ComicInk – AI tool that turns a prompt into a full comic book

Hacker News

Hi HN, I'm Sanjoy. I've always loved comic books and stories but can't draw. So I built ComicInk: describe a story, the AI generates a complete comic book — consistent characters across panels, dialogue, cover art, the whole thing. No-signup demo at comicink.ai/quick — type a prompt, watch Gemini write a dramatic teaser of your story in real-time while the cover + page 1 render (~2 min). Free signup unlocks the full 4-page comic. The hard problem was character consistency. Most AI image tools treat each generation independently, so your hero's face morphs between panels. ComicInk stores per-character reference images + structured attributes (age group, build, hair, distinguishing features) and injects them into every panel prompt. Not perfect, but substantially better than raw text-to-image. Translation worked out better than I expected: a click renders the comic in 10+ languages while the panel art stays identical — only the dialogue and captions change. The public reader auto-detects the visitor's browser language and serves the matching edition if one exists. Getting some interest from serious book creators as well so I introduced the "Book to comic" feature. Simply upload the PDF of a book of you have written. AI reads the book, analyzes it for important plot elements and characters. You can decide to convert the book into 1 issue or split it up across multiple issues, choose the art style you want and you have a comic version of your book. Stack: Next.js 16 / Supabase / Gemini (text + images) / fal.ai (backup image provider) / Stripe / Inngest. iOS app on the App Store. Happy to answer anything. Especially curious if anyone's solved character consistency better than reference-image injection — it holds up reasonably well with the Gemini image models I use, but drifts on other providers.

Visit product View on Hacker NewsApr 30, 2026

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

97%97% 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.

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

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

Product HuntUnlikely to reach the leaderboard · Strong signals: model, stripe, models · Missing: mac, agents, macos

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

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

31%31% 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, ide, io · Missing: https docs, excited, just released

21%21% 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

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · 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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