DEV Community: Akshat Jain

How ORMs Affect Performance: The Benefits, Trade-Offs, and Hidden Costs

Akshat Jain — Wed, 03 Jun 2026 18:34:14 +0000

Understanding where ORMs boost productivity, where they create bottlenecks, and how to use them without sacrificing performance.

If you’ve ever written something like user.posts.comments and moved on without thinking twice, you're not alone. ORMs make database operations feel almost effortless. Instead of writing complex SQL queries, you work with objects and methods that feel natural in your programming language. But that convenience comes with a trade-off. The easier database access becomes, the easier it is to overlook what's actually happening behind the scenes.

Why to Love ORMs

If you’ve worked on a modern web application, chances are you’ve used an Object-Relational Mapper (ORM), even if you didn’t think much about it at the time.

An ORM is a tool that lets developers interact with a database using programming language objects instead of writing raw SQL queries for every operation. Popular ORMs include Hibernate for Java, Entity Framework for .NET, Prisma and Sequelize for JavaScript, and SQLAlchemy for Python.

At first glance, it’s easy to see why ORMs became so popular.

Imagine building a feature that needs to create a user, update their profile, fetch their orders, and delete old records. Without an ORM, you would likely write multiple SQL statements by hand. With an ORM, those operations can often be handled through simple method calls that feel like working with regular objects in your code.

This abstraction dramatically improves developer productivity. Teams can move faster, write less repetitive code, and focus more on business logic than database syntax. For startups and small teams, that speed can be a significant advantage.

ORMs also make code easier to maintain. Instead of scattering SQL queries throughout an application, database operations are organized into models and repositories. This creates cleaner codebases and makes onboarding new developers much easier.

Another reason developers love ORMs is portability. In many cases, switching from one database system to another requires fewer code changes because the ORM handles much of the database-specific complexity.

These benefits explain why ORMs have become a standard part of modern software development. They solve real problems and save countless hours of engineering effort. However, the same abstraction that makes ORMs convenient can also hide important performance details. And that’s where things start getting interesting.

The Performance Benefits of Using an ORM

When developers talk about ORM performance, the conversation often focuses on the drawbacks. While those concerns are valid, it’s important to recognize that ORMs can also improve performance in several practical ways.

The biggest advantage is development speed.

Performance isn’t only about how fast an application runs. It’s also about how quickly a team can build, test, and improve software. By eliminating large amounts of repetitive SQL code, ORMs allow developers to deliver features faster and spend more time solving business problems.

Many ORMs also include built-in optimizations that developers would otherwise need to implement manually.

For example, connection pooling helps applications reuse database connections instead of constantly creating new ones. Some ORMs provide caching mechanisms that reduce unnecessary database requests. Others automatically generate parameterized queries, which can improve security while allowing databases to reuse execution plans more effectively.

Another benefit is consistency.

When developers write raw SQL throughout a codebase, query quality can vary significantly from one engineer to another. ORMs provide a standardized way to interact with data, reducing the risk of poorly written queries and improving maintainability over time.

Modern ORMs also offer tools for database migrations, schema management, and query logging. These features help teams manage database changes more safely and identify potential issues before they become production problems.

For small and medium-sized applications, the performance difference between a well-configured ORM and hand-written SQL is often negligible. In many cases, factors such as network latency, application architecture, and database indexing have a much greater impact on user experience than the ORM itself.

This is why ORMs remain the default choice for many development teams. They provide a strong balance between productivity and performance. The challenge arises when developers assume that convenience automatically leads to efficient database operations.

The Hidden Performance Costs Nobody Talks About

The biggest challenge with ORM performance is that most problems remain invisible until an application starts handling real traffic.

When using an ORM, developers interact with objects and methods instead of SQL queries. This abstraction is convenient, but it can also hide what is actually happening inside the database.

One of the most common issues is the N+1 query problem.

Imagine you fetch 100 users from a database. Then, for each user, the ORM automatically makes another query to retrieve their associated orders. What looked like a single operation in code suddenly becomes 101 database queries.

In a development environment with a small dataset, this might go unnoticed. In production, it can dramatically increase response times and database load.

Another common issue is over-fetching data.

Developers often request entire objects when they only need a few fields. For example, an API endpoint may only require a user’s name and email address, but the ORM retrieves every column in the table. As datasets grow larger, this unnecessary data transfer consumes memory, network bandwidth, and processing power.

ORMs can also generate SQL that isn’t always optimal.

The generated queries are designed to work across many scenarios, not necessarily to produce the most efficient execution plan for your specific use case. A query that looks simple in application code may translate into a complex SQL statement with unnecessary joins or subqueries.

Memory consumption is another overlooked cost.

Unlike raw SQL, which can return only the data you explicitly request, ORMs often create full object representations in memory. When processing thousands of records, this overhead can become significant and affect application performance.

The tricky part is that none of these issues mean ORMs are bad technology. The real problem is that ORMs make it easy to forget that every database operation still has a cost. The database doesn’t care whether a query came from elegant ORM code or hand-written SQL. It only cares about the work it has to perform.

Understanding that hidden layer is what separates developers who use ORMs effectively from those who accidentally turn them into performance bottlenecks.

ORM vs Raw SQL: Where the Difference Appears

The debate between ORMs and raw SQL has existed for years, but the reality is more nuanced than declaring one approach superior to the other.

For basic CRUD operations, ORMs perform remarkably well.

Creating users, updating records, deleting entries, and retrieving data by primary key are tasks that modern ORMs handle efficiently. In these scenarios, the productivity gains often outweigh any minor performance overhead.

The picture changes when queries become more complex.

Consider a reporting dashboard that combines data from multiple tables, performs aggregations, filters millions of rows, and calculates business metrics in real time. While an ORM may be capable of generating the required query, the resulting SQL is not always as efficient as a carefully crafted hand-written query.

This becomes even more important in high-traffic applications.

At small scale, an extra 20 milliseconds on a database query might not matter. At millions of requests per day, those milliseconds add up quickly. A query that consumes slightly more CPU, memory, or database resources can translate into significantly higher infrastructure costs.

Many large technology companies still use ORMs extensively, but they rarely rely on them for every database interaction.

Instead, they often use a hybrid approach. Standard application logic runs through the ORM, while performance-critical operations use optimized SQL queries, stored procedures, or specialized database tools. This allows teams to keep the development benefits of an ORM without sacrificing efficiency where it matters most.

The key takeaway is that raw SQL is not automatically faster simply because it is written by hand. Poorly written SQL can perform far worse than ORM-generated queries. Likewise, a well-configured ORM can often produce excellent results.

The real advantage of raw SQL is control. Developers can decide exactly what data to fetch, how tables should be joined, and how the database should execute a query. That level of control becomes increasingly valuable as applications grow in complexity and scale.

Rather than viewing ORMs and raw SQL as competing approaches, it’s more useful to see them as complementary tools. Each has strengths, and the best developers know when to use one, the other, or both together.

How to Use ORMs Without Killing Performance

The good news is that most ORM performance problems are preventable.

The issue is rarely the ORM itself. More often, it comes down to how the ORM is being used. Developers who understand what happens behind the scenes can enjoy the productivity benefits of an ORM while avoiding many of its common pitfalls.

The first step is to monitor your queries.

Many ORMs provide query logging and profiling tools that show the exact SQL being executed. Reviewing these queries regularly helps identify inefficient operations before they become production issues. If you’re not looking at the generated SQL occasionally, you’re essentially flying blind.

Another important practice is understanding lazy loading and eager loading.

Lazy loading retrieves related data only when it is requested. While convenient, it is one of the leading causes of N+1 query problems. Eager loading fetches related data upfront, often reducing the total number of database queries dramatically. Choosing the right strategy can have a major impact on application performance.

Developers should also avoid retrieving more data than necessary.

If an endpoint only needs three columns, request those three columns. Fetching entire records when only a small subset of information is required increases memory usage and database workload without providing any real benefit.

Database indexing remains critical as well.

Even the most optimized ORM query can perform poorly if the underlying database lacks proper indexes. Before blaming the ORM, it’s worth checking whether the database itself is configured efficiently.

Finally, don’t be afraid to write raw SQL when needed.

Most modern ORMs allow developers to execute custom queries for performance-critical operations. Using raw SQL for a handful of complex reports or analytics queries doesn’t mean abandoning the ORM. It simply means choosing the best tool for a specific task.

The most successful teams treat ORMs as powerful productivity tools rather than magical solutions. They enjoy the convenience of abstraction while maintaining a clear understanding of what happens inside the database. That balance is often the difference between an application that scales smoothly and one that struggles under load.

Should You Use an ORM?

After looking at both the benefits and the hidden costs, the answer to whether you should use an ORM is surprisingly simple: for most applications, yes.

ORMs solve real problems. They reduce boilerplate code, improve developer productivity, and make database interactions easier to maintain. For startups, internal tools, business applications, and many SaaS products, these advantages often outweigh the performance overhead.

However, ORMs are not a free performance optimization.

The convenience they provide can sometimes encourage developers to ignore what is happening at the database level. When applications begin to scale, those hidden inefficiencies can surface in the form of slow queries, excessive database load, and rising infrastructure costs.

The best approach is to think of an ORM as a productivity layer, not a replacement for database knowledge.

Developers who understand SQL, indexing, query execution plans, and database design tend to get the most value from ORMs because they can recognize when the abstraction is helping and when it is getting in the way.

In practice, many successful engineering teams adopt a balanced strategy. They use the ORM for the majority of everyday operations and switch to custom SQL only when performance requirements justify the additional complexity.

That approach delivers the best of both worlds: rapid development without sacrificing control over critical parts of the system.

The goal isn’t to avoid ORMs. The goal is to use them intelligently.

Conclusion

ORMs have transformed how modern applications interact with databases. They allow developers to build features faster, maintain cleaner codebases, and focus more on business logic than repetitive SQL.

At the same time, abstraction comes with trade-offs.

Problems such as N+1 queries, over-fetching, inefficient SQL generation, and memory overhead can quietly impact performance if left unchecked. These issues don’t make ORMs a bad choice. They simply highlight an important reality of software engineering: convenience and performance are rarely free.

The most effective developers understand both sides of the equation. They leverage ORMs to accelerate development while staying aware of the database operations occurring beneath the surface.

