Wonder – AI-native design tool on canvas

Hacker News

Hi everyone! I wanted to share something wonderful we've been working on for the past months. We found a way to leverage AI to make designs on a canvas, and we're building a design tool that can help with various tasks, from exploring different UX flows, layouts to complex tasks like building your design system. If you are a designer or worked with designs in the past, we would love to get your feedback on what things can be useful for you! We will be launching it with select people soon and hope that we can bring value to the design community.

Visit product View on Hacker NewsAug 19, 2025

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

91%91% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

TrustMRRFits verified-revenue profile · Strong signals: month, way · Missing: mobile apps, ios, personal

58%58% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

50%50% 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 · Strong signals: soon · Missing: plus, platform, intuitive

43%43% 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

15%15% 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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