Simple dynamic templating in JS

Hacker News

How do you guys deal with this scenario? const renderTemplate = (template, obj) => { const args = Object.keys(obj) const body = `return (\`${template}\`)` const renderFunc = new Function(...args.concat([body])) return renderFunc(...Object.values(obj)) } const dynamicallyLoadedTemplate = '${a} and ${b}' console.log( renderTemplate(dynamicallyLoadedTemplate, { 'a': '1', 'b': 2 }) ) Is this elegant enough?

Visit product View on Hacker NewsApr 29, 2018

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Actual performance

3points

5comments

Did not reach leaderboard

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BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

48%48% predicted probability of success on BetaList, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

44%44% 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

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

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

37%37% 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 HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos

32%32% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

14%14% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Correct prediction on native model

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