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Design Patterns Plugin for IntelliJ IDEA

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Design Patterns Plugin for IntelliJ IDEA

The Design Patterns Plugin is a plugin meant to help you by auto-implementing popular and useful design patterns based on the code you 've written. As this is still an on-going project we are continuously working on the implementation of new design patterns that the plugin supports, as well as upgrading the already existent ones. JetBrains Plugin Repo: https://plugins.jetbrains.com/plugin/10856-design-patterns-p... Github Repo: https://github.com/OrPolyzos/Design-Patterns-Intellij-IDEA-P... P.S. #1 To anyone who reads this and is going to use it, hope you enjoy it and please take some time to give a review and/or share your feedback! P.S. #2 We are currently looking for contributors, so we would be more than delighted and honored to welcome in our team anyone who wants to get involved and get his/her hands dirty!

Visit productView on Hacker NewsAug 12, 2018
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
72%72% 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 · Strong signals: new, code · Missing: mac, agents, macos
54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, ide, io · Missing: https docs, excited, just released
41%41% 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
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
24%24% 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
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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