As web developers, we often measure our work by a simple question:
“Does the feature work?”
The button works.
The API returns the expected response.
The database stores the data.
The page loads.
The deployment succeeds.
So we move the ticket to Done.
But sometimes a strange thing happens.
A few weeks later, users complain.
The application feels slow.
Customers cannot find an important feature.
Support receives the same question repeatedly.
The database has thousands of unnecessary records.
The dashboard works perfectly on a developer's laptop but becomes painful on a real user's phone.
Technically, the feature works.
But the product is still failing.
This is one of the most important lessons a web developer can learn:
Building working software and building useful software are not always the same thing.
Modern development has made it easier than ever to create features. AI coding assistants, component libraries, frameworks, APIs, cloud services, and reusable packages can dramatically reduce development time.
But faster development also creates a new risk.
We can become very good at building things that nobody actually needs.
The Difference Between “Working” and “Useful”
Imagine you are building an online shopping application.
The product team asks for a wishlist.
You create:
- a wishlist database table
- an API endpoint
- authentication
- a React component
- add/remove functionality
- loading states
- error handling
- tests
- deployment
Everything works.
You proudly demonstrate it.
But after launch, almost nobody uses it.
Why?
Maybe the wishlist button is difficult to find.
Maybe users don't understand what it does.
Maybe customers usually purchase products immediately.
Maybe the application has a bigger problem: checkout is confusing.
Maybe the wishlist feature was requested because another competitor had one, not because customers actually wanted it.
From a purely technical perspective, you succeeded.
From a product perspective, the result may be very different.
This is why developers should sometimes ask a question before asking:
“How should I build this?”
Ask:
“Why are we building this?”
That single question can save weeks of engineering work.
The Hidden Cost of Features
Every feature has a cost beyond development time.
When we add something to an application, we also add maintenance.
A new feature can introduce:
- more database tables
- more API endpoints
- more frontend components
- more tests
- more documentation
- more monitoring
- more security considerations
- more edge cases
- more support questions
- more deployment complexity
For example, adding a simple notification system might initially look easy.
You create a notifications table.
Then you need:
notification preferences.
Then email notifications.
Then push notifications.
Then notification history.
Then unread counts.
Then retry logic.
Then rate limiting.
Then background jobs.
Then monitoring.
Then users complain about receiving duplicate notifications.
Suddenly a feature that looked like a small task becomes another system that the team must maintain.
Software complexity grows not only from the amount of code we write, but from the number of behaviors we introduce.
This is why “just add one more feature” can be dangerous.
The Developer's Responsibility Doesn't End at the Commit
There is a common mindset in software development:
Product defines the requirements.
Developers implement them.
There is some truth in this division.
But developers are also users of technology.
We understand technical consequences that may not be obvious to everyone else.
Suppose a requirement says:
“Allow users to upload unlimited images.”
A developer should immediately think about:
- storage cost
- file size limits
- image compression
- malicious uploads
- file formats
- CDN usage
- database metadata
- deletion policies
- backups
- bandwidth
- performance
The requirement sounds simple.
The implementation is not.
A good developer doesn't simply say:
“Sure, I can build that.”
A better response might be:
“Yes, but let's clarify the expected file sizes, formats, storage policy, and how long we need to retain these files.”
That isn't being difficult.
That's engineering.
Start With the User's Problem
One of the biggest mistakes in software development is starting with technology instead of the problem.
A team says:
“We need a dashboard.”
The immediate discussion becomes:
Should we use React?
Should we use Vue?
Should we use Next.js?
Should we use GraphQL?
Should we use PostgreSQL?
Should we use Redis?
But none of these questions answer the most important question.
What problem will the dashboard solve?
Maybe the actual problem is that managers receive five different reports every morning.
If that's the problem, perhaps the solution isn't a giant dashboard.
Maybe it is a simple daily summary email.
Or perhaps a dashboard really is appropriate.
The important part is understanding the problem before deciding on the implementation.
Technology should support the solution.
It shouldn't become the solution by itself.
AI Has Made This Problem More Interesting
AI coding tools have changed the development workflow dramatically.
