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Start with a working, correct solution; optimize when there is a reason
The sequence “Make it work, make it right, make it fast” is attributed to Kent Beck in Ibrahima D.’s essay. Treat it as a useful order of attention, not a guarantee that every project should follow the same steps.
- Make it work: deliver the needed behavior. For a user list, fetch the users and render them.
- Make it right: improve clarity and correctness, and test the behavior. Refactor the list implementation if that makes it easier to understand or maintain.
- Make it fast: investigate performance and optimize when there is a real problem. Caching might help a slow page, but adding it before there is evidence of a need adds complexity without a demonstrated benefit.
The order helps resist premature optimization without treating performance as irrelevant. The key is to distinguish a measured or observed problem from a hypothetical one.
Keep the solution focused and unsurprising
Three principles help contain complexity: YAGNI, the Principle of Least Surprise, and KISS. They overlap, but each asks a different question.
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YAGNI: build for known needs
YAGNI means “You Aren’t Gonna Need It.” If the request is to export CSV, implement CSV rather than a generalized exporter for CSV, JSON, XML, and PDF. Add formats if actual requirements call for them. Speculative flexibility can become code that nobody needs to maintain.
Least Surprise: make behavior match expectations
A function named getUser() should not quietly update the user’s last-login timestamp. Hidden side effects make behavior harder to predict and can surprise the person calling the function. Prefer names and behavior that match local conventions; avoid clever compact solutions when they obscure what the code does.
KISS: make the code easy to read and change
“Keep It Simple” is not a command to compress everything into fewer lines. A 200-line function controlled by many flags may be harder to work with than smaller functions with clear names. Simplicity means that teammates can understand the behavior and make a change without decoding unnecessary complexity.
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Remove real duplication without creating the wrong abstraction
DRY means “Don’t Repeat Yourself”: keep each piece of knowledge in one authoritative place. If password rules are copied into signup, password reset, and backend validation, changing only some copies can leave inconsistent behavior.
But code that looks similar is not always the same knowledge. Two operations may have different requirements or may evolve independently. Extracting them into one abstraction too early can make later changes awkward or less clear. Before centralizing, ask whether the duplicated code represents a genuinely shared rule that should change together.
Use SOLID to address real design pressure
SOLID is a set of five object-oriented design principles. Ibrahima’s examples illustrate the ideas; they are teaching examples, not proof that applying the principles automatically improves every design.
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- Single Responsibility: a class that handles user data, notifications, and reporting has several distinct reasons to change. Separating responsibilities can make changes more focused.
- Open/Closed: structure code so new behavior can be added without repeatedly rewriting stable code. The essay uses payment methods as an example of behavior that may need to expand.
- Liskov Substitution: a subtype should be usable wherever its parent type is expected without breaking the expected behavior. The square/rectangle example shows how a seemingly reasonable inheritance relationship can violate expectations about setting dimensions.
- Interface Segregation: avoid forcing callers to depend on large interfaces containing operations they do not need. Smaller, focused interfaces can better match what different clients use.
- Dependency Inversion: keep high-level business logic from depending directly on a low-level detail, such as a particular database implementation. Depending on an abstraction can make the relationship more flexible.
These principles are most useful when they solve a concrete problem—such as a class with unrelated responsibilities or business logic tightly coupled to infrastructure. Applying them as a checklist can add indirection without addressing a real need.
Make risky changes in small, validated increments
Baby steps: shorten the feedback loop
Break a change into small pieces, and check each piece as you go. Short cycles of coding, testing, and committing make it easier to locate which change introduced a failure than a large, untested batch. A sequence of meaningful commits can also make tools such as git bisect more useful when tracking down when a regression began.
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The Mikado Method: map prerequisites for a large refactor
When a broad change depends on many other changes, the Mikado Method helps reveal that dependency chain without committing to a risky rewrite all at once:
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- Try the desired change—for example, upgrading a library.
- Record the failures and the prerequisite changes they reveal.
- Revert the attempted change so the working baseline is restored.
- Address the prerequisites in small, validated steps, then retry the original goal.
This approach turns an intimidating refactor into a set of smaller tasks while preserving a clear path back to the starting point.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose among principles when they conflict
Principles are decision aids, and sometimes they point in different directions. YAGNI may discourage an abstraction that a rigid application of SOLID seems to favor. DRY may encourage extracting shared code even when separate, explicit implementations are easier to understand. The right choice depends on the actual change pressure and the cost of complexity.
Ibrahima suggests this rough priority: a working solution, then YAGNI, least surprise, KISS, DRY, SOLID, and finally performance. That ordering is his personal decision aid, not an industry-wide standard. Use it to ask which principle should carry more weight in the current situation, not to override evidence or project constraints.
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- Build for a demonstrated need rather than a hypothetical future feature.
- Centralize a rule when the copies represent knowledge that should change together.
- Prefer predictable behavior over compactness when the two conflict.
- Add structural flexibility when it addresses likely or current change—not simply to satisfy a checklist.
- Optimize when there is a real performance concern, while still meeting the project’s requirements.
- For a risky change, reduce its size and validate each step.
A useful question to carry into a design decision is: “What’s the smallest, simplest thing that makes this work?” It keeps attention on the need at hand while leaving room to revisit the design when real requirements change.
Source: Ibrahima D., “The Principles I Code By: Small Rules, Big Difference,” DEV Community, June 6, 2025.
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