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ANX.GUID.Gen - Generate Geek-Friendly GUIDs

Hacker News

ANX.GUID.Gen - Generate Geek-Friendly GUIDs

I created this cause, I wanted to create few constant GUIDs for a game I am working on. The GUIDs generated might feel procedural-ly. Regenerate few times, to find something that you might like. Advantage: since the GUID are a bit readable than normal random GUIDs, this are easily spottable in log files. No uniqueness guarantees are made. Techstack:Hosted on Github Pages, Proxied via Cloudflare. HTML, JS, CSS, Bootstrap, Wordnet.

Visit product View on Hacker NewsDec 22, 2024

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

79%79% 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.

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

74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host, friendly · 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

14%14% 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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