Banana Studio – AI Image Editor Powered by Nano Banana

Hacker News

I built Banana Studio, an image editor that lets you modify specific regions inside an image using simple text instructions. It runs fully client-side in the browser and uses Google’s Gemini Nano Banana for fast, clean edits. Demo video: https://youtu.be/-DbDDsyT2MM Link: https://banana-studio-nano.vercel.app/ Why I built this Text-only prompts make it challenging to instruct the model precisely where to apply changes, so I developed a simple bounding-box interface that eliminates all the guesswork. The idea was to keep everything lightweight, browser-based, and still get clean, high-quality edits. How it works You upload an image, draw a box on the canvas, and type what you want changed. The app maps your box to the 1000x1000 coordinate grid Gemini expects. It then builds a structured prompt telling the model exactly which region to modify, sends it to the API, and gets an image back. Features - Edit specific regions by drawing one or more boxes - Different prompts for different boxes - Global enhancements when no region is selected - Works entirely client-side - Bring your own Gemini API key (stored locally in your browser) If you want to try it, I would love feedback from the HN crowd.

Visit product View on Hacker NewsNov 25, 2025

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

85%85% 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: gemini · Missing: supports, reddit linkedin, podcasting

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video, google · Missing: mobile apps, ios, personal

33%33% 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, 000, io · Missing: https docs, excited, just released

30%30% 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

25%25% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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