vibencode
AI-powered development
The code arrives faster than anyone can check it.
“Four times the code. Main is no faster.”
Evaluation built from real cases, merge gates that hold under volume, a test suite that grows faster than the output, and a codebase somebody still understands.
Typing was never the bottleneck, and now it is not even the work. Generation got roughly ten times cheaper; reviewing, integrating and operating did not, and all three now have ten times as much arriving at them. So the queue moved rather than cleared: more branches, a main branch that is no faster, and a growing pile of code that works and that nobody can account for. The answer is not less generation, but a checking half that grows as fast. Most of the list below is that.
- 01Evaluation built from real cases, not invented ones, so there is something to fail against before review becomes the only gate.
- 02Verification in CI that scales with volume: tests, static and architectural checks, plus the ones your failure modes need.
- 03The merge gate stays exactly where it was. Review does not get looser because the author was a machine.
- 04An audit trail on every suggestion that reached the codebase.
- 05Somebody owns it afterwards: a team that can say what a change does and why, without us.
- Evaluation & merge gatesWhat has to be true before a change can reach main, written down and enforced rather than assumed.
- Verification in CIThe checking half of delivery, wired into the pipeline your engineers already push to, growing as fast as the output does.
- A codebase somebody ownsYour engineers running it without us, and a written account of what to do the day it is confidently wrong.
The bottleneck was never typing. It is now deciding what is safe to merge.
Watch
Where the time actually goes, measured by sitting with engineers rather than by asking them.
Baseline
Cycle time, review latency, rework after merge, and how much reaches main. Recorded first, or the result is an opinion.
Wire it in
Evaluation from real cases, verification in CI, an audit trail on every suggestion, and the review path untouched.
Hand over
Your team runs it. We write down what to do on the day it is confidently wrong.
That is the shape of a full engagement. If it is more than you want to commit to yet, the same people will spend two days on one piece of it first, and you are free to stop there. Start a 48-hour sprint
- A repository we can readHistory included. Comprehension is what is being accelerated, and history is where it lives: the reverts, the hotfixes, the guard nobody removes.
- Two or three engineers who will use it dailyNot a champion who demos it. The people who will hit the annoying parts in week two and say so, which is the only useful feedback.
- Your current numbersCycle time, review latency, rework. If they do not exist we measure them first; without a baseline the result is only an opinion.
- A position on what may be generatedTests, migrations, production code. Someone senior holds that line, and it wants writing down before the first suggestion lands.
What actually is AI-powered development?
Building the half of delivery that decides what is safe to merge and safe to ship: evaluation from real cases, verification in CI, and a review path that holds under volume. Agents in the loop on the generating side too, with your repository and its history as context. It is a change to the workflow rather than to the tool list, and the part that makes it work is unglamorous: what the agent is allowed to see, where its output enters review, and a gate that does not move because the author was a machine.
Is this just buying an AI coding assistant for everyone?
No. And if that genuinely is all you need, we will say so on the first call rather than sell you eight weeks. The work here is the repository context, the review path and the measurement around them.
Does this replace engineers?
It has not in any engagement we have run. It moves where their time goes: less reconstruction of how things work, more judgement about what to change. That is exactly why comprehension is the thing we measure.
Will our code leave our environment?
That is a decision, not a default. If it cannot leave, the constraint shapes the toolchain from day one, which is a sovereign deployment question and gets answered first.
What about the code-quality argument?
It is a real argument, and it is the reason the merge gate does not move. We also measure rework after merge, because that is where quality problems actually surface rather than in review.
We build software for our customers, not for ourselves. Does this apply?
Yes. This is that work. Full-stack and SaaS delivery is where the company started; it now runs with agents in the loop. You can hire us to build the product, not only to change how you build it.
This is about how you build your own software. If what you want is an AI capability inside the product your customers use, that is a different engagement. That is GenAI implementation.
Can we build the software itself faster?
Two days in your repository. Written up at the end.