DEV Community: Soma

The AI Engineer's Reading List for 2026 (10 Books That Matter)

Soma — Sun, 16 Aug 2026 08:24:55 +0000

Disclosure: This post includes affiliate links; I may receive compensation if you purchase products or services from the different links provided in this article.

Hello Devs, the AI world is moving ridiculously fast. New models, new frameworks, new tools every week there's something new for AI engineers to learn. But if there's one thing that consistently separates great engineers from the rest, it's strong fundamentals.

And for that, books still beat everything else.

Over the past few months, I've been diving deep into some of the best books on **AI engineering, LLMs, and production AI systems. These are not just theoretical reads; they cover everything from building LLMs from scratch to designing scalable ML systems to deploying AI applications in production.

In this newsletter, I'm sharing 10 AI and LLM Engineering books I'm reading in 2026 that every developer, ML engineer, or AI enthusiast should have on their radar.

These include fantastic titles from experts like Paul Iusztin, instructor of the Agentic AI Engineering course, Chip Huyen, Sebastian Raschka, PhD, Maxime Labonne, Louis-François Bouchard (founder of Towards AI), Louie Peters, and others who are shaping how modern AI systems are built.

If you want to learn how to:

Build LLM-powered applications
Design scalable AI systems
Understand prompt engineering and agentic AI
Deploy LLMs in production

Then, these books are an excellent place to start.

Let's dive into the list.

10 Must-Read Books for AI and LLM Engineers in 2026

Without any further ado, here is a list of the 10 Best Books to learn AI and LLM Engineering in 2026. This includes books on AI, Machine Learning, and Large Language Models.

If you're serious about becoming an AI engineer or working with LLMs, this list is your roadmap.

1. AI Engineering by Chip Huyen

This is the first book you should read on AI Engineering, and if you don't like reading many books, then this single book is enough to learn all the skills you need to become an AI Engineer in 2026.

Chip Huyen, author of this book, brings a refreshing focus on AI systems design rather than just models.

If you don't know, Chip has worked as a researcher at Netflix, was a core developer at NVIDIA (building NeMo, NVIDIA's GenAI framework), and co-founded Claypot AI. She has also taught machine learning (ML) at Stanford University.

This book covers what an AI engineering stack looks like: the one that we software engineers must become experts in to be an AI engineer.

You'll learn how to turn machine learning models into real products --- handling data pipelines, model versioning, deployment, monitoring, and scaling.

It also covers what AI engineering is, how it differs from ML engineering, and the techniques AI engineers should be familiar with.

If your goal is to become a true AI Engineer (not just a Kaggle competition winner), this book is pure gold.

Here is the link to get this book --- AI Engineering by Chip Huyen

2. The LLM Engineering Handbook by Paul Iusztin and Maxime Labonne

This book is like an operations manual for LLM development.

It covers prompt engineering, model fine-tuning, retrieval-augmented generation (RAG), evaluation strategies, and production patterns.

The authors have real-world experience building LLM apps at scale.

Highly recommended if you want to move from "just using GPT" to designing serious LLM applications.

*Here is the link to get this book: The LLM Engineering Handbook by Paul Iusztin and Maxime Labonne

3. Designing Machine Learning Systems by Chip Huyen

This is another great book from Chip Huyen, one of my favorite authors when it comes to AI and LLM engineering

While "AI Engineering" focuses more on the systems side, this one gets into how to design and operate machine learning systems under real-world constraints like data drift, retraining, and model reliability.

You'll start thinking like a machine learning product engineer, not just a model builder.

Here is the link to get this book --- Designing Machine Learning Systems by Chip Huyen

4. Building LLMs for Production by Louis-François Bouchard and Louie Peters

This book shows you how to actually ship Large Language Models into production environments. You'll learn about fine-tuning, deploying, scaling, and maintaining LLMs like a real engineer.

It's packed with hands-on advice, architecture examples, and real deployment challenges.

If you're aiming for a career as an LLM engineer, this book should be your first read.

*Here is the link to get this book: Building LLMs for Production by Louis-François Bouchard and Louie Peters

5. Build a Large Language Model (from Scratch) by Sebastian Raschka, PhD

Sebastian Raschka is a legend in the machine learning community. This book teaches you how to build a transformer-based LLM from scratch using PyTorch, with no shortcuts.

You'll go deep into model architecture, tokenization, attention mechanisms, and training strategies.

Perfect for developers who want to understand LLMs at the code level, not just use APIs like OpenAI's.

Here is the link to get this book --- Build a Large Language Model (from Scratch) by Sebastian Raschka, PhD

6. Hands-On Large Language Models: Language Understanding and Generation

Jay Alammar and Maarten Grootendorst are two of the most respected names in the AI and NLP space.

This book walks you through building and fine-tuning large language models with modern tools like Hugging Face Transformers, LangChain, and more.

It's hands-on and practical --- ideal for developers, data scientists, and ML engineers who want to build and deploy LLMs that understand and generate human language effectively.

Here is the link to get this book --- Hands-On Large Language Models

7. Prompt Engineering for LLMs: The Art and Science of Building Large Language Model-Based Applications

If you're building AI products using OpenAI, Claude, or open-source LLMs, this book shows you how to write smarter prompts for better results.

It covers strategies like few-shot prompting, chain-of-thought, and using prompt patterns effectively.

Created by John Berryman and Albert Ziegler, this book dives into the evolving art and science of prompt engineering.

A must-read for AI developers and product designers.

Here is the link to get this book --- Prompt Engineering for LLMs

8. Building Agentic AI Systems: Create Intelligent, Autonomous AI Agents that can Reason, Plan, and Adapt

Written by Anjanava Biswas and Wrick Talukdar, this book explores how to build agentic AI systems that can go beyond static outputs.

This book shows you how to create autonomous AI agents that can interact with environments, reason, make decisions, and take actions.

If you're interested in building AI agents like Auto-GPT, BabyAGI, or LangGraph-based systems, this guide is a goldmine.

*Here is the link to get this book -- Building Agentic AI Systems

9. Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs

This is a comprehensive guide to prompt engineering techniques specifically designed for generative AI systems --- including text, image, and code generation.

The book emphasizes how to write prompts that are robust, consistent, and tailored for business and production environments.

Whether you're working with GPT, DALL-E, or other models, this Prompt Engineering book by James Phoenix and Mike Taylor will definitely help you future-proof your AI input strategies.

Here is the link to get this book --- Prompt Engineering for Generative AI

10. The AI Engineering Bible: The Complete and Up-to-Date Guide to Build, Develop and Scale Production Ready AI Systems

Thomas R. Caldwell's AI Engineering Bible is a must-have for software engineers and tech leaders.

It goes beyond models and APIs to show you how to engineer real-world AI systems that are scalable, maintainable, and production-ready.

From architecture to infrastructure, deployment to monitoring, it covers the entire AI lifecycle. This is the playbook for anyone who wants to lead AI implementation in their organization.

*Here is the link to get this book - The AI Engineering Bible

Why Read These AI Books?

Apart from my recommendations and several others on Reddit and HN, here are the top 5 reasons why you should read these AI and LLM Engineering books.

They're written by practitioners who have built production AI/LLM systems.
They focus on engineering, deployment, and real-world use cases, not just algorithms.
They don't waste your time with outdated academic theory.
They prepare you for the future of AI and LLM work: scalable, reliable, explainable systems.
Widely recommended by professionals on Reddit and Hacker News.

Reading books is powerful, but nothing beats building things.

If you want to accelerate your learning, combine these books with a hands-on course like: LLM Engineering: Master AI, Large Language Models & Agents to get some hands-on experience on building RAG RAG-based chatbot and learning LLM by watching.

Conclusion

That's all about the best books to learn AI and LLM Engineering in 2026. If you're serious about mastering AI and LLM engineering in 2026 and beyond, start with these must-read AI and LLM Engineering books.

They'll save you hundreds of hours of wasted time and help you actually build systems that work.

Want even faster progress?
If you want more fun and faster progress then you can also pair these books with hands-on projects like building your own RAG-based chatbot, fine-tuning a model on your own dataset, or deploying a real-world LLM app to the cloud.

I Found the Perfect Software Architecture Course for Senior Developers, and It’s Awesome

Soma — Sat, 08 Aug 2026 10:26:04 +0000

Disclosure: This post includes affiliate links; I may receive compensation if you purchase products or services from the different links provided in this article.

Hello friends, the brutal truth about senior engineering nobody tells you is that you can be brilliant at coding and still hit a career ceiling.

After 7 years as a senior software engineer, I watched less-technical colleagues get promoted to architect roles while I stayed stuck. Same salary. Same responsibilities. Same frustration.

The reason wasn't my code quality or work ethic. The problem was simple: I didn't understand software architecture.

I could write elegant algorithms and debug complex systems, but when discussions turned to architectural decisions --- choosing between microservices and monoliths, designing for scalability, making infrastructure tradeoffs --- I froze. I realized coding skills alone won't make you an architect.

Here's what changed everything: I discovered Certified Professional for Software Architecture (CPSA-F®) by Michael Pogrebinsky.

Six months later:

Promoted to Software Architect
Cracked Senior Engineer interviews
Internationally recognized CPSA-F® certification

This isn't a typical online course review. This is about the career transformation that happens when you finally learn to think like an architect instead of just a coder.

Here's why this training changed my career --- and why it might be exactly what you need.

The Gap Nobody Talks About: From Coding to Architecture

The problem with most senior engineers:

We're taught to write code. We're NOT taught to design systems.

What this means in practice:

✅ You can implement features flawlessly
✅ You can debug the most complex bugs
✅ You can write clean, maintainable code

❌ You can't design a system for 10M users
❌ You can't make trade-off decisions confidently
❌ You can't communicate architectural vision
❌ You can't evaluate and choose between patterns

The reality: Senior engineer skills ≠ Architect skills

They're completely different disciplines. And nobody teaches the transition from one to the other.

That's the gap this course fills.

Why I Chose This Software Architecture Training?

I tested 5 different architecture courses before finding this one. Here's what made it different:

1. Internationally Recognized Certification

This isn't just a certificate of completion. It's preparation for iSAQB® CPSA-F® (Certified Professional for Software Architecture --- Foundation Level) certification.

What this means:

Never expires --- Unlike AWS, Azure, or Google certifications
Globally recognized --- Respected across industries and countries
Industry standard --- Created by actual architects, not course creators
Career leverage --- Immediate signal you're architect-ready

The difference: Most courses teach theory. This course prepares you for an internationally recognized standard.

