Nano Banana 2 – Sub-second AI image gen via Gemini 3.1 Flash

Hacker News

Hi HN, I wanted to see how far I could push the "Flash" in Gemini 3.1. Most AI interfaces feel bloated and slow, so I built Nano Banana 2 to focus on raw speed. Technical Highlights: Stack: Next.js + Edge Runtime to minimize TTFB. Optimization: Custom streaming pipeline for Nano Banana 2 image preview tokens. Locales: Automated i18n for 13 regions (MS, IL, PL, etc.) based on real-time search breakout data. The goal was to build a globally responsive UI that feels native regardless of where the user is. Would love your feedback on the latency and the streaming implementation. Free to try for the HN community: https://nano-banana2.me/

Visit product View on Hacker NewsFeb 27, 2026

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

81%81% 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: user, gemini · Missing: mac, agents, macos

73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

25%25% 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

22%22% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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