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✅ **Pedagogical Improvements:** - Removed complex SimpleProfiler dependency - Added simple time_kernel() function using time.perf_counter() - Displays timing in microseconds (realistic for kernel operations) - Focused learning on kernel optimization vs profiling complexity ✅ **Clean Learning Progression:** - Module 11 (Kernels): Simple timing - 'Can I make this faster?' - Module 12 (Benchmarking): Professional profiling - 'How do I measure systematically?' - Module 13 (MLOps): Production monitoring - 'How do I track in production?' ✅ **Implementation Details:** - Fixed imports to use matmul_naive from TinyTorch layers - Simplified baseline implementation using NumPy dot product - Reduced cognitive load by removing measurement complexity - Maintained all kernel optimization concepts ⚠️ **Note:** Cache-friendly implementation needs debugging but core timing functionality works 🎯 **Impact:** Students can now focus on building optimized kernels with immediate microsecond-level performance feedback, setting up perfect progression to comprehensive benchmarking in Module 12.