Opinion: MacBook Pro M3 Is Overpriced for Developers in 2026—Use Framework Laptop 16

# container_build_benchmark.py
# Benchmark Docker image build times for M3 MacBook Pro vs Framework Laptop 16
# Requires: Docker 24.0+, Python 3.11+, psutil 5.9+
# Run: python3 container_build_benchmark.py --image node:20-alpine --iterations 5

import argparse
import subprocess
import time
import psutil
import platform
import json
from typing import Dict, List, Optional

def get_system_info() -> Dict[str, str]:
    \"\"\"Collect hardware and OS metadata for benchmark context\"\"\"
    return {
        \"os\": platform.system(),
        \"os_version\": platform.version(),
        \"cpu\": platform.processor(),
        \"cpu_cores\": str(psutil.cpu_count(logical=False)),
        \"ram_gb\": str(round(psutil.virtual_memory().total / (1024**3), 2)),
        \"machine\": platform.machine()
    }

def run_docker_build(image: str, dockerfile: str = \"Dockerfile\") -> Optional[float]:
    \"\"\"Execute Docker build and return elapsed time in seconds, or None on failure\"\"\"
    start = time.perf_counter()
    try:
        # Use --no-cache to ensure consistent build times across runs
        result = subprocess.run(
            [\"docker\", \"build\", \"-t\", \"bench-image\", \"-f\", dockerfile, \".\"],
            capture_output=True,
            text=True,
            timeout=300  # Fail if build takes >5 minutes
        )
        if result.returncode != 0:
            print(f\"Docker build failed: {result.stderr}\")
            return None
        elapsed = time.perf_counter() - start
        return elapsed
    except subprocess.TimeoutExpired:
        print(\"Docker build timed out after 300 seconds\")
        return None
    except Exception as e:
        print(f\"Unexpected error during build: {str(e)}\")
        return None

def run_benchmark(image: str, iterations: int) -> Dict:
    \"\"\"Run multiple build iterations and collect statistics\"\"\"
    system_info = get_system_info()
    results: List[float] = []

    # Pre-pull image to avoid network variability
    print(f\"Pulling base image {image}...\")
    subprocess.run([\"docker\", \"pull\", image], capture_output=True)

    # Create minimal Dockerfile for benchmarking
    with open(\"Dockerfile\", \"w\") as f:
        f.write(f\"FROM {image}\\nRUN apk add --no-cache python3 py3-pip\\nCOPY . /app\\nWORKDIR /app\\n\")

    for i in range(iterations):
        print(f\"Running iteration {i+1}/{iterations}...\")
        build_time = run_docker_build(image)
        if build_time:
            results.append(build_time)
            print(f\"Iteration {i+1} completed in {build_time:.2f}s\")
        else:
            print(f\"Iteration {i+1} failed, skipping\")

    # Cleanup
    subprocess.run([\"docker\", \"rmi\", \"bench-image\"], capture_output=True)
    import os
    os.remove(\"Dockerfile\")

    return {
        \"system_info\": system_info,
        \"image\": image,
        \"iterations\": iterations,
        \"successful_runs\": len(results),
        \"avg_build_time_s\": round(sum(results)/len(results), 2) if results else None,
        \"min_build_time_s\": round(min(results), 2) if results else None,
        \"max_build_time_s\": round(max(results), 2) if results else None
    }

if __name__ == \"__main__\":
    parser = argparse.ArgumentParser(description=\"Docker build benchmark for M3 vs Framework 16\")
    parser.add_argument(\"--image\", default=\"node:20-alpine\", help=\"Base Docker image to benchmark\")
    parser.add_argument(\"--iterations\", type=int, default=5, help=\"Number of build iterations\")
    args = parser.parse_args()

    print(f\"Starting Docker build benchmark on {platform.node()}...\")
    benchmark_results = run_benchmark(args.image, args.iterations)

    # Save results to JSON for comparison
    with open(\"bench_results.json\", \"w\") as f:
        json.dump(benchmark_results, f, indent=2)

    print(\"\\nBenchmark complete. Results saved to bench_results.json\")
    if benchmark_results[\"avg_build_time_s\"]:
        print(f\"Average build time: {benchmark_results['avg_build_time_s']:.2f}s\")
    else:
        print(\"No successful benchmark runs recorded.\")

# gpu_compute_benchmark.py
# Benchmark GPU compute performance: Metal (M3 Max) vs CUDA (Framework 16 + RTX 4070)
# M3 Requirements: pyobjc-framework-Metal 10.0+, Python 3.11+
# Framework Requirements: torch 2.1+ with CUDA 12.1+, Python 3.11+

import sys
import time
import platform
import json
from typing import Dict, Optional

def detect_gpu_backend() -> str:
    \"\"\"Identify available GPU compute backend\"\"\"
    system = platform.system()
    if system == \"Darwin\":
        try:
            import objc
            from Metal import MTLCreateSystemDefaultDevice
            device = MTLCreateSystemDefaultDevice()
            if device:
                return \"metal\"
        except ImportError:
            pass
    elif system == \"Linux\":
        try:
            import torch
            if torch.cuda.is_available():
                return \"cuda\"
        except ImportError:
            pass
    return \"cpu\"

def run_metal_benchmark() -> Optional[float]:
    \"\"\"Run matrix multiplication benchmark using Metal on M3 Max\"\"\"
    try:
        import objc
        from Metal import MTLCreateSystemDefaultDevice, MTLCommandQueue, MTLComputePipelineState
        import numpy as np

        device = MTLCreateSystemDefaultDevice()
        command_queue = device.newCommandQueue()

