DEV Community: Kevin Ly

AlgoSync — a new social media platform for developers, founders, and tech creators

Kevin Ly — Wed, 22 Oct 2025 21:53:17 +0000

Hey everyone 👋

I’m excited to introduce AlgoSync — a new social media platform built for developers, founders, and tech creators.

AlgoSync is where tech people share what they’re building, exchange ideas, open discussion, and connect with others in the industry — all in a clean, dev-focused environment.

We officially launched just a few days ago, and the response has been amazing:

🚀 40+ daily active users in the first 4–5 days

🎓 Developers and top students from schools like Stanford, Berkeley, and Waterloo have already joined

🌍 Growing purely through word of mouth

AlgoSync is now live (web only for now, mobile responsive).

Come join the early community and be part of the conversation 👇

👉 https://www.algosyncverse.com

#developers #webdev #startup #community #tech

Open Source vs. Closed Source Software

Kevin Ly — Wed, 06 Aug 2025 02:46:20 +0000

In today’s fast-evolving tech landscape, the debate between open source and closed source software continues to spark interest — and for good reason. Whether you're a developer, tech enthusiast, startup founder, or simply a curious user, understanding the difference between these two approaches is essential.

In this blog, we’ll explore what open source and closed source software really are, compare their pros and cons, and discuss notable examples — including the controversial case of OpenAI, which started as an open source initiative.

What is Open Source Software?

Open source software (OSS) is software whose source code is made available to the public. Anyone can inspect, modify, and enhance it. This model promotes transparency, collaboration, and community-driven development so which is why it attracts many programmers around the world to an open-source project.

Examples include:

Linux – the foundation of many operating systems and servers.
VS Code – a free, powerful code editor maintained by Microsoft.
Firefox – the open-source web browser by Mozilla.
TensorFlow – an open-source machine learning framework developed by Google.

Most OSS projects are hosted on platforms like GitHub or GitLab and licensed under terms like MIT, GPL, or Apache to define how the code can be used or modified.

What is Closed Source Software?

Closed-source software, on the other hand, is proprietary software whose source code is kept secret. Only the original creators — typically companies — can legally view or alter the code.

Examples include:

Microsoft Windows
macOS
Adobe Photoshop
OpenAI’s GPT models (as of today)

Users of closed-source tools can access the software’s features, but not the internal code that powers it. If you're trying to access the closed-source software, you might face some serious legal penalties.

So... Which Should You Choose for Your Project?

Let’s say you’re building something.
A passion project. A startup. A game. A tool.
Should you go open source… or closed?

Here’s how to think about it:

✅ Choose Open Source if:

You want to build in public, attract contributors, and grow a community.
You value transparency, especially for tools used by other developers.
You’re creating something educational, experimental, or mission-driven.
You want to earn trust quickly — especially in fields like security or infrastructure.

Or… you simply believe in the philosophy:

“Code should be open, for everyone.”

🔐 Choose Closed Source if:

You're building a product you plan to monetize or license.
You need to protect intellectual property or competitive advantages.
You want full control over features, branding, and distribution.
You have legal or security obligations to keep the code private.

Or you’re targeting an audience that doesn’t care how the sausage is made — just that it works.

Still not sure?

Many companies today actually choose a hybrid model:

They open-source parts of their infrastructure to build trust and adoption.
But they keep the core proprietary to sustain the business.

Think of it this way:

Open source is a great way to build reach. Closed source is a great way to build revenue. And in some cases, you can do both.

So instead of asking “Which is better?”
Maybe the better question is:

“What kind of relationship do I want with my users?”

The OpenAI Controversy: From Open to Closed

One of the most fascinating case studies is OpenAI.

Founded in 2015 as a non-profit research organization, OpenAI's mission was to ensure that artificial general intelligence (AGI) benefits all of humanity. It pledged to make research and code open source — as stated in its founding charter.

In its early years, OpenAI released models like GPT-2 and tools like OpenAI Gym openly. However, in 2020, OpenAI made a pivotal shift. It withheld GPT-3’s code and full model weights, citing concerns over misuse. Later, OpenAI became a “capped-profit” company and began monetizing its models through APIs and partnerships (notably with Microsoft).

“We were wrong to think we could release these powerful models openly without seeing harmful use,” said OpenAI CEO Sam Altman.

