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I've build a prompt generator for Lovable

Hacker News

I've build a prompt generator for Lovable

I'm always thinking of ideas to promote my agency directory and bring more visitors to it. I've noticed that free tools do very well in this kind of situations. So after talking to a friend I came up with an idea. He uses Lovable for some of his agency work and he was complaining about the fact that depending on the prompt you provide, working with this AI coding tools becomes very very tedious. So basically, the premise was simple: if you start with a very good prompt, the back and forth of tweaking changes and prompting again and again becomes WAY easier. A very good thing is that all this AI coding tools (like Lovable, Bolt, Vercel v0) already provide a "prompting bible". I got to work and a came up with a very simple yet effective Prompt Generator for Lovable. It follows the guidelines of Lovable and I have tried it with different examples and it works! Let's see if it can be useful for anyone and even bring some more people to the main agency directory. I would love to know what you guys think. Any feedback is welcome!

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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 · Missing: supports, reddit linkedin, podcasting
83%83% 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: lovable, coding, plain · Missing: mac, agents, macos
75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
39%39% 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
35%35% predicted probability of success on TrustMRR, 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
33%33% 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
16%16% 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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