In the end, ORMs aren’t inherently fast or slow. They’re tools. And like any tool, their impact depends on how they’re used.

The next time you write a simple ORM query, take a moment to consider what SQL is actually being executed behind the scenes. That small habit can make the difference between an application that performs well today and one that continues to perform well as it grows.

If You’re Not Creative use Generative AI

Akshat Jain — Sun, 31 May 2026 15:45:54 +0000

Boost your creativity with AI — turn ideas into reality faster by collaborating with tools like ChatGPT instead of waiting for inspiration.

A few months ago, I stared at a blank screen for over an hour.

I had ideas in my head but nothing felt good enough to write. Every sentence I typed, I deleted. It wasn’t writer’s block exactly… it was something worse: self-doubt.

Out of frustration, I opened “some Ai app” and typed a simple prompt: “Give me 10 ideas about creativity.”

Within seconds, I had more ideas than I knew what to do with.

That moment changed how I think about creativity forever.

Creativity today feels harder than ever not because we have fewer ideas, but because we have too many expectations.

We scroll through perfectly crafted posts, viral content, and polished work every day. Subconsciously, we start comparing. We feel like whatever we create has to be original, smart, and impressive all at once.

That pressure kills creativity before it even begins.

Instead of exploring ideas freely, we overthink. We hesitate. We wait for the “perfect idea” that rarely comes.

And this is where something interesting happens.

Tools powered by AI for creativity, like ChatGPT, nano banana, sora etc are changing the way we approach this problem. Not by replacing human creativity but by removing the friction that stops us from starting.

Think of it this way: creativity isn’t just about having ideas. It’s about unlocking them.

Sometimes, all you need is a small push — a different angle, a fresh perspective, or even just a starting point. That’s exactly what generative AI provides.

It doesn’t do the creative thinking for you. It helps you think better, faster, and without the fear of starting from zero.

And once you experience that shift, creativity stops feeling like a struggle… and starts feeling like a process you can actually control.

What Is Generative AI in Simple Terms?

Before we go deeper, let’s simplify something that sounds complicated: generative AI.

At its core, generative AI is a type of technology that can create new content — like text, images, ideas, or even music — based on patterns it has learned from massive amounts of data.

That might sound technical, but here’s an easier way to think about it.

Imagine you’ve read thousands of books, watched hundreds of videos, and listened to countless conversations. Over time, your brain starts recognizing patterns — how stories are structured, how ideas connect, how people express themselves.

Generative AI works in a similar way.

Tools like ChatGPT are trained on huge datasets, which allow them to generate responses, suggest ideas, and help you build on your thoughts. When you ask a question or give a prompt, the AI doesn’t “think” like a human — it predicts what kind of response would make sense based on everything it has learned.

And that’s what makes it powerful for AI for creativity.

You don’t need to be a writer, designer, or expert to use it. You just need a starting point. A simple prompt like:

“Give me blog ideas about travel”
“Help me write a story opening”
“Suggest creative business ideas”

…can instantly give you direction.

But here’s the key thing to understand: AI isn’t replacing creativity — it’s accelerating it.

It gives you raw material. Possibilities. Angles you might not have considered.

What you do with those ideas — that’s where your creativity comes in.

In a way, generative AI is like a brainstorming partner that never runs out of energy, never judges your ideas, and is always ready to help you move forward.

And once you start using it that way, everything begins to change.

How AI Boosts Creativity

Now that you understand what generative AI is, let’s talk about where things get really practical: how it actually helps you become more creative.

Because the real power of tools like ChatGPT isn’t just that they generate content — it’s how they support your thinking process.

First, let’s talk about ideas.

One of the biggest creative struggles is simply knowing what to create. You might want to write, build something, or start a project — but you don’t know where to begin.

This is where AI for creativity becomes incredibly useful.

You can ask ChatGPT to brainstorm ideas on any topic, and within seconds, you have multiple directions to explore. Instead of being stuck with zero ideas, you suddenly have ten. And often, one of those sparks something even better in your mind.

That’s how creativity really works — not from one perfect idea, but from many small ones.

Then comes the second challenge: getting stuck halfway.

Maybe you started writing, designing, or planning — but now you’re unsure how to continue. ChatGPT can help you expand your thoughts, suggest new angles, or even challenge your assumptions.

It’s like having someone sit next to you and say, “What if you tried this instead?”

Another powerful use is refinement.

You don’t have to rely on AI to create everything from scratch. You can give it your rough ideas, messy drafts, or incomplete thoughts — and it can help you organize, improve, or restructure them.

In this way, ChatGPT doesn’t replace your creativity — it amplifies it.

And finally, there’s something subtle but important: momentum.

Creativity often dies when we pause too long. When you can instantly generate ideas, get feedback, or explore variations, you stay in motion. And once you’re in motion, creating becomes easier.

So instead of staring at a blank page, you’re constantly interacting, shaping, and building.

That’s the real shift.

You’re no longer creating alone — you’re creating with a tool that helps you think faster, explore more, and push your ideas further than you normally would.

Real-Life Ways to Use AI for Creativity

By now, this might all sound useful in theory — but what does it actually look like in real life?

The good news is that AI for creativity isn’t limited to one field. You can use it in surprisingly practical ways, no matter what you’re working on.

Let’s start with writing.

Whether you’re creating blog posts, stories, or even social media captions, ChatGPT can help you generate ideas, structure your content, or rewrite sentences more clearly. You don’t have to struggle with the first draft anymore — you can start messy and improve as you go.

Then there’s business and side projects.

If you’ve ever thought about starting something but didn’t know what, AI can help you brainstorm ideas based on your interests. It can suggest business concepts, content strategies, or even ways to solve everyday problems.

Sometimes, all it takes is one idea to get started.

AI is also becoming a powerful tool in visual creativity.

With image-generation tools, you can create artwork, design concepts, or even branding ideas without being a professional designer. It lowers the barrier to entry, allowing more people to express their creativity visually.

But it doesn’t stop there.

You can also use AI for learning and problem-solving. If you’re trying to understand a concept, improve a skill, or think through a challenge, AI can explain things in simple terms or offer different perspectives.

And that’s what makes it so valuable.

It’s not just a tool for “creative people.” It’s a tool for anyone who wants to think better, explore ideas, and bring something new into the world.

At the end of the day, creativity isn’t about the medium — it’s about expression.

And AI simply gives you more ways to express what’s already inside your mind.

The Right Way to Use AI Without Losing Your Creativity

At this point, it’s easy to get excited about what AI can do.

But there’s an important question we need to address: Can relying on AI actually make you less creative?

The honest answer is — yes, if you use it the wrong way.

If you simply copy and paste whatever AI generates, you’re not really creating. You’re outsourcing your thinking. And over time, that can make your ideas feel generic and disconnected.

But that’s not what AI for creativity is meant for.

The right way to use AI is as a tool, not a shortcut.

Think of it like this: a calculator doesn’t make you bad at math — it helps you solve problems faster. But if you stop understanding the logic behind the numbers, that’s when it becomes a problem.

The same applies here.

You should use AI to generate ideas, explore perspectives, and break through creative blocks — but always bring your own voice into the final result.

Because your experiences, opinions, and way of seeing the world are something AI cannot replicate.

Another important part is editing.

AI can give you a strong starting point, but it’s rarely perfect. The real creativity comes from refining, adjusting, and shaping the output into something that feels authentic to you.

And then there’s intention.

Instead of asking AI to “do everything,” guide it with clear prompts. The better your input, the better the output. This turns the process into a collaboration rather than dependency.

In the end, AI doesn’t replace creativity — it reveals how you use it.

Used passively, it can make your work feel flat.

Used actively, it can push your thinking further than you ever expected.

That balance is what separates average content from truly creative work.

Common Mistakes Beginners Make

When people first start using AI tools, the excitement is real.

You type a prompt, and within seconds, you get a complete answer. It feels almost magical.

But this is also where most beginners go wrong.

The first mistake is copy-pasting without thinking.

It’s tempting to take what AI gives you and use it as-is. But the problem is, AI-generated content often lacks personality. It sounds fine — but not you. Without adding your own perspective, your work can feel generic and forgettable.

The second mistake is over-relying on AI.

Remember, AI for creativity is meant to support your thinking — not replace it. If you depend on it for every idea, you stop challenging yourself. Creativity grows when you engage, question, and experiment — not when you just accept the first answer.

Another common issue is asking vague prompts.

If you type something like “give me ideas,” you’ll get broad, average results. But if you’re specific — “give me 10 unique blog ideas about creativity for beginners who feel stuck” — the output becomes much more useful.

Better input leads to better output.

And finally, many people don’t experiment enough.

They try AI once or twice, don’t get amazing results, and give up. But like any tool, it takes a bit of practice. The more you play around with prompts, styles, and ideas, the more powerful it becomes.

Think of it less like a search engine — and more like a conversation.

The biggest advantage of AI isn’t that it gives you answers.

It’s that it helps you explore possibilities.

And the more curious you are, the more value you’ll get from it.

The Future of Creativity with AI

If tools like ChatGPT already feel powerful today, it’s worth asking: where is all of this going?

The future of creativity isn’t about humans vs. AI.

It’s about humans with AI.

For a long time, creativity was limited by skill. If you couldn’t write well, design, or express your ideas clearly, it was hard to bring your thoughts to life.

But now, AI for creativity is lowering those barriers.

You don’t need to be an expert to start. You just need an idea — and the willingness to explore it.

This shift is opening doors for more people than ever before. Someone with no formal training can now write articles, design visuals, build projects, or even start a business with the help of AI.

That doesn’t make creativity less valuable.

It makes it more important.

Because when everyone has access to tools, the real difference comes from how you think. Your perspective, your taste, your judgment — these become your biggest strengths.

At the same time, this also means the role of a creator is changing.

Instead of doing everything manually, creators are becoming curators, editors, and decision-makers. You guide the process. You choose what works. You shape the final output.

AI handles the heavy lifting — but you provide direction.

And that’s a powerful combination.

Looking ahead, the people who benefit the most won’t be the ones who avoid AI.

They’ll be the ones who learn how to use it thoughtfully.

Because creativity isn’t disappearing.

It’s evolving.

And those who adapt will find themselves with more tools, more opportunities, and more ways to turn ideas into reality than ever before.

Conclusion

So here’s the truth.

Creativity was never about having perfect ideas.

