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Grok Bikini – A technical Grok prompt engine for fashion synthesis

Hacker News

Grok Bikini – A technical Grok prompt engine for fashion synthesis

EN: Hi HN, I’ve been building Grok bikini (grok-bikini.com), a technical project focused on streamlining the Grok prompt experience for high-fidelity fashion synthesis. The Implementation: The goal is to move beyond simple text inputs. I developed a system that programmatically expands a basic Grok prompt into a structured instruction set—incorporating specific photography metadata, lighting parameters, and material textures optimized for the Vision API's latent space. Experimental Logic: To test the global reach of this Grok prompt engine, I implemented an automated SEO framework for 19 languages. This allows me to observe how search intent varies across different cultural contexts while using /swimwear paths as a semantic buffer to maintain platform safety. Click https://grok-bikini.com/ to use Grok prompt.

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Actual performance

1points
Did not reach leaderboard

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Product HuntOn track for Day 1 leaderboard · Strong signals: context, grok, using · Missing: mac, agents, macos
60%60% 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: para · Missing: supports, reddit linkedin, podcasting
57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews
53%53% predicted probability of success on AppSumo, 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
45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal
35%35% 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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