That's why I created batchai. The idea is simple: less copy-pasting, fewer clicks on 'Add to Chat' or 'Apply.' batchai traverses files and processing each of them. Since AI isn’t always perfect, I’ve designed it to run only on a Git directory, so we can easily diff the changes and choose to either commit or revert them.

Currently, batchai only supports code review and fixing common issues (think of it as a local AI-driven SonarQube). The next feature in progress is generating unit test code in batches, which I plan to use in a few of my personal projects (including this batchai), as they have very few unit tests. Other planned features include code explanation, comment generation, and refactoring — all of which will be handled in batches. Additionally, I’m working on enabling batchai to have an overall insight of the project’s code, such as building cross-file code symbol indexing, which should help the AI perform better.

Here are some interesting findings from testing batchai on my personal projects over the past two weeks:

    It can identify issues that traditional tools, such as SonarQube, tend to miss.
    It may not report all issues in one go, so I need to run it multiple times.
    Due to outdated LLM training data and hallucinations, it's crucial to confirm the changes for accuracy by myself - That's why I make batchai work only on clean Git repository directories.