It was about starting — even when your thoughts feel messy, incomplete, or uncertain.

That’s exactly where tools like ChatGPT make a difference.

They don’t give you brilliance instantly. They give you momentum. A way to move from “I don’t know what to do” to “let me try this.”

And that small shift changes everything.

Because once you start, you improve. You refine. You discover ideas you didn’t even know you had.

That’s the real power of AI for creativity.

It turns creativity from something mysterious into something practical. Something you can actually do, not just admire in others.

But remember — AI is just the spark.

The meaning, the emotion, the originality — that still comes from you.

So don’t wait for the perfect idea.

Open a blank page. Ask a question. Try something small.

And let your creativity build from there.

How to Choose the Right Tech Stack for Your Project

Akshat Jain — Mon, 25 May 2026 16:37:37 +0000

If you’ve ever tried to start a project, you’ve probably faced this moment.

You open your laptop, ready to build something.

Then the questions start.

Should you use React or something simpler?

Is Node.js enough, or should you go with Django?

Do you need MongoDB, or is PostgreSQL a better choice?

And then there’s Svelte, Next.js, Flask, Ruby on Rails… the list just keeps growing.

Suddenly, instead of building, you’re stuck comparing tools.

This is where most beginners get trapped. Not because they lack skills, but because the ecosystem is overwhelming. Every framework claims to be faster. Every database promises better performance. Every developer online has a different opinion.

And the worst part is, it feels like choosing the wrong tech stack could ruin your entire project.

But here’s the reality.

There is no perfect tech stack.

There is no universal combination of frontend, backend, and database that works for every project. What works for a startup building fast might completely fail in an enterprise system. What works for a real-time app might be overkill for a simple blog.

The goal is not to find the best stack.

In this guide, we’re going to break things down from first principles. You’ll learn how frontend, backend, and databases actually differ, how popular technologies compare in real-world scenarios, and most importantly, how to make decisions without getting stuck.

By the end, you won’t just know options.

You’ll know how to think.

Breaking Down a Tech Stack (Conceptual Understanding)

Before comparing frameworks or databases, you need a clear mental model.

A tech stack is not just a list of tools. It’s a system made of three core layers working together.

Think of it like this.

Every application, whether it’s a simple blog or a complex SaaS product, follows the same flow. A user interacts with something on the screen. That action sends a request somewhere. That request gets processed. Data is stored or retrieved. And then a response is sent back.

That entire cycle is your tech stack in action.

Let’s break it down.

At the top, you have the frontend. This is everything the user sees and interacts with. Buttons, forms, dashboards, animations. Technologies like React, Vue, or even simple HTML and CSS live here. Its job is not to make decisions, but to present information and capture user input.

Behind that sits the backend. This is where decisions happen. When a user logs in, submits a form, or requests data, the backend processes that request. It applies business logic, handles authentication, and communicates with the database. Frameworks like Node.js with Express, Django, Flask, Spring Boot, or Ruby on Rails operate in this layer.

Finally, there’s the database. This is the memory of your application. It stores users, transactions, content, and everything your system needs to remember. Whether it’s structured systems like PostgreSQL or flexible ones like MongoDB, this layer is responsible for persistence.

What’s important is not just knowing these layers exist, but understanding how they interact.

The frontend never talks directly to the database. The backend acts as a bridge. It receives requests from the frontend, fetches or updates data in the database, and sends back a response.

Once you understand this flow, choosing technologies becomes much simpler.

You’re no longer picking random tools.

You’re designing a system.

Frontend Technologies (How to Think About Them)

When people start learning frontend, they usually jump straight into comparing frameworks.

React vs Vue. Angular vs Svelte. Next.js vs everything else.

But that’s the wrong starting point.

Before choosing a framework, you need to understand the type of frontend you actually need. Because in many cases, you might not need a heavy framework at all.

Let’s start from the simplest level.

At one end, you have static pages. These are built using plain HTML, CSS, and a little JavaScript. There’s no complex state, no heavy interactivity. Think blogs, landing pages, portfolios. They load fast, are easy to deploy, and require minimal maintenance. For many projects, this is more than enough.

As your application becomes more interactive, you move toward Single Page Applications (SPAs). This is where frameworks like React, Vue, and Angular come in. Instead of reloading the entire page, the UI updates dynamically. This is ideal for dashboards, admin panels, or apps where users interact frequently.

Then comes a more advanced approach: Server-Side Rendering (SSR) and Static Site Generation (SSG). Tools like Next.js combine frontend and backend thinking. Pages can be rendered on the server for better performance and SEO, or pre-generated for speed. This is why modern applications often prefer these hybrid approaches.

Now let’s talk about frameworks, not as tools, but as philosophies.

React is flexible. It gives you control, but also responsibility. You decide how things are structured. This is why it dominates startups and large-scale apps.

Vue sits in the middle. It’s easier to learn, more guided than React, but still flexible enough for most use cases. A great choice if you want simplicity without losing power.

Angular is different. It’s opinionated and structured. Everything has a defined way. That makes it harder to learn, but extremely powerful for large teams building enterprise systems.

Svelte takes a completely different approach. It compiles your code at build time, which means less runtime overhead and better performance. It’s simple, fast, and increasingly popular for lightweight applications.

And then there’s Next.js, which is not just a frontend tool anymore. It brings routing, backend capabilities, and rendering strategies together, making it a strong default choice for modern full stack applications.

The key idea is this.

You don’t choose a frontend framework because it’s popular.

You choose it based on how much complexity your interface actually needs.

Backend Technologies (System Design Perspective)

If the frontend is what users see, the backend is what actually makes your application work.

This is where most of the real decision-making happens.

Every time a user logs in, uploads something, or requests data, the backend is responsible for handling it. It validates input, applies business rules, talks to the database, and sends a response back. In simple terms, it is the brain of your system.

But unlike frontend, backend choices are less about visuals and more about tradeoffs.

Different backend technologies are built with different philosophies.

Start with JavaScript-based backends.

Node.js with frameworks like Express or NestJS is extremely popular, especially for startups. The biggest advantage is using the same language across frontend and backend. It’s lightweight, fast to build with, and works particularly well for real-time applications like chats or live dashboards because of its non-blocking nature.

Then you have Python-based frameworks.

Django is structured and comes with a lot of built-in features. Authentication, admin panels, database handling, everything is already there. This makes it great for building products quickly without worrying about too many low-level decisions.

Flask, on the other hand, is minimal. It gives you freedom, but also requires you to make more choices. This makes it flexible, but slightly harder for beginners who don’t yet know what to pick.

Now move to more enterprise-focused ecosystems.

Spring Boot, built on Java, is designed for large, scalable systems. It’s powerful, reliable, and widely used in enterprise environments. But it comes with complexity. It takes longer to learn and build with, but scales extremely well.

Similarly, the .NET ecosystem with C# is strong in enterprise applications, especially in organizations that rely on Microsoft technologies. It offers performance, stability, and solid tooling.

Then there’s Ruby on Rails.

Rails follows a philosophy called “convention over configuration.” Instead of giving you endless choices, it gives you a predefined way of doing things. This makes it incredibly fast for building MVPs and shipping products quickly. Many successful startups started this way.

So how do you think about all of this?

It comes down to a few key tradeoffs.

Some frameworks optimize for speed of development, like Django or Rails.

Some optimize for flexibility and control, like Node.js or Flask.

Others optimize for scalability and structure, like Spring Boot or .NET.

There is no universally better backend.

There is only a backend that aligns with your project’s needs, your team’s experience, and how fast you want to move.

Databases (The Most Misunderstood Layer)

If the backend is the brain of your system, the database is its memory.

And yet, this is the layer most beginners misunderstand.

People often ask, “Should I use MongoDB or PostgreSQL?” as if one is universally better. But just like frontend and backend, the right choice depends entirely on how your data behaves.

Let’s start from first principles.

There are two broad ways to think about databases.

The first is relational databases, often called SQL databases. Examples include PostgreSQL and MySQL. These systems store data in structured tables with predefined schemas. Every row follows a fixed format, and relationships between data are clearly defined.

This structure is their biggest strength.

If you’re building something like a banking system, an e-commerce platform, or anything where data consistency matters, relational databases are extremely reliable. They enforce rules, support complex queries, and ensure transactions are handled safely.

But that structure also makes them less flexible.

If your data changes frequently or doesn’t fit neatly into tables, you’ll start feeling constrained.

That’s where NoSQL databases come in.

Databases like MongoDB or Firebase allow you to store data in flexible formats, often as JSON-like documents. You don’t need to define a strict schema upfront. This makes development faster, especially in early stages when your data model is still evolving.

It’s a big reason why many startups prefer NoSQL when building MVPs.

But flexibility comes with tradeoffs.

You often lose strong guarantees around consistency, and complex queries can become harder to manage as your application grows.

Then there are specialized systems like Redis, which operate in memory. These are not typically used as primary databases, but as performance layers. They are incredibly fast and are commonly used for caching, session storage, or real-time features.

So how should you think about choosing a database?

If your system depends on structured data and strong consistency, SQL databases like PostgreSQL are usually the right choice.

If your priority is speed of development and flexibility, NoSQL options like MongoDB make more sense.

And if performance becomes critical, you don’t replace your database.

You add layers like Redis on top of it.

The key idea is simple.

Your database choice is not about trends.

It’s about how your data behaves over time.

Full Stack Combinations (How Things Come Together)

Up to this point, we’ve looked at frontend, backend, and databases as separate pieces.

But in real-world applications, you never use them in isolation.

You combine them into a stack, and that combination shapes how your entire system behaves.

This is where many beginners get confused again. They try to find the “perfect stack” like MERN, MEAN, or something trending online.

But stacks are not magic formulas.

They are just commonly used combinations that work well together.

Let’s understand them from a thinking perspective.

Take the MERN stack. It combines MongoDB, Express, React, and Node.js. The biggest advantage here is consistency. You’re using JavaScript everywhere. That makes development faster, especially for small teams or solo developers. It’s one of the reasons startups love it.

Then there’s a combination like Django with React and PostgreSQL. This is more balanced. Django handles the backend with strong structure and built-in features, React manages the frontend, and PostgreSQL ensures reliable data storage. This kind of stack is often used when you need both speed and long-term stability.

