This is fundamentally a scaling problem, not a tooling problem. When AI generates PRs that no single person can fully grasp, the question isn't "how do we make reviewing 5,000 lines more comfortable" – it's "who is actually vouching for this code?"
The answer is already deeply embedded in Git's tooling: every commit carries both an author and a committer field. The author wrote the code, the committer is the person who put it into the codebase. With git blame you always know who is to blame – in both senses. In the age of AI-generated code, this distinction matters more than ever: the author might be an LLM, but the committer is the human who vouches for it.
Disclosure: non-native English speaker, used AI to help articulate these thoughts – the ideas are my own.
Ah, co-authored, but that's different thing, isn't it?
Tbh I only know it from squashed PRs.
However:
> "Co-author" is not a git concept. It is a convention in commit messages used by some services, including GitHub. So, the solution is to edit the actual commit message with git commit --amend and add a line to the end:
I mean, i love those kind of cli tools but in my current mood, instead looking for it on github, I'd probably ask an frontier model:
“Create a cross-platform CLI tool that scans multiple Git projects (grouped by category) and reports their status (clean, modified, ahead, error) based on a YAML config.”
Allways surprised how far this gets me. Most of my dotfiles now got created this way.
I’m using SuperWhisper for voice dictation and casually chatting with Claude Code, which I just start in the Obsidian vault directory.
I’m currently onboarding into a new project and need to gather and structure information quickly.
So I just jot down whatever comes to mind — in natural language, no structure.
Claude takes care of organizing it: generating #tags, creating [[links]], and making things retrievable later.
After a couple of weeks, I can ask things like:
• What did I do yesterday?
• Who is “John Doe”?
• Which project uses Java version 21?
• …
Happy to share more if anyone’s curious.
(English reviewed with a bit of help from ChatGPT — non-native speaker here.)
I'm experimenting with a somewhat similar workflow.
I have a Boox tablet I use to take handwritten notes on any relevant topics during the day. And with that I create the following workflow: 1. transform handwritten notes into a markdown document using chatgpt 2. with this document I ask chatgpt to create a summary of yesterdays activities and a list of unfinished things I still need to work on today.
This approach isn't perfect but is surprisingly useful.
That's definitely a big upside. Things like dishes and laundry are great background tasks. Doesn't take long to set up, then a machine handles the rest, swap out the laundry and keep going. Barely interrupts my day job, and the impetus to get out of my chair is welcome anyway.
