January 8, 2026
Borrowed speed, no returns
Recent Optimizations in Python's Reference Counting
Python quietly gets faster—and commenters want confetti, not footnotes
TLDR: Python added a new instruction that reads local variables without extra memory bookkeeping, speeding tight loops. Commenters say it’s a sleeper feature—split between applause for the speed push and annoyance that it was buried—while asking if Python’s long-awaited performance era is truly here.
Python just snuck in a speed hack and the comments lit up. A new instruction called LOAD_FAST_BORROW lets Python read local variables without bumping a tiny “how many references?” counter, cutting busywork in tight loops. Translation: fewer fussy memory writes, more zip. It’s in 3.14+, but some missed the memo.
The top vibe? “Why wasn’t this shouted from the rooftops?” User zahlman called it a big deal buried in release notes, pointing to the issue. Others cheered the trend: is Python entering its performance era? Waterluvian asked if speed has become a major focus lately or if we’re just noticing it more.
Then came the drama: folks bickered over marketing vs engineering—should the core team hype wins like this, or keep heads down and ship? Jokes flew about Python “hitting the gym” and “borrowing speed without paying interest.” A few worried about safety (“will my code break?”), and were soothed by the note that the compiler applies this only when it’s safe. The 3.15 bytecode even bundles two variable loads into one opcode for extra neatness. The meta-meme: Python keeps getting faster, yet the big improvements are tucked away behind arcane words like “bytecode.” Fans want confetti; devs prefer diff logs. And yes, everyone wants benchmarks.
Key Points
- •CPython introduces a new bytecode instruction, LOAD_FAST_BORROW, available since Python 3.14.
- •LOAD_FAST_BORROW loads local variables without incrementing reference counts, reducing overhead in hot loops.
- •A bytecode comparison shows Python 3.13 using LOAD_FAST while Python 3.15 uses LOAD_FAST_BORROW and a combined dual-load variant.
- •Reference counting in CPython causes frequent memory writes on variable access, impacting performance and CPU caches.
- •The optimization is applied only under specific criteria determined by static lifetime analysis (details not provided in the excerpt).