Another interesting approach is Ruby on Rails with PostgreSQL. Rails allows you to move extremely fast in the early stages. You can build and launch products quickly without making too many architectural decisions. It’s a classic choice for MVPs.

Now look at a different direction.

Instead of heavy frameworks everywhere, many modern applications use static frontend + APIs. For example, a statically generated frontend using tools like Next.js, connected to backend APIs and cloud services. This approach improves performance and simplifies deployment.

What’s important is not memorizing stacks, but understanding why they exist.

Some stacks prioritize:

Speed of development
Simplicity
Performance
Scalability

And different combinations optimize for different goals.

As your application grows, your stack can also evolve. Many large systems don’t stick to a single stack forever. They adapt over time.

So instead of asking “Which stack is best?”, ask a better question:

“Which combination helps me solve my problem efficiently right now?”

Security (Often Ignored, Always Critical)

When people think about a tech stack, they usually focus on frontend, backend, and databases.

Security is treated like an afterthought.

That’s a mistake.

Because no matter how well your system is built, a single security flaw can break everything.

At a basic level, security is about protecting three things:

User data
System access
Communication between components

Let’s break it down practically.

Most applications need authentication and authorization. This is how you verify who a user is and what they’re allowed to do. Frameworks like Django and Spring Boot often provide built-in support for this, while in Node.js you usually implement it using libraries and tokens like JWT.

Then comes data protection.

Sensitive data should never be stored in plain text. Passwords must be hashed. Communication between frontend and backend should always happen over HTTPS. These are not advanced practices, they are the baseline.

Another important concept is input validation.

Your backend should never trust incoming data. Whether it’s a form submission or an API request, validating and sanitizing input prevents common attacks.

As your system grows, security becomes layered:

API protection
Rate limiting
Role-based access
Secure deployments

The key idea is simple.

Security is not a feature you add later.

It is something that should be considered while choosing your stack and designing your system from day one.

Deployment and Infrastructure (Where Your App Lives)

Writing code is only half the job.

Your application needs to run somewhere.

This is where deployment and infrastructure come in, and it’s a part many beginners overlook while choosing a tech stack.

Different technologies come with different deployment complexities.

For example, a static site can be deployed in minutes using platforms like hosting CDNs. It’s simple, fast, and requires almost no backend management.

But as soon as you introduce a backend, things change.

Now you need:

Servers (or serverless platforms)
Environment configuration
Database hosting
Scaling strategies

Modern applications often rely on cloud platforms to handle this complexity. Instead of managing physical servers, you deploy your application to cloud environments that handle scaling, uptime, and performance.

There are generally three approaches:

First, traditional servers. You manage everything yourself. This gives control but increases complexity.

Second, Platform as a Service (PaaS). You deploy your code, and the platform handles most of the infrastructure. This is beginner-friendly and widely used.

Third, serverless architecture. You don’t manage servers at all. Your backend runs as functions that scale automatically. This is powerful but requires a different way of thinking.

Deployment also affects your tech stack choices.

Some frameworks are easier to deploy than others. Some databases integrate better with certain cloud providers. These practical constraints matter more than theoretical comparisons.

At the end of the day, a good tech stack is not just easy to build.

It is easy to deploy, run, and scale.

How to Choose a Tech Stack (The Real Decision Framework)

Now that you understand the pieces, the real question is:

How do you actually choose a tech stack without overthinking it?

Instead of chasing trends, you need a simple decision framework. Think in terms of constraints, not tools.

Start with your project type.

If you’re building an MVP or a startup idea, your priority is speed. You want to test an idea quickly, not design a perfect system. In this case, choose technologies that let you move fast. Frameworks like Node.js with Express, Django, or Ruby on Rails are great here. Pair them with something simple on the frontend, and you’re ready to ship.

If you’re building a real-time application like chat systems or live dashboards, your backend choice matters more. Systems like Node.js work well because they handle concurrent connections efficiently. You might also introduce tools like Redis for fast data access.

For enterprise-level systems, the priorities change. Stability, scalability, and maintainability become more important than speed. This is where structured frameworks like Spring Boot or .NET start to make sense. They require more effort upfront but pay off as the system grows.

Now think about your team.

If you’re a beginner or working solo, avoid complex stacks. A simpler setup like React with Node.js or Django is easier to manage. If your team already knows a specific language, that should heavily influence your choice. Learning a completely new ecosystem while building a product can slow you down significantly.

Next comes constraints.

Time, budget, and scalability expectations all play a role. If you have limited time, choose tools with strong ecosystems and ready-made solutions. If you expect rapid growth, think about how easily your stack can scale.

Finally, think about the future, but don’t over-optimize for it.

Many developers try to design systems for millions of users before they even have ten. This leads to overengineering. It’s better to start simple and evolve your stack as your needs grow.

At the end of the day, choosing a tech stack is not about finding the perfect combination.

It’s about making a practical decision based on your current situation.

Common Mistakes When Choosing a Tech Stack

Even with all this understanding, developers still make the same mistakes again and again.

Not because they don’t know the options, but because they approach the decision the wrong way.

The first mistake is choosing based on trends.

A new framework becomes popular, everyone starts talking about it, and suddenly it feels like you have to use it. But popularity does not mean suitability. A tool can be great and still be completely wrong for your use case. Many developers end up using complex stacks for simple problems, just because they are trending.

The second mistake is overengineering too early.

This usually comes from fear. The fear that your app might scale someday, so you try to prepare for it from day one. You introduce microservices, complex architectures, multiple databases, and unnecessary layers before your product even has users.

The result is not scalability.

It’s complexity.

And complexity slows everything down.

Another common mistake is ignoring your team’s skillset.

A tech stack is not just about technology, it’s about the people building it. If your team is comfortable with JavaScript, forcing a shift to Java or Go might reduce productivity. The best stack is often the one your team can use efficiently.

Then there’s trying to find a perfect stack.

This is the biggest trap.

Developers spend days or even weeks comparing React vs Vue, Django vs Node.js, SQL vs NoSQL, hoping to make a flawless decision. But no matter what you choose, there will always be tradeoffs.

Waiting for the perfect choice only delays progress.

And finally, many people underestimate simplicity.

Simple stacks are easier to build, debug, deploy, and maintain. Complexity should be introduced only when it is actually needed, not assumed.

If you avoid these mistakes, you’re already ahead of most developers.

Because choosing a tech stack is not about knowing everything.

It’s about avoiding the wrong decisions.

Practical Decision Guide

At this point, you understand the theory.

Now let’s simplify everything into something you can actually use.

Because in the real world, you don’t sit and analyze for hours. You make a decision and start building.

So instead of giving you a rigid formula, here’s a practical way to think.

If your goal is to build fast and validate an idea, keep your stack simple. A combination like React on the frontend, Node.js on the backend, and a flexible database like MongoDB works well. It reduces friction and lets you focus on the product instead of infrastructure.

If you’re working on something that requires structured data and reliability, like dashboards, internal tools, or business systems, a more structured approach makes sense. Pair a solid backend like Django or Spring Boot with a relational database like PostgreSQL. This gives you consistency and long-term stability.

For content-driven or SEO-heavy websites, you don’t always need a heavy backend. Static site generation or server-side rendering with tools like Next.js can handle most requirements while keeping performance high.

If your application involves real-time features, such as chat systems or live updates, prioritize technologies that handle concurrency well. Node.js is a common choice here, often combined with in-memory systems like Redis to improve performance.

And if you’re completely new and just trying to learn or build your first project, don’t overcomplicate it.

Pick one ecosystem and stick to it.

A simple full stack JavaScript setup, or a Django-based backend with a basic frontend, is more than enough to get started.

The key is not to find the smartest stack.

It’s to choose a good enough stack that lets you move forward.

Because progress teaches you more than planning ever will.

Conclusion

Choosing a tech stack feels like a big decision.

And in some ways, it is.

But not in the way most people think.

The mistake is believing that your entire project depends on picking the perfect combination of frontend, backend, and database from the start. That one wrong choice will somehow break everything.

That’s not how real systems are built.

Good products are not the result of perfect stacks.

They are the result of good decisions made over time.

Almost every successful application you see today has evolved. Technologies were replaced, architectures were redesigned, and systems were rebuilt as requirements changed. What mattered was not the initial stack, but the ability to adapt.

So instead of asking, “What is the best tech stack?”, ask yourself something more practical.

What can I build with right now?

What can I maintain?

What helps me move forward?

Because at the end of the day, a tech stack is just a set of tools.

And tools don’t build products.

You do.

Let’s Investigate Social Media Algorithm and how they hook you in

Akshat Jain — Thu, 21 May 2026 17:09:39 +0000

You don’t control your feed.

Your feed controls you.

So, what exactly are social media algorithms?

In the simplest terms, a social media algorithm is a system that decides what content you see and in what order. Instead of showing posts chronologically (like the old days), platforms now use algorithms to personalize your feed.

That means two people can open the same app at the same time and see completely different content.

Why? Because the algorithm is constantly trying to answer one question:

“What will keep this person engaged the longest?”

Every platform — Instagram, TikTok, YouTube, Facebook uses its own version of this system. But the goal is always the same show you content you’re most likely to watch, like, share, or interact with.

And here’s where it gets interesting.

These algorithms don’t just look at what you like.

They look at everything:

How long you watch a video
Whether you scroll quickly or pause
What you comment on
Even what you don’t interact with

Over time, they build a detailed profile of your preferences what you enjoy, what grabs your attention, and what makes you stay.

Read This for Digital profiling :- LINK

This is why your feed starts to feel “perfect.”

Almost like it understands you.

But remember it’s not magic.

It’s data.

And once you understand that, you start to see social media very differently.

The Real Goal: Your Attention

At first glance, social media platforms seem like they exist to connect people, share ideas, or entertain us.

But behind the scenes, their real goal is much simpler capture and hold your attention for as long as possible.

Why? Because attention is money.

The longer you stay on a platform, the more ads you see.

The more ads you see, the more revenue the platform generates.

It’s that straightforward.

This is why social media algorithms aren’t designed to show you what’s most important they’re designed to show you what’s most engaging.

And “engaging” doesn’t always mean positive or useful.

Sometimes it means:

Content that makes you emotional
Content that surprises you
Content that keeps you curious enough to scroll just one more time

Think about it have you ever gone down a random rabbit hole of videos you didn’t even plan to watch?

That’s not random. That’s the algorithm doing its job perfectly.