2. Taught by Someone Who Actually Built Large-Scale Systems

Michael Pogrebinsky isn't a career instructor. He's a practicing software architect who's worked at:

Google --- Large-scale distributed systems
Intel --- Performance-critical architectures
DAQRI (acquired by Snap Inc.) --- AR/VR systems at scale

What this brings:

Real examples from systems handling millions of users. Not theoretical "imagine if..." scenarios, but actual architectural decisions he made at billion-dollar companies.

He's also trained 200,000+ engineers and is an officially accredited iSAQB® CPSA-F® trainer.

My take: Taking this course felt like having a senior architect mentor walking me through every decision.

3. Skills That AI Can't Replace

With AI tools advancing rapidly, coding is becoming commoditized. Architecture is not.

Why architecture remains human:

Requires understanding business context
Involves complex tradeoffs (performance vs. cost vs. time)
Needs stakeholder communication
Demands long-term strategic thinking

The insight: AI can write code. It can't design systems that balance technical constraints, business goals, and organizational realities.

This course teaches skills that make you more valuable as AI advances, not less.

Here's the link to this training: Certified Professional for Software Architecture

What You Actually Learn (The Curriculum Breakdown)?

This software architecture training resource isn't a surface-level overview course. It's 58 comprehensive lessons covering everything architects need to know.

Module 1: Software Architecture Fundamentals

What you'll master:

What software architecture actually is (most engineers don't know)
The architect's role vs. the senior engineer's role
How architecture connects to business goals
Design constraints and how to work with them
Making architecture decisions that survive organizational changes

Why this matters:

Understanding the why behind architectural decisions is what separates architects from engineers. This module builds that foundation.

Real impact: I stopped proposing technical solutions without understanding business context. This alone improved my influence 10x.

Module 2: Risk Analysis and Quality Attributes

What you'll master:

Identifying architectural risks before they become problems
Quality attributes: scalability, availability, performance, security
Quantifying non-functional requirements
Making tradeoffs between competing quality attributes
Risk mitigation strategies

Practical application:

Before this module: "We should make it scalable!"\
After this module: "Given our growth projections of 50% annually and budget constraints, I recommend horizontal scaling with auto-scaling groups, accepting 99.9% availability instead of 99.99% to reduce costs by 40%."

Career impact: This is how architects think. Speaking this language changed how leadership perceived me.

Module 3: Architectural Patterns (The Core Toolkit)

What you'll master:

Layered Architecture:

When to use (most enterprise apps)
Common pitfalls
Variations and tradeoffs

Pipes and Filters:

Data processing pipelines
Streaming architectures
ETL systems

Microservices Architecture:

When microservices make sense (and when they don't)
Service boundaries
Communication patterns
Data management in distributed systems

Event-Driven Architecture:

Event sourcing
CQRS (Command Query Responsibility Segregation)
Event processing patterns
Eventual consistency

Why patterns matter:

Instead of reinventing solutions, you recognize problems and apply proven patterns. This accelerates decision-making and reduces risk.

My experience: Recognized that our monolith didn't need microservices (yet). Saved the company 6 months of unnecessary complexity.

Module 4: Interface Design and Data Modeling

What you'll master:

API design principles
Interface contracts and versioning
Data modeling for distributed systems
Database selection (SQL vs. NoSQL vs. NewSQL)
Consistency models and CAP theorem in practice

Real-world application:
Designed RESTful APIs that didn't break when requirements changed. Understanding interface design prevented the "rewrite everything" cycle.

Module 5: Design Tactics and Coupling Strategies

What you'll master:

Reducing coupling between components
Dependency management at scale
Module design and cohesion
Refactoring strategies for large systems
Technical debt management

The paradigm shift:

This module taught me to think in systems, not just code. Understanding coupling changed how I approached every design decision.

Module 6: Architecture Documentation and Communication

What you'll master:

Documenting architecture decisions (ADRs)
Creating architecture diagrams that stakeholders understand
Communicating with non-technical audiences
Presenting architecture to executives
Building consensus around architectural decisions

Career-changing insight:

Technical brilliance without communication = invisible. This module taught me to make architecture decisions clear to everyone from developers to CEOs.

Before: Technical documents nobody read\
After: Visual diagrams that drove decision-making

Module 7: Architecture Evaluation

What you'll master:

ATAM (Architecture Tradeoff Analysis Method)
Evaluating existing architectures
Identifying architectural anti-patterns
Measuring architectural quality
Making data-driven architecture improvements

Real impact: Evaluated our existing system, identified 3 critical bottlenecks, proposed solutions that 3x'd our throughput.

Understanding iSAQB® and CPSA-F® Certification

Now, let's learn a bit about these two popular software architecture certifications.

What is iSAQB®?

The International Software Architecture Qualification Board (iSAQB®) is a global, non-profit organization that:

Develops standardized software architecture curricula
Maintains certification programs for architects
Ensures professional competence standards
Operates internationally across industries

Think of it as: The PMI of software architecture, but more technical and specialized.

What is CPSA-F®?

CPSA-F® = Certified Professional for Software Architecture --- Foundation Level

Key facts:

First level in the CPSA® certification scheme
Internationally recognized across 60+ countries
Never expires --- Unlike cloud certifications
Accredited training required for best preparation
Foundation for advanced certifications (CPSA-A®, CPSA-E®)

Career significance:
Holding CPSA-F® certification demonstrates:

Fundamental software architecture competence
Commitment to professional standards
Readiness for architect-level responsibilities
International professional recognition

The advantage: When two candidates apply for an architect role, CPSA-F® certification is a decisive differentiator.

Is This Training Officially Accredited?

Yes. Top Developer Academy (Michael's organization) is an officially accredited training provider by iSAQB®.

What this means:

Curriculum fully complies with CPSA-F® exam requirements
Content reviewed and approved by iSAQB®
Training certificate qualifies you to take the exam
Learning outcomes aligned with certification standards

The difference: Non-accredited courses teach "architecture." This course prepares you for a professional standard.

Who Should Take This Course?

This course is suitable for all senior engineers, developers, and tech leads, but its Perfect For:

✅ Senior Software Engineers --- Ready to make the architect leap
✅ Technical Leads --- Want formal architecture training
✅ Engineering Managers --- Need to understand architecture decisions
✅ Software Architects (self-taught) --- Want certification and validation
✅ Senior Developers --- Feeling stuck in current role

Ideal candidate profile:

18+ months practical software development experience
Comfortable with multiple programming languages
Worked on production systems
Ready to think beyond code
Seeking career advancement

Also Valuable For:

✅ Software Analysts --- Understanding architecture context
✅ DevOps Engineers --- Architectural implications of infrastructure
✅ Product Managers --- Technical depth for better decisions

Not Ideal For:

❌ Complete beginners --- Need coding experience first
❌ Junior developers --- Focus on fundamentals first
❌ Those seeking quick certifications --- This requires serious study

My observation: The engineers who got the most value had 3+ years of experience and felt ready for "the next level" but didn't know how to get there.

Final Verdict: Is This Worth It?

For senior engineers ready to become architects: This is the most valuable investment you can make.

What you get:

✅ Structured path from engineer to architect
✅ Internationally recognized CPSA-F® preparation
✅ 58 comprehensive lessons from a practicing architect
✅ Lifetime-valid certification
✅ Skills that AI can't replace
✅ Clear ROI in salary and career growth

What you won't get:

❌ Quick certification with minimal effort
❌ Shallow overview of architecture
❌ Outdated patterns and practices

My honest recommendation:

If you're a senior engineer who:

Feels stuck at current level
Wants to lead technical decisions
Seeks architect-level compensation
Values internationally recognized credentials

Take this training. It's the fastest, most structured path to architect-level skills and certification.

The salary increase alone justifies the investment. The career transformation is priceless.

Here's the link: Certified Professional for Software Architecture

Additional Resources for Your Journey

More from Michael Pogrebinsky:

The Bottom Line

Six months ago, I was a frustrated senior engineer wondering why my career wasn't progressing despite my technical skills.

Today, I'm a Software Architect earning $93K more, leading technical decisions, and shaping product direction.

The difference: Understanding that coding skills alone aren't enough. Architecture is a separate discipline that must be learned intentionally.

This CPSA-F® training provided the structured path, professional certification, and practical knowledge that transformed my career.

If you're a senior engineer ready to leap to architect, this is your roadmap.

Don't wait. Start now.

Your future architect self will thank you.

P.S. --- This training transformed my career in 6 months. The investment pays for itself immediately through salary increase alone. If you're serious about becoming an architect, join this training today.

I Found the LeetCode for System Design Interview, and It's Awesome

Soma — Sat, 25 Jul 2026 09:08:08 +0000

Disclosure: This post includes affiliate links; I may receive compensation if you purchase products or services from the different links provided in this article.

Credit: codemia.io

Hello Devs, if you're preparing for software engineering interviews, particularly in MAANG, you already know that Data Structures & Algorithms (DSA) and System Design are two key areas where you will be rigorously tested.

While LeetCode is the go-to platform for DSA, system design has always been a challenge.

These are great resources to learn fundamentals, go through case studies, and understand the theory part of system design, but LeetCode-style practice is one thing that is missing - until now.

I recently found Codemia.io, and I must say, it feels like the LeetCode for System Design.

If you've struggled with structuring your system design answers, getting real feedback, or knowing whether your approach is correct, Codemia.io is a game-changer.

They not only have the biggest collection of System Design and OOP Design problems for practice, but they also have a free System Design course called Tackling System Design Interview Problems, which is a great free resource to learn essential System Design concepts.

It's a short course with 2 hours of content, but it's powerful and also has quizzes to test your skills. Here are all the key System Design topics you can learn on this free course:

Now, let's check out how Codemia.io can help you to prepare better for your System design and OOP Design interview, and why I think it's like Leetcode for System design.

Most system design resources today are long, text-heavy articles or expensive courses. The problem?

No hands-on practice - Reading about system design isn't enough; you need to actively design solutions.
No structured progression --- Unlike DSA, where problems are categorized by difficulty, system design prep often feels random and overwhelming.
No evaluation or expert feedback --- You don't just need an answer; you need to know why one design is better than another.

This is where Codemia.io changes the game.

Instead of just giving you theory-heavy content, Codemia.io provides an interactive, structured, and guided approach to system design. Here's what I loved:

1. Real-World System Design Problems

Codemia.io has over 150+ system design problems, each with a structured approach similar to how LeetCode presents DSA problems.

For example, consider the classic "Design a URL Shortening Service" problem.

You start with:
1. Functional and Non-Functional Requirements
2. Capacity Estimation
3. Database Schema and API Design
4. High-Level & Low-Level Design

It guides you step by step so you don't feel lost.

Bonus: You also get access to a drawing board where you can sketch your architecture, just like in a real system design interview.

