VibeDriftAI technical debt: the hidden cost of agent-written code
AI writes code faster than ever, and a new kind of technical debt piles up just as fast: drift. What AI technical debt is, why it's invisible, and how to measure and pay it down.
The VibeDrift blog.
Field notes and guides on drift in AI-written code — what it is, how to measure it, and how to keep your codebase coherent as agents write more of it.
Claude Code is drifting your repo. Here's how to stop it.
Claude Code is brilliant in a fresh repo and drifts in a big one, because every session is stateless. The practices that help, the ones that don't, and the MCP fix that makes it check before it writes.
What is an MCP server? A practical guide for coding agents
An MCP server lets an AI coding agent call tools while it works. What the Model Context Protocol is, how a server works, how it differs from a plugin or API, and how to add one to Claude Code or Cursor.
Why AI-generated code passes review but breaks your codebase
AI code compiles, passes tests, passes the linter, and passes review, then quietly breaks your codebase anyway. Why review misses behavioral drift, and what actually catches it.
Cursor rules vs MCP: why config files aren't enough
Cursor rules nudge the model up front but can't guarantee it follows them. MCP checks the real codebase mid-task. Why config files aren't enough, and why you want both.
CLAUDE.md best practices (and where they quietly fail)
How to write a CLAUDE.md your agent actually follows, and the honest part nobody mentions: where it quietly fails, and what to pair it with so your conventions don't erode.
5 MCP tools that make your AI agent write better code
Five MCP tools that let your AI agent ask the codebase how it already works before it writes, so it stops contradicting your conventions and reinventing code that already exists.
How to keep a large codebase consistent when AI writes most of it
A practical playbook for keeping a large codebase consistent when AI writes most of it: declare conventions, check them in-loop, measure drift, and gate CI so regressions can't merge.
Adding a drift gate to CI: block incoherent AI code before merge
Make coherence a merge requirement, not a hope. How to add a VibeDrift drift gate to GitHub Actions, pick a --fail-on-score threshold, and ratchet it up over time.
Measuring code coherence: what a Vibe Drift Score actually tells you
What code coherence means, and how the Vibe Drift Score measures whether your codebase agrees with itself. How to read the 0–100 score, the five detectors, and your scan-over-scan delta.
VibeDrift now runs inside your coding agent (MCP), and we measured the impact
The full controlled eval: with and without VibeDrift, graded by an independent pass. The signal cut introduced drift where the agent couldn't infer the convention, and did nothing where it could.
From detection to prevention: the missing feedback loop in AI coding
AI moved the bottleneck from writing code to keeping a codebase coherent. VibeDrift now runs as an MCP server so your agent checks the repo's conventions before it writes, and we measured the impact.
Your AI-written codebase is drifting. Here's how to measure it.
AI coding tools don't just introduce bugs, they introduce behavioral deviation. Your codebase slowly stops agreeing with itself, and no existing tool measures it.