A GPT Image 1.5 UI for developers tired of rewriting prompts

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Hey HN, After burning through dozens of credits rephrasing the same prompt in different AI image tools, I built gpt2image.io - a conversational interface for GPT Image 1.5. **The core idea:** Instead of prompt archaeology ("was it 'modern minimalist' or 'contemporary minimal' that worked?"), you iterate through dialogue: - "Change the background to midnight blue" - "Make the text bolder" - "Keep everything but replace the laptop" Context persists across turns. Style consistency is maintained. It's how humans actually work. **Built for:** - Developers mocking up UIs - Designers exploring concepts rapidly - Product teams creating marketing assets - Anyone who needs images that evolve with ideas **Tech highlights:** - Built on Next.js 16 + React 19 (using React Compiler for optimization) - Turbopack for stupid-fast local dev - Multi-language support out of the box - Clean API for integrations **What it's not:** - Not trying to replace Photoshop - Not a magic solution for everything - Still bound by underlying model capabilities Try it: https://www.gpt2image.io (first 2 images free) Looking for feedback on: - What editing patterns break down - What controls matter most in production workflows - API features you'd actually use Contact: contact@gpt2image.io

Visit product View on Hacker NewsDec 23, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, context, using · Missing: mac, agents, macos

82%82% 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 · Missing: supports, reddit linkedin, podcasting

75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

70%70% 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 · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive

33%33% 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

19%19% 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.

Incorrect prediction on native model

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