Today, OpenAI’s latest models (like GPT-4) are entirely closed source, and even the training data remains secret. This has sparked ongoing debate about transparency, ethics, and the future of AI research.

Conclusion

The open vs. closed source debate isn't just technical — it's philosophical. It's about who gets to innovate, who controls the tools we use, and how we shape the future of technology.

As users and developers, it’s important to stay informed, make conscious choices, and support initiatives that align with your values.

And as OpenAI’s shift shows us, the line between open and closed isn't always fixed — it evolves with power, progress, and responsibility.

Should You Learn to Code or Pursue a CS Career in 2025? A Wake-Up Call for the AI Era

Kevin Ly — Mon, 02 Jun 2025 20:52:23 +0000

It's 2025

You’ve just graduated with a Computer Science degree — or maybe you're self-taught, having put in countless hours on LeetCode, bootcamps, and personal projects. You believed in the dream: that tech is the future, that software engineers are in demand, that maybe you’d build the next big thing.

But now?

Layoffs are everywhere.
AI writes code.
Even “entry-level” jobs want 3+ years of experience.

And you're left wondering:

“Did I mess up? Should I have even learned to code?”

If you’re feeling confused, frustrated, or just uncertain, you're not alone. Many people are struggling to find a job right now, even if that is an internship or entry-level, and if they do find work, they’re not sure what AI will do or improve tomorrow, as layoffs show no signs of abating.

Let’s take a step back, breathe, and figure out what’s really going on and how we can move forward with clarity, purpose, and confidence.

🕰️ A Look Back: When Machines Replaced Humans… or Did They?

Let’s rewind to the 19th century.

Back then, skilled textile workers in England and the U.S. — tailors, seamstresses, weavers — made clothes by hand. Their work required talent, precision, and years of practice.

And then what? The Industrial Revolution.

The invention of mechanized looms could do in minutes what took humans hours. Fear swept through the workforce. Jobs were at risk. The Luddites even smashed machines in protest.

Sound familiar? It should — because it's the same narrative we hear today from tech CEOs, news articles, and podcasts. AI is advancing rapidly, they say. It’s reshaping industries, automating tasks, and yes, replacing jobs. In tech, especially, new grads now find themselves competing not just with each other but also with recently laid-off employees from big tech and alumni from top-tier universities. The pressure is real.

If we compare today’s AI boom with the Industrial Revolution of the 19th century, the pattern is strikingly similar. Machines are once again taking over tasks once done by humans, and we’re living through a new kind of revolution.

So, what does history teach us?

Those who resisted change were left behind.
Those who adapted — who learned to work with the new tools — found opportunity.

Back then, traditional roles like tailors and weavers didn’t disappear overnight, but they evolved. The people who embraced sewing machines and learned to work with looms didn’t lose their value — they shifted their skills into a new form.

🧭 Start With Purpose: What Is Ikigai?

So, what is Ikigai?

Because when everything around you feels shaky — trends, headlines, maybe even your confidence — you need something solid to guide you.

That something is Ikigai (生き甲斐) — a Japanese concept that means “a reason for being.”
It lives at the intersection of:

❤️ What you love
✅ What you're good at
🌍 What the world needs
💵 What you can be paid for

If you’re feeling lost on your CS journey, this is a great place to start.

Ikigai isn’t just about finding a job — it’s about building a meaningful life.

Ask yourself:

Question	Your Thoughts
Do I enjoy building things, solving problems, and making ideas real?	...
Can I get better at it, even if it’s hard now?	...
Can this skill help real people or solve real problems?	...
Are there paths where I can earn from this eventually?	...

If your gut says yes, then don’t quit. Let AI be your co-pilot. Let rejections build resilience. Let the community, like us, lift you.

But if your gut says no, that’s okay too. You’re not a failure. You’re just refining your direction. Not everyone needs to be a coder.

But everyone needs a calling.

💡 So... Should You Still Learn to Code?

Let’s be real:

Right now, AI is evolving so quickly that we can't predict exactly what tomorrow will bring. One thing is certain, though: we have to adapt to the changes shaping the tech industry. But beyond technical skills, there’s something else we need to build — and that’s trust.

In a world where tools like InterviewCoder — built by Roy Lee, a former Columbia student — can help people cheat in their technical interviews, or where AI can assist someone in earning a degree without truly understanding a single line of code, trust becomes more valuable than ever.

