Stitchwink – Cross-stitch pattern making with AI

Hacker News

My wife has been cross stitching since she was a child and I wanted to make a tool where she could generate her own custom patterns: https://stitchwink.com/ You can try chatgpt or other AI tools but the problem is that they don't create real patterns. They look good at first glance but it's not actually a physically realizable pattern when you look at the details. The way it works is pretty simple, you use text (& optional images) to create and edit patterns. There's also a built in editor to manually change any of the stitches. It's not perfect but with a few manual edits, she's been able to create some awesome patterns. Here are a few one the ones she's made: https://stitchwink.com/share/8efdff80-2a21-4e7f-8122-553ca94... https://stitchwink.com/share/ab47a0bf-afbc-44ff-90ad-9128535... https://stitchwink.com/share/201fa791-39a1-4684-9f3e-2539a62... https://stitchwink.com/share/dcdf06be-fe2b-4353-85e5-3eede41...

Visit product View on Hacker NewsSep 10, 2026

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

82%82% 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.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: chatgpt, physical · Missing: mac, agents, macos

50%50% 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

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

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

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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