Cr
Crystaldoc.info – Crystal Shards API Documentation Hosting
Hacker NewsCrystaldoc.info – Crystal Shards API Documentation Hosting
This is a website I developed in Crystal for hosting API documentation for Crystal shards (aka gems, crates, libraries, etc). It's built in Crystal itself and is using Postgres, Kemal, the built-in doc generation tooling, and Lexbor. I've been working on this for a number of months and I'm proud of where it's gotten. All feedback welcome! Repo: https://github.com/nobodywasishere/crystaldoc.info
Share cardActual performance
43points
13comments
Made the leaderboard
Launch Intel predictions
Analyze your own launch →Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released64%64% 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 · Missing: supports, reddit linkedin, podcasting60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: using · Missing: mac, agents, macos56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal41%41% 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, revenue15%15% 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, crypto2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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