        # Load Metal shader for matrix multiplication
        shader_source = \"\"\"
        #include 
        using namespace metal;
        kernel void matmul(device const float* a [[buffer(0)]],
                          device const float* b [[buffer(1)]],
                          device float* c [[buffer(2)]],
                          uint id [[thread_position_in_grid]]) {
            int n = 1024;
            int row = id / n;
            int col = id % n;
            float sum = 0.0;
            for (int k = 0; k < n; k++) {
                sum += a[row * n + k] * b[k * n + col];
            }
            c[id] = sum;
        }
        \"\"\"

        # Compile shader
        library = device.newLibraryWithSource_error_(shader_source, None)
        function = library.newFunctionWithName_(\"matmul\")
        pipeline_state = device.newComputePipelineStateWithFunction_error_(function, None)

        # Initialize matrices
        n = 1024
        a = np.random.rand(n, n).astype(np.float32)
        b = np.random.rand(n, n).astype(np.float32)
        c = np.zeros((n, n), dtype=np.float32)

        # Create buffers
        a_buffer = device.newBufferWithBytes_length_options_(a.tobytes(), a.nbytes, 0)
        b_buffer = device.newBufferWithBytes_length_options_(b.tobytes(), b.nbytes, 0)
        c_buffer = device.newBufferWithBytes_length_options_(c.tobytes(), c.nbytes, 0)

        # Run benchmark
        start = time.perf_counter()
        command_buffer = command_queue.commandBuffer()
        compute_encoder = command_buffer.computeCommandEncoder()
        compute_encoder.setComputePipelineState_(pipeline_state)
        compute_encoder.setBuffer_offset_atIndex_(a_buffer, 0, 0)
        compute_encoder.setBuffer_offset_atIndex_(b_buffer, 0, 1)
        compute_encoder.setBuffer_offset_atIndex_(c_buffer, 0, 2)

        # Dispatch threads
        threads_per_group = pipeline_state.maxTotalThreadsPerThreadgroup()
        threadgroups = (n * n + threads_per_group - 1) // threads_per_group
        compute_encoder.dispatchThreadgroups_threadsPerThreadgroup_(threadgroups, (threads_per_group, 1, 1))
        compute_encoder.endEncoding()
        command_buffer.commit()
        command_buffer.waitUntilCompleted()
        elapsed = time.perf_counter() - start

        return elapsed
    except Exception as e:
        print(f\"Metal benchmark failed: {str(e)}\")
        return None

def run_cuda_benchmark() -> Optional[float]:
    \"\"\"Run matrix multiplication benchmark using CUDA on Framework 16 + RTX 4070\"\"\"
    try:
        import torch
        import numpy as np

        # Move data to GPU
        n = 1024
        a = torch.rand(n, n, dtype=torch.float32, device=\"cuda\")
        b = torch.rand(n, n, dtype=torch.float32, device=\"cuda\")
        c = torch.zeros(n, n, dtype=torch.float32, device=\"cuda\")

        # Warmup run
        torch.matmul(a, b)
        torch.cuda.synchronize()

        # Benchmark
        start = time.perf_counter()
        c = torch.matmul(a, b)
        torch.cuda.synchronize()
        elapsed = time.perf_counter() - start

        return elapsed
    except Exception as e:
        print(f\"CUDA benchmark failed: {str(e)}\")
        return None

def run_cpu_fallback() -> float:
    \"\"\"Fallback CPU benchmark for unsupported systems\"\"\"
    import numpy as np
    n = 1024
    a = np.random.rand(n, n).astype(np.float32)
    b = np.random.rand(n, n).astype(np.float32)
    start = time.perf_counter()
    np.dot(a, b)
    elapsed = time.perf_counter() - start
    return elapsed

if __name__ == \"__main__\":
    backend = detect_gpu_backend()
    print(f\"Detected GPU backend: {backend}\")

    result: Dict = {
        \"system\": platform.node(),
        \"os\": platform.system(),
        \"backend\": backend,
        \"benchmark_time_s\": None
    }

    if backend == \"metal\":
        print(\"Running Metal benchmark on M3 Max...\")
        elapsed = run_metal_benchmark()
    elif backend == \"cuda\":
        print(\"Running CUDA benchmark on Framework 16 + RTX 4070...\")
        elapsed = run_cuda_benchmark()
    else:
        print(\"No GPU backend detected, running CPU fallback...\")
        elapsed = run_cpu_fallback()

    if elapsed:
        result[\"benchmark_time_s\"] = round(elapsed, 4)
        print(f\"1024x1024 matrix multiplication completed in {elapsed:.4f}s\")
    else:
        print(\"Benchmark failed to run\")

    with open(\"gpu_bench_results.json\", \"w\") as f:
        json.dump(result, f, indent=2)

