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Exploring how different AI prompts change outputs

Hacker News

Exploring how different AI prompts change outputs

One of the recent sections I added is focused on “figurine-style” prompts — the kind that make an image look like a collectible toy or a commercial product photo. Here’s the page if you want to try it: [Nano Banana 3D Figurine Maker]( https://editimg.ai/nano-banana-3d-figurine-maker ). The idea isn’t a model or API of my own — it’s more like a library of tested prompts. I found that structuring prompts as if you were briefing a photographer or product designer (“scale,” “packaging,” “studio lighting”) consistently improved results.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
72%72% 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 · Missing: mac, agents, macos
64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
48%48% 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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
23%23% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% 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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

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