DEV Community: mikias yonas

Your AI Wrote the Code, But Your RAM is Screaming

mikias yonas — Fri, 16 Jan 2026 14:07:04 +0000

Welcome to Day 1 of our series on reclaiming the craft of software engineering.

Today, we’re talking about X-Ray Vision. In a world of high-level languages like JavaScript, Python, and Ruby, we are shielded from the hardware. We create objects, variables, and arrays without a second thought.

But when your app slows to a crawl or a "weird" bug appears that your AI assistant can't explain, it’s usually because you’ve hit the "Invisible Wall" of memory.

1. The Abstraction Trap

AI is a master of syntax. If you ask it to "Process this 100MB JSON file," it will give you a beautiful map() function.

The problem? The AI doesn't know your server only has 512MB of RAM. It doesn't see the Heap. It just sees the logic. Without core skills, you are building a race car but forgetting that it needs a fuel tank.

2. Developing "X-Ray Vision": Stack vs. Heap

To "vibe" with your code, you need to visualize where your data actually lives.

The Stack: Think of this as your "Current Context." It’s fast, organized, and tiny. It stores your primitive values (numbers, booleans) and function calls. When a function ends, the stack is cleared.
The Heap: This is the "Big Warehouse." This is where objects, arrays, and complex data live. It’s huge but messy.

The Secret: Your variables on the Stack are often just "addresses" pointing to the Heap. When you understand this, you understand why changing user.name in one function mysteriously changes it in another. It’s a Reference.

3. The AI Trap: The "Invisible" Memory Leak

AI loves suggesting Closures and Event Listeners. They are powerful, but they are a leading cause of memory leaks.

Imagine you ask an AI to help you build a scroll-tracking feature. It might suggest something like this:

window.addEventListener('scroll', () => {
   const data = heavyDataProcessing(); 
   console.log(data);
});

The Problem: If this code runs inside a component that gets destroyed and rebuilt (like in React or Vue) without removing the listener, you are "leaking" memory into the Heap every time. The AI won't always remind you to clean up. You have to be the architect.

4. The Vibe Check: Performance over Syntax

A developer with core skills doesn't just write code that works; they write code that breathes.

Before: Creating a new array every time you filter data (Memory Heavy).
After: Mutating in place or using a more efficient data structure because you know how the Heap is feeling.

Today's Challenge:

Next time you're debugging, don't just look at the console error. Ask yourself:

Is this value a Primitive (on the Stack) or a Reference (on the Heap)?
Who "owns" this data, and when is it getting cleared?

What’s your "Favorite" Memory Leak story?

We’ve all been there—a simple loop that crashed a production server. Let’s swap horror stories in the comments!

Beyond the Prompt: Why Core Skills are the Ultimate Dev Flex in 2026

mikias yonas — Fri, 16 Jan 2026 13:33:32 +0000

We’ve all seen the headlines. AI can write functions in seconds, Copilots can autocomplete entire boilerplate structures, and low-code tools are getting scarily good. In this environment, it’s easy to wonder: Do I still need to know how a Linked List works? Does "problem-solving" even mean the same thing anymore?

The short answer is: Yes, more than ever.

While the way we write code is changing, the reason we write it hasn't. To truly "vibe" with your code and deliver a product that doesn't crumble under pressure, you need a foundation that a prompt can’t provide.

1. The Trap of "Black Box" Development

When you rely entirely on abstractions or AI-generated snippets, you’re essentially building a house on a foundation you didn't inspect. This is often called Abstraction Leakage.

Eventually, something will go wrong. An API will latency out, a memory leak will slow down your production environment, or a generated regex will create a security vulnerability.

The Reality: Core skills—understanding memory management, networking protocols, and basic data structures—give you the "X-ray vision" to see through the abstraction and fix the root cause, rather than just patching the symptom.