It learns what holds your attention and then keeps feeding you more of it. Not necessarily what you need, but what you won’t resist.

Over time, this creates a loop:

And the cycle repeats.

That’s why it often feels hard to stop scrolling.

Because the system isn’t built for you to leave — it’s built for you to stay.

The Psychology They Use to Hook You

If social media algorithms are the engine, psychology is the fuel.

These platforms don’t just rely on data they rely on how your brain naturally works. And once you understand this, the whole system starts to make sense.

Let’s start with something powerful: dopamine.

Dopamine is a chemical in your brain linked to pleasure and reward. Every time you see something interesting, funny, or emotionally satisfying, your brain gives you a small dopamine hit.

Now imagine this happening over and over again as you scroll.

That’s not a coincidence it’s a loop.

And here’s the clever part: the rewards are unpredictable.

Sometimes the next post is amazing. Sometimes it’s boring. But you don’t know which one is coming next. This is called a variable reward system the same principle used in slot machines.

You keep scrolling because the next post might be the one.

Then there’s social validation.

Likes, comments, shares these aren’t just features. They’re signals. They tell your brain people noticed you. That feels good. And naturally, you want more of it.

So you post again. You check again. You stay longer.

And finally, there’s FOMO — the fear of missing out.

The feeling that something important, funny, or trending is happening right now… and if you stop scrolling, you’ll miss it.

All of this combines into a powerful system:

You’re rewarded just enough to stay
You’re curious enough to continue
And you’re emotionally invested enough not to leave

That’s why it’s not just about weak willpower.

It’s design meeting human psychology.

How Algorithms Learn You Better Than You Know Yourself

By now, you might be wondering how does the algorithm get so accurate?

The answer is simple it watches everything you do.

Not in a creepy, human way but in a data-driven, pattern-recognition way.

Every small action you take sends a signal:

You paused on a video for 3 seconds? That’s interest.
You watched till the end? Strong interest.
You rewatched it? Even better.
You skipped something instantly? Not relevant.

Over time, these tiny signals add up.

The algorithm doesn’t need you to tell it what you like it infers it from your behavior. And it does this at a scale and speed no human ever could.

This is where social media algorithms become incredibly powerful.

They build a detailed profile of you:

Your interests
Your mood patterns
The kind of content you engage with at different times of the day

That’s why your feed often feels “too accurate.”

You might just think about something and suddenly, related content starts showing up. It feels almost like the app can read your mind.

But what’s really happening is pattern prediction.

And there’s a side effect of this: echo chambers.

Read This for Echo chamber :- LINK

Because the algorithm keeps showing you content similar to what you already engage with, your world becomes narrower. You see more of what you agree with, and less of what challenges you.

Over time, this can shape your opinions, reinforce beliefs, and even influence how you see reality.

So it’s not just about entertainment anymore.

It’s about influence.

The Dark Side: When Personalization Becomes Manipulation

Personalization sounds helpful and in many ways, it is.

You get content you enjoy. You discover new creators. You don’t waste time on things you don’t care about.

But there’s a point where personalization quietly crosses into something else: manipulation.

Because social media algorithms aren’t just learning from you they’re also shaping you.

The more you engage with certain types of content, the more you’re shown similar content. Over time, this can create habits that feel hard to break.

You don’t just use the platform anymore.

You start to depend on it.

This is where addictive patterns begin to form.

Endless scrolling, constant checking, losing track of time these aren’t random behaviors. They’re outcomes of a system designed to keep you engaged at all costs.

And it doesn’t stop there.

Because algorithms prioritize engagement, they often push content that is:

More extreme
More emotional
More attention-grabbing

Why? Because that’s what keeps people hooked.

This can lead to polarization, where people are pushed deeper into one-sided viewpoints. It can also amplify misinformation because controversial or shocking content spreads faster.

Then there’s the impact on mental health.

Constant comparison, unrealistic standards, and the pressure of social validation can quietly affect how you feel about yourself. Even if you don’t notice it immediately.

So while the experience feels personal and tailored…

it’s also carefully engineered.

And the line between helping you and influencing you is thinner than it seems.

Can You Beat the Algorithm?

At this point, it might feel like the system is too powerful to escape.

But here’s the truth you don’t need to “beat” the algorithm.

You just need to stop playing unconsciously.

Because social media algorithms only work well when you’re passive.

The moment you become intentional, the dynamic starts to shift.

Start with something simple: notice your behavior.

Pay attention to when you open an app.

Is it boredom? Habit? Avoiding something else?

That awareness alone creates a small gap between impulse and action.

Next, take control of what you engage with.

Remember, the algorithm learns from your actions. So if you:

Like more meaningful content
Follow creators who add value
Skip or ignore content you don’t want

…you’re actively training your feed.

You’re not just consuming you’re shaping the system.

Another powerful step is setting boundaries.

Simple things like:

Turning off notifications
Setting time limits
Keeping your phone out of reach during focused work

These aren’t extreme measures. They’re practical ways to reduce unconscious usage.

And if your feed feels overwhelming, you can always reset it — by unfollowing accounts, clearing watch history, or even taking a short break.

The goal isn’t to quit social media completely.

It’s to use it on your terms, not the algorithm’s.

Because once you understand how it works, you realize something important:

You’re not as powerless as it seems.

Final Thoughts

At the end of the day, social media isn’t evil.

The technology itself is neutral. It can inform you, entertain you, connect you with people, and even inspire you.

The real issue is how it’s designed and how unconsciously we use it.

Social media algorithms are incredibly powerful because they tap into something very human: our attention, our emotions, and our need for connection.

They don’t force you to stay.

But they make it very easy not to leave.

And that’s the subtle difference.

The goal of this article isn’t to make you delete your apps or fear technology. It’s simply to make you more aware.

Because awareness changes everything.

Once you see the patterns, you start questioning them:

“Why am I still scrolling?”
“Do I actually want to watch this?”
“Is this adding value to my time?”

These small questions break the automatic loop.

And when that happens, you move from being controlled by the algorithm…

to coexisting with it.

That’s the real win.

Not quitting. Not fighting.

Just understanding — and choosing consciously.

Building a Subscription Management System That Actually Scales

Akshat Jain — Wed, 13 May 2026 16:38:21 +0000

Here’s what I learned building a subscription management system from scratch.

If you’ve ever built an app, you’ve probably started the same way I did a frontend, a backend, and a database.

It works. It feels clean. You ship features quickly.

But then the app grows.

You add things like subscription tracking, renewal reminders, analytics, maybe even notifications. Suddenly, what used to feel simple starts getting messy. Logic spreads across different parts of the codebase. Small changes start breaking unrelated features. Debugging takes longer than building.

This is exactly where most subscription management systems start to struggle.

The problem isn’t that the system is “wrong.” It’s that it was designed for a smaller version of reality.

In the beginning, a subscription system looks simple:

Store subscription details
Track billing cycles
Send reminders

But in a real-world scenario, things expand quickly:

Users can have multiple subscriptions
Each subscription has different billing cycles
Notifications need to be timely and reliable
Data needs to be consistent across the system

What started as a few database tables becomes a system that needs to handle coordination, timing, and scale.

And here’s the key insight I learned the hard way:

A system that works for 10 users is not the same system that works for 10,000 users.

Most failures don’t happen because of bad code. They happen because the system wasn’t designed to evolve.

That’s when I stopped thinking in terms of “features”… and started thinking in terms of system design.

Starting with Back-of-the-Envelope System Design

Before writing more code, I stepped back and did something I should’ve done earlier a quick back-of-the-envelope design.

Nothing fancy. No diagrams. Just rough thinking.

If this system actually grows, what does it need to handle?

Let’s say:

10,000 users
Each user has ~5 subscriptions
That’s 50,000 active subscriptions
Each subscription may trigger 1–2 notifications per cycle

Now you’re looking at:

Thousands of reads per day (dashboard, listings)
Frequent writes (new subscriptions, updates)
Time-based events (renewals, reminders)

Suddenly, this is no longer a “simple app.”

It’s a system with different types of workloads:

User authentication
Subscription management
Scheduled processing (renewals, alerts)
Notification delivery

And here’s where things get interesting.

Not all parts of the system behave the same way.

Authentication needs to be fast and secure
Subscription data needs consistency
Notifications need reliability, not just speed
Analytics is read-heavy

Trying to handle all of this in a single backend quickly becomes messy.

You either:

Overload one system with too many responsibilities
Or start adding hacks to make things “just work”

Neither scales well.

This quick exercise changed how I approached the system.

Instead of thinking:

“What feature do I build next?”

I started asking:

“What kind of system does this feature belong to?”

That shift is what naturally led me toward a more scalable architecture.

Breaking the Monolith and moving to a Scalable Architecture

At this point, it became clear that adding more features to a single backend was only going to make things worse.

Everything was tightly connected.

Authentication logic mixed with business logic
Subscription updates triggering notification logic directly
Small changes forcing full redeploys

It worked but it didn’t scale.

So instead of patching the system again, I made a bigger shift:

I started breaking the system into separate services.

Not because microservices are trendy, but because the system needed clear boundaries.

I split the application based on responsibilities:

Authentication became its own service
Subscription logic became isolated
Notifications were handled independently

This immediately brought clarity.

Each part of the system now had a single responsibility.

Changes in one service didn’t risk breaking everything else.

But let’s be honest this didn’t magically make things easier.

In fact, it introduced a new set of problems:

How do services talk to each other?
How do you manage authentication across services?
Where does request routing happen?

This is where the architecture evolved further.

Instead of clients directly calling multiple services, I introduced an API Gateway as the single entry point. Every request flows through it, and it decides where to route it.

This solved a lot of chaos:

Centralized routing
Consistent authentication checks
Cleaner client-side logic

Now the system looked less like a single app and more like a coordinated set of services.

And that’s the key difference.

You’re no longer building “an app.”

You’re building a system where multiple components work together.

It’s more complex, yes.

But it’s also the first step toward something that can actually scale.

Inside the System: How Each Service Works

Once the system was split into services, the next challenge was making sure each one had a clear role. Without that, you just end up with distributed chaos instead of a clean architecture.

Here’s how the system is structured today.

At the front, there’s the mobile application built with React Native. This is what users interact with — adding subscriptions, viewing dashboards, and receiving alerts.

