If AI Coding Trends Continue, I’ll Be Merging 20 PRs a Day

In December, I merged six PRs to my dots repository. In the 31 days ending July 11, I merged 55.

dots is basically my home base for personal tooling, configs, skills, and whatever else I am currently using to make computers do things for me. It is also where most of my agent workflow compounds.

Across all my personal repositories, I merged 481 PRs between July 12, 2025 and July 11, 2026. None of these are work repositories.

The first 13 weeks averaged 3.4 merged PRs per week. The latest 13 averaged 19.2. That is a 5.7x increase.

agents go brrrrr

Simon Willison called late 2025 the “November 2025 inflection point.” That matches my experience. Around December, coding models got good enough to produce half-decent code.

My GitHub history shows another jump a few months later.

The first sustained acceleration starts around the February 5 release of Claude Opus 4.6. The curve takes off again after GPT-5.5 arrived in Codex in April. GPT-5.5 with medium reasoning became my daily driver and a real workhorse.

I think the change is that the models got better at following directions and working autonomously for longer. They can handle larger scopes and one-shot things more often, especially if I give them the right context, validation framework, and definition of done.

Sometimes this is annoying. The models disappear for too long and do too much before checking back. I have seen that too. But if the repository has good context and validation, the benefit outweighs the annoyance.

This is where compound engineering and harness engineering really start to pay off.

I have invested a lot of time into making these repositories agent-friendly. Every improvement makes the next change easier to ship. Context keeps the agent from taking obvious wrong turns. Tests let it work longer without me. The validation lanes catch mistakes before I need to read through the implementation.

The chart makes this look like a clean model story. It isn’t. My harness was getting better at the same time.

Sebastian Raschka called RLVR one of the defining post-training trends of 2025. He also makes the point that a coding agent is the model plus its harness. That feels like the right way to look at this chart.

I do not know how much of the curve is better post-training versus my harness getting better. Probably both. Actually, it is probably the companies spending billions of dollars on compute and research at an unprecedented scale driving most of this change.

We are at the point where the outer loop of automation makes sense and is not just theoretical.

PR count is not a quality or impact metric. It does show how much code my setup can get from idea to merged PR without falling apart. If this keeps increasing, I cannot supervise every run and read every diff closely.

The recent quarter averaged 19.2 merged PRs per week. The quarter before that averaged 11.6. That is a 1.65x increase.

If that kind of jump keeps happening every three months, the sequence goes from 19 PRs per week to 32, then 52, then 86.

By next July, I will be merging 142 PRs per week, a little over 20 per day.

Obviously, no. I still have to review the code, decide what is worth building, and occasionally do something other than merge pull requests.

But at 142 PRs per week, I am not reviewing every diff or supervising every agent run. I am operating a system. That is where the outer loop of automation stops being theoretical.

It is also why there is so much emphasis on harnesses. Once the model can work for longer and reliably hit a definition of done, every improvement to the system makes the next PR cheaper.

Draw that curve out another year and you can see the dollar signs in every VC’s eyes.