And most importantly, they also share expert solutions for most of the problems to see how an expert should answer that question. For example, here is the solution on how to design a URL shortening service, and it's also available for free

2. Difficulty-Based Categorization

Just like LeetCode, both System design and object-oriented design problems are categorized as:

Easy --- Foundational concepts like Load Balancing, Caching, Message Queues
Medium --- Scenarios like Rate Limiting, Search Autocomplete, Real-Time Chat Systems
Hard --- Complex designs like Distributed Databases, Cloud Storage, and Microservices Architecture

Each problem also includes company tags so you can focus on what's asked at FAANG and other top tech companies. For example, this how to design Twitter problem is a medium-scale problem, which you can see to get some experience. It's also free.

3. AI-Powered Hints & Evaluations

One of the best features is AI-assisted guidance. You get to chat with the coach --- the AI agent that will help you with the question and solution.

1. If you get stuck, AI provides hints --- just like LeetCode's hint system.

2. If you submit a design proposal, AI evaluates it against industry best practices.

3. You get automated feedback on scalability, fault tolerance, and efficiency.

This bridges the gap between self-learning and expert mentorship.

Beyond System Design: Object-Oriented Design & AI Interviews

Codemia.io goes beyond just system design. It also covers Object-Oriented Design (OOD) Questions. If you don't know, Object-Oriented Design is a common low-level design (LLD) round at FAANG.

Here are OOP problems you can solve on codemia.io to get some practice:

Classic problems like Parking Lot, ATM Machine, and Hotel Booking System
Step-by-step UML diagrams and class structure explanations
Comparison of different design patterns

If you struggle with OOD interviews, this is a goldmine.

Here is also all the OOP design problems available on Codemia.io

Now, let's see how codemia.io compares with other popular resources we have used in the past to prepare for System Design interview, notably DesignGurus.io, whose Grokking the System Design Interview course is also one of my favorites, and Educative.io, whose modern System design course is also a great resource.

You can see that codemia.io has the highest number of practice problems, 120+ and growing. No other platform has that many questions, which is also a big plus point of this platform, and that's why I called it "Leetcode of System Design".

The second important point is an expert-written solution, also known as an editorial solution on this website. There are more than 80+ such solutions, which are a great resource to compare your solution, learn from them, and also find how to answer such questions.

What point to mention, what should you discuss, tradeoffs, and so much more.

Another plus point of codemia.io is that it has object-oriented programming questions, which are not available in other courses, though DesignGurus.io has Grokking the Object Oriented Design Interview course, which you can use for OOP design practice, but again, when it comes to numbers, codemia.io wins.

So far, we have compared the content, but if we talk about the platform, codemia.io has been especially designed for online practice, particularly for design questions.

It comes with Interactive learning tools, interactive feedback and evaluation, a chatbot, and a great design tool for drawing. No other platform supports so many features that are essential to master system design.

Then there is the community, and you can also see the solutions of other folks to learn from them. I love this feature as it allows me to see what other folks are thinking and how they solve that problem. This comparative analysis has helped me a lot to learn key system design concepts.

Overall, I think codemia.io is a great platform to prepare for System design and OOP design interviews. You may find courses, but it's hard to find a platform like this.

I highly recommend it for anyone preparing for System Design and Tech interviews. And, if you want to join now, you can also use the discount code PROMOTION20 to get 20% off now.

Here is the link --- 20% OFf on Codemia.io

System Design Problems to Practice in LeetCode Style

Here is a list of Easy System Design problems to start with:

While a few questions from this list are absolutely free for others, you need to take a membership, but that's worth it because you get AI feedback and also access to 75+ high-quality solutions **.

You can see the full list of System design problem on Codemia here

Final words

If you're preparing for FAANG system design interviews, Codemia.io is a must-try. It solves the biggest problems in system design prep:

Structured learning path (instead of random articles)
Real-world hands-on practice (instead of just reading)
Expert evaluation & AI feedback (so you know what to improve)
Mock interviews to simulate real conditions

I'm still exploring Codemia.io, but what I've seen so far is truly impressive.

If you've tried it, let me know what you liked or didn't like.

Thanks for reading this article so far. If you like this article, then please share it with your friends and colleagues. If you have any questions, feel free to ask in the comments.

P. S. - They are also offering a 20% discount now, and you can get access for just $59 for one year. The original price is $120, so you have a chance to save $60 and also learn System design better.

The System Design Framework I Used to Solve 100+ Problems

Soma — Sat, 27 Jun 2026 06:54:24 +0000

Hello Devs, for months, I felt confident about system design interviews.

I'd watched endless YouTube videos. I'd studied architecture diagrams. I could explain how Netflix builds recommendation systems. I understood Kafka, Redis, load balancers, and microservices. I'd memorized the designs of Twitter, Uber, YouTube, and TinyURL.

Then I sat down for my first real system design interview and froze.

The interviewer asked: "How would you design a notification system?"

I had memorized notification systems. I knew about push notifications, email queues, delivery workers, and retry logic. I could recite architectural patterns.

But suddenly, none of that helped.

I didn't know which questions to ask first. I started designing before understanding the actual requirements. I built architecture for problems that didn't exist. I missed obvious bottlenecks.

I couldn't articulate why I made specific trade-offs. When the interviewer pushed back, I had no framework to adjust.

I failed that interview.

But that failure taught me something crucial: System design interviews aren't about knowing technologies. They're about knowing how to think.

After that, I went back and systematically practiced 20 system design problems. Not passively watching solutions. Actually designing. Making mistakes and refining my approach. And somewhere around problem 12, a pattern emerged.

The best candidates didn't know more technologies than anyone else. They had a framework.

They asked the same questions in the same order. They structured their thinking consistently. They could handle curveballs because their framework was flexible. They reasoned through trade-offs explicitly.

Here's the framework that finally made it click for me.

The Problem with Memorization

Before I share the framework, let me explain why memorizing designs fails.

When you memorize "How to Design Twitter," you learn:

Use relational databases for users and tweets
Use NoSQL for timelines
Cache with Redis
Use message queues for fanout
Shard by user ID
etc.

But here's the problem: The next problem won't be Twitter.

It might be Design TicketMaster (a reservation system with high volume and tight deadlines). Suddenly, your Twitter knowledge is partially relevant but mostly confusing.

You start applying Twitter patterns to a problem with completely different constraints. You over-engineer or under-engineer. You miss the actual bottleneck.

Memorization creates false confidence. You think you're prepared because you know technologies. But system design isn't about knowing technologies. It's about knowing how to apply them to a specific problem.

The real skill is: How do you take an open-ended problem, ask the right questions, structure your thinking, reason through trade-offs, and evolve the design under pressure?

That's what a framework gives you.

The Framework: 8 Steps to Structured Thinking

After practicing dozens of problems, I distilled system design into this simple framework:

1. Clarify the Requirements

What: Ask clarifying questions before designing anything.

Why: False assumptions sink designs. You need to understand what you're actually building.

Questions to ask:

What are the functional requirements? (What should the system do?)
What are the non-functional requirements? (Scale, latency, consistency, availability?)
Who are the users? What's their usage pattern?
What's the expected traffic? Read/write ratio?
Do we need strong consistency or eventual consistency?
What's acceptable latency?

Example: "Design a notification system"

Clarify: Is this in-app notifications, push notifications, email, SMS, or all?
What's the scale? Millions of users?
What's the expected latency for notification delivery?
Does every notification need to be delivered exactly once, or is some loss acceptable?
Do we need real-time delivery, or can we batch?

Don't skip this step. Most engineers jump straight to architecture. But clarification prevents wasted effort on problems that don't exist.

2. Estimate the Scale

What: Do back-of-envelope calculations to understand scale.

Why: Scale determines architecture. A system for 1000 users needs a different architecture than 1 billion users.

Key metrics:

Daily Active Users (DAU)
Requests per second (QPS)
Data storage needed
Bandwidth requirements

Example: For a notification system with 1 billion users:

If 10% are active daily = 100 million DAU
If each active user generates 5 notifications = 500 million notifications/day
500M / 86400 seconds ≈ 5,787 QPS
This tells you: you need a system that handles thousands of notifications per second

This simple calculation shapes everything that follows. It tells you whether you can get away with simple solutions or need distributed systems.

3. Define the Core APIs

What: Write the API contracts for the system.

Why: This forces you to clarify the interface before building internals. It's the contract between clients and your system.

Example: For a notification system:

createNotification(userId, title, body, type, metadata)\
getUserNotifications(userId, limit, offset)\
markAsRead(notificationId)\
getUserPreferences(userId)\
updateUserPreferences(userId, preferences)

These APIs are simple, but they define what your system must support. Everything else is an implementation detail.

4. Design the Data Model

What: Define database schema and data structures.

Why: This reveals how data flows through the system. Bad data models cascade into bad architecture.

Example: For notifications:

Users table:\

userId (PK)\
username\
email\
preferences

Notifications table:\

notificationId (PK)\
userId (FK)\
title\
body\
type\
createdAt\
readAt\ UserPreferences table:\
userId (PK)\
pushEnabled\
emailEnabled\
smsEnabled\
quietHours

Now you're thinking about:

How do you query user preferences efficiently?
How do you find unread notifications for a user?
How do you handle delivery status?

The data model reveals design requirements.

5. Build the High-Level Architecture

What: Sketch the major components and how they interact.

Why: This is where technologies come in. But only after you understand the problem.

Example: High-level architecture for notifications:

API Server\
↓\
Notification Service (validates, stores)\
↓\
Message Queue (decouples creation from delivery)\
↓\
Delivery Workers (push, email, SMS)\
↓\
Delivery Services (external APIs)

Each component has a specific responsibility. The message queue decouples creation from delivery (allowing backpressure). Delivery workers handle multiple channels.

6. Identify Bottlenecks

What: Explicitly state where the system will break under load.

Why: This prevents over-engineering in the wrong places and under-engineering in critical ones.

Example: For notifications, bottlenecks might be:

Database writes when millions of notifications are created simultaneously
Message queue throughput
Delivery worker capacity
External API rate limits

Identifying bottlenecks reveals where you need optimization.

7. Discuss Trade-Offs

What: Explain why you chose specific technologies and what you're sacrificing.

Why: Every architecture decision has trade-offs. Being explicit shows thinking, not just pattern matching.

Example:

Why Kafka for the queue? Durability (survives failures), high throughput, replay capability. Trade-off: complexity vs. simpler queue.
Why eventual consistency for preferences? Users don't need real-time preference updates. This allows caching and reduces database load. Trade-off: brief window where old preferences are used.
Why shard by userId? Distributes load evenly. Trade-off: cross-user queries become complex.

Notice the pattern: Every choice has a reason and a cost.

8. Evolve the Design Based on Constraints

What: When the interviewer introduces new requirements, adjust systematically using the framework.

Why: Interviews include surprise requirements. A good framework handles change.