Every request from the app goes through the API Gateway. Think of it as the system’s front door. It handles routing, basic filtering, and ensures requests reach the correct service without the client needing to know internal details.

Behind that, the system is divided into focused services.

The Auth Service is responsible for authentication and security. It handles login, registration, JWT token generation, and role-based access control. This keeps security isolated and consistent across the system.

The User Service manages user-related data profiles, preferences, and account settings. It doesn’t deal with subscriptions directly, which keeps responsibilities clean.

The core of the system is the Subscription Service. This is where all the main business logic lives creating subscriptions, managing billing cycles, calculating renewals, and powering analytics.

The Help/Support Service provides a guided resolution system using a decision-tree approach, helping users troubleshoot common issues step-by-step and reducing the need for manual support intervention.

Then comes the Notification Service, which handles sending alerts. Whether it’s email, in-app notifications, or push alerts, this service ensures users are notified at the right time without blocking the main application flow.

And then there’s the most interesting part — the LLM Service.

Instead of forcing users to manually fill forms, this service allows them to send a simple message like:

“Netflix ₹499 monthly”

The service processes that input, extracts structured data, and creates a subscription event automatically in the database. It sits alongside the system, enhancing it without tightly coupling with core logic.

Each service focuses on one thing.

They communicate through APIs, not shared logic.

This separation is what makes the system maintainable as it grows.

Because at scale, clarity matters more than cleverness.

Making the System Feel Intelligent (LLM + Automation)

Up to this point, the system was scalable. It was structured. It could handle growth.

But it still felt… like a system.

Users had to:

Open the app
Fill forms
Enter subscription details manually

It worked. But it wasn’t great.

That’s when I started thinking about a different problem:

What if users didn’t have to “use” the system at all?

What if they could just say what they did, and the system handled the rest?

That idea led to integrating an LLM-based service into the architecture.

Now instead of filling a form, a user can type something like:

“Bought Spotify for ₹199 per month”

That message goes to the LLM service, which processes it and extracts:

Subscription name
Cost
Billing cycle
Relevant timing details

Once parsed, it creates a structured event and pushes it into the system just like any manually created subscription.

From the user’s perspective, it feels almost invisible.

No forms. No friction. Just intent → action.

And importantly, this didn’t break the architecture.

The LLM service is not tightly coupled with the core system. It acts as an intelligent layer on top:

It receives input
Transforms it
Sends structured data to the Subscription Service

That’s it.

This design keeps the system clean while still enabling powerful behavior.

I’ve written in detail about how this LLM pipeline works and how it processes natural language into database-ready events in a separate articles — [LINK]. But at a high level, the goal here wasn’t just automation.

It was about reducing user effort to near zero.

Because scalability isn’t just about handling more users.

It’s also about making the system easier to use as it grows.

What I Learned Building for Scale

Looking back, the biggest lesson wasn’t about microservices, APIs, or even system design.

It was about how systems evolve.

I didn’t start with a perfect architecture. I started with something simple that worked. And that’s important because trying to over-engineer from day one usually slows you down more than it helps.

But at some point, the system outgrows its original design.

That’s where most developers struggle.

You can either:

Keep patching the existing system
Or step back and rethink the architecture

I learned that scalability isn’t about adding more code.

It’s about creating the right boundaries.

Breaking the system into services wasn’t just about scaling traffic. It was about:

Isolating failures
Making changes safer
Keeping logic understandable

At the same time, microservices are not a silver bullet.

They introduce:

Network complexity
Distributed debugging
More infrastructure to manage

So the goal isn’t “use microservices.” The goal is:

Use the right level of complexity for the problem you have.

Another key shift was thinking beyond features.

Early on, I was focused on:

“Add subscriptions”
“Add notifications”

Later, the thinking changed to:

“How do these parts interact?”
“What happens when this grows 10x?”

And finally, the most interesting realization came from adding the LLM layer.

A system can be scalable and still feel heavy to use.

But when you reduce friction when users don’t have to think in terms of forms and fields you unlock a completely different experience.

That’s when the system stops feeling like software… and starts feeling intuitive.

Conclusion

It’s about evolving the system as the problem grows.

I began with a simple setup — one backend, one database, straightforward APIs. And for a while, that was enough. But as soon as real-world complexity entered the picture — multiple subscriptions, notifications, automation — the cracks started to show.

That’s when the shift happened.

From thinking in terms of features… to thinking in terms of systems.

Breaking the application into services brought structure. Introducing an API Gateway brought control. Separating responsibilities made the system easier to reason about.

And then, adding an intelligent layer with the LLM service changed how users interact with the system entirely.

Because in the end, scalability isn’t just about handling more traffic.

It’s about:

keeping systems maintainable
reducing friction for users
and designing something that can grow without collapsing under its own complexity

If there’s one thing I’d take away from this journey, it’s this:

Don’t try to build a perfect system from day one.

Build something that works and be ready to redesign it when it stops working.

That’s how real systems are built.

Scaling Myths That Mislead Developers

Akshat Jain — Sat, 09 May 2026 15:28:37 +0000

Why common assumptions about scaling lead to fragile systems

Scaling is often seen as a technical problem.

More users arrive, and the system needs to handle increased load.

However, many scaling failures are not caused by lack of resources.

They are caused by incorrect assumptions.

These assumptions shape how systems are designed.

When they are wrong, scaling becomes difficult, expensive, and unreliable.

Understanding these myths is important for building systems that perform well under real conditions.

The “just add more servers” myth

A common belief is that scaling can be solved by adding more machines.

Horizontal scaling does increase capacity, but it does not fix underlying issues.

If the system has:

inefficient queries
tight coupling
shared bottlenecks

Adding more servers only distributes the problem.

In some cases, it can make things worse by increasing coordination overhead and system complexity.

Scaling works only when the architecture supports it.

Premature optimization

Optimization is often applied before understanding real bottlenecks.

Developers may try to:

reduce latency early
optimize code paths unnecessarily
introduce complexity without clear need

This leads to systems that are harder to maintain and reason about.

Without real usage data, optimization decisions are based on assumptions.

Effective scaling requires understanding where the system actually struggles, not where it might struggle.

Tech over fundamentals

There is a tendency to rely on tools and technologies as solutions.

new frameworks
distributed systems
advanced infrastructure

While these tools are useful, they do not solve fundamental design problems.

Poor data modeling, inefficient workflows, and lack of clear boundaries cannot be fixed by adding new technology.

Scaling is primarily a design problem, not a tooling problem.

Confusing performance with scalability

Performance and scalability are related but different.

Performance refers to how fast a system responds under a given load.

Scalability refers to how well a system maintains performance as load increases.

A system can be fast at low traffic but fail under higher load.

Similarly, a scalable system may not be extremely fast, but it maintains stability as usage grows.

Focusing only on performance can hide scalability issues.

Ignoring system limits

Every system has limits.

database capacity
network throughput
processing power

Scaling requires understanding these limits and how they are reached.

Ignoring them leads to unexpected failures when the system is pushed beyond its capacity.

Design decisions should consider where limits exist and how they can be managed.

Assuming linear growth

Another common assumption is that systems scale linearly.

If a system handles 100 requests per second, it is expected to handle 200 with double the resources.

In practice, this is rarely true.

Contention, coordination overhead, and shared dependencies introduce nonlinear behavior.

Performance often degrades faster than expected as load increases.

Conclusion

Scaling is not just about handling more traffic.

It is about how systems behave as conditions change.

Misconceptions about scaling lead to incorrect design decisions, which become visible under pressure.

By focusing on fundamentals, understanding limits, and avoiding common myths, systems can be designed to scale more reliably.

This concludes the series.

Thanks for reading.

Why Your APIs Feel Slow (Even When They Aren’t)

Akshat Jain — Thu, 07 May 2026 16:21:12 +0000

Understanding the gap between actual performance and perceived latency

In previous parts, we explored how backend systems behave under load and how design decisions impact performance.

However, not all performance issues come from slow systems.

In many cases, the backend is fast, but the API still feels slow.

This difference comes from how latency is experienced, not just how it is measured.

API performance is not only about execution time. It is also about network behavior, data transfer, and how requests are structured.

Network latency matters

Every API call travels over a network.

Even if the backend processes a request quickly, the total time includes:

travel time from client to server
routing through multiple network hops
return time for the response

This delay exists even when the backend is efficient.

For users located far from the server, or on unstable networks, this latency becomes noticeable.

As a result, a fast backend can still feel slow due to network distance and conditions.

Payload size issues

The size of the response directly affects how long it takes to deliver.

Larger payloads require more time to:

transfer over the network
process on the client side

Even small increases in payload size can add noticeable delay, especially on slower connections.

Returning more data than necessary increases latency without improving functionality.

Efficient APIs focus on sending only what is required.

Too many API calls

Frontend applications often depend on multiple API calls.

Instead of one request, the system may perform several smaller requests to gather data.

For example:

one call for user data
another for related items
another for additional details

Even if each call is fast, the total time adds up.

Sequential calls increase delay further, as each request waits for the previous one.

This creates the perception of a slow system, even when individual endpoints are efficient.

Serialization and deserialization cost

Data needs to be converted before it is sent and after it is received.

On the server:

objects are serialized into formats like JSON

On the client:

responses are parsed back into usable data

This process takes time.

While the cost is small per request, it becomes noticeable with large payloads or frequent calls.

It adds hidden overhead that is often ignored during performance evaluation.

Frontend rendering delays

API performance is often judged by how quickly users see results.

Even after the response arrives:

data must be processed
UI must be updated
components must render

These steps add delay beyond the API response time.

From the user’s perspective, the system feels slow, even if the backend responded quickly.

Lack of parallelism in requests

When API calls are made sequentially, total latency increases.

Each request waits for the previous one to complete.

If multiple independent requests are needed, this approach wastes time.

Parallel execution can reduce total wait time, but it is not always implemented.

This leads to unnecessary delays in response delivery.

Conclusion

API performance is not only about backend speed.

It is influenced by network latency, payload size, request patterns, and client-side processing.

A system can be technically fast but still feel slow to users.

Understanding this difference helps in designing APIs that are efficient not only in execution, but also in experience.

In the next part, we will explore load testing and why many systems fail to identify performance limits early.

Thanks for reading.

How I Built an LLM Service That Converts Natural Language into Database Events

Akshat Jain — Tue, 05 May 2026 16:08:45 +0000

You open the app, fill fields, select options, and submit.

