gomponents, HTML components in pure Go

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Hey everyone! I’ve built gomponents over the past couple of years because I wasn’t satisfied with using templating languages in Go. In particular, it was really hard to pass data around when using stdlib html/template, and it just didn’t sit right with me. Using just Go, you get so much for free from the language, and building HTML components becomes much easier, I think. The way you use it basically looks like using a DSL, but it’s not! It’s using the often-disliked but occasionally useful dot import mechanism of the language. This way, there’s full autocompletion built right in if your editor supports Go. Anyhoo, I hope you find some use in it. I know a lot of people already do. :) Enjoy! PS: There's gomponents-htmx as well. The two work really well together.

Visit product View on Hacker NewsMar 13, 2024

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

78%78% predicted probability of success on Product Hunt, 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

72%72% 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 · Strong signals: supports · Missing: reddit linkedin, podcasting, created

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

34%34% 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

16%16% 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.

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