Example: Interviewer adds: "Delivery must be guaranteed. No lost notifications."
Now you need to:

Add delivery status tracking (Step 4 --- update data model)
Add retry logic and dead-letter queues (Step 5 --- architecture)
Discuss idempotency for retries (Step 7 --- trade-offs)

You don't panic. You follow your framework and adjust each section.

Walking Through a Complete Example: Notification System

Let me apply this framework end-to-end so you see how it works.

1. Clarify Requirements

Me (candidate): "Before I design, let me clarify the requirements:

Functional: We need to send notifications to users through multiple channels: in-app, push, email
Non-functional: 1 billion users, 10% daily active. Need to handle 5,000+ notifications per second
Consistency: Eventual consistency is fine. Users don't need real-time preference updates
Latency: In-app notifications should appear within seconds. Email can be minutes
Delivery: Best effort initially. We can improve to guarantee delivery later"

Interviewer: "Good. Add: We need analytics. Track delivery success/failure rates."

2. Estimate Scale

"So with 1B users, 10% DAU = 100M active users.

If each generates 5 notifications/day = 500M notifications/day
500M / 86,400 = ~5,787 QPS baseline
But peaks could be 5--10x, so we need to handle 30,000+ QPS
Storage: 500M notifications/day × 365 days × 500 bytes ≈ 90TB/year
This tells me we need: a distributed database, a queuing system, and batch processing"

3. Define Core APIs

POST /notifications/create\

userId: string\
title: string\
body: string\
channels: [in-app, push, email]\
metadata: object

GET /users/{userId}/notifications\

Returns: paginated list of notifications\ PUT /notifications/{notificationId}/read\
Mark's notification as read\ GET /users/{userId}/preferences\
Returns: user's notification preferences\ PUT /users/{userId}/preferences\
Updates: user's notification preferences\ GET /analytics/delivery-stats\
Returns: delivery success/failure rates

4. Design Data Model

Users:\

userId (UUID, PK)\
email\
phone\
createdAt

Notifications:\

notificationId (UUID, PK)\
userId (FK, indexed)\
title\
body\
channels (array/enum)\
createdAt (indexed)\
readAt\
metadata (JSONB)

DeliveryLog:\

deliveryId (UUID, PK)\
notificationId (FK)\
userId (FK)\
channel (enum: in-app, push, email)\
status (pending, sent, failed)\
attemptCount\
lastAttemptAt\
error

UserPreferences:\

userId (PK)\
pushEnabled\
emailEnabled\
smsEnabled\
quietHours\
unsubscribedCategories (array)\
updatedAt

NotificationQueue:\

queueId (UUID)\
notificationId (FK)\
status (pending, processing, completed, failed)\
createdAt\
processedAt

Notice: I'm not overthinking this. Simple schema that answers questions: "How do I find unread notifications?" (query by userId, readAt IS NULL). "How do I track delivery?" (DeliveryLog table).

5. Build High-Level Architecture

┌─────────────────────────────────────────┐\
│ Client Applications │\
│ (Web, Mobile, Admin Dashboard) │\
└──────────────┬──────────────────────────┘\
│\
┌──────────────▼──────────────────────────┐\
│ Notification API Service │\
│ - Validates requests │\
│ - Stores notifications │\
│ - Checks user preferences │\
└──────────────┬──────────────────────────┘\
│\
┌──────────────▼──────────────────────────┐\
│ Apache Kafka (Message Queue) │\
│ - Decouples creation from delivery │\
│ - Handles backpressure │\
│ - Enables replay for debugging │\
└──────────────┬──────────────────────────┘\
│\
┌──────────┼──────────┐\
│ │ │\
┌───▼──┐ ┌───▼──┐ ┌───▼───┐\
│In-App│ │Push │ │Email │\
│Worker│ │Worker│ │Worker │\
└───┬──┘ └───┬──┘ └───┬───┘\
│ │ │\
└─────────┼─────────┘\
│\
┌─────────▼─────────┐\
│ Notification DB │\
│ (PostgreSQL) │\
└───────────────────┘

┌──────────────────────────┐\
│ External Services │\
│ (Firebase, Twilio, etc.) │\
└──────────────────────────┘

Each worker handles one delivery channel. They pull from Kafka, attempt delivery, and log results. Simple but scalable.

6. Identify Bottlenecks

"The bottlenecks are:

Database writes: Creating 5,000+ notifications/second. Solution: Shard by userId, use a write-optimized database
Kafka throughput: Need to handle 5,000+ msgs/sec. Solution: Partition by userId, scale consumer groups
Delivery workers: Must keep up with Kafka output. Solution: Auto-scale based on queue depth
External API limits: Firebase and email services have rate limits. Solution: Queue locally, respect rate limits, retry backoff
Storage growth: 90TB/year. Solution: Archive old notifications, tiering strategy"

Now I know where to optimize, not where to panic.

7. Discuss Trade-Offs

"Important trade-offs:

Message Queue (Kafka vs RabbitMQ):

Choose Kafka: High throughput (5K+ msgs/sec), persistent (survives worker crashes), partition support
Trade-off: More complex to operate than RabbitMQ

Eventual Consistency vs Strong Consistency:

Choose eventual: Preferences cached in workers, updated every 5 minutes
Trade-off: Users might see outdated preferences briefly. Worth it for 10x less database load

Shard by userId vs Shard by Time:

Choose userId: Distributes load evenly, straightforward
Trade-off: Some queries (all notifications in time range) become complex

Best-Effort Delivery vs Guaranteed:

Starting with best-effort: Simpler, sufficient for most notifications
To upgrade: Add deduplication, idempotency, and longer retries. Tradeoff: Added complexity

These aren't random choices. Each is explicitly reasoned."

8. Evolve on Feedback

Interviewer: "What if we need to guarantee notification delivery? No lost notifications."

"Then we need:

Idempotency keys (Step 3): Each notification gets a unique key. Retries with the same key are idempotent.
Deduplication (Step 5): If a notification is retried, we don't create duplicates.
Longer retention (Step 4): Keep delivery logs longer for audit trails.
Checkpoints (Step 5): Mark notifications as delivered only after confirmation.
Dead-letter queue (Step 5): Send undeliverable notifications to a queue for manual review.

We still use the same core architecture. We're just adding layers of reliability."

Notice: I didn't redesign from scratch. I followed the framework, identified what needs to change, and evolved systematically.

Why This Framework Works?

This framework works because:

It's systematic --- You follow the same steps for every problem. Consistency reduces panic.
It's independent of technologies --- You clarify requirements before choosing technologies. You're not forcing Kafka into every problem.
It's flexible --- When requirements change, you know which step to revisit. New requirement? Update clarifications → re-estimate → adjust architecture.
It's interviewable --- Interviewers see clear thinking, not pattern matching. When they push back, you can adjust confidently.
It's complete --- You think about requirements, scale, APIs, data, architecture, bottlenecks, and trade-offs. Nothing is forgotten.

Building Intuition Through Practice

Knowing the framework is one thing. Applying it under pressure is another.

I practiced this framework on problems like:

Design a Simple URL Shortening Service (TinyURL) --- Simple requirements, teaches API design
Design Twitter --- Complex scaling, feeds, fanout
Design TiketMaster --- Time-sensitive, high concurrency, inventory management
Design an API Rate Limiter --- Small problem, teaches precision
Design Pastebin --- File storage, sharing, expiration
Design a Video View Count System --- High-frequency writes, eventual consistency
Design Craigslist --- Search, filtering, inventory
Design a Nested Comments System --- Hierarchical data, efficiency
Design an Online Presence Indicator Service --- Real-time updates, scale
Design a Tagging Service --- Performance, caching, consistency
Design a Fitness Tracking App --- Mobile-first, time-series data
Design a Weather Reporting System --- Data aggregation, multiple sources
Design a Multi-Device Screenshot Capture System --- Distributed processing, file handling
Design a Conference Room Booking System --- Scheduling, conflicts, availability
Design an Employee Swap System --- Complex state machine, transactions

Plus object-oriented design problems like:

Design a Parking Lot System --- Teaches low-level design
Design an Elevator System --- State machines, coordination
Design a Vending Machine System --- State, inventory, transactions
Design a Resource Management System --- Allocation, scheduling

Each problem reinforced the framework. Some required emphasis on different parts (TinyURL: API and data model; Twitter: architecture and bottlenecks; Rate Limiter: algorithms and precision), but the framework held.

You can see the full list of system design problems here:

The Path from Theory to Confidence

This is the journey:

Week 1: Watch YouTube videos, feel confident about technologies
Week 2--3: Practice 5 problems, realize you don't have a system
Week 4--6: Practice 10 more problems, start seeing patterns
Week 7--10: Practice 10 more, the framework crystallizes
Week 11+: You trust the framework. New problems don't panic you

By problem 20, you're not memorizing designs. You're executing a proven system under pressure. That confidence is what actually matters.

Here is also a curated list of System Design problems you can start solving to crack System Design Interviews:

Where to Practice This Framework?

If you want to build intuition with this framework, you need a platform designed for practice. Reading articles or watching videos is passive. Actually designing, getting feedback, and iterating is how it clicks.

Codemia.io is purpose-built for this. It's not a course. It's a practice platform with:

120+ system design problems across difficulty levels (Easy, Medium, Hard)
Expert solutions for each problem, showing how professionals think
Interactive design tools to sketch architecture while practicing
AI-powered feedback that evaluates your design against best practices
Difficulty progression (Easy → Medium → Hard) so you build gradually
Company tags so you practice problems actually asked at FAANG

For structure, they also offer:

Free course: Tackling System Design Interview Problems --- Teaches the fundamentals
System Design Fundamentals --- Deeper dive into core concepts
Mock interviews --- Simulate real interview pressure
Object-Oriented Design section --- For low-level design interviews

This is where you apply the framework repeatedly until it becomes intuition.

Final Thoughts

System design interviews have intimidated me for years. I thought I needed to know more technologies, memorize more designs, and understand more patterns.

But after practicing 20 problems and refining this framework, I realized: The skill isn't in knowing more. It's about thinking more systematically.

The framework gives you that system. Use it:

Clarify before you build
Estimate to understand scale
Define APIs to clarify contracts
Design data to reveal problems
Build architecture only after understanding the problem
Identify bottlenecks explicitly
Discuss trade-offs to show reasoning
Evolve smoothly when requirements change

Apply this framework consistently. Practice on real problems. Get feedback. Refine.

By your 20th problem, you won't memorize designs. You'll execute a proven system under pressure. And that's what separates candidates who fail from candidates who pass.

Good luck. You've got this.

P.S. --- If you're serious about system design interviews, start practicing with this framework on Codemia.io. The combination of a solid framework + structured practice is unbeatable.