It works but it’s friction.

I wanted something simpler.

What if a user could just say:

_**“Netflix ₹499 monthly”** …and the system handles everything?_

The Core Idea

Instead of forcing users to adapt to the system…

Make the system adapt to the user.

The pipeline looks like this:

Each step reduces ambiguity and moves toward structured data.

Step 1 — Handling Voice & Text Input

The system doesn’t just rely on one type of input.

Users can either:

Speak (“Netflix ₹499 monthly”)
Type a quick message (just like a notification or note)

So the first step is to normalize everything into plain text.

If the input is voice, we convert it using a speech-to-text service.

If it’s already text, we process it directly.

The goal is simple: everything becomes text before any processing begins.

Example Input

\# Case 1: User typed a message (like a quick note)  
user\_input = "Netflix 499 monthly"  

\# Case 2: Voice input (after speech-to-text conversion)  
voice\_transcribed = "Spotify 199 per month"

Basic Handling Layer

def normalize\_input(input\_data, input\_type="text"):  
    if input\_type == "voice":  
        # Simulated speech-to-text (replace with real API)  
        text = input\_data  # already transcribed  
    else:  
        text = input\_data  
    return text.lower().strip()  

\# Example usage  
text\_input = normalize\_input(user\_input, "text")  
voice\_input = normalize\_input(voice\_transcribed, "voice")  
print(text\_input)  
print(voice\_input)

Why This Step Matters

This step might look simple, but it’s critical.

Because:

It creates a single entry point for all inputs
It keeps downstream logic clean
It allows you to support multiple input methods easily

And more importantly:

It makes the system feel natural — users can just “say” or “type” what they did.

Step 2 — Lightweight Regex Filtering

Before sending everything to the LLM, I added a simple filter.

Why?

Because not all inputs are subscription-related.

This saves cost and improves accuracy.

import re  

def is\_subscription(text):  
    patterns = \[  
        r'\\b(monthly|yearly|weekly)\\b',  
        r'₹\\d+',  
        r'\\b(netflix|spotify|amazon|prime)\\b'  
    \]  

    return any(re.search(p, text.lower()) for p in patterns)  
\# Example  
print(is\_subscription(user\_input))  # True

If it’s not a subscription, we can route it elsewhere.

Step 3 — LLM Parsing

Now comes the important part — extracting structured data.

We send the filtered input to an LLM with a strict prompt.

from openai import OpenAI  

client = OpenAI()  
def parse\_subscription(text):  
    prompt = f"""  
    Extract subscription details from the input.  
    Return JSON with fields:  
    name, cost, billing\_cycle  
    Input: "{text}"  
    """  
    response = client.chat.completions.create(  
        model="gpt-4o-mini",  
        messages=\[{"role": "user", "content": prompt}\]  
    )  
    return response.choices\[0\].message.content  
\# Example  
result = parse\_subscription(user\_input)  
print(result)

Expected output:

{  
  "name": "Netflix",  
  "cost": 499,  
  "billing\_cycle": "monthly"  
}

Step 4 — Structuring the Event

Now we convert this into a system event.

import json  
from datetime import datetime  

def create\_event(parsed\_json):  
    data = json.loads(parsed\_json)  

    event = {  
        "type": "SUBSCRIPTION\_CREATED",  
        "timestamp": datetime.utcnow().isoformat(),  
        "payload": {  
            "name": data\["name"\],  
            "cost": data\["cost"\],  
            "billing\_cycle": data\["billing\_cycle"\]  
        }  
    }  

    return event  
event = create\_event(result)  
print(event)

Step 5 — Saving to Database

Finally, store it.

def save\_to\_db(event):  
    # Replace with actual DB logic  
    print("Saving to DB:", event)  

save\_to\_db(event)

Why This Works

This system feels simple, but a few design decisions make it powerful:

1. Regex Before LLM

Filters irrelevant input
Reduces cost
Improves signal

2. LLM for Structure, Not Logic

LLM extracts meaning
System enforces rules

3. Event-Based Design

Everything becomes an event
Easy to extend (notifications, analytics, etc.)

Where This Gets Interesting

Once this pipeline is in place, you can extend it easily:

Add reminders automatically
Trigger notifications
Detect duplicates
Categorize spending

And most importantly:

The user doesn’t feel like they’re using a system.

They just type or speak naturally or we can take permission and extract messages from cell phone.

Final Thought

This isn’t about AI.

It’s about reducing friction.

Forms make users adapt to systems.

Natural language lets systems adapt to users.

And that small shift makes everything feel… effortless.

Observability: You Can’t Fix What You Can’t See

Akshat Jain — Sun, 03 May 2026 15:09:41 +0000

Understanding system behavior beyond logs and dashboards

In previous parts, we explored how systems fail under load and how design decisions influence performance.

But identifying failures is a different challenge.

A system may be slow, unstable, or partially broken, yet the cause is not always visible.

This is where observability becomes important.

Observability is not just about collecting data.

It is about understanding how a system behaves internally by looking at its outputs.

Logs, metrics, and traces

Observability is built on three main signals.

Logs provide discrete records of events.

They show what happened at a specific point in time.

Metrics provide aggregated numerical data.

They show trends such as latency, error rates, and throughput.

Traces provide request level visibility.

They show how a single request moves through different components.

Each of these serves a different purpose.

Logs help in understanding specific events.

Metrics help in identifying patterns.

Traces help in connecting events across the system.

None of them is sufficient on its own.

Lack of visibility delays fixes

When systems lack observability, problems remain hidden.

Failures may exist in small forms:

slight latency increases
occasional errors
resource usage spikes

These signals are often missed without proper visibility.

Over time, these small issues grow.

By the time they become noticeable, the system is already under stress or failing.

Lack of visibility does not prevent problems.

It delays their discovery.

Correlation is key

Modern systems are distributed.

A single request may pass through multiple services, databases, and external APIs.

Observing each component separately is not enough.

The key is to connect events across components.

Correlation allows understanding of:

how one service affects another
where latency is introduced
how failures propagate

Without correlation, data remains fragmented.

With correlation, it becomes possible to identify root causes instead of symptoms.

The problem of too many metrics

Collecting more data does not always improve observability.

Large systems often generate thousands of metrics.

This creates noise.

When everything is measured, it becomes harder to identify what actually matters.

Important signals get lost among less relevant data.

Effective observability focuses on meaningful metrics:

latency
error rates
system saturation

The goal is not to measure everything, but to measure what reflects system behavior.

Observability as a system property

Observability is not something added later.

It must be part of system design.

Systems should be built in a way that their internal state can be inferred from external outputs.

This includes:

structured logging
consistent metrics
traceable request flows

Without this, understanding system behavior becomes difficult, especially under load.

Conclusion

Observability defines how well a system can be understood from the outside.

Without it, diagnosing issues becomes slow and uncertain.

With it, systems become easier to analyze, debug, and improve.

Performance issues, failures, and bottlenecks are not always obvious.

They must be observed, connected, and interpreted.

In the next part, we will look at common scaling myths that often mislead developers when designing systems.

Thanks for reading.

Load Testing: Why Most Developers Do It Wrong

Akshat Jain — Fri, 01 May 2026 15:31:42 +0000

Why testing for stability often hides the real limits of your system

In previous parts, we explored how systems behave under pressure.

Load testing is meant to reveal those behaviors before they appear in production.

However, many systems still fail unexpectedly, even after being tested.

The issue is not the absence of testing.

It is how testing is approached.

Load testing is often treated as a validation step, rather than a method to understand system limits.

Load Testing

Testing average instead of peak

Most load tests simulate normal conditions.

expected number of users
typical request patterns
stable traffic levels

Under these conditions, systems usually perform well.

However, real failures occur under peak conditions, not average ones.

Traffic spikes, sudden bursts, and extreme concurrency reveal issues that normal testing cannot.

Testing only average load gives a false sense of confidence.

It confirms that the system works, but not how it behaves under stress.

Unrealistic test scenarios

Load tests often use simplified or artificial traffic patterns.

uniform request distribution
predictable intervals
identical requests

Real user behavior is different.

traffic comes in bursts
request patterns vary
some endpoints are used more than others

Because of this mismatch, tests fail to capture real-world complexity.

The system passes the test but fails in production, where conditions are less predictable.

Ignoring system limits

A key purpose of load testing is to identify limits.

maximum throughput
latency thresholds
resource saturation points

However, many tests stop once the system appears stable.

They measure success instead of exploring failure.

Without pushing the system to its limits, it is not possible to understand:

when performance starts degrading
how quickly failures spread
which component fails first

Understanding limits is more valuable than confirming stability.

No continuous testing

Load testing is often treated as a one-time activity.

It is performed before release and then ignored.

However, systems evolve over time.

new features are added
traffic patterns change
dependencies are updated

These changes affect performance.

A system that was stable earlier may degrade gradually.

Without continuous testing, these changes go unnoticed until failure occurs in production.

Lack of failure analysis

Many load tests focus on metrics like response time and throughput.

But they do not analyze how the system fails.

Important questions are often ignored:

does the system degrade gradually or suddenly
which component fails first
how failures propagate

Understanding failure behavior is essential for improving system design.

Without it, testing provides limited insight.

No correlation with real metrics

Load testing results are often viewed in isolation.

They are not always compared with real system metrics such as:

CPU usage
memory consumption
database performance

Without this correlation, it is difficult to identify the root cause of performance issues.

Testing shows that a problem exists, but not why it exists.

Conclusion

Load testing is not just about checking if a system works.

It is about understanding how the system behaves under pressure.

Testing average conditions, using unrealistic scenarios, and avoiding system limits leads to incomplete results.

To be effective, load testing must explore extremes, reflect real-world usage, and evolve with the system.

In the next part, we will look at observability and why understanding system behavior is essential for fixing performance issues.

Thanks for reading.

How I Built a Decision-Tree Based Help and Support System

Akshat Jain — Wed, 29 Apr 2026 16:10:54 +0000

80% of user problems are repeated patterns.

So why are we solving them manually every time?

If you’ve ever built a help and support system, you’ve probably done this

Add a few FAQs, maybe a help page, and a “Contact Us” button.

It feels enough.

But then users start reaching out… and you notice something strange.

They’re asking the same questions. Over and over again.

“How do I add a subscription?”

