macOS Hardware Info : AI performance benchmarking

samuraidev — Sun, 15 Feb 2026 00:41:13 +0000

This is a submission for the GitHub Copilot CLI Challenge

https://github.com/CodeGeekR/macOS-hardware-info

What I Built

I built macOS Hardware Info – a unified diagnostic tool that consolidates SSD
health monitoring with AI performance benchmarking.

The tool gives you real-time S.M.A.R.T. data, calculates Total Bytes Written in
Terabytes, measures actual disk speeds with 1GB benchmarks, and runs AI
performance tests on CPU, GPU, and Apple's Neural Engine. What makes it special
is the native Apple Silicon support – it uses multi-layer fallback detection for
T2, M1, M2, M3, and M4 chips, handling Apple's proprietary controllers that
standard tools miss.

Built it in under 5 hours and got all 18 tests passing. It means a lot to me because it proves you can write
professional, production-ready code fast when you have the right workflow.

My Experience with GitHub Copilot CLI

Honestly? Game-changer. I was skeptical at first, but Copilot CLI turned what
would've been a full weekend project into a sub-5-hour sprint.

Where it shined:

 - It mapped out the architecture, identified duplicate code, and suggested a clean dataclass-based structure. Saved me hours of manual refactoring.
 - When dealing with Apple's proprietary SSD controllers, I'd type a comment like "handle Apple NVMe vendor logs" and it would generate the multi-method fallback strategy. It knew about the IOKit framework and NVMe

Log Page 0xC0.
- It auto-completed type annotations as I wrote functions. No more context-switching to docs for dict[str, Any] | None syntax.
- Created 18 unit tests by describing what I wanted to test. It caught edge cases I hadn't thought of (like the power_cycles vs power_cycle_count field issue).

I had a bug where power cycles showed "N/A". Copilot suggested
log.get('power_cycle_count') or log.get('power_cycles') – turns out different
SSDs use different field names. That's knowledge I would've spent 30 minutes
Googling.

I'd describe intent in comments, Copilot would draft the code, I'd
review/adjust, then move to the next function. Felt like pair programming with
someone who'd read the entire smartmontools documentation.

The speed boost wasn't just about typing faster – it was about staying in flow.
No mental context switches, no documentation rabbit holes, just continuous
forward progress. Finished with time to spare for proper documentation and a
professional README.

Would I have built this without Copilot CLI? Sure, eventually. But it would've
taken 2-3 days instead of one afternoon, and I'd probably have skipped the
comprehensive tests and detailed docs due to time pressure.

Copilot CLI doesn't replace thinking, but it eliminates the boring
parts so you can focus on architecture and problem-solving. That's where the
real engineering happens.

Forem: samuraidev

macOS Hardware Info : AI performance benchmarking

What I Built

My Experience with GitHub Copilot CLI