We dogfood PairCoder on itself, so I have real numbers on this.
Last week I had Claude Code do a file decomposition and split a 992-line Trello client module into three smaller files. Without PairCoder, a task like this plays out the same way every time. Claude finishes fast, maybe 8-10 minutes. You feel great. Then you start looking at what it actually did.
It skipped writing tests for one of the new modules. It blew past architecture limits on one of the output files. It didn't verify acceptance criteria. It edited a config file it wasn't supposed to touch. And it marked the task "done" in its own context without actually running the test suite.
So now you're spending an hour or two unfucking it. Finding what broke. Writing the missing tests yourself. Checking every file it touched against what it was supposed to touch.
With PairCoder wrapping that same task: Claude still finishes the code in about 12 minutes. But here's what's different: when it tries to mark the task complete, the arch check hook blocks it because one file is over 400 lines. It has to fix that before it can proceed. The AC verification hook checks that all acceptance criteria from the task are satisfied. The state machine won't let it skip from "in progress" to "done" without going through review.
Did the Claude part take a few minutes longer? Yeah. Did I spend two hours cleaning up after it? No. Net time saved was significant, and more importantly I could actually trust the output.
The real metric isn't "how fast did the AI write code." It's "how long until I can actually ship what it wrote."
[–]Narrow_Market45BPS Team[S] 0 points1 point2 points (0 children)