“Why is my billing date wrong?”

“Where can I see my payments?”

At first, it feels like users aren’t reading.

But that’s not the real problem.

The real problem is this:

Most help systems are designed for information.

Users need guidance.

A static FAQ assumes the user already knows:

what their problem is
what to search for
which answer applies to them

In reality, most users are confused at the first step.

They don’t think in terms of categories like:

“billing issues”
“subscription errors”

They think in situations:

“something is not working”
“I don’t understand this screen”

And here’s where things get interesting.

When I started looking at support requests closely, I realized something:

A large percentage of problems were repeated patterns.

Not unique cases.

Just the same issues showing up again and again:

multiple users struggling to add a subscription
users misunderstanding renewal dates
confusion around monthly vs yearly billing

This changed how I looked at the problem.

Instead of building a better FAQ…

I started thinking:

What if the system could guide users step-by-step to the solution instead of expecting them to find it?

That idea is what led to building a decision-tree based help system.

Thinking Like a System, Not a Page

After noticing that most user issues were repetitive, the problem became clearer.

The issue wasn’t lack of content.

It was lack of direction.

Traditional help systems are built like documentation:

Lists of FAQs
Search bars
Static categories

But users don’t navigate problems like that.

They don’t think:

“Let me go to the billing section and read all options.”

They think:

“Something is wrong what do I do next?”

That shift is important.

Instead of designing a help page, I started designing a guided system.

A system that:

asks the right questions
narrows down the problem
leads the user to a solution

Almost like how a support agent would think.

And that’s where the idea of a decision tree fits naturally.

Instead of overwhelming users with options, you guide them step by step:

What’s the issue?
What exactly went wrong?
When did it happen?

Each answer moves them closer to the solution.

This approach does two things really well:

Reduces user confusion
Reduces repeated support requests

Because now, instead of 20 users asking:

“How do I add a subscription?”

The system guides them through the exact steps automatically.

At this point, the help system stops being passive.

It becomes interactive and problem-solving.

Designing the Decision Tree Structure

Once the idea of a guided system was clear, the next step was structuring it properly.

At its core, the help system is just a decision tree.

Simple concept:

Each node = a question
Each branch = a user choice
Each leaf = a solution or a real person/agent

Instead of showing everything at once, the system reveals only what’s needed at each step.

Here’s a simple example:

Tree Structure

Now compare this to a typical FAQ page.

Instead of scanning 10–15 questions, the user just answers 2–3 guided steps and reaches the solution.

Why This Structure Works

This works well because of one key observation:

Most user problems fall into a limited number of patterns.

For example:

Many users struggle with adding subscriptions
Many get confused about billing cycles
Many face similar payment issues

So instead of handling each request individually, we categorize and guide.

This reduces:

repeated support queries
manual intervention
user frustration

Designing It Properly

While building this, a few principles mattered:

Keep questions simple
Avoid deep nesting (3–5 levels max)
Always provide an exit (contact support)
Log where users drop off

Because if users abandon the flow, that’s where your system needs improvement.

At this point, the structure is clear.

Next step is making it work in code.

Implementing the Decision Tree (Python Code)

Once the structure was clear, implementing it was surprisingly simple.

You don’t need complex frameworks.

A decision tree can be represented using basic objects.

At its core, each node needs:

a question (or condition)
possible next steps
or a final action (solution)

Basic Implementation

Here’s a clean and minimal version:

class Node:  
    def \_\_init\_\_(self, question=None, options=None, action=None):  
        self.question = question  
        self.options = options or {}  
        self.action = action  

    def evaluate(self, context):  
        if self.action:  
            return self.action(context)  

        answer = context.get(self.question)  

        if answer in self.options:  
            return self.options\[answer\].evaluate(context)  

        return escalate\_to\_agent(context)  

\# Actions  
def resolved(ctx):  
    return "Issue Resolved"  

def retry\_payment(ctx):  
    if ctx.get("retry\_success"):  
        return "Payment Successful"  
    return escalate\_to\_agent(ctx)  

def escalate\_to\_agent(ctx):  
    return "Escalating to Customer Support Agent"  

\# Tree Construction  

tree = Node(  
    question="issue\_type",  
    options={  
        "subscription": Node(  
            question="subscription\_problem",  
            options={  
                "add": Node(  
                    question="add\_issue\_type",  
                    options={  
                        "ui": Node(action=resolved),  
                        "error": Node(action=escalate\_to\_agent)  
                    }  
                ),  
                "manage": Node(  
                    question="manage\_issue",  
                    options={  
                        "edit": Node(action=resolved),  
                        "delete": Node(action=escalate\_to\_agent)  
                    }  
                )  
            }  
        ),  
        "payment": Node(  
            question="payment\_problem",  
            options={  
                "failed": Node(action=retry\_payment),  
                "incorrect\_charge": Node(action=escalate\_to\_agent)  
            }  
        )  
    }  
)  

\# Example Context  
context = {  
    "issue\_type": "payment",  
    "payment\_problem": "failed",  
    "retry\_success": False  
}  

print(tree.evaluate(context))

What This Gives You

A flexible structure
Easy to extend (just add nodes)
Clear separation of logic
No hardcoded if-else chains

And most importantly:

You can model real user journeys instead of writing scattered logic.

In a real system, this wouldn’t use input().

Instead:

UI handles selections
Backend returns next node
State is maintained per sessionNow the final step is connecting this to your actual system.

Integrating It into the Real Project

The best part about this help and support system is that it doesn’t need to live separately from the main application.

I integrated it as its own Help/Support Service inside the project architecture.

The flow is simple:

When a user taps Help, the mobile app sends the selected category or current screen context to the service.

For example, if the user is on the Add Subscription page and opens support, the system can already start from a relevant branch of the tree instead of asking generic questions.

This makes the experience feel much smarter.

Reducing Human Effort

The biggest win was reducing repeated manual support effort.

Earlier, if 20 users had trouble adding a new subscription, all 20 would either:

read the same FAQ
message support
wait for a response

Now, the tree handles the majority of these repeated issues automatically.

Some common examples:

unable to add a new subscription
confusion between monthly and yearly plans
payment failure after renewal
missing notification alerts
dashboard analytics not updating

These are pattern-based problems, which makes them perfect for tree traversal.

This means human agents only need to handle edge cases.

Escalation Path

Every branch ends with one of two outcomes:

Resolved automatically
Escalate to human agent

That fallback is important.

Because no matter how good the tree is, some cases will always need human judgment.

The system should help users first, not trap them.

That balance is what makes it practical inside a larger product.

What I Learned Building This

Building a help and support system like this taught me something simple but important:

Most problems are not unique they’re repeated patterns.

Once you accept that, the solution becomes clearer.

You don’t need:

more FAQs
more documentation
more support agents

You need a system that can recognize patterns and guide users.

The decision-tree approach worked well because:

it simplifies user choices
it reduces cognitive load
it scales without increasing support effort

But it’s not perfect.

Some trade-offs:

Deep trees can become hard to manage
Poorly designed questions can confuse users
Edge cases still require human support

So the goal isn’t to replace support.

It’s to handle the predictable 70–80% of issues automatically.

Conclusion

A help system shouldn’t just exist it should actively solve problems.

Most applications treat support as a secondary feature:

static pages
long FAQs
contact forms

But users don’t want information.

They want resolution.

By turning the help system into a decision-tree based flow, you shift from:

passive content → guided experience
repeated queries → automated solutions
manual effort → scalable support

And the result is something that feels natural.

Users don’t feel like they’re navigating a system.

They feel like the system understands them.

That’s when support stops being a feature…

and starts becoming part of the product experience.

Async Processing: The Secret to Surviving Spikes

Akshat Jain — Mon, 27 Apr 2026 16:05:51 +0000

How decoupling work from requests helps systems stay stable under load

In the previous part, we saw the limitations of synchronous systems.

When every request waits for all operations to complete, performance suffers under load. Resources remain blocked, and slow dependencies affect the entire flow.

Asynchronous processing takes a different approach.

Instead of doing all work during the request, it separates immediate responses from background work.

This shift changes how systems handle load, especially during traffic spikes.

Decoupling work from requests

In an asynchronous system, not all work is done in real time.

The request handles only what is necessary for an immediate response.

The remaining work is moved to background processing.

This reduces:

request duration
resource usage during the request
dependency on slow operations

By decoupling work, the system avoids holding resources for long periods and improves overall throughput.

Queues absorb traffic spikes

Queues are a core part of asynchronous systems.

Instead of processing all requests immediately, incoming tasks are stored in a queue and processed at a controlled rate.

This creates a buffer between incoming traffic and system capacity.

During traffic spikes:

requests are queued instead of rejected
processing happens gradually
system load remains stable

Queues do not eliminate load, but they prevent sudden overload.

Improved user experience

Asynchronous systems improve perceived performance.

Users receive faster responses because the system does not wait for all operations to complete.

For example:

a request can be accepted immediately
heavy processing happens in the background
results are delivered later

This reduces user wait time and makes the system feel more responsive.

Event driven architecture basics

Asynchronous systems are often built around events.

Instead of calling services directly and waiting for responses, components emit events when something happens.

Other components react to these events independently.

This model:

reduces direct dependencies between services
allows work to happen in parallel
improves system flexibility

Event driven systems shift the focus from request flow to state changes.

Better resource utilization

Asynchronous processing allows better use of system resources.

Since requests are shorter and less blocking:

threads are freed faster
connections are reused efficiently
overall throughput increases

Background workers can process tasks independently, making better use of available capacity.

Isolation of failures

In synchronous systems, failure in one step affects the entire request.

In asynchronous systems, failures can be isolated.

a background job can fail without blocking user requests
retries can be handled separately
issues remain contained within specific components

This reduces the impact of failures on the overall system.

Trade offs of asynchronous systems

Asynchronous systems are not without challenges.

They introduce:

increased system complexity
delayed consistency
need for monitoring background jobs

Debugging becomes harder because work is distributed across multiple components.

Despite these trade offs, the benefits are significant for systems under variable load.

Conclusion

Asynchronous processing changes how systems handle work.

By separating immediate responses from background tasks, systems can reduce load, improve responsiveness, and handle traffic spikes more effectively.

This approach is especially useful in environments where demand is unpredictable.

In the next part, we will explore why APIs feel slow even when backend systems are fast.

Thanks for reading.