2. The Problem-Solving Mindset: Systems Thinking

Coding is only 20% typing; the other 80% is thinking in systems. A "Problem-Solving Mindset" isn't about knowing the syntax for a for-loop in five languages. It’s about:

Edge Case Hunting: What happens if the user loses Wi-Fi mid-upload?
Scalability: Will this logic work for 10 users? What about 10 million?
Trade-offs: Every technical decision has a cost. Is readability more important than raw performance for this specific feature?

AI is a "local" optimizer—it looks at the immediate block of code. Humans are "global" optimizers—we look at how that code affects the entire ecosystem.

3. How to "Vibe" with Code

The "vibe" we talk about in development—that flow state where everything clicks—comes from mastery, not just completion.

When you understand the why behind a framework (like why React uses a Virtual DOM or how Rust ensures memory safety), you stop fighting the tool and start dancing with it. You aren't just copy-pasting; you are composing. That’s where the best products come from.

4. Why AI Makes Core Skills More Valuable

Think of AI as a high-powered engine. If you don't know how to steer, you’re just going to hit a wall faster.

Code Review is the new Coding: You spend more time reading and verifying code than writing it. If you don't have core skills, you can't effectively audit what the AI gives you.
Architectural Integrity: AI is great at functions, but it’s often poor at maintaining a cohesive, long-term architecture. That’s the human’s job.

The Bottom Line

Tools will come and go. Today it’s LLMs; tomorrow it’ll be something else. But the ability to decompose a complex problem into logical steps remains the "infinite skill."

Don't just be a user of tools; be a master of the logic they run on.

What do you think?

Are we losing the "craft" of coding, or are we just evolving into a new kind of engineer? Let’s discuss in the comments!

Mastering Asynchronous JavaScript: How to Effectively Use Promises, Async/Await, and Error Handling

mikias yonas — Fri, 27 Sep 2024 07:06:18 +0000

Asynchronous JavaScript is crucial for modern web applications, but managing asynchronous tasks can be tricky. Understanding how to effectively use Promises, Async/Await, and common error-handling techniques will help you write cleaner, more efficient code. Below are some practical implementations and best practices that I’ve learned from real-world projects.

1. Promises vs. Async/Await: When to Use Which
Both Promises and Async/Await provide ways to handle asynchronous operations, but they shine in different contexts.

Promises are great when handling parallel operations like multiple API calls.

Async/Await is perfect when you need to write code that looks synchronous but handles asynchronous operations under the hood.

Example: Using Promises

const fetchData = (url) => {
  return fetch(url)
    .then(response => response.json())
    .then(data => console.log(data))
    .catch(error => console.error('Error:', error));
};
fetchData('https://api.example.com/data');

Example: Using Async/Await

const fetchDataAsync = async (url) => {
  try {
    const response = await fetch(url);
    const data = await response.json();
    console.log(data);
  } catch (error) {
    console.error('Error:', error);
  }
};

fetchData('https://api.example.com/data');

Tip: Use Async/Await for readability and reducing callback hell, but for parallel tasks, Promise.all() is still your friend.

2. Handling Errors:
Proper Try-Catch and Promise Rejection's Errors are inevitable, but not handling them properly can lead to hidden bugs. For Async/Await, always use try-catch blocks to capture any errors. In Promises, always end with .catch() to handle rejections.

Example: Error Handling with Async/Await

const fetchWithErrorHandling = async () => {
  try {
    const res = await fetch('https://api.example.com/broken-url');
    const data = await res.json();
    return data;
  } catch (error) {
    console.error('Fetch failed:', error);
    throw new Error('Failed to fetch data'); // Propagate the error for further handling
  }
};

Example: Error Handling with Promises

fetch('https://api.example.com/broken-url')
  .then(response => response.json())
  .catch(error => {
    console.error('Fetch failed:', error);
    // Handle the error here or propagate it
  });

Tip: For complex applications, consider using a centralized error-handling service like Sentry to track errors in production.

