ThePrimeagen did not need SIMD.

He says so himself. A profile of the game he is building shows very little time in the part of the program where this kind of optimization might matter. He had encountered the concept before and used functions optimized by other people, but had never written a practical SIMD implementation himself.

Then Mitchell Hashimoto published "Everyone Should Know SIMD." The discussion quickly became very interested in the words "everyone" and "should."

Prime responded to an Internet argument in a surprisingly productive way: instead of arguing about the headline for another hour, he tried the thing.

Recognizing a shape before you need it

SIMD means Single Instruction, Multiple Data.

The basic idea is less mystical than the acronym. Instead of applying the same operation to one value, then the next, then the next, some CPU instructions can operate on several data lanes at once.

Hashimoto is not arguing that every developer should become an expert in AVX or NEON instruction sets. His article sets a smaller target: understand the pattern well enough to notice when a piece of work might fit SIMD.

His practical shape is fairly consistent. Prepare vector constants, process input in chunks matching the vector width, perform the SIMD operation, reduce or store the result, then handle the scalar tail that does not fit a complete chunk.

This is not something to paste into every for loop because you read a blog post before breakfast. That is exactly why the idea is interesting.

Knowledge can become useful before it has a current use.

Prime invents a problem on purpose

Instead of forcing SIMD somewhere into his game, Prime creates a small exercise that actually suits it: scanning lots of bytes for particular characters.

He writes a naive version, then one that processes several bytes together. His local benchmark shows a large speed difference in favor of the SIMD implementation.

The exact number is not the interesting part. His test says nothing universal about your application, processor or language. Hashimoto's own Ghostty example also produces results tied to a specific machine and specific code path.

What Prime keeps from the exercise is something else.

At the end, he says he probably will not use SIMD any time soon. His game still does not need it. But he can now recognize the shape of a future problem where this family of instructions might matter.

Before: "SIMD, low-level performance thing."

After: "if I have a large loop applying the same operation to lots of independent data, there may be another way to look at it."

That shift in mental model is often more valuable than memorizing an API.

AI makes this kind of learning stranger, not obsolete

There is an obvious counterargument now: why learn the mechanics if a model can write the implementation on the day you finally need it?

Because asking for a solution still requires suspecting that the solution exists.

An agent may suggest vectorization. It may also miss it, suggest it in the wrong place, or produce a more complicated implementation that improves nothing. Without a minimal model of the concept, you are mostly trading one black box for another and choosing whichever one sounds more confident.

Knowing what SIMD is does not mean rewriting a media codec by hand. It may simply give you enough vocabulary to ask, "is this repetitive operation vectorizable?" and then measure the answer.

That distinction gets more useful in agent-assisted development. The better machines become at producing specialized code, the more valuable it is to recognize families of solutions.

Not necessarily to reproduce them from memory. To see them.

Knowledge does not need a Jira ticket

Prime says the exercise took him roughly an hour to understand, implement and debug. That is his experience, not a promise that SIMD has a sixty-minute learning curve.

Still, I like the idea of spending an occasional hour on something you do not currently need.

Software work easily becomes aggressively just-in-time: learn exactly what today's ticket demands, ship it, move on. Efficient, until every problem begins to look like one of the five tools you already know.

There is a difference between learning a technology because you expect to use it and learning a concept because it expands the map.

SIMD might sit unused in your head for five years. If a genuinely expensive loop appears later, having seen that shape before could save far more than an hour.

Or it may never help at all.

That is not necessarily failure. Technical curiosity does not have to produce measurable ROI before the sprint ends to be allowed to exist.