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A trained generator of high converting copy for MVP landing pages

Hacker News

A trained generator of high converting copy for MVP landing pages

Hello HN! I’m Eric from the Dominican Republic and I’ve created Maze, an AI generator of text copy for MVP landing pages, focused on creating clear, high converting text, to be able to test your project ideas in an easier manner. This is honestly my, (let’s say above 9th attempt, to save embarrasement) to build something. I’m also new to HN, so forgive me for my lack of karma. * Wonder if that’s got to do with my lack of success *. But anyway. Bit of a backstory: I was about to embark with yet another project that I was not too crazy about, web design services. Not because I don’t like it but because I don’t want to occupy my time in essentially a job that I couldn’t really scale later and would take all my time. Then, while trying to come up with some options for my landing page content, I thought “I don’t want to painstakinly come up with this whole landing page copy, yet again”. * Fade in memories of war, past traumas * I used to completely redesign underperforming ecommerce stores including their copy, sometimes for lots of products. So I knew what each landing page takes. But while doing that, I learned a lot about copywriting, landing pages, online sales, advertising, buyer psycology, conversions, etc. Also learned the most common problem is that as beginners, most of us don’t have a clue on how to explain our product or service in a clear manner in our websites. We think we do, but we really don’t. So I decided to look for a tool to make it * cough!... chatGPT * easier. Found something that would generate a landing page automatically including the copy, filled my heart with hope, but quickly saw that just like chatGPT, the resulting copy was essentially useless unless heavily modified afterwards. Aha! I heard. (Who said that?). Just kidding. Why don’t I fine tune an AI model with the loads of effective, high converting copy I’ve stashed in all these years. Headlines, subtitles, social proof techniques, persuasive words and phrases, and lots of other elements. Then I can input some details about my project and it delivers an A+ job? The always energised serial entrepreneur in me said: - Hey! Who else needs that? Tired me responded: - I don’t… I.. * sighs *… Well, right now I do. So I built it. Please try it and let me know your thoughts.

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Strong signals: created, including · Missing: supports, reddit linkedin, podcasting
96%96% 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: model, new, chatgpt · Missing: mac, agents, macos
77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io, including · Missing: https docs, excited, just released
55%55% 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
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
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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