Eternal – A no-nonsense, browser-based bulk SVG optimizer for FREE

Hacker News

I built Eternal, a straightforward bulk SVG optimizer that works entirely in your browser. No sign-ups, no installations, just drag-and-drop SVG optimization. Some of the features: - FREE: Yes, that's true, no subscription and no - Browser-based: No installation required - Privacy-focused: All processing happens locally - Bulk processing: Optimize multiple SVGs at once - File customization: Add prefixes and suffixes to optimized files - Download options: Get individual SVGs or all as a zip file As a designer who creates a lot of icons. I created Eternal because I needed a no-fuss SVG optimizer for my projects and couldn't find one that was both simple and powerful. Try it out -> https://eternal.simpleglyphs.com . Try it out -> https://eternal.simpleglyphs.com . Try it out -> https://eternal.simpleglyphs.com . I'd love to hear your thoughts and suggestions for improvement!

Visit product View on Hacker NewsAug 30, 2024

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Actual performance

2points

Did not reach leaderboard

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

71%71% 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.

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

50%50% 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

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

24%24% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

23%23% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: subscription · 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

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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