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I Built a Free Text-to-SVG Generator

Hacker News

I Built a Free Text-to-SVG Generator

Hey HN, Last weekend, I got bored and decided to build something fun: https://text2s.vg – a simple tool that lets you type a prompt and generate a free vector SVG image. Why I built this: I wanted to explore combining text-to-image generation with lightweight vector outputs, all while keeping it fast and accessible. It's a good way to progress at creating a project from A to Z, from backend workflows to frontend design. Plus, I thought it might be useful for developers, designers, or anyone working on creative projects. How it works: Type a prompt, hit "Generate SVG," and you instantly get an SVG file ready to use. It’s completely free, no ads, no signup – just something I made for fun. The tech stack: Backend: A custom ComfyUI workflow (using Flux, BiRefNet, and a custom node ComfyUI-ToSVG,) all running serverlessly on Runpod. Frontend: React with Tailwind CSS (no Next.js, no need for SSR). Database/Hosting: Firebase handles Firestore, storage, and hosting. What’s next: I’m looking for feedback to improve it or explore new features. Maybe adding style controls or export options? Would love to hear your thoughts, questions, or ideas!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
89%89% 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: new, using · Missing: mac, agents, macos
59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: plus, host · Missing: platform, intuitive, reviews
52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
32%32% 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
14%14% 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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