Today, a developer can describe a feature and quickly receive:
- frontend components
- backend APIs
- database schemas
- tests
- documentation
- validation
- authentication flows
- deployment configurations
This can be extremely useful.
But there is a hidden danger.
When implementation becomes cheaper, unnecessary implementation becomes cheaper too.
In the past, building an unnecessary feature might require two weeks.
Today, an AI-assisted developer might create a prototype in a few hours.
That sounds like a pure advantage.
But imagine creating ten unnecessary features instead of one.
The speed itself doesn't guarantee better software.
AI can help answer:
“How can I build this?”
But humans still need to answer:
“Should we build this?”
And:
“What happens after we build it?”
That distinction will become increasingly important.
Don't Let the Demo Become the Product
Another common problem is optimizing software for demonstrations.
A feature looks impressive during a product demo.
Everything works.
The developer uses:
- fast internet
- a powerful laptop
- clean test data
- a modern browser
- perfect inputs
Then real users arrive.
They have:
- slow connections
- older phones
- large datasets
- unexpected input
- accessibility needs
- different browsers
- poor network conditions
The feature suddenly feels very different.
This is why developers need to test beyond the happy path.
Ask:
What happens when things go wrong?
What happens if the API takes 10 seconds?
What happens if the user refreshes during a payment?
What happens if the database contains one million records?
What happens if the user submits the same form twice?
What happens if the user loses internet connection?
What happens if JavaScript fails?
What happens if the user enters unexpected data?
These questions often reveal more about software quality than a successful demo does.
Developers sometimes treat performance as an optimization task that can be handled later.
But users experience performance as part of the product.
If a page takes too long to load, the user doesn't think:
“Interesting, this application probably has an inefficient database query.”
They think:
“This website is slow.”
That difference matters.
A web developer should consider performance from the beginning.
For example:
Instead of loading every record immediately, use pagination.
Instead of sending huge images, optimize them.
Instead of making unnecessary API requests, cache appropriate data.
Instead of rendering everything on the client, consider whether server-side rendering or other strategies make sense for the application.
Instead of optimizing based on assumptions, measure real performance.
And importantly:
Don't optimize everything. Optimize what actually affects users.
A developer can spend two days optimizing a function that runs in 5 milliseconds while ignoring a page that takes five seconds to load.
Measurement helps us focus.
The Database Is Part of the Product
Frontend developers sometimes think primarily about screens.
Backend developers may focus on APIs.
But the database quietly determines how much pain the application will experience later.
A poor schema can create problems that become increasingly expensive to fix.
Consider a system storing customer activity.
At first:
10,000 records.
Everything is fast.
Then:
1 million records.
Still manageable.
Then:
50 million records.
Suddenly queries become slow.
Reports take minutes.
Indexes need to be reconsidered.
Storage costs increase.
Backups become larger.
Data retention becomes important.
The original feature may still work exactly as designed.
The problem is that the system wasn't designed with growth in mind.
This doesn't mean every small application needs a massive distributed architecture.
It means developers should understand the expected scale.
Don't build for imaginary scale, but don't completely ignore predictable scale either.
Simplicity Is an Engineering Skill
Developers often celebrate sophisticated architecture.
Microservices.
Event-driven systems.
Distributed queues.
Multiple databases.
Complex caching layers.
Service meshes.
These technologies can be valuable.
But complexity itself isn't a sign of engineering quality.
Sometimes the best architecture is boring.
One application.
One database.
A few clear APIs.
Simple deployment.
Good monitoring.
Good tests.
Clear documentation.
That can be much easier to understand and maintain.
The goal isn't to use the most advanced architecture.
The goal is to build a system that solves the problem reliably.
Complexity should be justified by a real requirement.
Not by the desire to make the architecture look impressive.
Maintenance Is Where Software Quality Becomes Visible
Launching software is exciting.
Maintaining it is where reality appears.
After six months, someone needs to understand your code.
After one year, the original developer may not even be on the project.
A dependency becomes outdated.
An API changes.
A security vulnerability is discovered.
A database grows.
A customer asks for a new workflow.
A bug appears in a feature nobody has touched for months.
This is why maintainability matters.
