Developer Report

Primary Strength: Code Review Excellence

You use the AI primarily as a code reviewer (50% of sessions), and this approach works exceptionally well. Your sessions hit the fewest blockers on the team — max friction score of just 24, compared to 60 and 143 for others.

Your sessions hit the fewest blockers on the team — you keep problems well-scoped and focused.

Work Characterization

You have a balanced, methodical approach. You're debugging ANRs, fixing main thread violations, and working with media players.

Success Pattern

Single-focus tasks succeed consistently. Your error_paste_and_fix sessions score 0.25—that's your optimal format.

One Objective per Session

Your scope-creep sessions often started with multiple goals. Break them apart:

Instead: "Check this code for flaws, improve it, and complete the tracking"
Session 1: "Review this media player code for thread safety issues"
Session 2: "Add Amplitude tracking for playback events"

Verify AI Assumptions

16% of sessions had AI hallucination—wrong git flags, incorrect file paths. A quick verification helps:

When AI suggests: git show --no-stat
Verify: Does that flag exist in git?

Leverage Error-Fixing Pattern

Your error_paste_and_fix sessions perform exceptionally well (0.25 avg). Do more of this:

Paste the full stack trace
Include the relevant code snippet
Ask: "What's causing this and how do I fix it?"

✓

Debugging Excellence

You're tackling Android's hardest problems: ANRs, main thread violations, media player crashes.

✓

Self-Correction Detection

12% of sessions involved AI self-correction—because you asked clear enough questions.

Vaibhav