Introduction to Web Fonts and Intro to Web APIs (Tutorials)

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Hey everyone. I wrote a few tutorials using Mozilla's Webmaker (webmaker.org) and was asked by the Webmaker team to convert them into what they call teaching kits and teaching activities for their curriculum. There's already enough HTML/CSS/JS tutorials for beginners online, which is why I went with something a little more advanced (some basic knowledge of HTML/CSS/JS is necessary to follow). Plus the tutorials have been checked by people working for Mozilla, so they passed some pretty good quality control. Make Your Own Web Mashup: Introduction to Web APIs https://fourtonfish.makes.org/thimble/make-your-own-web-mashup-introduction-to-web-apis Introduction to Web Fonts https://fourtonfish.makes.org/thimble/make-your-own-about-me-page-part-i-introduction-to-web-fonts A Web Fonts Exercise https://fourtonfish.makes.org/thimble/make-your-own-social-media-sharing-buttons I hope you'll find these useful.

Visit product View on Hacker NewsApr 14, 2014

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

80%80% 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

65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: using, apis · Missing: mac, agents, macos

61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: plus · Missing: platform, intuitive, reviews

46%46% 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

33%33% 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

23%23% 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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