Totogram – Turn your photos into Studio Ghibli-style AI art

Hacker News

Hey HN, I recently built Totogram, an AI-powered web app that transforms ordinary photos into illustrations inspired by the visual style of Studio Ghibli films. It started as a side project—I wanted a way to turn personal photos into something more magical and nostalgic, like a scene out of Spirited Away or My Neighbor Totoro. The result is a tool that applies a custom-trained AI model to give photos a soft, hand-drawn, Ghibli-inspired aesthetic. Right now, it's in pre-launch with early access open (no login needed, just a waitlist). Key features: -Authentic Ghibli-style transformations (custom model) -Before/after viewer with slider -High-res output for printing or gifting -No subscriptions—one-time access The goal is to offer a meaningful way to preserve memories with an emotional/artistic twist. Would love feedback on: #The concept in general—too niche or just right? #The user experience and clarity of the landing page #Any ideas for other use cases (prints? social sharing? etc.) Thanks for checking it out! → https://totogram.io

Visit product View on Hacker NewsApr 21, 2025

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

73%73% 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: model, user, visual · Missing: mac, agents, macos

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

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

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

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

47%47% 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

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 · Strong signals: subscription · Missing: arr, mrr, revenue

11%11% 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.

Correct prediction on native model

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