SpaceBadgers – Free and Libre SVG Badges

Hacker News

Greetings, Hacker News community! I am thrilled to present SpaceBadgers, a new free and open-source SVG badge generator I've been working on. It's located at badgers.space. SpaceBadgers is born out of the desire to offer more flexibility and customization for project badges, often used in open-source projects. It's fully open source, provided under the permissive MIT license, and will always be provided for free. The core badge worker is written in Rust, and so is the library behind it, which you can also find on crates.io under the name spacebadgers. I am excited to receive your feedback and suggestions. Check it out and let me know what you think in the comments. Contributions are also welcomed and appreciated. You can find the source code here: https://github.com/splittydev/spacebadgers .

Visit product View on Hacker NewsMay 21, 2023

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Hacker NewsStrong engagement from HN community · Strong signals: excited, open source, hacker news · Missing: https docs, just released, exist

84%84% 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 · Missing: supports, reddit linkedin, podcasting

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

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

56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · 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 · Missing: arr, mrr, revenue

13%13% 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.

Correct prediction on native model

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