Code should be understandable.
Naming should be clear.
Important decisions should be documented.
Tests should cover meaningful behavior.
Logs should help diagnose failures.
Monitoring should tell the team when something goes wrong.
Good software isn't simply software that works today.
Good software is software that a team can continue changing tomorrow.
Security Shouldn't Be an Afterthought
Another example of “working but failing” is security.
Imagine a user profile endpoint.
It correctly returns profile information.
The feature works.
But the endpoint doesn't properly verify authorization.
A user might access another user's data by changing an ID in the URL.
Technically:
The endpoint works.
Functionally:
It returns data.
From a security perspective:
The design is broken.
Security needs to be part of the feature itself.
Developers should think about:
- authentication
- authorization
- input validation
- sensitive data
- secrets
- file uploads
- rate limiting
- dependency vulnerabilities
- logging
- access control
A feature isn't complete simply because the normal user flow works.
Accessibility Is Also Part of “Working”
Consider a website that works perfectly with a mouse.
Can someone navigate it using a keyboard?
Can screen readers understand the content?
Do buttons have meaningful labels?
Is the color contrast sufficient?
Can users identify form errors?
Are interactive elements accessible?
If the answer is no, the application may technically work for the developer who built it, while failing for part of the intended audience.
Accessibility shouldn't be treated as decoration.
It is part of building usable software.
Even small improvements can matter:
- semantic HTML
- proper labels
- keyboard navigation
- meaningful alt text
- visible focus states
- accessible error messages
These practices often improve the experience for everyone.
How Can Developers Build More Meaningful Software?
A useful approach is to add a few questions to the development process.
Before building a feature, ask:
What user problem are we solving?
Who actually experiences this problem?
How frequently does it happen?
What does success look like?
What is the simplest solution we can test?
Before launching, ask:
What can go wrong?
What happens with bad input?
What happens under slow network conditions?
What happens when the system has much more data?
Is the feature secure?
Is it accessible?
Can we monitor it?
After launching, ask:
Are users actually using it?
Are support requests increasing or decreasing?
Did the feature solve the original problem?
What did we learn?
These questions don't require a new framework.
They require a different mindset.
The New Developer Skill: Knowing What Not to Build
For a long time, software engineering rewarded the ability to build.
Now, the ability to decide what not to build is becoming equally important.
If AI can generate code quickly, typing code becomes less of a bottleneck.
The bottleneck becomes judgment.
Understanding requirements.
Identifying risks.
Choosing trade-offs.
Recognizing unnecessary complexity.
Understanding users.
Testing assumptions.
Communicating with product teams.
Measuring outcomes.
Maintaining systems.
These are not separate from engineering.
They are engineering.
A developer who can create 10,000 lines of code quickly isn't necessarily more valuable than someone who prevents 10,000 unnecessary lines from being written.
Sometimes the best code is the code you decide not to create.
A Simple Rule I Try to Remember
When I work on a feature, I think about three different questions:
Does it work?
This is the technical question.
Does it help?
This is the product question.
Can we maintain it?
This is the engineering question.
A feature should ideally survive all three.
Because software isn't created just to exist.
It exists to help someone accomplish something.
The future of web development isn't simply about writing code faster.
It is about understanding problems better, making better trade-offs, and creating software that remains useful after the excitement of launch disappears.
AI will continue to change how quickly we can build.
Frameworks will continue to evolve.
Browsers will continue to become more capable.
Cloud infrastructure will continue to become easier to access.
But one principle will remain:
The goal of software development isn't to produce more software. The goal is to solve meaningful problems with software.
And sometimes, the most valuable thing a developer can say isn't:
“I can build that.”
It is:
“Let's make sure we're solving the right problem first.”
What Do You Think?
As developers, we often celebrate shipping features, closing tickets, and releasing updates.
But how often do we go back and ask whether those features actually improved the user's experience?
Have you ever built a feature that worked perfectly technically but turned out not to solve the real problem?
What did you learn from that experience?
And with AI making software development faster, do you think developers will spend more time solving meaningful problems—or will teams simply produce more features?
I'd love to hear how other developers approach this.
Sumita
Web Developer