MetalGraph – Visual node editor for SwiftUI Metal shaders

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Hi HN, I just released MetalGraph, a visual node-based editor for building Metal shaders for SwiftUI. The motivation: the feedback loop when iterating on SwiftUI + Metal can be pretty rough. I wanted something closer to a node editor workflow where you can tweak values, rewire the graph, and immediately see the result. It became a great tool for learning metal for myself. What it does: - Node graph editor (50+ nodes) with a real-time preview - 30+ built-in examples (color effects, layer effects like glass/chromatic aberration, distortions, touch-driven effects) - Exports production-ready code: Metal Shading Language + SwiftUI-ready .colorEffect / .layerEffect variants - Supports reusable custom nodes + simple for-loops for iterative computations - Optional AI assistant (Claude / OpenAI) for shader questions and help building graphs If you are thinking about learning Metal shaders for SwiftUI or have any experience, I’d love feedback! The app is free to download (you can't add/remove nodes though) and you can load and play with all examples. Happy to answer any technical questions about how the graph compiles down to shader code. And happy to hear any ideas on how to take this app further! Here is a long-ish demo video of what the app can do: https://www.youtube.com/watch?v=FH2GdFuk9nI

Visit product View on Hacker NewsJan 30, 2026

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

80%80% 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: claude, visual, openai · Missing: mac, agents, macos

72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

52%52% 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 · Missing: plus, platform, intuitive

50%50% 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

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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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