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Structured JSON Prompts for AI Image Generation

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Structured JSON Prompts for AI Image Generation

I've been experimenting with structured approaches to text-to-image generation with chatgpt and google imagen 3.0 and built two complementary tools: image-json-gen: A simple ts to create structured image prompts as JSON for generative models. generation-schemas: An evolving schema spec to standardize and validate image generation prompts across tools. The goal is to move away from messy natural language prompts and toward reproducible, composable, and tool-friendly formats. Useful for devs building image pipelines, creative tooling, or anyone who wants stricter control over prompt structure. Feedback, ideas, and contributions welcome. Cheers!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
59%59% 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, google, models · Missing: mac, agents, macos
50%50% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: friendly · Missing: plus, platform, intuitive
50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google, way · Missing: mobile apps, ios, personal
49%49% 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
34%34% 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
17%17% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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