Node Based Vector Designer Mac App

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Hi! I've been working for a few weeks on a node based vector design tool. It's non-destructive and exports to Sketch and Illustrator. You can create graphs of Vector Nodes which feed into each other and produce a new Vector shape. I've created a quick video on how to use the grid component, and also a longer video where I go other a few more features. Longer overview: https://www.youtube.com/watch?v=dM8fgL-ibjI Grid tool: https://www.youtube.com/watch?v=BwPYTu8_XwE I wrote it in Swift 4.2, using Cocoa (It's a Mac app), It also uses Metal for rendering the canvas and a SpriteKit view for the node editor.

Visit product View on Hacker NewsFeb 22, 2019

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

77%77% 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.

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

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video · 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

12%12% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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