Python: SIMD Speed Boost and JIT Improvements
The Python core team merged 5 pull requests bringing significant performance improvements, including an impressive SIMD optimization for bytes.hex() that delivers up to 11x faster performance on large data. The day also saw important JIT fixes, workflow refactoring, and small but meaningful performance tweaks to difflib.
Duration: PT3M36S
Episode overview
This episode is a short developer briefing from Python.
It explains recent repository work in plain language.
- Show: Python
- Published: 2026-02-23T11:10:33Z
- Audio duration: PT3M36S
Transcript excerpt
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Hey there, Python developers! Welcome back to another episode of the Python podcast. I'm so glad you're here with me today - grab your favorite beverage because we've got some absolutely fantastic news to dive into from February 23rd, 2026.
You know those days when you wake up and just feel like everything's clicking? Well, that's exactly what happened in the Python core development world yesterday. We saw 5 merged pull requests that are going to make your Python code faster, more reliable, and just plain better.
Let's start with the absolute star of the show - Gregory P. Smith's incredible work on SIMD optimization for bytes.hex(). Now, I know SIMD might sound like tech jargon, but here's what you need to know: if you're working with bytes.hex(), bytearray.hex(), or even hashlib's hexdigest methods, your code just got a…
What I love about this change is how thoughtfully it was implemented. Gregory used platform-agnostic vector extensions that work beautifully on Linux and macOS, and if you're on a platform that doesn't support SIMD, no worries - it gracefully falls back to the existing code with no performance hit.
Now, speaking of performance, we also got a lovely little…
The JIT…
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