I built Magic Canvas – A visual space for context engineering

Hacker News

Hey HN, I got frustrated with the endless back-and-forth prompting when building apps with AI. You know the drill: describe what you want, AI generates code, you ask for changes, rinse and repeat. It felt like we were missing something fundamental about how humans and AI should work together. So I built Magic Canvas: what I call a visual space for context engineering. Instead of describing your app in text, you work directly on a canvas where the AI agent can see your layout, understand your design intentions, and write production code that matches exactly what you've created visually. The key insight: when you can show instead of tell, the AI understands your context much better. Drag a button, resize a section, change colors — all of it translates directly to code changes. I've been using it to build multi-page applications, and it feels like a completely different way of working with AI. The visual context gives the agent so much more information than text prompts ever could. Still early days, but I think this might be how we'll all be building with AI in the future. Would love to hear what you think. https://www.youtube.com/watch?v=19FEqqSq3y8

Visit product View on Hacker NewsJul 23, 2025

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

97%97% 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.

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

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

36%36% 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

23%23% 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.

Incorrect prediction on native model

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