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Writing rough code helped How-To Geek contributor Zunaid Ali move a side project from the starting line to a meaningful result in two weeks or less, after a previous attempt took about a month. That is Ali’s personal comparison, not a measured productivity result or a guarantee for other developers. His catch: fast code can become a burden when you cannot understand, debug, or maintain what you have written.
Why writing less-than-perfect code helped
Ali says concern about following professional software-engineering conventions had contributed to side projects stalling or being abandoned. He tried a different approach: write code that was rushed, messy, or sometimes wrong, then focus on reaching a useful result instead of polishing every line along the way.
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In his account, that change helped him make progress faster. A project that had previously taken about a month to reach a meaningful point got there in two weeks or less. The comparison comes from one developer’s experience; it does not establish that deliberately writing bad code will make other developers twice as productive.
The useful idea is narrower: perfectionism can block a prototype from becoming something you can evaluate. If a project is a private experiment and you can safely discard it, quick, imperfect work may help you learn whether the idea is worth pursuing.
Deliberate mistakes can make programming behavior memorable
Ali also describes writing incorrect or broken code as a way to learn a language, framework, or concept. Instead of merely reading a rule, he tries a mistake and observes the result. The point is to make a language behavior concrete—not to recommend shipping broken code.
Destructuring a value that is null
In JavaScript, trying to destructure a property from null throws a TypeError. Deliberately triggering that error can make the limitation easier to remember than encountering it only while debugging a larger program.
Using the default sort on numbers
Ali’s other JavaScript example is [10, 1, 2, 25].sort(), which produces [1, 10, 2, 25] with the default comparison. The result can surprise someone expecting numerical order. Trying it makes the behavior visible and gives the learner a specific edge case to investigate.
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Ali relates this learning tactic to a reported 2022 study in which learners who wrote incorrect definitions and corrected them did better on later tests than learners who copied correct definitions. He also notes that a 2024 replication attempt did not find the same effect, and that the studies discussed were not about programming. The article provides no study details that would support treating this as settled evidence for coding education. At most, the comparison offers a reason to experiment with mistakes as one learning technique, not proof that it works for every learner or topic.
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The catch: you still have to read the code you write
Fast code generation can leave behind files and lines that make sense only in fragments, rather than as a system. Ali says that when a bug appeared in his AI-assisted work, asking AI to fix it did not solve the problem. He had to inspect the code and found he could not trace how data moved through it or what the different components were responsible for.
That is the cost of treating typing speed as productivity. If you cannot follow the relationships between components, a quick fix may add another layer you do not understand. The time saved while creating the code can be lost when diagnosing a bug, changing behavior, or explaining the work to someone else.
Decide how much care code deserves by how long it must live
The practical boundary in Ali’s argument is whether the code is disposable or likely to survive. A throwaway experiment can be messy if you know it will be discarded. A feature that will remain in a real system needs to be understandable enough to read, extend, and maintain.
Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problems| Question | Disposable experiment | Code likely to survive |
|---|---|---|
| Who needs to understand it? | Usually just you, briefly | You, collaborators, or future maintainers |
| What happens when it breaks? | You can discard it or restart | Someone must trace the fault and restore expected behavior |
| What happens when requirements change? | Starting over may be acceptable | The code may need to be read and extended |
These are practical distinctions, not measured categories. A prototype can also become the starting point for a lasting feature. Ali invokes The Pragmatic Programmer in discussing disposable prototypes and “tracer code” that remains in a finished project; the important decision is not whether early code is rough, but whether you know what you intend to keep.
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A practical way to move fast without losing control
- Label the experiment’s purpose. Know whether you are testing an idea, learning a behavior, or building something others will rely on.
- Use deliberate mistakes in isolation. Try a language edge case in a small example so its behavior is easy to observe and does not contaminate production code.
- Check whether the result is understandable. Before building on generated or rushed code, make sure you can trace its data flow and explain the role of its components.
- Clean up what survives. If the experiment becomes part of a real system, revisit the code for clarity and maintainability before treating it as finished.
Rough code can be a useful way to get unstuck or learn by observing consequences. It stops being a shortcut when the code has to stay and nobody—including its author—can confidently work out what it does.
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