yoDawgMemesFactory

thank you. i think this tool have really room to grow, but still concept of manipulate each task is quite usefule

1. Im sorry. i it was typo on path, i fixed it so you can see now.

2. from now, i only allow to use gpt-4o, because the requests involve relatively long context windows, which require high-quality reasoning. Only recent high-performance models like GPT-4o or Claude Sonnet are capable of reducing the manual workload for this kind of task.

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but still, if user want to use other models , i can make adapter features for various models

It uses openai.

This uses OpenAI's GPT-4o model.

The requests involve relatively long context windows, which require high-quality reasoning. Only recent high-performance models like GPT-4o or Claude Sonnet are capable of reducing the manual workload for this kind of task.

I've always wondered how long until we reach this. If every pc can run models locally, with a given seed and prompt it could be the ultimate compression. It's also hilarious.

Thank you — I’ll consider adding that feature.

Actually, I'm currently thinking about creating a small community for sharing pattern definitions.

I think IntelliJ is a great tool on its own. Recently, they even added a feature that auto-injects dependencies when you declare them as private final — super convenient.

I can’t help but wonder if the folks at JetBrains are starting to feel a bit of pressure from tools like Cursor or Windsurf

I've also got some mileage from live templates for repetitive code. However, at some point I built[0] an IntelliJ IDEA plugin to help me generate setters and field assignments that I felt live templates weren't a good solution for (for my case). I don't know if JavaFactory solves this kind of problem, keen to try it out.

[0]: https://github.com/nndi-oss/intellij-gensett

Your point is valid. In real-world work, tests should focus on parts that are difficult to verify, and if everything passes on the first try, it's often a sign that something deserves a closer look.

That said, what I wanted to highlight in the example was a contrast — tools like Cursor and other general-purpose models often fail to even generate simple tests correctly, or can't produce tests that pass. So the goal was to show the difference in reliability.

Related to this, consider that when an LLM writes tests for code, it's writing them based on what the code actually does, not what it's supposed to do. This is equally true when the code itself was written by the LLM. Sure the tests pass, but that doesn't prove the code is correct.