Check out their pricing --- affordable for the value you get. I have got their lifetime plan, which provides the best value, and I recommend the same. It's not costly; in fact, it's similar to what other platforms charge for their annual membership.

11 Must-Read Software Architecture and Design Books for Developers

Soma — Tue, 23 Jun 2026 15:52:53 +0000

Disclosure: This post includes affiliate links; I may receive compensation if you purchase products or services from the different links provided in this article.

Hello friends, System design ** and ** Software design are two important topics for tech interviews and two important skills for Software developers.

Without knowing how to design a system, you cannot create new software, and it will also be difficult to learn and understand existing software and systems.

That's why big tech companies like FAANG/MAANG pay special attention to System design skills and test candidates thoroughly.

Earlier, I have shared system design interview questions like API Gateway vs Load Balancer, Horizontal vs Vertical Scaling, Forward proxy vs reverse proxy, and common System design concepts, and in this article, I am going to share with you the best System design books to learn Software design.

Whether you are a beginner or an experienced developer, you can read these books, as you will definitely find valuable stuff.

I have read them, and even though I have been doing Software development for more than 15 years, I have learned a lot.

System design** is a complex process, and you need to know a lot of stuff to actually design a system that can withstand the test of time in production.

Software architecture is another field where you are expected to learn a lot of things. It's simply impossible to become a software architect by reading a few books, but if you have experience and a hunger to learn, then these books can be a gold mine.

These books allow you to learn from other people's experiences. You can read these books to find what challenges they face when they design a real-world system like Spotify, Google, or Amazon, and how they overcome.

Each story is a journey in itself, and you will learn a thing or two by reading and then relating with your own experience.

I love to read books, and they are my primary source of learning, along with online courses nowadays. In this article, you will find a few books that I have gone through in recent times to improve my knowledge about System Design and Software Architecture.

If you love these books, give a high five, and if you have any other excellent books to share, feel free to drop a comment.

By the way, if you are preparing for System design interviews and want to learn System Design in depth then you can also checkout sites like ByteByteGo, Design Guru, Exponent, Educative, Codemia.io, and Udemy which have many great System design courses

P.S. Keep reading until the end. I have a free bonus for you.

11 Best System Architecture and System Design Books for Developers

Here are my favorite 10 books on system design, software architecture, and software development. These are really good books, and it's not just that I am recommending them; you go to Reddit or Hacker News, and everywhere these books are recommended. They are also recommended to me by many people in the past.

1. Head First Software Architecture

I love Head First books; they are the simplest books you can read on programming and software development, so when they released a book on Software Architecture, I immediately ordered a copy.

Having gone through their Head First Design Patterns and Head First Object Oriented Analysis, I had very high hopes for this book, and I must say I wasn't disappointed.

It teaches you key principles to design software in a fun and engaging way, the Head First way. You will appreciate their simple-to-do examples and exercises in this complex topic.

While I loved this book, I know many folks who really hate anything Head First, for them, it's like a comic book and not worth their time.

If you are one of them, I am sorry, but this book definitely is a must, with valuable insights, advice, and examples on different topics that you need to consider when designing and architecting applications.

If you are a senior developer and want to become a tech lead or a software architect, you should definitely read this book.

2. Designing Data-Intensive Applications By Martin Kleppmann

This book was recommended to me by my tech lead, but when I started reading this book, I literally fell asleep. It was too complex for me to understand, but I didn't give up, and slowly I found that this is one heck of a book for anyone who wants to work on real systems.

In this fantastic book on system design, Martin Kleppmann will help you understand the pros and cons of all the different technologies that are used for storing and processing data.

It is a book that is written in a lucid style and presents a very broad overview of data storage systems.

You will get a very good grasp of fundamental concepts, algorithms, and practical applications of various technologies.

This is also one of the most popular books when it comes to learning Software design and System Design, and I highly recommend this book to all kinds of software developers.

The book is also good for beginners and experienced developers, software architects, and anyone who wants to be better at software design in 2026.

If you want, you can also combine this book with the Mastering the System Design Interview by Frank Kane (Ex Amazon Hiring Manager) on Udemy for better preparation.

3. System Design Interview -- An insider's guide By Alex Xu (Part 1 and 2)

This book was recommended to me by one of my colleagues who cracked the Google interview. When I asked how he prepared, he shared a few books and courses apart from grinding LeetCode, and this was one of them.

As the title suggests, this is the perfect book for everyone who is preparing for a system interview. Trust me, this book is the finest on the internet right now. This book was created by Alex Xu, who has gone through the same process.

You will get access to several drawings and diagrams that will assist you in gaining an understanding of the real system. You will be able to understand what the recruiters are looking for in your answers to questions.

Alex also has a companion System design course on ByteByteGo, where you will not only find all the content of this book, but also the second part of the System Design Interview Book
but also new content, a deep dive into popular system questions like how to design YouTube and WhatsApp, as well as a proven System design framework to solve Software design problems.

In short, if you read this book, you will be able to confidently answer questions on your next system design interview.

This is also one of the most recommended System design interview books on Reddit, Quora, Hacker News, Twitter, and other online platforms, and it's obvious from the number of reviews this book has on Amazon.

4. Software Engineering at Google: Lessons Learned from Programming Over Time

If you are a senior developer who wants to expand your knowledge about Software Engineering by learning from those who have done that in companies like Google, then this book is for you.

This book is an interesting read on the intricacies of developing and maintaining a sustainable and healthy codebase, emphasizing the distinction between programming and software engineering.

Drawing on their experiences at Google, the authors provide a detailed look at the practices employed by some of the world's leading software engineers to navigate the challenges of evolving codebases in response to changing requirements and demands.

This software design book also delves into Google's unique engineering culture, processes, and tools, shedding light on how these elements contribute to the effectiveness of their engineering organization.

Throughout the book, three fundamental principles are highlighted: the impact of time on software sustainability, the influence of scale on software practices within an organization, and the trade-offs engineers must consider when making design and development decisions.

With a focus on practical insights and real-world examples, this book serves as a valuable resource for software engineers seeking to enhance their understanding of software engineering principles and practices.

While this book book is not exclusively focused on System design, it has many valuable lessons on trade-offs developers must consider when making design and development decisions, which is quite important for senior developers and software architects.

5. Clean Architecture By Robert C. Martin

If you are looking for a Software Architecture book to start with, then this book is for you. Along with Head First Software Architecture, this is the best book I have read for senior developers who want to become software architects.

System design, Software Design, and Software architecture are closely related. When you read this book, you will learn everything there is to know about Software structure and Design.

You will also be able to make your code smoother and integrate agile methodology into your solutions.

This book is created by Robert C. Martin, fondly known as Uncle Bob, who is also the author of the famous Clean Code and Clean Coder book. This is the third in the series, and many people read all three to become better software developers.

This book focuses on the different principles related to system analysis and design, as well as the different challenges that system designers face while developing a solution in the real world.

In addition, you will learn how to run unit tests and use a test-driven strategy for improving the efficiency of your systems.

And, if you want, you can also combine this book with Coursera's **Software Design and Architecture Specialization **to learn better.

6. System Analysis And Design By Scott Tilley

According to industry experts, this is one of the most important systems design textbooks that is available on the market today. That is because this book will make you realize what has changed in the industry as well as why a change was necessary in the first place.

Also, this book has sections that are dedicated to object-oriented coding and agile methodology, which are two of the most used methodologies these days. You will also learn how systems function, as well as about cloud computing and mobile applications.

The 12th edition of System Analysis and Design by Scott Tilley is the latest book in this series, and you should read it to get the updated knowledge.

If you are preparing for a System design interview, then you can also check out System Design Interview Guide by Sandeep Kaul (Tech Lead @ Facebook) for better preparation.

7. Head First Design Patterns By Eric Freeman And Elisabeth Robson

This is another interesting book that explains what patterns are important as well as how to apply these patterns to your designs. It focuses on object-oriented principles that are the basis of these patterns.

Using this book, you will be able to vastly improve your knowledge of software development. You will get to know about important design principles that can be useful for solving software design problems.

The 2nd edition of this book is now available, and you should read it to learn design patterns with Java 8 and beyond. Most importantly, how to use Lambdas and Streams to implement classic design patterns in Java.

And, if you need an online course to go along with this book, Design Patterns in Java course by Dmitri Nestruk on Udemy is best as it shows the modern implementation of classic design patterns in Java.

8. Hacking the System Design Interview: Real Big Tech Interview Questions and In-depth Solutions

This is another book you can read to prepare for the FAANG System Design interview. This book not just cover essential System design concepts that every software architect should know, but also covers many popular System design questions and coding problems.

Created by Stanley Chiang, a Google Software Engineer, this is also one of the best-selling books on System design on Amazon.

The best thing about this book is that it walks you through key components which are used to build any system, like below:

Web server
API gateway
Load balancer
Distributed cache
Asynchronous queue
Object storage
CDN
Fan-out service
Unique ID generator

This book also includes real interview questions based on hundreds of interviews conducted at big tech companies like Google and Meta, and their detailed solutions. I highly recommend this book to anyone preparing for technical interviews.

You can also combine this with the Algomonster or Exponent System design course for better preparation.

9. Fundamentals of Software Architecture: An Engineering Approach by Mark Richards and Neal Ford

This is another highly recommended book on Software architecture. Written by Mark Richards and Neal Ford stands as an invaluable guide for developers aspiring to transition into the role of a software architect, a position consistently ranked among the top 10 best jobs in salary surveys globally.

This first-of-its-kind book offers a comprehensive overview of software architecture, covering a wide array of topics such as architectural characteristics, patterns, component determination, diagramming, evolutionary architecture, and more.

Written by hands-on practitioners with extensive experience in teaching software architecture classes, Mark Richards and Neal Ford focus on universal architecture principles applicable across various technology stacks.

The book delves into critical aspects like architecture patterns, component identification, soft skills, modern engineering practices, and treating architecture as an engineering discipline.

With a modern perspective that incorporates innovations from the past decade, this book equips both aspiring and existing architects with the necessary tools and insights to navigate the complexities of software architecture, making it an indispensable resource in the field.

I highly recommend this book to any senior developer who also wants to become a software architect.

10. System Analysis and Design by Dennis, Wixom, and Wroth

This is a great book that will give you a very good understanding of system analysis and design, as the title suggests. You will get access to extensive descriptions, as well as practical projects that reflect real-life situations.

The 6th Edition of this book is also made up of a number of case studies and many examples, along with deep explanations for all the case studies and examples.

This book will definitely help you in achieving your goals as a design student. You will get a solid grasp of all the principles related to system analysis and design.

If you want, you can also combine this book with the **Grokking the System Design Interview **course on DesignGuru.io, one of the best interactive courses for System Design.

