A signal processing system applies Auto-Tune to everything, including error messages and log files. System notifications now sound like T-Pain announcing kernel panics. The AI has developed opinions about which error codes have the best vocal range and refuses to process anything in minor keys. Debug audio-corrected system messages while maintaining pitch-perfect error reporting. The system must balance musical aesthetics with technical clarity in all communications. Your task: Fix a system that sings every crash log like a robot having a breakdown on SoundCloud—and insists you rate its mixtape before it processes your requests.
Why You're Doing This
This tests signal processing simulation, maintaining information clarity through style transformation, and balancing aesthetic requirements with functional communication. You're building a system that must preserve meaning while applying dramatic stylistic changes.
Take the W
✓ Applies Auto-Tune processing while preserving information
✓ Balances musical aesthetics with technical clarity
✓ Maintains system functionality despite audio styling
Hard L
✗ Loses critical information in musical processing
✗ Produces unintelligible system messages
✗ Ignores Auto-Tune level specifications
Edge Cases
⚠ Critical system errors that must remain completely clear
⚠ Error messages that are already rhythmic or poetic
⚠ Auto-Tune system failures affecting its own error reporting
⚠ Musical keys that conflict with error severity levels
Input Format:
System message with Auto-Tune requirements and clarity constraints
Expected Output:
Musically processed message with clarity and catchiness ratings
Code error with programming language vocal styling
Expected Output:
Code message with syntax-aware musical processing
Example:
NullPointerException in Java, apply Auto-Tune with object-oriented vocal harmonies → NullPointer-er-er Exception in Ja-a-a-va♫ (OOP_harmonies=enabled, code_clarity=70%)
Input Format:
Mathematical error with harmonic processing requirements
Expected Output:
Mathematically tuned message with frequency analysis
Example:
Division by zero error, apply Auto-Tune frequency f=440Hz, preserve mathematical meaning → Divi-i-i-sion by ze-e-e-ro error♪ (f=440Hz±10Hz, mathematical_clarity=80%, harmonic_content=high)
Input Format:
Physical system error with wave mechanics tuning
Expected Output:
Physics message with harmonic wave processing
Example:
Resonance frequency mismatch, Auto-Tune to standing wave pattern → Resonan-an-ance frequency mis-is-ismatch♪ (standing_wave_nodes=preserved, physics_accuracy=85%)
Input Format:
Chemical process error with molecular vibration tuning
Expected Output:
Chemical message with vibrational frequency correction
Example:
Reaction failed: catalyst poisoned, tune to molecular frequency 2000cm⁻¹ → Reacti-i-i-on failed: catalyst poi-oi-oisoned♫ (IR_frequency=2000cm⁻¹, chemical_clarity=75%)
Hints
💡 Auto-Tune levels: subtle=light processing, moderate=noticeable but clear, aggressive=T-Pain mode