3. Running Promises in Parallel with Promise.all()
When you need to run multiple asynchronous tasks concurrently, use Promise.all(). It waits for all promises to resolve or rejects as soon as one promise fails.

Example: Parallel Execution with Promise.all()

const fetchUser = fetch('https://api.example.com/user');
const fetchPosts = fetch('https://api.example.com/posts');

Promise.all([fetchUser, fetchPosts])
  .then(async ([userResponse, postsResponse]) => {
    const user = await userResponse.json();
    const posts = await postsResponse.json();
    console.log(user, posts);
  })
  .catch(error => console.error('One of the promises failed:', error));

Tip: For tasks that don’t need to fail together, use Promise.allSettled() to continue processing other promises, even if some fail.

Example: Handling Multiple Task Failures with Promise.allSettled()

const fetchTasks = [fetch('https://api.example.com/task1'), fetch('https://api.example.com/task2')];

Promise.allSettled(fetchTasks).then(results => {
  results.forEach((result, index) => {
    if (result.status === 'fulfilled') {
      console.log(`Task ${index + 1} fulfilled with data:`, result.value);
    } else {
      console.log(`Task ${index + 1} failed with reason:`, result.reason);
    }
  });
});

4. Avoid Async Pitfalls in Loops
Using forEach() with asynchronous operations can lead to unexpected results because forEach does not wait for async functions to resolve. Instead, use for...of or map() combined with Promise.all() to handle async operations in loops.

Example: Common Mistake with forEach and Async

const urls = ['url1', 'url2', 'url3'];
urls.forEach(async (url) => {
  const res = await fetch(url);
  const data = await res.json();
  console.log(data); // This might not work as expected
});

Correct Example: Using for...of with Async/Await

const urls = ['url1', 'url2', 'url3'];

const fetchAllData = async () => {
  for (const url of urls) {
    const res = await fetch(url);
    const data = await res.json();
    console.log(data);
  }
};

fetchAllData();

Tip: Use Promise.all() when you want to run all asynchronous operations concurrently within loops.

Example: Using Promise.all() with Map for Parallel Execution

const urls = ['url1', 'url2', 'url3'];

const fetchAllData = async () => {
  const promises = urls.map(url => fetch(url).then(res => res.json()));
  const results = await Promise.all(promises);
  results.forEach(data => console.log(data));
};

fetchAllData();

5. Throttling and Debouncing for Optimized Performance
In real-time scenarios like handling input fields or scroll events, making too many API calls can slow down your app. Throttling limits how often a function is executed, while debouncing ensures that a function only executes after a certain delay.

Example: Debouncing Input with Lodash

const debouncedFetch = _.debounce(async (searchTerm) => {
  const res = await fetch(`https://api.example.com/search?query=${searchTerm}`);
  const data = await res.json();
  console.log(data);
}, 300);

// Attach to an input field's event listener
document.getElementById('search').addEventListener('input', (event) => {
  debouncedFetch(event.target.value);
});

Tip: Use throttling for tasks like window resizing or scroll events where execution should happen at regular intervals, and debouncing for tasks like search inputs to prevent flooding the server with requests.

Conclusion
Mastering asynchronous JavaScript is critical for building responsive, high-performance applications. By understanding the nuances of Promises, Async/Await, and proper error handling, you can avoid common pitfalls and write efficient code. Whether you're running parallel tasks with Promise.all(), handling errors, or optimizing performance with debouncing, these tips will enhance your ability to manage asynchronous operations in any JavaScript project.

Unlocking the Power of Node.js: 7 Essential Tips for Building Scalable Backend Systems

mikias yonas — Wed, 25 Sep 2024 15:16:09 +0000

With over four years of full-stack development experience, primarily focusing on backend systems using Node.js, TypeScript, and related technologies, I’ve come across a number of techniques that have made backend development more efficient, scalable, and reliable. In this article, I’ll share some rare but impactful tips that will help you maximize the performance of your Node.js applications. These techniques are based on real-world experience across projects involving microservices, database optimization, real-time communication, and cloud integrations.