11. Modern Systems Analysis And Design By Joseph Valacich and Joey George

This is another Software design book that will teach you all about the skills, ideas, and techniques that are connected with system analysis and design. Trust me when I say that this is the perfect and most comprehensive system design book for you.

This System Design and Analysis book is written by Joshep Valacich and Joey George, and also includes a section on agile methods, which will ensure that this is a perfect student learning system for system designers.

The writing style is very simple but informative, which means that it will be accessible to everyone, including those whose mother tongue is not English.

There are multiple editions of this book to make sure you choose the latest edition, the 9th Edition, and if you need a Udemy course to go with this book, I recommend the Pragmatic System Design course on Udemy.

I have found that by reading books and watching courses is the best way to learn System design.

That's all about the best Software architecture and System Design Books. You can read these books to learn essential Software Architecture design concepts and how to architect a system.

If you are learning software architecture for a System design interview, then The System Design Interview Insider Guide book by Alex Xu also covers common Software design problems and shows you how to solve them, which can be really beneficial for interviews.

Bonus

As promised, here is the bonus for you, a free book. I just found a new free book to learn Distributed System Design, you can also read it here on Microsoft --- https://info.microsoft.com/rs/157-GQE-382/images/EN-CNTNT-eBook-DesigningDistributedSystems.pdf

Thank you and all the best for your System Design interview and learning journey!!

The AI Engineer’s Reading List for 2026 (10 Books That Matter)

Soma — Tue, 16 Jun 2026 10:38:20 +0000

Hello Devs, the AI world is moving ridiculously fast.

New models, new frameworks, new tools, every week there's something new for AI engineers to learn. But if there's one thing that consistently separates great engineers from the rest, it's strong fundamentals.

Javarevisited Newsletter is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

And for that, books still beat everything else.

Over the past few months, I've been diving deep into some of the best books on AI engineering, LLMs, and production AI systems. These are not just theoretical reads --- they cover everything from building LLMs from scratchto designing scalable ML systemsto deploying AI applications in production.

In this newsletter, I'm sharing 10 AI and LLM Engineering books I'm reading in 2026, books that every developer, ML engineer, or AI enthusiast should have on their radar.

These include fantastic titles from experts such as Paul Iusztin, Chip Huyen, Sebastian Raschka, PhD, Maxime Labonne, Louis-François Bouchard, Louie Peters, and others who are shaping the way modern AI systems are built.

If you want to learn how to:

Build LLM-powered applications
Design scalable AI systems
Understand prompt engineering and agentic AI
Deploy LLMs in production

Then these books are an excellent place to start.

Let's dive into the list.

10 AI and LLM Engineering Books Every Developer Should Read in 2026

Without any further ado, here is a list of the 10 Best Books to Learn AI and LLM Engineering in 2026. This includes books on AI, Machine Learning, and Large Language Models.

If you're serious about becoming an AI engineer or working with LLMs, this list is your roadmap.

1. AI Engineering by Chip Huyen

This is the first book you should read on AI Engineering, and if you don't like reading many books, then this single book is enough to learn all the skills you need to become an AI Engineer in 2026.

Chip Huyen, author of this book, brings a refreshing focus on AI systems design rather than just models.

If you don't know, Chip has worked as a researcher at Netflix, was a core developer at NVIDIA (building NeMo, NVIDIA's GenAI framework), and cofounded Claypot AI. She has also taught machine learning (ML) at Stanford University.

This book covers what an AI engineering stack looks like: the one that we software engineers must become experts in to be an AI engineer.

You'll learn how to turn machine learning models into real products --- handling data pipelines, model versioning, deployment, monitoring, and scaling.

It also covers what AI engineering is, how it differs from ML engineering, and the techniques AI engineers should be familiar with.

If your goal is to become a true AI Engineer (not just a Kaggle competition winner), this book is pure gold.

Here is the link to get this book --- AI Engineering by Chip Huyen

And, if you want, you can also combine this book with the Full Stack AI Engineering --- Towards AI Academy course for active learning. It's one of the most well structured course you can find.

2. The LLM Engineering Handbook by Paul Iusztin and Maxime Labonne

This book is like an operations manual for LLM development.

It covers prompt engineering, model fine-tuning, retrieval-augmented generation (RAG), evaluation strategies, and production patterns.

The authors have real-world experience building LLM apps at scale.

Highly recommended if you want to move from "just using GPT" to designing serious LLM applications.

Here is the link to get this book --- The LLM Engineering Handbook by Paul Iusztin and Maxime Labonne

If you are into Agentic AI, you can also check his course,- Agentic AI Engineering on Towards AI.

3. Designing Machine Learning Systems by Chip Huyen

This is another great book from Chip Huyen, one of my favorite authors when it comes to AI and LLM engineering

You'll start thinking like a machine learning product engineer, not just a model builder.

Here is the link to get this book --- Designing Machine Learning Systems by Chip Huyen

If you need a course, you can also checkout Associate AI Engineer for Developers --- DataCamp course, its a great course for beginner to start with.

4. Building LLMs for Production by Louis-François Bouchard and Louie Peters

This book shows you how to actually ship Large Language Models into production environments. You'll learn about fine-tuning, deploying, scaling, and maintaining LLMs like a real engineer.

It's packed with hands-on advice, architecture examples, and real deployment challenges.

If you're aiming for a career as an LLM engineer, this book should be your first read.

Here is the link to get this book --- Building LLMs for Production by Louis-François Bouchard and Louie Peters

If you like courses, you can also checkout 10-hour Crash Course to Expert LLM Developer on Towards Academy. It's a great course to go along with this book.

5. Build a Large Language Model (from Scratch) by Sebastian Raschka, PhD

Sebastian Raschka is a legend in the machine learning community. This book teaches you how to build a transformer-based LLM from scratch using PyTorch, with no shortcuts.

You'll go deep into model architecture, tokenization, attention mechanisms, and training strategies.

Perfect for developers who want to understand LLMs at the code level, not just use APIs like OpenAI's.

Here is the link to get this book --- Build a Large Language Model (from Scratch) by Sebastian Raschka, PhD

If you need a course, then you can also check out Master LLM Engineering & AI Agents: Build 14 Projects --- Udemy, it's one of the most affordable courses on LLM Engineering.

6. Hands-On Large Language Models: Language Understanding and Generation

Jay Alammar and Maarten Grootendorst are two of the most respected names in the AI and NLP space.

This book walks you through building and fine-tuning large language models with modern tools like Hugging Face Transformers, LangChain, and more.

It's hands-on and practical --- ideal for developers, data scientists, and ML engineers who want to build and deploy LLMs that understand and generate human language effectively.

Here is the link to get this book --- Hands-On Large Language Models

You can also combine this book with the Generative AI Engineering with LLMs Specialization from Coursera for more project-based and in-depth learning.

7. Prompt Engineering for LLMs: The Art and Science of Building Large Language Model-Based Applications

If you're building AI products using OpenAI, Claude, or open-source LLMs, this book shows you how to write smarter prompts for better results.

It covers strategies like few-shot prompting, chain-of-thought, and using prompt patterns effectively.

Created by John Berryman and Albert Ziegler, this book dives into the evolving art and science of prompt engineering.

A must-read for AI developers and product designers.

Here is the link to get this book --- Prompt Engineering for LLMs

If you need a course, you can also combine this with The Complete Prompt Engineering for AI Bootcamp (2026) on Udemy. This course is a comprehensive guide not only to ChatGPT and prompting, but also to GitHub Copilot

8. Building Agentic AI Systems: Create Intelligent, Autonomous AI Agents that can Reason, Plan, and Adapt

Written by Anjanava Biswas and Wrick Talukdar, this book explores how to build agentic AI systems that can go beyond static outputs.

This book shows you how to create autonomous AI agents that can interact with environments, reason, make decisions, and take actions.

If you're interested in building AI agents like Auto-GPT, BabyAGI, or LangGraph-based systems, this guide is a goldmine.

Here is the link to get this book --- Building Agentic AI Systems

You can also combine this book with the Agentic AI Nanodegree from Udacity for a project-based and in-depth learning.

9. Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs

This is a comprehensive guide to prompt engineering techniques specifically designed for generative AI systems --- including text, image, and code generation.

The book emphasizes how to write prompts that are robust, consistent, and tailored for business and production environments.

Whether you're working with GPT, DALL-E, or other models, this Prompt Engineering book by James Phoenix and Mike Taylor will definitely help you future-proof your AI input strategies.

Here is the link to get this book --- Prompt Engineering for Generative AI

10. The AI Engineering Bible: The Complete and Up-to-Date Guide to Build, Develop, and Scale Production Ready AI Systems

Thomas R. Caldwell's AI Engineering Bible is a must-have for software engineers and tech leaders.

It goes beyond models and APIs to show you how to engineer real-world AI systems that are scalable, maintainable, and production-ready.

From architecture to infrastructure, deployment to monitoring, it covers the entire AI lifecycle. This is the playbook for anyone who wants to lead AI implementation in their organization.

Here is the link to get this book --- The AI Engineering Bible

Why You Should Read These AI and LLM Engineering Books?

Apart from my recommendations and several others on Reddit and HN, here are the top 5 reasons why you should read these AI and LLM Engineering books.

They're written by practitioners who have built production AI/LLM systems.
They focus on engineering, deployment, and real-world use cases --- not just algorithms.
They don't waste your time with outdated academic theory.
They prepare you for the future of AI and LLM work: scalable, reliable, explainable systems.
Widely recommended by professionals on Reddit and Hacker News.

Reading books is powerful, but nothing beats building things.

If you want to accelerate your learning, combine these books with a hands-on course like: LLM Engineering: Master AI, Large Language Models & Agents to get some hands-on experience on building a RAG RAG-based chatbot and learning LLM by watching.

Conclusion

They'll save you hundreds of hours of wasted time and help you actually build systems that work.

Want even faster progress? If you want more fun and faster progress then you can also pair these books with hands-on projects like building your own RAG-based chatbot, fine-tuning a model on your own dataset, or deploying a real-world LLM app to the cloud.

System Design Interview Roadmap (10 Concepts That Matter Most)

Soma — Sat, 13 Jun 2026 04:56:50 +0000

Disclosure: This post includes affiliate links; I may receive compensation if you purchase products or services from the different links provided in this article.

image_credit - codemia.io

Hello friends, if you are preparing for coding interviews but are wondering how to tackle the crucial System design topic and are looking for step-by-step guidance, the right approach, tips, and questions, then you have come to the right place.

In this article, I will share a complete guide to cracking System design interviews.

The Software Engineering job market is on fire! Especially if you have a couple of years of experience under your belt.

