I’m Ayman Atif, a freelance software engineer focused on business software, backend systems, automation, and applications that have to survive real-world use.
A lot of my work starts with a deceptively simple sentence:
“The application works, but something is becoming a problem.”
Sometimes it is performance.
Sometimes it is a workflow that still depends on spreadsheets.
Sometimes a codebase has become difficult to change without breaking something else.
And sometimes the application works perfectly well in development, but production tells a different story.
One of the most interesting examples I’ve worked on started with something that didn’t look like a performance problem at all.
It was a business application.
The users needed to work with a lot of data, switch between different sections quickly, edit records, generate documents, process financial information, and keep several parts of their workflow open at the same time.
The application worked.
Until it didn’t feel fast anymore.
The deceptive phase of a successful application
There is a counterintuitive stage in software development where an application becomes a victim of its own success.
At the beginning, everything looks reasonable.
A database contains a few hundred records.
Pages load quickly.
Queries finish almost immediately.
The server has plenty of room.
Developers make decisions based on what exists today.
Then the business grows.
A few hundred records become thousands.
Users spend more time inside the application.
More tabs are opened.
More documents are processed.
More people interact with the same data.
Nothing is necessarily “broken.”
The problem is that the assumptions behind the original implementation are no longer true.
That changes the equation.
A query that was perfectly acceptable with 200 records might become expensive with 20,000.
A page that felt instant when it loaded 50 objects might become noticeably slower when it loads thousands.
A workflow that worked beautifully for one person might behave differently when several users are modifying shared state.
The code didn't suddenly become bad.
The environment changed.
The requirement that made everything harder
One requirement in particular made the problem interesting.
The users wanted the application to behave more like a desktop application.
They didn't want to constantly navigate between pages.
They wanted to open different sections, switch between them, keep their context, and work with large datasets directly inside grids.
And in some areas, they wanted thousands of records visible without traditional pagination.
From a user's perspective, that makes perfect sense.
If you're working with financial or business data all day, constantly clicking through pages can become friction.
You want to search, sort, inspect, edit, and move on.
But every convenient interaction has a cost somewhere else.
The browser has work to do.
The server has work to do.
The database has work to do.
And memory has to go somewhere.
The more desktop-like the web application becomes, the more important it is to understand what is actually happening underneath the interface.
I stopped looking at the page
The first instinct when a page becomes slow is often to look at the page.
Maybe the JavaScript is inefficient.
Maybe the rendering is expensive.
Maybe the browser is struggling.
Those things can certainly happen.
But I learned to resist fixing the first thing that looks suspicious.
Instead, I started following the entire path.
Database.
↓
Backend.
↓
Serialization.
↓
Network.
↓
Browser.
↓
DOM.
↓
User interaction.
That sounds obvious, but it changes how you investigate a performance problem.
If you only look at the frontend, you can spend hours optimizing something that represents a small percentage of the actual problem.
The same is true in reverse.
A database query might be fast in isolation while the application still feels slow because too much data is being transferred, processed, or rendered afterward.
Performance is a chain.
The user experiences the slowest part of that chain.
More data exposed the real problem
As the amount of data increased, some operations that had previously been invisible became measurable.
Queries mattered more.
Object loading mattered more.
Serialization mattered more.
Browser rendering mattered more.
Memory usage mattered more.
And suddenly, “just load the records” wasn't a harmless instruction anymore.
Every record had a cost.
Every relationship had a cost.
Every extra field had a cost.
Every duplicated piece of state had a cost.
This is one of the lessons I now try to keep in mind when designing business applications:
Data volume is part of the architecture.
It shouldn't be treated as something that will magically take care of itself later.
If an application is expected to grow, the question isn't only:
“How do we make this work?”
It is also:
“What happens when this becomes ten times larger?”
The server has a vote too
There was another constraint that made the problem more interesting: the server wasn't unlimited.
The application had to operate within a relatively small amount of memory.
That changes how you think about architecture.
You can't simply throw more infrastructure at every problem.
Sometimes the right answer is optimization.
Sometimes it is changing when data is loaded.
Sometimes it is reducing unnecessary work.
Sometimes it is processing things differently.
And sometimes you have to question the original design.
This is where I think business software differs from many toy projects and tutorials.
In a tutorial, you can say:
“Let's add another server.”
In production, someone has to pay for it.
And even if you can afford it, more infrastructure doesn't automatically fix inefficient data access or excessive memory usage.
You still need to understand the system.
Then there was shared state
Performance wasn't the only problem.
Once multiple people are working with the same information, another category of problems appears.
Who owns this change?
What happens if two users edit the same thing?
Which version should win?
When should state be saved?
What happens if the browser closes halfway through an operation?
What happens if a user opens several sections and modifies information in one while another section still contains an older representation?
These aren't UI questions anymore.
They're system-design questions.
A business application is full of shared state.
That means reliability depends on more than making individual functions work correctly.
You have to think about the relationships between those functions.
The lesson I took from it
The biggest lesson wasn't a particular optimization technique.
It was a change in how I think about software.
A feature doesn't exist in isolation.
It exists inside a business.
The business grows.
The data grows.
Users change their behavior.
Requirements evolve.
Infrastructure has limits.
And eventually, assumptions that looked completely reasonable during development are challenged by reality.
That's why I prefer investigating a system before proposing a rewrite.
When something becomes slow, fragile, or difficult to maintain, the answer isn't automatically “replace it.”
First, understand it.
Find where the real constraint is.
Measure what is actually happening.
Then change the smallest thing that meaningfully improves the situation.
Sometimes that leads to a refactor.
Sometimes it leads to a database change.
Sometimes it requires changing how data moves through the application.
Sometimes the architecture itself needs to evolve.
But you should know why you're making the change.
Software gets interesting when it meets reality
I've been building software since 2014, and most of the lessons that stayed with me didn't come from writing the first version of an application.
They came afterward.
When users started depending on it.
When the database became larger.
When a server had less memory than expected.
When two people changed the same data.
When a feature that looked simple turned out to affect five different parts of the system.
That's the point where software engineering becomes less about making something work and more about making something people can continue to rely on.
That's also the kind of work I enjoy most.
I build and improve business applications, backend systems, automation workflows, APIs, and existing software that has reached the point where it needs careful engineering rather than another quick patch.
If you're dealing with a business process that has outgrown spreadsheets, an application that has become difficult to maintain, or software that needs to become more reliable as it grows, you can find my work and get in touch with me at ayman-atif.vercel.app.
The goal isn't just to write software.
It's to build something the business can actually rely on.
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