TUICR: Code Review for the Age of AI-Generated Code

Here's a scenario that's become painfully familiar. You ask Cursor, Claude Code, or Copilot to refactor a module. Seconds later, hundreds of lines have changed. Maybe it even looks right. So you do what developers have learned to do: copy the diff, scan it manually, run a quick git diff, paste snippets back into your editor, and run your tests, hoping nothing slipped through.

Then the AI suggests a follow-up change. And you do it again.

This is the copy–paste–check–repeat loop, and it quietly becomes the bottleneck of AI-assisted development. The generation is fast. The review is not.

Traditional code review tools were designed for human-paced, human-scoped changes: a few files, deliberate commits, meaningful PR descriptions. They weren't built for a world where your coding assistant can produce 500-line diffs in under ten seconds.

TUICR (Terminal UI Code Reviewer) is a terminal-first code review tool designed specifically for AI-generated changes build by Almog Garva, co-founder of Responsive.

TUICR lives in your terminal, works with standard patch files and git diffs, and gives you a fast, keyboard-driven interface for reviewing, accepting, and rejecting changes without leaving the command line.

The core thesis is simple: if AI is generating code at machine speed, your review tooling needs to match that cadence. TUICR doesn't try to replace GitHub PRs or your existing git workflow. It inserts cleanly between AI generation and your commit, giving you a structured moment to say yes, I understand and accept this at every change, not just at merge time.

The mismatch between AI output and existing review tools is more structural than it first appears. A few specific pain points:

Large diffs, limited context : A PR with 400 changed lines from a human usually has a story behind it a ticket, a discussion thread, incremental commits. An AI-generated diff of the same size arrives without any of that scaffolding. Reviewing it feels like reading a chapter without the book.

Manual patch validation : When you're iterating with an AI, you often end up with overlapping or superseded patches. Without tooling to track what's been applied, what's been approved, and what's pending, it's easy for changes to accumulate faster than your mental model of the codebase can absorb them.

Context loss between sessions : AI tools don't retain memory of everything they've suggested in a session. If you walk away mid-review, that context evaporates. You're left with a pile of modified files and no clear audit trail of which changes you intentionally approved.

Slower cycles masquerading as fast ones : AI generation is fast. But if review is slow and error-prone, the net velocity gain is smaller than it looks and the error rate is higher.

TUICR is designed to feel immediately familiar to anyone who lives in the terminal. Here's a practical walkthrough from install to first review.

Installation

Basic commands

Reviewing an AI-generated patch

Once you run tuicr review, you're dropped into the interactive diff viewer. Files are listed in a sidebar; the main pane shows the diff with syntax highlighting. Navigation is entirely keyboard-driven:

Approving or rejecting changes

TUICR operates at the hunk level the same granularity as git add -p. You can approve individual hunks within a file while rejecting others. Rejected hunks are cleanly reverted; approved hunks are staged automatically. The review session produces a structured log you can commit alongside your changes.

Integrating into your workflow

Let's walk through a realistic scenario. You're building an API endpoint and ask Claude Code to add input validation and rate limiting to an existing handler.

AI generates the changes

Claude Code modifies three files: the handler, a new middleware, and the route config. Total diff: ~180 lines.

Stage and open TUICR

Run git add -A && tuicr review.

Terminal UI opens with all three files listed, diff ready to navigate.

Review hunk by hunk

The rate-limiting logic looks correct. The middleware wiring looks right. But one hunk modifies error handling in a way that suppresses a log you need press r to reject it.

Approve the rest, commit

Press a across the remaining hunks. TUICR stages only approved changes. One clean git commit and you're done with a review log attached.

The payoff: The whole review took under three minutes. You caught one real issue and moved on. Without TUICR, that rejected hunk would likely have shipped.

In TUICR, “review hunk by hunk” means reviewing each small section of a code change (a hunk) individually rather than reviewing the entire file or patch at once.

In Git terminology, a hunk is a group of contiguous lines that were added, removed, or modified in a file within a diff.

Example diff:

That block between the @@ markers is one hunk.

If multiple parts of the file changed, Git creates multiple hunks.

Instead of scanning an entire large diff (which AI tools often generate), TUICR lets you step through each change block sequentially.

The workflow looks like:

This makes reviewing large AI-generated patches much faster and safer.

AI tools like GitHub Copilot, Cursor IDE, or Claude Code often generate big multi-file changes.

Reviewing everything at once can cause:

Hunk-by-hunk review solves this by:

TUICR isn't trying to replace any of your existing tools it fills a specific gap between AI generation and commit. Here's how it slots into a modern AI-assisted stack:

TUICR doesn't skip your CI pipeline or GitHub review it adds a structured human checkpoint before code enters that funnel. Think of it as the first gate, not a replacement for the others.

Development velocity. Counter-intuitively, adding a review step speeds things up. By catching issues before they're committed, you avoid the debugging cycles that quietly eat hours downstream. Review-as-you-go is faster than review-after-you've-merged.

Code safety. AI tools are remarkably good at plausible-looking code. The issues they introduce are often subtle a suppressed error, a missing null check, a slightly wrong default. Hunk-level review catches these before they compound.

Human oversight at the right granularity. TUICR enforces a simple principle: every line of AI-generated code gets a deliberate human decision. Not a skimmed glance, not a rubber-stamp merge a yes or no for each meaningful change. That's the oversight model that actually holds up under scale.

We're in a transitional moment. AI coding assistants have already changed how fast code gets written. What hasn't kept pace is how thoughtfully it gets reviewed. The risk isn't that AI writes bad code it's that the sheer volume of AI output gradually erodes the human judgment that was always the backstop.

TUICR is a small, principled intervention: give developers a fast, structured, terminal-native way to review AI-generated changes and stay genuinely in control of their codebase. Not as a slowdown, but as a practice.

The developers who will thrive with AI tools aren't the ones who review less they're the ones who've learned to review smarter, at higher velocity, without dropping the thread of what's actually in their code.

TUICR is worth ten minutes to install. Your future self, debugging a production issue introduced by an unreviewed AI hunk, will agree.