A clip from a 1984 movie about a teen learning karate in the San Fernando Valley is analyzed by film students for its narrative economy and is now cited by cognitive scientists in publications on skill acquisition. Daniel LaRusso is instructed by Mr. Miyagi to wax his vehicle. circular movements. Left hand, right hand. Day after day, seemingly meaningless, seemingly unconnected to anything Daniel truly learned. Then Miyagi delivers a blow, and Daniel instinctively and unconsciously blocks it with the same motion he’d been practicing for days without understanding why. Before the learner grasped the purpose of the lecture, the muscle memory had already been established thru repetition.
That structure is being purposefully and more rigorously borrowed by software engineering educators than the casual remark may imply. Before any student meets a real-world codebase of significant complexity, the “Miyagi Method”—as some developer communities have begun to refer to it—applies the same idea to foundational coding practice: straightforward, restricted exercises repeated until the logic flows without conscious effort. The exercises themselves are referred to as katas, a term taken directly from martial arts that describes a structured routine that is practiced until it comes naturally. Sorting a list, reversing a string, or creating a basic function that verifies input are examples of coding kata. These appear insignificant at first glance. That’s exactly the point.
Any seasoned engineer who has coached a junior developer would identify the issue these workouts tackle right away. It would be like asking Daniel LaRusso to compete in a tournament before he’s learned to stand if a new programr were thrown into a sizable production codebase with external dependencies, layered abstractions, legacy decisions made by people who left the company years ago, and a user interface that behaves differently from what the documentation describes. The intricacy doesn’t provide knowledge. It paralyzes. Additionally, graduates who were adept at using certain tools but found it difficult when those tools behaved strangely were the result of many bootcamps and university programs’ tendency to push students into “real” projects as soon as feasible.
Perhaps the most obvious example of a method that needs the Miyagi treatment is test-driven development. Although it sounds simple, the fundamental practice of writing a failing test first, writing the bare minimum of code to make it pass, and then refactoring doesn’t come naturally. Repetition makes it normal at a level that, when done, feels halfway between boring and contemplative. Developers who learnt the concept via a slide presentation and moved on handle new problems differently from those who have hammered TDD thru katas until the sequence is reflexive. Before the habit can be trusted under duress, it must be ingrained.
The pedagogy becomes really intriguing when it comes to the AI aspect of this. Junior engineers can now create usable boilerplate code without fully comprehending its functionality thanks to automated coding tools. That is helpful up until a certain point, at which point it becomes a liability. This is especially true when the AI output contains an error, makes an architectural assumption that is inappropriate for the project, or generates code that functions well in the limited test scenario but fails in production. The underlying skill that the Miyagi Method is intended to develop is precisely what is needed to identify those mistakes. An engineer who has never had to create a sorting algorithm by hand because it has always been done by a tool might not be able to spot subtle errors in the tool’s version.