And in the world of Software Engineering, *if you are applying for a Senior Engineer / Lead / Architect, or a more senior role, System Design is the most sought-after skill, and hence one of the most important rounds in the whole process.

If you mess this up, nothing else would matter. If you get it right, though, you're looking at a raise of at least tens of thousands of dollars annually.

So how do you ace your system design round? Well, here's what I did while preparing for my interviews with Facebook, Google, and Amazon, and it worked out rather well.

I did end up creating a checklist for myself, which got me through most of my rounds, so if you follow a similar path, you should be able to come up with something that works for you as well.

Before we get into the details, though, what is a system design interview? What do the interviewers really expect from the candidates?

What is expected in System Design interviews?

The first step to prepare for a system design interview is to understand what is expected of you. Once you know that, you can align your preparation strategy to meet those expectations.

So here are the key expectations from you on a System design interview:

You should be able to design a system that satisfies the requirements given to you and scales well, for example, design Twitter, design a vending machine, or design a trade position aggregator.
Your design should be pluggable and not restrict the addition of new features.
You should be able to compare various alternatives and choose the most optimal one. Things like, which database is the most important, or which protocol should you use, or what's the best approach to scale a system, etc.
You should know the basics that are relevant from a system design standpoint, like:
Load balancers
APIs
Caches
Databases
Network Protocols
Message queues
CDNs
High-level details about ML and Big Data
CAP Theorem
Monitoring and analytics

These are actually the essential System design topics and concepts every developer should prepare. If you have knowledge about these topics, then you will definitely do well on system design interviews.

By the way, don't get overwhelmed by the number of topics. Most of the good System design courses, like System Design Interview Guide by CodeKarleon Udemy or Grokking the System Design Interview on DesignGurus.io cover all these topics. This course by Design Gurus has helped 60k+ subscribers to crack their system design interview (SDI).

How to prepare for System Design Interviews?

While many things have changed in the last couple of years, the System design interview has also changed with more focus on API, performance, and Microservices.

Anyway, here is a simple four-step process to ace any System Design round, not just this year but also in the coming years.

Master Essential System Design Concepts like Scalability, Consistency, Caching, Database sharding, and replication, etc.
Learn from the tech giants by reading their blogs, like Uber and Netflix
Practice System design questions like designing YouTube, WhatsApp, etc. on sites like Codemia.io, which contains more than 120+ system design problems. It's a Leetcode for system design.
Practice and Mock Interviews (like tryExponent, Codemia.io, Bugfree.ai or DesignGuru )

1. System Design Concepts

Any system design interview will definitely require you to come up with a basic high-level design for whatever system you are trying to build. Some components will be needed for sure. Make a note of these components.

Here are a few of them:

1. Load Balancer

You cannot have a distributed system without a load balancer to distribute the incoming requests among various nodes. This ensures proper resource utilization and that there is no single point of failure in your system. Here's how Nginx does it.

While learning about Load balancer its also a good idea to learn about API Gateway and carefully understand the difference between load balancer and API Gateway, which is quite important from an interview point of view. This question has already been asked of me a couple of times in interviews.

2. Caching

If you have ever done performance improvement, then you know that the Caching is probably the first thing that comes to mind.

Most systems have some read-heavy interactions, some information that the user will access frequently but not update as much.

It makes sense to cache this information in such a way that it can be easily fetched without the need for a DB lookup.

Think low latency.

Also, based on your use case, you might need to store more frequently accessed information or more recently accessed information. So, read up on various eviction policies like least recently used (LRU).

If you want to learn more, then ByteByteGo System Design course has a lot of information on Caching strategies and how to effectively use caches. You can refer them to learn more.

3. Database

Database is one of the most important skills to have, as most of the real-world applications you will build will have some sort of data store like a relational database or NoSQL.

In fact, there can be no system without some form of data storage. Whether you want to store files, images, product information, financial transactions, or simply dump all the data from various user interactions to run analytics later.

It all needs databases. So read up on it. Find out what matters when you are selecting a database, read about SQL/NoSQL, query patterns, and how CAP theorem might come into play while making tradeoffs.

Sandeep has explained this quite well on his System Design course on Udemy. If you want to learn further, you can refer to that as well.

4. Message queues

Sometimes our system needs to perform some tasks that need to be done but not necessarily immediately, or their outcome does not affect the user's journey.

In such cases, rather than making a service call and waiting for the response, it makes sense to just write the message to the queue so that it can be executed later.

This is also known as asynchronous messaging, which is the backbone for many Microservices.

What if you need to insert information in your database, and bulk insert could be more efficient? It would make sense to just keep track of these inserts in a message queue and perform 1 bulk insert instead of hundreds of 1-to-1 inserts to optimize your resources.

A good knowledge of Messages queues like RabbitMQ, ActiveMQ, and Apache Kafka is essential for any Software Engineer and this is one topic which is covered quite well on Educative.io's Grokking Modern System Design for Software Engineers & Managers course. If you want to learn about not just message queue but other essential System design topics, this is one resource which I personally use.

5. CDN

When your users are distributed geographically, getting your content to them in a reasonable amount of time becomes a real challenge.

CDNs allow us to maintain a copy of our data in various data centers located closer to the users' locations to reduce the latency.

Here is a short video about how Akamai does it.

6. Analytics and Monitoring

This is something that is needed in every system you create. This is a hidden requirement; no one calls it out in the requirement gathering, but every interviewer wants this.

User logs in or logs out? Wish listed an item? Payment failed? It is all the information for us! Anything of importance happens, fire an event, and save it in your messaging queue.

You can perform real-time analytics on data or just dump it in a Hadoop cluster to use later. Similarly, if an API call is regularly failing, or if your servers are about to run out of resources, wouldn't you like to know about it beforehand?

A good knowledge of tools like Grafana, Prometheus can help you with Analytics and monitoring, and also to impress your interviewer during a system design interview.

Alex Xu has also shared a lot of good information on Monitoring on his book and ByteByteGo course. You can refer to them as well.

7. Network Protocols

Based on your requirement, based on the type of content you are sharing, you might need to make a decision on which network protocol to use. Read up on various network protocols like TCP and UDP and when they are relevant, what might be the compromises you might need to make, etc.

The first step is to know about all these concepts. And by knowing concepts, I don't mean just some theoretical knowledge of what these are, but more practical hands-on experience about what should be used when.

You need to know things like:

Which is the best alternative to choose from, given the use case.
What are the tradeoffs that you need to consider while making these decisions?
Best practices for certain use-cases.

To know most of these things, I'll recommend going through this course on System Design by CodeKarle, which covers all of the above with concrete examples from the real world.

2. Learn from the tech giants like Uber and Netflix

This is probably not going to help in the short term. But in the long term, to become an expert in System Design, it's best to look at the Tech blogs of various tech companies and see how they are solving various technical problems.

This would paint a clear picture of the real problems that they face and how innovatively they solve them.

Understanding these things would help you become better at system design and also keep you up to date with the latest innovations in tech.

Some of the best blogs to follow are:

If you don't like reading and prefer watching, then you can also join online courses created by ex-FAANG employees like the Mastering the System Design Interview by Frank Kane, (Ex Amazon Hiring Manager). This is one of the best resources to learn System design for interviews.

3. Solve Frequently asked System Design questions

An obvious way to get started with your interview prep! Knowing the basics is definitely important, but it won't be enough!

The most basic way to get started with your practice is to look at some frequently asked questions and their solutions.

Most system design interviews revolve around some 5--6 commonly asked System design questions and if you know the solutions to those, you are more than likely to clear this interview.

The most common questions are (with solution, click the link to see the solution when you get stuck):

How to design Twitter?
How to design a Chat system like WhatsApp or Facebook Messenger
How to design Ticketmaster?
How to design an efficient Parking Lot?
Whatsapp System Design
Airbnb System Design
Uber System Design
Tinyurl System Design

If you need resources to solve these questions, something which not only solves the question but also explains the underlying concepts and approach to solving system design questions, then thishighly-rated course by CodeKarle discusses most of these case studiesand some more problems, which has helped many people crack their interviews for companies like Google, Facebook, Microsoft, Amazon, etc.

4. Practice (Mock Interview)

Practice, practice, practice! Did I say practice? There are a lot of resources out there. I have shared a few that discuss some of the most popular system design problems in detail.

Once you go through a few of them, you will start noticing a pattern and will soon be able to come up with solutions on your own.

You should also get a better understanding of how your systems are designed in your organization. How are the other teams doing things? What factors do they take into consideration?

The next best thing is to practice with a friend. Make sure you also go through a few mock interviewsbefore your actual interview to avoid some common but easily avoidable pitfalls.

Since chances for companies like Google and Meta are hard to come by, you should prepare as best as possible, and Mock interviews are really important for them. They allow you to see your actual preparation level before real interviews.

At the same time, its also important that your mock interview should mimic the real interview and for that I recommend you to join sites like tryExponent, Codemia.io, Bugfree.ai, Designgurus.io or , which allows you to take mock interview with real experts.

Codemia.iois like the Airbnb for mock interviews and career coaching. It connects job seekers with seasoned FAANG interviewers who provide invaluable feedback, getting you ready for real-life interviews.

Many of my friends, readers, and colleagues, along with numerous job seekers, have already reaped the benefits of this platform.

What makes Codemia stand out?
1. Affordability: The average cost per session is significantly lower compared to other platforms.
2. Expert interviewers: Veterans from FAANG and top-tier companies bring years of experience, offering priceless feedback that truly makes a difference.
3. Direct communication: Job seekers can directly message interviewers to address any questions or concerns, such as experience sharing or rescheduling, ensuring better preparation for both parties.
4. All-in-one tools: Meetapro equips you with the essential tools for an effective interview session, including recording options for post-session review.

That's why I highly recommend adding Codemia.ioin your preparation journey for best results. You can learn more about it

Common Mistakes to Avoid on System Design Interviews

There are two important things you can do to become successful in life: do what other successful candidates do and avoid the mistakes of those who failed. Some of the most common mistakes that I have seen people make are:

Not driving the interview
Not asking questions
Not structuring the interview properly
Running out of time
Not considering the requirements
Not exploring all the alternate design options

All these mistakes can be easily avoided by having a few mock interviews with someone who knows System Design well. And time your interviews. The target must be to solve within 40 minutes, including time for some discussion.

Well, that's it, folks. **With this 4-step process, you'll soon be ready to ace any of your system design interviews! Hopefully, this should be a good starting point for you. Happy learning!

For mock interviews, you can also check out Exponent website, which offers 1--1 coaching and classes for in-person learning and also runs a free service called Prampthat offers peer-to-peer interviews for software engineers in data structures and system design.

