If you ship Go services and care about latency and cost, this will save you time and nasty surprises in prod!

What’s inside:
- Bench patterns that actually measure your handlers, not the harness
- A tiny DummyResponseWriter to kill recorder overhead
- b.Loop() usage with Go 1.24
- Ready-to-use GitHub Action with CodSpeed and codspeed-macro runners
- PR comments + checks to block regressions before merge

Learn more: codspeed.io/changelog/20...

🦀 @apas.tel - The Impact of Memory Allocators on Performance

Memory allocators are often overlooked. But Arthur makes the case that they’re a key lever for performance in Rust. Learn how they work and when to care.

🧵 5/11

📺 Watch: youtu.be/pJ-FRRB5E84

Now, even small teams and indie devs can get reliable, high-precision performance insights without breaking the bank.

Learn more: codspeed.io/changelog/20...

Check it out: codspeed.io/changelog/20...

Source: https://github.com/karlrupp/microprocessor-trend-data

But physics is catching up. As transistors shrink, heat and power constraints limit clock speed gains. But to keep Moore’s Law going, we stack more cores. GPUs push this further with thousands of parallel cores, mentioning exponential performance increase but omitting the implementation complexity.

We can expect the same for most compiled languages. Though, when dealing with an interpreted language, this becomes much harder to optimize before any specialization or JIT compilation.

So for VM bytecode instrumentation, external execution path like native FFI calls need specific handling. Just like system calls need specific handing for performance measurement with native binary instrumentation.