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SVG Weave. A node graph editor that animates SVGs with AI

Hacker News

SVG Weave. A node graph editor that animates SVGs with AI

Hey HN, I'm a solo dev and I kept wasting hours hand-writing CSS @keyframes to animate SVGs. Write a keyframe, preview, tweak the timing, preview again, repeat. For anything beyond a simple fade it turns into dozens of rules across multiple elements. I wanted something where I could just describe the motion and get working animations back. SVG Weave is a visual node graph editor for this. You place nodes on a canvas (SVG input, prompt, render) and connect them with wires. Type what you want the animation to do, hit render, and the AI streams CSS @keyframes back in real time. You see the SVG come alive as tokens arrive. Things that might be interesting technically: - Style-inject mode: when only animations are needed, the AI outputs just a <style> block instead of rewriting the full SVG. Faster and avoids corrupting path data. - Overlap detection: the system prompt makes the model analyze element layering and restrict partially-covered elements to opacity/scale only, preventing hidden edges from being revealed during translation. - State transitions: connect two SVGs (start and end) and AI generates a single animated SVG that morphs between them using CSS transforms, opacity, and clip-path. - Chaining: output of one render feeds as input to the next prompt, so you can build complex animations step by step. - Shadow DOM isolation in the preview modal so SVG styles don't leak into the host page. You can also generate SVGs from text descriptions or vectorize raster images in the editor. Stack: Next.js, React Flow, Convex, Gemini via OpenRouter. Free signup gets you 20 credits (one render costs about 10). Requires an account to save projects but you can see the full editor immediately. svgweave.com

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, visual, single · Missing: mac, agents, macos
84%84% 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: gemini · Missing: supports, reddit linkedin, podcasting
78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
58%58% predicted probability of success on TrustMRR, 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
44%44% 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: host · Missing: plus, platform, intuitive
31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real time · 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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