So if you're genuinely passionate about programming or if coding aligns with your Ikigai, keep learning. Keep building. Show your projects on GitHub. There's still space for you in tech. But if you're here just for the money? Then it's time for a reality check — because this journey isn’t as glamorous as some YouTubers make it seem.

Because the truth is:

The future belongs to those who collaborate with AI, not fear it.
The job market will reward those with grit and curiosity, not just certificates.
The world still needs builders, dreamers, and problem solvers.

“The machine might know the syntax — but only you know the soul behind the software.”

So, should you learn to code or pursue a CS career in 2025? If it’s your purpose or passion, then yes.

Quick & Easy Intro to Data Structures & Algorithms for Beginners

Kevin Ly — Wed, 16 Apr 2025 21:24:14 +0000

In programming, data structures and algorithms (DSA) are the foundational building blocks that dictate how software applications handle data and perform tasks efficiently. Understanding these concepts is crucial for any software developer, as they directly impact programs' performance, scalability, and efficiency.

Definition of Data Structures

A data structure is a specialized way to organize, manage, and store data for efficient access and modification. Different data structures are designed to suit specific types of operations, enabling developers to store and manipulate data effectively.

What is an Algorithm?

An algorithm is a set of well-defined instructions for solving a particular problem or performing a specific task. Algorithms take data as input and produce an output, often through a series of logical steps. The efficiency of an algorithm is measured in terms of its time complexity (how fast it runs) and space complexity (how much memory it uses).

Real-Life Example of Data Structures and Algorithms

Imagine you are organizing a bookshelf. How you arrange your books and the method you use to find a particular book are akin to data structures and algorithms.

Data Structure: The bookshelf is a data structure. If you arrange books alphabetically by author, you are using an array-like structure where each position corresponds to a specific book.
Algorithm: When you want to find a specific book, you might start from one end and check each book one by one until you find the one you need.

Basic Data Structures

Here are some of the most commonly used data structures:

Arrays: A collection of elements, identified by index or key. Arrays are fixed in size and allow constant access to elements.
Linked Lists: A sequence of nodes where each node points to the next. Linked lists allow for dynamic memory allocation and efficient insertions and deletions.
Stacks: A linear data structure that follows the Last-In-First-Out (LIFO) principle. It's used in scenarios like undo functionality in text editors.
Queues: A linear structure that follows the First-In-First-Out (FIFO) principle, often used in task scheduling and handling requests.
Trees: A hierarchical structure with a root node and child nodes, commonly used for storing hierarchical data like file systems or databases (e.g., Binary Trees, AVL Trees).
Graphs: A collection of nodes (vertices) connected by edges, used to represent networks, such as social media connections or city maps.
Hash Tables: A data structure that maps keys to values, providing fast access to elements based on their keys.

Basic Algorithms

Some of the most commonly used algorithms include:

Sorting Algorithms: Methods to arrange data in a particular order. Examples include QuickSort, MergeSort, and BubbleSort.
Searching Algorithms: Techniques to find an element within a data structure. Examples include Binary Search and Linear Search.
Traversal Algorithms: Ways to navigate through data structures, especially trees and graphs. Examples include Depth-First Search (DFS) and Breadth-First Search (BFS).
Dynamic Programming: A method to solve complex problems by breaking them down into simpler subproblems. This technique is often used in optimization problems.
Greedy Algorithms: Algorithms that make locally optimal choices at each step to find a global optimum, are often used in tasks like scheduling and resource allocation.
Divide and Conquer: A technique that divides a problem into smaller subproblems, solves each one independently, and combines their results. QuickSort and MergeSort are classic examples.

Importance of Data Structures and Algorithms

Mastering data structures and algorithms is essential for several reasons:

Efficiency: Well-designed data structures and algorithms improve the efficiency of software by reducing time and space complexity. This is particularly important for applications handling large amounts of data or requiring fast response times.
Problem Solving: Many real-world problems can be modeled and solved using data structures and algorithms. Understanding these concepts allows developers to design solutions that are both correct and efficient.
Scalability: Efficient algorithms ensure that programs can handle growth in data size or user base without significant degradation in performance.
Competitive Advantage: In coding interviews, knowledge of DSA is often tested as it reflects a candidate's ability to think logically and solve complex problems.

