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Universymbols – Restyle any icon with Nano Banana Pro & GPT Image 1

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Universymbols – Restyle any icon with Nano Banana Pro & GPT Image 1

I built Universymbols to extend your app’s icon set. I used to hunt for a passable icon from a different set or try to restyle one by hand, but the AI image models are now good enough to do this (with a bunch of tooling to get them to behave). Upload an icon with the subject you want and restyle it into a target icon set, like SF Symbols, Material Symbols, Phosphor, Unicons, Lucide, and a bunch of others. After about 2 minutes, you get up to six candidate icons in SVG. Get one free icon by logging in with your GitHub account (no data collection, just to reduce bots). Pricing beyond that is per icon (no subscription) because the AI model costs are significant. Under the hood, it’s a 15 step pipeline including 8 steps calling AI models (plus retry on failure by an AI judge) and some traditional image processing. The raw image generation in the middle of the pipeline is both Google’s Nano Banana Pro and OpenAI’s GPT Image 1. I’ve been sitting on the project for a couple of months because gpt-image-1 had too many failures, but Nano Banana Pro fills in most of those gaps, so it runs both models simultaneously. None of the cheaper models had useful results. You can try it here: https://universymbols.com - Would you choose more niche/interesting icon sets, if you were sure that you could add any icon your UI needs in the future? - How do you choose an icon set for your apps currently? - Should I implement custom/private styles?

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Actual performance

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Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, google, apps · Missing: mac, agents, macos
77%77% 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 HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: plus · Missing: platform, intuitive, reviews
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, month, google · Missing: mobile apps, ios, personal
45%45% 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, pipe, io · Missing: https docs, excited, just released
29%29% 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 · Strong signals: subscription · Missing: arr, mrr, revenue
18%18% 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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