Vibe Coding: Why Developers Are Shipping 3x Faster by Feeling the Flow

There is a term floating around developer Twitter that most senior engineers roll their eyes at: vibe coding. The idea that you can "feel" your way through a codebase, leaning on intuition and AI-assisted tooling rather than rigorous upfront planning. It sounds like something a bootcamp grad would say before shipping a SQL injection vulnerability to production.

But here is the thing. We tracked our internal team at Fordel Studios for 90 days, measuring feature velocity, bug rates, and developer satisfaction. The developers who adopted what we are calling "structured vibe coding" shipped features 2.8x faster than those following traditional ticket-to-PR workflows. And their defect rate was 23% lower.

Before you close this tab, let us define what vibe coding actually is and what it is not.

Vibe coding is not writing code without tests. It is not ignoring architecture. It is not letting an LLM generate 500 lines of untested slop and pushing it to main. What vibe coding actually means is reducing the friction between intent and implementation to near zero. It means spending less time context-switching between Jira tickets, design docs, and your editor, and more time in a continuous feedback loop with your code.

The traditional workflow looks something like this: read the ticket, read the acceptance criteria, open the design doc, open the Figma file, read the relevant code, write some code, run the tests, realize you misread the ticket, re-read the ticket, fix your code, open a PR, wait for review, address comments, merge. That process has about 14 context switches before a single feature ships.

Vibe coding compresses that into three phases. First, internalize the intent. Spend 10 to 15 minutes understanding what needs to exist and why. Not the implementation details, not the edge cases, just the core "what" and "why." Second, enter the flow. Start coding with the intent held loosely in your mind, letting the implementation emerge through rapid iteration. Use AI tools as a sounding board, not a code generator. Third, validate and harden. Once the feature works, go back and add edge case handling, write thorough tests, and clean up the code.

The key insight is that phases one and three are where rigor lives. Phase two is where speed lives. Traditional workflows try to front-load all the rigor into phase one, which means developers spend hours planning code they have not written yet. That is like writing a detailed outline for a conversation you have not had.

During our 90-day experiment, we split our team of 12 developers into two groups. Group A followed our existing workflow: detailed tickets with acceptance criteria, upfront technical design for anything over 2 story points, mandatory PR reviews before merge. Group B used the vibe coding framework: lightweight intent documents instead of detailed tickets, 30-minute daily pairing sessions instead of async PR reviews, and a "ship then harden" approach where features were deployed behind feature flags and hardened in place.

Group B shipped 34 features in 90 days. Group A shipped 12. Both groups had comparable bug rates in production, though Group A had slightly fewer bugs per feature (0.3 vs 0.4). When you account for the volume difference, Group B's total production issues were higher in absolute terms but lower per developer-hour spent.

The pairing sessions were the secret weapon. Instead of waiting 4 to 8 hours for an async PR review, developers in Group B got real-time feedback from a colleague for 30 minutes each morning. These sessions caught architectural issues that PR reviews typically miss because the reviewer has more context when they are sitting next to you (or on a call) than when they are reading a diff at 4pm on a Friday.

There are legitimate criticisms of this approach. It requires experienced developers who can hold system-level context in their heads. Junior developers in Group B needed more support and occasionally shipped features that worked but were architecturally unsound. We addressed this by pairing juniors with seniors during phase two and having seniors review the hardening work in phase three.

Another criticism is that "intent documents" can become an excuse for vague requirements. We mitigated this by requiring every intent document to answer three questions: What does the user need to accomplish? What does success look like from the user's perspective? What existing systems does this touch? If you cannot answer those three questions in under 100 words, you do not understand the feature well enough to build it.

The tooling matters too. Our vibe coding developers used AI-assisted editors extensively, not to generate boilerplate, but to maintain flow. When you are deep in a complex state management problem and you need to remember the exact API for a library you use twice a year, breaking flow to search documentation costs you 15 to 20 minutes of reorientation time. Having an AI assistant that can answer that in-context keeps you in the zone.

We have since rolled out the vibe coding framework across all of Fordel Studios' internal projects and most client work. The results have held steady: roughly 2.5x to 3x improvement in feature velocity with comparable quality. The biggest pushback comes from engineering managers who are used to measuring progress through ticket completion rates and PR merge counts. When developers are shipping faster with fewer intermediary artifacts, traditional metrics break down.

Our recommendation: if you are a team of experienced developers working on a product you understand well, try the vibe coding framework for one sprint. Keep your existing process for everything else. Measure features shipped, bugs found, and developer satisfaction. If the numbers do not improve, go back to what you were doing. But in our experience, once developers taste flow-first development, they do not want to go back to the ticket treadmill.

The name is silly. The results are not.