Accepted Diffs, Tab Completions, Agent Lines of Code — 3 Metrics That Show How Well You Use AI Coding Tools

Many developers use Cursor or GitHub Copilot every day but never really know if they're using it effectively. The feeling of "AI is helpful" isn't enough — you need concrete numbers.

Cursor's Usage Leaderboard tracks 3 key metrics. Understanding them will tell you exactly where you stand and what to improve.

The Example Leaderboard

Here's what a team leaderboard looks like. We'll use these numbers throughout the article to explain each metric.

1. Accepted Diffs — Do You Trust Your AI?

When AI suggests a code change, you see a diff — red for removed lines, green for added lines. If you click Accept, that's 1 accepted diff.

# AI suggests:
- def get_user(id):
-     return db.query(id)

+ def get_user(user_id: int) -> User:
+     return db.session.query(User).filter_by(id=user_id).first()

What does a high number mean?

• ✅ You actively assign tasks to AI frequently
• ✅ AI suggestions are relevant enough that you accept them
• ❌ If high but not reviewed carefully → technical debt accumulates

Looking at the leaderboard: Ethan Kowalski leads with 189 diffs — he prompts AI most frequently. But his Agent LoC of 13,017 means each accept averages ~69 lines — smaller tasks. Compare to James Mitchell's 142 diffs generating 31,204 LoC (~220 lines/diff) — he's assigning much larger tasks per session.

Reference range: 80–150 diffs/month is active AI usage.

2. Tab Completions — Is AI Woven Into Your Typing Flow?

While you're typing, AI automatically suggests what comes next (shown in gray). Press Tab to accept.

# You type:
def calculate_

# AI suggests (gray):
def calculate_total_price(items: list[Item]) -> float:

This metric reflects how deeply AI is integrated into your daily coding flow — no need to stop and prompt, AI runs alongside you.

In the leaderboard, Lucas Fernandez stands out with 117 tab completions — far ahead of everyone else. He's coding in a continuous "collaboration" mode with AI, letting it complete line by line rather than waiting for large task results.

A low number isn't necessarily bad — some developers prefer prompting large tasks over autocomplete. But if you've never tried it, it's worth enabling and building the habit.

3. Agent Lines of Code — Do You Delegate Big Tasks to AI?

This is the metric that separates basic from advanced AI users.

Agent mode is where AI takes multi-step actions autonomously:

You assign: "Create authentication module with JWT"

AI does:
├── Reads project structure
├── Creates auth.service.ts
├── Creates auth.controller.ts
├── Creates auth.middleware.ts
├── Runs build → finds error
└── Self-fixes error → reports done

Every line of code produced during that process gets added to Agent LoC.

The LoC per Diff ratio is the most revealing number:

In our example, James Mitchell averages ~220 LoC/diff — clearly delegating large, complex tasks. Noah Patel has only 33 diffs but 9,510 LoC (~288 LoC/diff) — he rarely accepts but when he does, it's a big chunk. Worth checking whether those large accepts are being reviewed carefully.

How to Read a Team Leaderboard

Don't just look at the overall rank. Look at the combination of all 3 metrics:

High Diffs + High LoC     → Good Agent usage, large tasks ✅
High Diffs + Low LoC      → Frequent prompts but small tasks 🟡
Low Diffs  + High Tabs    → Autocomplete-focused style 🟡
Low Diffs  + High LoC     → Rare accepts but large ones — review carefully ⚠️

Conclusion

These 3 metrics measure 3 different dimensions of AI usage:

• Accepted Diffs → interaction frequency
• Tab Completions → depth of workflow integration
• Agent Lines of Code → trust level and task complexity

The goal isn't the highest number — it's a balanced combination that fits the type of work you're doing. A senior dev working on complex features should have high LoC/Diff. A developer working on quick bug fixes will naturally have more diffs but lower LoC. Both can be using AI effectively.