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Online parser for arbitrary CRDs with sample YAML
Hacker NewsOnline parser for arbitrary CRDs with sample YAML
Hello peeps. I’ve wrote a tool to nicely display crds. I know of docs.crds how is this different? You can paste in the crd and it will generate a sample yaml to boot and you don’t need to point it at a repository. Also it has a cli version. Here is the repo https://github.com/Skarlso/crd-to-sample-yaml . I hope someone finds this useful. Any feedback is always appreciated. :) Thanks.
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AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting49%49% 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, personal39%39% 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 released37%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 · Missing: mac, agents, macos35%35% 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, revenue12%12% 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, crypto1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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