CodeKerdos

Most developers think System Design is difficult.

But almost every System Design topic can actually be classified into these 5 major parts.

And before appearing for any big tech System Design interview, you should be thorough with each one of them. 🚀

𝗦𝘁𝗲𝗽 𝟭: 𝗕𝘂𝗶𝗹𝗱 𝗦𝘁𝗿𝗼𝗻𝗴 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀
→ Networking Concepts (HTTP, TCP/IP, DNS, Load Balancing)
→ API Design Principles (REST, GraphQL, Authentication, Rate Limiting)
→ Database Fundamentals (SQL, NoSQL, Indexing, Partitioning)
→ Caching Techniques (Redis, CDN, In-Memory Caching)
Without strong fundamentals, advanced architectures become confusing.

𝗦𝘁𝗲𝗽 𝟮: 𝗟𝗲𝗮𝗿𝗻 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆
→ Vertical vs Horizontal Scaling
→ Traffic Distribution Strategies
→ Replication & Database Sharding
→ Message Queues & Asynchronous Processing
This is where systems start handling millions of users efficiently.

𝗦𝘁𝗲𝗽 𝟯: 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗦𝘆𝘀𝘁𝗲𝗺 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲
→ Monolithic vs Microservices
→ Event-Driven Architectures
→ CQRS & Event Sourcing
→ High Availability & Fault Tolerance
Most interview discussions revolve around architectural trade-offs.

𝗦𝘁𝗲𝗽 𝟰: 𝗠𝗮𝘀𝘁𝗲𝗿 𝗗𝗮𝘁𝗮 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴
→ CAP Theorem & Consistency Trade-offs
→ Distributed Storage Systems
→ Data Replication & Partitioning
→ Query Optimization & Efficient Indexing
Good System Design engineers understand data deeply.

𝗦𝘁𝗲𝗽 𝟱: 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗗𝗲𝘀𝗶𝗴𝗻
→ Architect scalable applications
→ Explain technical trade-offs clearly
→ Participate in mock design interviews
→ Improve designs through continuous feedback

Because System Design is not just theory.

It is the ability to think, discuss, justify, and optimize systems under constraints.
Master these 5 areas properly, and System Design interviews become far less intimidating. 🔥

If you want to learn these concepts from developers who work at companies like Amazon, Google, Microsoft, Nvidia, etc, then do join

Senior DevOps engineers watching vibe coders deploy infrastructure using only AI prompts

These engineers spent years learning:
- Linux
- networking
- Docker
- Kubernetes
and dealt with a lot many complex systems

And now there’s someone “vibe coding” entire deployments with AI tools.

The industry is changing fast.
→ Knowing how these tools work still matter.
→ Deep engineering knowledge still matters.

But AI-native workflows are becoming part of modern engineering whether people accept it yet or not.

The engineers who adapt early are probably going to have a very unfair advantage.

How AI agents work is very different from how most people use AI today.

AI agents don’t just respond to prompts.

They can observe systems, access tools, use memory, analyze environments, make decisions, and take actions autonomously.

This is exactly why Agentic AI is becoming important for DevOps Engineers and SREs working with Kubernetes and cloud systems.

The future is moving from:
“Ask AI a question”

to:
“Let AI operate systems intelligently.”

At CodeKerdos, our Agentic AI Bootcamp focuses on practical implementation of MCP, RAG, AI workflows, and building your own Kubernetes troubleshooting AI agent.

We still have a few seats open.

DM us to join the bootcamp.

SRE ArtificialIntelligence CloudComputing MCP RAG CodeKerdos

Kubernetes expertise in DevOps can make you irreplaceable today.

But maybe not after the next 1–2 years.

Because the future SRE in your team might not be a human alone.

It could be an AI agent watching metrics, analyzing logs, correlating alerts, running kubectl commands, identifying root causes, and even suggesting or applying fixes automatically.

And honestly, this shift has already started.

Modern infrastructure is becoming too large, too distributed, and too fast-moving for purely manual operations.

The next generation of DevOps Engineers and SREs will not just manage infrastructure.

They will manage AI-powered operational systems.

That’s why learning Agentic AI is becoming extremely important for engineers working with Kubernetes, cloud, observability, and automation.

Imagine an AI system that:

detects Kubernetes issues automatically
understands observability data
analyzes incidents
performs troubleshooting
optimizes infrastructure
learns from previous failures

This is where infrastructure engineering is heading.

And the engineers who understand both systems and AI will become extremely valuable.

At CodeKerdos, we have started our Agentic AI Bootcamp focused on practical implementation, real-world infrastructure use cases, Kubernetes AI agents, MCP, RAG, observability, and automation workflows.

By the end of the bootcamp, you will build your own Kubernetes troubleshooting AI agent.

We still have a few seats open.

DM us to join the bootcamp.

ArtificialIntelligence CloudComputing PlatformEngineering AIAgents GenerativeAI MCP RAG CodeKerdos

Pods may crash, but Kubernetes ensures your application stays alive with self-healing, auto-scaling, and high availability. 🚀☸️
This is the power of modern DevOps infrastructure — maintaining the desired state even during failures. 🔥

BackendDevelopment SoftwareEngineering Tech Infrastructure CloudNative Programming SystemDesign Automation Developers Coding AWS Scalability HighAvailability Linux DevOpsEngineer

One of the biggest misconceptions about LLMs is that they “know” the truth.

They don’t.

LLMs are prediction systems.

Their job is to predict the next most probable word or token based on patterns learned from massive amounts of data.

And sometimes, when the model doesn’t actually know the answer, it still generates a response that sounds confident, fluent, and believable.

That’s called hallucination.

This becomes extremely important in engineering and AI systems because hallucinations can lead to:

* wrong troubleshooting
* fake APIs
* incorrect configurations
* misleading automation decisions

And this is exactly why modern AI systems are moving toward:

* RAG
* MCP
* tool calling
* observability
* AI agents with real-world context

At CodeKerdos, we are covering these concepts practically in our Agentic AI Bootcamp, where you will learn how modern AI systems actually work behind the scenes and build your own Kubernetes troubleshooting AI agent.

We still have a few seats left.

DM us to join the bootcamp.

Kubernetes DevOps MCP RAG MachineLearning CloudComputing AIAgents CodeKerdos