He

Headmaker – HTML <head> generator and reference

Hacker News

Headmaker – HTML <head> generator and reference

Hello HN! I made a site for creating head elements. The included tags are somewhat curated and should cover the majority of use cases without too many outdated/irrelevant entries. I recently started learning JavaScript and wanted to make a simple interactive site to apply what I've learned; I was also a bit lost regarding head elements, so it seemed a good way to kill two birds with one stone. After looking for comparable sites and being disappointed in what's available I decided to flesh it out and make it public. It ended up being more of an undertaking than I was hoping for from a first project because of scattered and inconsistent information sources. I think it turned out pretty well though. Any feedback is appreciated.

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Indie HackersFits the IH revenue-focused audience · Strong signals: started, para · Missing: supports, reddit linkedin, podcasting
66%66% 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: ide, io · Missing: https docs, excited, just released
59%59% 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 · Missing: mac, agents, macos
54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way, para · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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