects. MakeBrand LogoMaker uses modern generative image models to create truly unique LogoMarks that perfectly fit your brand or project. The logo can be further customized using the built-in editor, where you can adjust fonts, sizing, spacing, and colors to suit your needs. The pricing is transparent and credit based. For just 10€ you can generate 20 different LogoMarks you can choose from! You can export your Logo for Free in high-quality SVG or in a bundled Package. If you sign up now you get 40 free credits! If you have any questions or feedback, please let me know! Thanks, Felix

Visit product View on Hacker NewsOct 11, 2024

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

92%92% 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 HuntUnlikely to reach the leaderboard · Strong signals: model, models, using · Missing: mac, agents, macos

47%47% predicted probability of success on Product Hunt, 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

39%39% 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 · Missing: plus, platform, intuitive

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

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

27%27% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

15%15% 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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