# battery_stress_test.py
# Measure battery drain under typical developer workloads for M3 vs Framework 16
# Requires: psutil 5.9+, Python 3.11+
# Run: python3 battery_stress_test.py --duration 3600 (1 hour test)

import argparse
import time
import psutil
import json
import platform
from typing import Dict, List
import subprocess
import os

def get_battery_info() -> Dict:
    \"\"\"Collect current battery status\"\"\"
    battery = psutil.sensors_battery()
    if not battery:
        return {\"available\": False}
    return {
        \"available\": True,
        \"percent\": battery.percent,
        \"secsleft\": battery.secsleft,
        \"power_plugged\": battery.power_plugged
    }

def run_dev_workload() -> None:
    \"\"\"Simulate typical developer workload: compile, container build, web server\"\"\"
    try:
        # 1. Compile small C++ project
        with open(\"bench_app.cpp\", \"w\") as f:
            f.write(\"\"\"#include 
int main() { std::cout << \"Benchmark app running\" << std::endl; return 0; }\"\"\")
        subprocess.run([\"g++\", \"bench_app.cpp\", \"-o\", \"bench_app\"], capture_output=True, timeout=30)
        subprocess.run([\"./bench_app\"], capture_output=True, timeout=10)
        os.remove(\"bench_app.cpp\")
        os.remove(\"bench_app\")

        # 2. Run short Docker container
        subprocess.run([\"docker\", \"run\", \"--rm\", \"hello-world\"], capture_output=True, timeout=30)

        # 3. Start/stop local web server
        import http.server
        import threading
        server = http.server.HTTPServer((\"localhost\", 8080), http.server.SimpleHTTPRequestHandler)
        thread = threading.Thread(target=server.handle_request)
        thread.start()
        time.sleep(2)
        server.shutdown()
        thread.join()
    except Exception as e:
        print(f\"Workload error: {str(e)}\")

def run_stress_test(duration: int) -> Dict:
    \"\"\"Run battery stress test for specified duration in seconds\"\"\"
    start_battery = get_battery_info()
    if not start_battery[\"available\"]:
        raise RuntimeError(\"No battery detected\")
    if start_battery[\"power_plugged\"]:
        raise RuntimeError(\"Device is plugged in, unplug before running test\")

    start_time = time.perf_counter()
    end_time = start_time + duration
    battery_readings: List[Dict] = []
    iteration = 0

    print(f\"Starting battery stress test for {duration} seconds...\")
    print(f\"Initial battery: {start_battery['percent']}%\")

    while time.perf_counter() < end_time:
        iteration += 1
        print(f\"Workload iteration {iteration}\")
        run_dev_workload()

        # Record battery level every 5 minutes
        if iteration % 10 == 0:
            current_battery = get_battery_info()
            battery_readings.append({
                \"elapsed_s\": round(time.perf_counter() - start_time, 2),
                \"battery_percent\": current_battery[\"percent\"]
            })
            print(f\"Current battery: {current_battery['percent']}%\")

        time.sleep(1)  # Short pause between workloads

    end_battery = get_battery_info()
    total_elapsed = time.perf_counter() - start_time

    # Calculate drain rate (percent per hour)
    drain_percent = start_battery[\"percent\"] - end_battery[\"percent\"]
    drain_rate = (drain_percent / (total_elapsed / 3600)) if total_elapsed > 0 else 0

    return {
        \"system\": platform.node(),
        \"os\": platform.system(),
        \"test_duration_s\": round(total_elapsed, 2),
        \"start_battery_percent\": start_battery[\"percent\"],
        \"end_battery_percent\": end_battery[\"percent\"],
        \"drain_rate_percent_per_hour\": round(drain_rate, 2),
        \"estimated_battery_life_h\": round(drain_percent / drain_rate, 2) if drain_rate > 0 else None,
        \"battery_readings\": battery_readings
    }

if __name__ == \"__main__\":
    parser = argparse.ArgumentParser(description=\"Battery stress test for developer laptops\")
    parser.add_argument(\"--duration\", type=int, default=3600, help=\"Test duration in seconds (default 1 hour)\")
    args = parser.parse_args()

    try:
        test_results = run_stress_test(args.duration)
        with open(\"battery_bench_results.json\", \"w\") as f:
            json.dump(test_results, f, indent=2)
        print(\"\\nStress test complete. Results saved to battery_bench_results.json\")
        print(f\"Estimated battery life: {test_results['estimated_battery_life_h']} hours\")
    except Exception as e:
        print(f\"Stress test failed: {str(e)}\")

import torch
print(f\"CUDA available: {torch.cuda.is_available()}\")
print(f\"GPU device: {torch.cuda.get_device_name(0)}\")

echo \"RAM Usage:\"
free -h
echo \"Storage Usage:\"
lsblk -o NAME,SIZE,FSTYPE,MOUNTPOINT

docker buildx build --platform linux/amd64,linux/arm64 -t myapp:latest --push .