JavaFactory – IntelliJ plugin to generate Java code

Hacker News

Hi HN, I built a code generator plugin for IntelliJ that uses LLMs to create repetitive Java code like implementations, tests, and fixtures — based on custom natural-language patterns and annotation-based references. Most tools like Copilot or Cursor aim to be general, but fail to produce code that actually fits a project structure or passes tests. So I made something more explicit: define patterns + reference scope, and generate code consistently. In this demo, 400 lines of Java were generated in 20 seconds — and all tests passed: https://www.youtube.com/watch?v=ReBCXKOpW3M GitHub: https://github.com/JavaFactoryPluginDev/javafactory-plugin

Visit product View on Hacker NewsMay 20, 2025

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

71%71% 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 HuntUnlikely to reach the leaderboard · Strong signals: cursor, code · Missing: mac, agents, macos

47%47% 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: io · Missing: https docs, excited, just released

47%47% 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

45%45% 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

37%37% 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

18%18% 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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