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Grüße aus HH nach DD von einem Ex-Sachsen (1992 bin ich umgezogen)!

>Apple Silicon's unified memory architecture is arguably the best hardware for data science workloads, but almost nobody in the data science world takes it seriously. The reason? Python.

Python is the lingua franca of data science, but it's an interpreted, single-threaded language that can't take advantage of Apple's multi-core hardware. Running Python on a Mac is like buying a Lamborghini and never leaving first gear.

In this video, I make the case that Apple's real barrier isn't hardware: it's the missing library ecosystem. Swift, as a compiled language with native access to Metal GPU acceleration, can unlock performance that Python simply can't match.

To prove it, I built a direct comparison: Swift with Metal acceleration was more than 80x faster than Python on the same analytical workload. In CFO terms, a three-day Python job finishes in one hour.

If Swift gets even a fraction of Python's library coverage running natively on Apple Silicon, it changes the economics of data processing entirely: local hardware instead of cloud compute, real-time analytics instead of overnight batch jobs, and deterministic processing instead of unpredictable agents.

The hardware is ready.
The language is ready.
The ecosystem just needs to be built.

Recording of the call