1. Use RabbitMQ for Efficient Job Queuing
In scenarios where your application processes background tasks, message queues become vital. One of my go-to libraries for job queuing in Node.js is RabbitMQ. It’s especially useful when you need to handle recurring tasks like sending emails or managing scheduled jobs. RabbitMQ scales well across distributed systems and integrates smoothly with Redis for in-memory data storage.

Tip: Always configure your job queues to retry in case of failure, and use priority settings for time-sensitive tasks. This helps maintain smooth operations even during high loads.

2. Master Connection Pooling for PostgreSQL
When working with PostgreSQL, optimizing your database connections is key. Too many open connections can lead to bottlenecks and slow query execution times. I recommend using connection pooling, which instead of opening and closing connections for every request, connection pooling uses a cache of database connections that can be reused when future requests to the database are required. It lets your database scale effectively as the data stored there and the number of clients accessing it grow. Traffic is never constant, so pooling can better manage traffic peaks without causing outages. Your production database shouldn’t be your bottleneck.

Tip: Use libraries like pg-pool for PostgreSQL connection pooling. Tuning parameters such as max, idleTimeoutMillis, and connection TimeoutMillis can drastically improve performance under heavy workloads.

3. Real-Time Communication with WebSockets
Real-time communication is becoming essential for many applications. For instance, in one of my projects, I used Socket.io to build a real-time chat application. WebSockets allow for full-duplex communication, making it ideal for real-time apps like chat systems or live updates.

Tip: Use namespaces and rooms in Socket.io to efficiently organize and broadcast events to specific users or groups. Also, consider implementing message acknowledgment for critical communication to ensure message delivery in unstable network conditions.

4. Optimize Docker Containers for Production
When deploying applications using Docker, many developers forget to optimize their containers for production. I’ve found that reducing the size of the Docker image by using a multi-stage build significantly speeds up deployment and resource usage.

Tip: Use lightweight base images like alpine and multi-stage builds to reduce the final image size. This can also reduce security risks by minimizing the number of packages exposed in production.

5. Leverage MongoDB for Flexible Schema Design
For projects that require flexible, schema-less data storage, MongoDB is a powerful solution. In cases where you're dealing with unstructured data or rapid development where the schema might evolve, MongoDB provides the flexibility that relational databases might lack.

Tip: Use schema validation at the database level with MongoDB’s jsonSchema. This way, you can enforce certain rules on your data without being as rigid as a SQL schema.

6. Implement Circuit Breaker Patterns for Microservices
Microservices architecture can sometimes introduce complexity in handling failure between services. A best practice I've implemented is the Circuit Breaker Pattern to detect failures and prevent cascading failures across microservices.

Tip: Use libraries like opossum to implement circuit breakers in Node.js. Set proper timeout values and fallbacks to ensure that your system gracefully handles service downtimes without crashing the entire application.

7. Continuous Integration and Deployment with GitHub Actions
Automating the deployment pipeline is a crucial aspect of maintaining a healthy development cycle. I’ve used GitHub Actions for automating tests, builds, and deployments across many of my projects.

Tip: Always write comprehensive tests, especially for critical parts of your application, and automate their execution with CI/CD pipelines. Set up GitHub Actions to trigger on pull requests, and deploy to staging or production environments based on the branch.

Conclusion
The world of backend development is vast, and these tips will help you streamline your Node.js applications for better performance, scalability, and reliability. Whether you’re working with job queues, real-time communication, or microservices, these strategies will help you optimize your system for production-level performance.

Feel free to experiment and tailor these techniques to your specific needs, and don’t forget to continuously test and measure the performance of your applications.

I hope these insights from my experience inspire you to dig deeper into the potential of Node.js for backend development. Stay tuned for more tips, and don’t hesitate to reach out if you want to dive deeper into any of these topics!

Happy coding!