Conclusion

Understanding data structures and algorithms is crucial for writing efficient code and solving complex problems. From everyday tasks like organizing a bookshelf to building scalable software systems, DSA plays a vital role in how data is managed and processed. Whether you're sorting a list, searching for a particular item, or managing complex hierarchical data, the right choice of data structure and algorithm can significantly impact the performance and scalability of your solution.

By mastering these concepts, developers can design systems that are not only correct but also optimized for efficiency, making DSA a cornerstone of computer science education and practice.

Additional:
This blog post has been transformed into a video on our AlgoSync YouTube channel.

Link: Video

Web Development: What is that?

Kevin Ly — Sat, 15 Feb 2025 04:14:01 +0000

These days, it's hard to imagine life without our devices—phones, computers, or iPads. They help us relax after a long day and assist us with countless tasks. One thing that we are all familiar with from the moment we first touched a computer until now is a web browser, most typically Microsoft's Internet Explorer, and Google Chrome. Web browsers help us access a larger world online by giving us access to all kinds of web applications from social media to daily newspaper applications. But have you ever stopped to wonder what a "web application" really is and how it works? In today's blog post, I'll break down how a web application works and the key elements that make up an application.

What is a web application?

A website or web application can be understood as a collection of web pages containing information, including images, articles, music, or anything else that the website creator wants to put up. For example, when accessing the Amazon website, we will see web pages such as the home page, product page, category page, and cart page. These web pages are typically accessible via the Internet and can be viewed using a web browser such as Google Chrome, Firefox, Safari, or Edge.

How does a web application work?

How a web application works can be summarized as follows:

After accessing the web browser via the Internet, the user will start typing on the URL bar above to go to a desired website. After the user presses Enter, the browser will find the place where the website is being stored (which we call the server). To find the server, first, our web browser will contact something called a DNS Server to find the IP address of that website by translating the website name we just entered in the URL bar, such as www.example.com, into a series of numbers called an IP address, such as 192.168.1.1.

After getting that IP address, which means we get the address of the server, our browser will send a request (or we often know it as HTTP requests) to the server where the website's data information is being stored and request the specific data that the user wants to access.

After the server receives the browser's request, it will respond to the browser with the status (whether it was successful or not) and the requested data.

Finally, when the browser receives a response from the server stating that it has been successful, it will start rendering the page.

Imagine this process as looking up a friend's phone number in a phonebook. You know the friend's name (the website address), but you need their phone number (the IP address) to call them. The DNS Server acts like the phonebook's lookup service and the IP address is like the phone number you need to dial to reach your friend.

What exactly is the term “Web Development”?

Web development is the process of creating websites and applications for
the Internet. It encompasses various aspects, including web design, web
content development, client-side/server-side scripting, and network security configuration. Web development can be divided into two main categories: Front-End and Back-End.

Front-End Development:
Front-end development is all about what you see and interact with on a website. Think of it as the "face" of the website. Here are the three basic parts that all website front ends must have:

HTML: HTML is like the skeleton of a webpage. It gives structure to the site, laying out where everything goes. Imagine the web application is like a house, HTML is like the wall, bricks, and everything that builds the house but we're not painting it yet. Every element on the page, like headings, paragraphs, and images, is defined using HTML.
CSS: CSS is what makes the website look good. It’s like the paint, furniture, and decorations you add to your house to make it stylish and comfortable. CSS lets you change colors, fonts, spacing, and layout to make everything look just right. Without CSS, websites would look plain and boring.
Javascript: JavaScript brings the website to life. It’s the magic that makes things move and respond when you click, type, or scroll. Think of it as the electrical wiring and plumbing in a house, making sure everything works smoothly and interactively. With JavaScript, you can create cool features like slideshows, form validations, and interactive maps.

Back-End Development:
Back-end development focuses on the server side of web applications. It involves managing databases, server logic, and application programming interfaces (APIs). Key components include:

Server: The server is a powerful computer that hosts websites and web applications. It handles requests from the client's browser and processes them. Servers can be set up using various technologies such as Node.js, Python (Django, Flask), Ruby on Rails, Java, and PHP.
Database: A database stores and manages the data used by a website or web application. Common databases include MySQL, PostgreSQL, MongoDB, and SQLite. Databases are crucial for storing user information, content, and other dynamic data.
API (Application Programming Interfaces): APIs allow different software systems to communicate with each other. In web development, APIs enable the front end to interact with the back end. They facilitate data exchange and functionality between the client and server.

