It doesn’t work to let the model rip and clean up after - well, it works the first time. But the second time you do it, it’ll make duplicates of stuff you didn’

It doesn’t work to let the model rip and clean up after - well, it works the first time. But the second time you do it, it’ll make duplicates of stuff you didn’t know you already had because you’re not fully across your own codebase and neither is the model. Instead treat the model output as a spike. By this I mean - first ask for the feature, then put the changes into a patch or stash. Reset with `git reset --hard`, then hand-author the change using the LLM output you stashed as a reference. It is also awesome to one-shot a giant feature with Gemini, thanks to its long context: > cd $REPO_ROOT && repomix && llm -m gemini-2.5-pro-exp 'attached is my entire codebase. Please add $FEATURE. In your output give all file paths and find/replace blocks needed for the implementation plus any commands and other instructions I will need.' -a repomix-output.xml (Thanks Simon Willison and the creators of repomix.) Welcome to the wild west of what we should call “agentic coding”. Serious professionals are doing this stuff, it isn't pure vibes. You’re at the forefront, upending the way software has been built for decades. Theres huge ROI right now hacking together your own workflows. While it’s still DIY, enjoy being part of the shared learning process & please share what you learn :)