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Stop explaining the obvious to Nano Banana

Hacker News

Stop explaining the obvious to Nano Banana

AI image editing felt like testing my ability to explain what I see: “exactly X, preserve Y, the person on the Z..." and it would still edit the wrong stuff. So I built a paint-and-prompt editor. (1) You paint the region (2) Type a short prompt, and done Prompt before: "Remove the person in the far-left corner behind the person wearing a black jacket. Preserve everything else in the image, no identity change... etc" Prompt now: "Remove him." Website: https://swisy.app If you try it, it will break. Please do let me know what sucked and why not worth trying. I'll fix it.

Visit product View on Hacker NewsDec 30, 2025

Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

63%63% 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: plain · Missing: mac, agents, macos

55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

46%46% 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, io · Missing: https docs, excited, just released

29%29% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

25%25% 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

24%24% 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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