Conclusion

In terms of "Web Development", we still have a lot of things to discuss in advance like responsive design, package management, front-end and back-end framework, database, and a lot of other things but by understanding these key elements, we can appreciate the complexity and intricacies involved in creating and maintaining websites. Web development requires a blend of creativity, technical skills, and problem-solving abilities to build functional and visually appealing web experiences.

Additional:
This blog post has been transformed into a video on our AlgoSync YouTube channel to reach a wider audience and share knowledge more broadly.

Link: Video's link

Introduction to Programming Languages

Kevin Ly — Sun, 09 Feb 2025 22:57:09 +0000

Programming language is the foundation of software development, enabling us to communicate instructions to a computer in a structured and understandable manner. Just as there are many spoken languages around the world, there are numerous programming languages, each with its syntax and semantics suited for different purposes. We can joke that programming language is what turns the cool gadgets and machines we dreamed about in childhood fantasy movies into the reality we live in today. Before we discuss and talk about what programming language is and how many types there are, we talk about the history and background first, so that we can understand who created the first programming languages and what was their purpose back then.

History & Background:

The history of programming language began in 1843 with Ada Lovelace's pioneering algorithm for Charles Babbage's Analytical Engine, which is considered the first computer.
From 1949 to 1957, we saw the advent of Assembly Language and the creation of FORTRAN for scientific calculations.
The 1950s also introduced COBOL for business data processing and ALGOL language (which is the platform for many other programming languages such as C, C++, Java, and Pascal later on).
In the 1970s, C was developed for system software, while Pascal emerged for educational purposes.
The 1980s brought C++ with object-oriented features and Python for simplicity and readability.
The 1990s saw the rise of Java for platform independence and JavaScript for web development.
Since the 2000s, languages like Ruby, PHP, and Swift have been developed to meet various specific needs, reflecting programming languages' continuous evolution and specialization.

What is a programming language?

A programming language is like a special set of instructions that tells a computer what to do. It's what programmers use to write code, make software, and solve problems. Each programming language has its own rules for writing code, which we call the syntax, and how that code behaves

Types of programming language

Programming language can be broadly categorized into several types:

1. High-Level vs Low-level Languages:

High-level languages are designed to be easy for humans to read and write. These are languages we mostly see programmers code, and students learn in school like Java, Python, C++, JavaScript, etc.
Low-level languages are the languages closer to machine code and hardware, such as Assembly Language, offering more direct control over computer resources.

2. Functional vs Object-Oriented vs Procedural Languages:

Functional Languages: These focus on using math functions and data that don’t change. Think of them like a calculator where you get a result without changing the numbers. Examples include Haskell and Lisp.
Object-Oriented Languages: These organize code around "objects" and "classes," like how you might organize tools in a toolbox. This makes code easier to manage and reuse. Examples are C++, Java, and Python.
Procedural Languages: These focus on a sequence of instructions, like following a recipe step-by-step. Examples include C and Pascal.

3. Scripting Languages: Interpreted languages often used for automating tasks and web development, such as JavaScript, PHP, and Ruby.

What language should I choose if I'm new to programming?

The choice of programming language depends on factors such as the nature of the project, performance requirements, ease of learning, and community support. Beginners often start with high-level languages like Python due to its simplicity and versatility. But in my opinion, I would choose C++ when starting to learn code. C++ is a powerful language that provides a deep understanding of how computers work. It allows you to learn detailed concepts like memory management and low-level programming, which can give you a solid foundation for understanding more complex programming topics later on.

Conclusion:

Programming language is the lifeblood of our digital world, translating human ideas into executable instructions that computers understand. Just as spoken languages evolve and diversify, programming languages have a rich history shaped by pioneers like Ada Lovelace and developments spanning from Assembly to modern-day languages like Python and JavaScript.

Understanding this evolution not only unveils the origins of our digital landscape but also highlights the diverse roles each language plays. From the raw efficiency of low-level languages like Assembly to the user-friendly syntax of high-level languages such as Python, each serves distinct purposes in software development.

As technology advances, so too do programming languages, adapting to new challenges and opportunities. Whether you're drawn to the precision of C++ or the versatility of Python, choosing a programming language is like choosing a tool for a job—a decision influenced by project requirements, learning preferences, and community support.

Additional:

This blog post has been transformed into a video on our AlgoSync YouTube channel to reach a wider audience and share knowledge more broadly.

Link: Video's link