Dynamic SVG Icons Generator

Hacker News

I have created a mini website that generates dynamic SVG icons that can be used wherever you want. The origin was that when using a Dashboard like "homarr" or "homepage" not all applications have an "automatic" icon (using favicon in homarr, "Dashboard icons" in homepage, etc.). I started looking for a website that offered this functionality but I couldn't find it (maybe I searched wrong or it really doesn't exist). This is how this website was born where with a request it returns a simple but personalized icon. Ex: /generate_svg?font_size=30&text=Hello+World&text_length=5&fill_color=%23ff0000&text_color=%23ffffff&shape=circle This request would generate an SVG image with the text "HW" (short for "Hello World") in a font size of 30, with a red fill color and a white text color, and a circle shape. More information about the configuration parameters on the web. PS: English is not my native language, so please forgive my mistakes.

Visit product View on Hacker NewsJun 10, 2024

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

85%85% 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: using · Missing: mac, agents, macos

64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

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

Hacker NewsStrong engagement from HN community · Strong signals: exist, 000, io · Missing: https docs, excited, just released

56%56% 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: personal, para · Missing: mobile apps, ios, entrepreneurs

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

19%19% 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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