An AI image editor that understands context (FLUX.1 Kontext)

Hacker News

Hey HN! I've been working on FLUX.1 Kontext, an AI-powered image editing platform that leverages Black Forest Labs' revolutionary FLUX.1 Kontext models. *What makes it different:* - Context-aware editing: The AI understands relationships between objects in your image - Natural language prompts: "Change the background to a sunset" actually works as expected - Professional-grade results: Built on the same tech stack powering enterprise AI tools - Real-time processing: Most edits complete in under 30 seconds *Technical details:* - Frontend: Next.js 14 with TypeScript - AI Models: FLUX.1 Kontext Pro/Max from Black Forest Labs - Security: Cloudflare Turnstile integration - Image processing: Optimized pipeline for various aspect ratios *Why I built this:* Existing AI image editors often miss context or produce unrealistic results. FLUX.1 Kontext models excel at understanding spatial relationships and maintaining consistency across edits. *Try it free:* [ https://konfluxart.com ] Would love feedback from the HN community! What image editing challenges have you faced with current AI tools? Tech stack details and implementation notes in comments if anyone's interested.

Visit product View on Hacker NewsMay 30, 2025

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, pipe · Missing: https docs, excited, just released

42%42% 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: ios · Missing: mobile apps, personal, entrepreneurs

39%39% 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

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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.

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