If you need another option, then DesignGuru also has mock interviews, which cost around $239, and you get a chance to be interviewed by experts and a former Facebook Engineer and founder of DesignGuru.io

Best System Design Interview Resources

If you need more resources like books and online courses to prepare for the System Design Interview, here are my recommendations:

Thanks for reading this article so far. If you like this System design interview roadmap, questions, tips, and resources, then please share it with your friends and colleagues who are preparing for tech interviews.

If you have any questions or feedback, please drop a note.

I Found LeetCode for Software Design, and It’s Awesome

Soma — Sat, 09 May 2026 07:17:35 +0000

Disclosure: This post includes affiliate links; I may receive compensation if you purchase products or services from the different links provided in this article.

If you're preparing for FAANG interviews, you already know that Data Structures & Algorithms (DSA) and System Design are the two critical areas where you'll be tested rigorously.

While LeetCode has become the standard platform for DSA practice, system design preparation has always been fragmented --- scattered across expensive courses, text-heavy articles, and YouTube videos with no hands-on practice.

While there are many websites and platforms to prepare for System Design Interviews like ByteByteGo, DesignGurus.io, Exponent, Educative, and Udemy, there is nothing like LeetCode, where you can practice the real, interview like system design problems online and also get evaluated.

That changed when I discovered BugFree.ai.

After using it to prepare for my recent FAANG interviews, I can confidently say: BugFree.ai is the LeetCode for System Design --- and it's exactly what the interview prep landscape has been missing.

If you've struggled with structuring system design answers, getting real feedback, or knowing whether your approach matches industry standards, BugFree.a solves all of these problems.

Why System Design Needs a Platform Like LeetCode or BugFree.ai?

Most system design resources today fall into one of these categories:

1. Long, Text-Heavy Courses

Passive learning with no hands-on practice
You read about designs but never actually create them
No way to validate your understanding

2. Expensive Mock Interview Platforms

$200--500 per session
Limited feedback
Not scalable for comprehensive preparation

3. YouTube Videos and Blog Posts

Fragmented information
No structured progression
No evaluation of your solutions

The fundamental problem: Reading about system design isn't enough. You need to design solutions actively and get expert feedback on your approach.

This is where BugFree.ai changes the game completely.

What Makes BugFree.ai Special?

Instead of passive learning, BugFree.ai provides an interactive, hands-on, and AI-powered approach to mastering system design. Here's what sets it apart:

1. Real-World System Design Problems with Interactive Practice

BugFree.ai offers a comprehensive collection of system design problems structured exactly like LeetCode presents DSA problems but for architecture and design.

Example: Design URL Shortener

You work through:

Functional and Non-Functional Requirements
Capacity Estimation and Constraints
API Design and Data Models
High-Level Architecture
Deep Dives into Critical Components
Scalability and Trade-offs

The difference: You don't just read about URL shorteners --- you actually design one step-by-step, with guidance and validation at each stage.

Built-in Drawing Tools:

Just like in real interviews, you get access to an interactive whiteboard where you can sketch architectures, draw component diagrams, and visualize data flows

Here are some of the popular System Design questions you can solve on Bugfree.ai for practice:

2. Difficulty-Based Categorization (Similar to LeetCode)

BugFree.ai categorizes problems by difficulty level, making it easy to progress systematically:

Easy --- Foundational concepts:

Load Balancing
Caching Strategies
Message Queues
Basic API Design

Medium --- Real-world scenarios:

Rate Limiting Systems
Search Autocomplete
Real-Time Chat Applications
Notification Systems

Hard --- Complex distributed systems:

Design YouTube/Netflix
Distributed Databases
Cloud Storage Systems
Microservices Architecture
Design Web Crawler

Each problem includes company tags showing which FAANG and top tech companies ask them, so you can focus your preparation strategically.

3. AI-Powered Coaching and Feedback

This is where BugFree.ai truly shines, AI-assisted learning that feels like having an expert mentor available 24/7.

How it works:

Smart Hints: Stuck on a problem? The AI provides hints without giving away the answer.
Design Evaluation: Submit your design and get automated feedback on scalability, reliability, and best practices.
Industry Standards: Your solution is evaluated against real-world architectural patterns.
Interactive Q&A: Ask questions about your design decisions and get immediate clarification

This bridges the gap between self-study and expensive coaching --- you get expert-level feedback at a fraction of the cost.

4. AI-Powered Mock Interviews

Here's the feature that sets BugFree.ai apart from every other platform: AI-driven mock interviews.

The Problem with Traditional Mock Interviews:

Expensive ($200- 500 per session)
Limited availability
Inconsistent interviewer quality
Can't practice as much as you want

BugFree.ai's Solution:

Unlimited mock interviews powered by AI that simulate real FAANG interview conditions:

Realistic Interview Experience: Timed sessions with ambiguous requirements (just like real interviews).
Follow-up Questions: AI interviewer asks probing questions about your design decisions.
Performance Evaluation: Detailed feedback on communication, technical depth, and trade-off analysis.
Unlimited Practice: Take as many mock interviews as you need to build confidence

Example: Web Crawler System Design Mock Interview

The AI interviewer guides you through designing a web crawler, asks follow-ups about politeness policies, distributed crawling, and URL deduplication, exactly like a real interview.

My experience: After 10+ mock interviews on BugFree.ai, my real FAANG interview felt comfortable. I'd seen the patterns, practiced the communication, and knew how to structure my answers.

5. Object-Oriented Design (OOD) Practice

System design isn't the only thing tested at FAANG. Object-Oriented Design (Low-Level Design) is a critical round, especially for senior roles.

BugFree.ai covers this comprehensively:

Classic OOD Problems:

Parking Lot System
Elevator System
ATM Machine
Library Management System
Hotel Booking System
Vending Machine

What you get:

Step-by-step class design guidance
UML diagrams and relationships
Design pattern applications
SOLID principles in practice
Trade-offs between different approaches

If you've struggled with OOD interviews (many developers do), this is invaluable preparation.

You can see all the OOD problems here.

6. Resume Analysis and Optimization

Here's an unexpected bonus: AI-powered resume review.

Upload your resume and get:

ATS optimization --- Make sure your resume passes applicant tracking systems
Keyword suggestions --- Align with job descriptions for roles you're targeting
Formatting feedback --- Professional structure and readability
Content improvements --- Stronger bullet points highlighting impact

Why this matters: Your resume gets you the interview. BugFree.ai helps you get past the first filter.

Getting Started: My Recommendations

If you're completely new to system design:

Week 1: Easy Problems

Start with URL Shortener
Focus on understanding the framework
Don't worry about perfect solutions
Get comfortable with the platform

Week 2: Build Fundamentals

Solve 3--4 easy problems
Learn capacity estimation
Practice API design
Understand database selection

Week 3: Medium Problems

Tackle real-world scenarios
Focus on scalability patterns
Learn caching strategies
Practice load balancing

Week 4: First Mock Interview

Take your first mock interview
Don't expect perfection
Learn from the feedback
Identify gaps in knowledge

Then: Continue cycling through problems, mock interviews, and focused learning in weak areas.

What I Wish I Knew Before Starting

This is another section which is close to my hearth, here I will share my learning so that you can do better:

1. Don't Skip the Easy Problems
Even if you're experienced, start with easy problems. They teach the framework for approaching system design that applies to harder problems.

2. Mock Interviews Are Essential
Solving problems alone isn't enough. Mock interviews teach:

Time management (45--60 minute constraints)
Communication under pressure
Handling ambiguous requirements
Explaining trade-offs clearly

3. Review Community Solutions
After solving a problem, always check how others approached it. You'll discover patterns and optimizations you missed.

4. Focus on Communication
System design interviews test communication as much as technical knowledge. Practice explaining your thinking process clearly.

5. Use the AI Feedback
Don't just collect problem checkmarks. Study the AI feedback carefully. That's where real learning happens.

Is BugFree.ai Really Worth It?

After three months of intensive preparation using BugFree.ai, here's my honest assessment:

What BugFree.ai does exceptionally well:

Hands-on practice --- Actually design systems, don't just read about them
AI-powered feedback --- Get expert-level guidance on every design
Unlimited mock interviews --- Practice as much as you need
Structured progression --- Clear path from beginner to expert
Cost-effective --- Fraction of traditional mock interview costs
Comprehensive coverage --- System design + OOD + resume help

What could be improved:
⚠️ Human interaction isn't quite the same (supplement with 1--2 human mock interviews)
⚠️ Community features could be stronger (though improving)

My verdict:
For system design interview preparation, BugFree.ai is the most effective platform I've found. It's truly the LeetCode for system design—combining structured practice, expert feedback, and unlimited mock interviews in one place.

The ROI is obvious: The platform costs less than a single traditional mock interview session yet provides unlimited practice and comprehensive preparation.

Start here: BugFree.ai

Final Thoughts

System design interviews are intimidating, but they're also learnable and masterable with the right approach.

What doesn't work:

Reading endless blog posts without practicing
Watching YouTube videos passively
Hoping to "wing it" in interviews
Paying for expensive mock interviews you can't afford to repeat

What works:

Structured hands-on practice
Regular feedback on your designs
Mock interviews simulating real conditions
Systematic improvement over time

BugFree.ai provides all of this in one platform.

After using it to pass FAANG system design interviews, I can confidently say: This is the platform I wish had existed when I first started preparing.

Don't make the same mistakes I did --- scattered preparation across fragmented resources, expensive one-off mock interviews, and no systematic way to validate improvement.

Start practicing on BugFree.ai today. Your future self (and your compensation package) will thank you.

P.S. - I used BugFree.ai while working full-time. The focused, interactive practice was far more effective than months of random article reading. Share this with engineers preparing for senior roles.

I Reviewed 40+ Resources — Here Are the 12 Best for Learning Agentic AI in 2026

Soma — Sat, 18 Apr 2026 15:42:34 +0000

Disclosure: This post includes affiliate links; I may receive compensation if you purchase products or services from the different links provided in this article.

credit - Agentic AI Engineering by Paul Iustzin

Hello guys, Agentic AI is rapidly becoming one of the most exciting frontiers in artificial intelligence. Unlike traditional AI systems, Agentic AI focuses on building autonomous agents, intelligent programs that can reason, plan, and act independently to achieve specific goals.

From AI assistants that manage complex workflows to autonomous trading systems, agentic architectures are now at the heart of cutting-edge applications.

As AI agents move from research labs to real-world use cases, the demand for engineers who can design and deploy these systems is exploding.

Whether you want to create multi-agent workflows, automate business processes, or simply stay ahead in the AI job market, learning Agentic AI in 2026 is a smart career move.

If you want to learn and master Agentic AI and are looking for resources, then you have come to the right place. Earlier, I have shared best Agentic AI courses and books, and in this article, I am sharing the best of both worlds.

This post contains 10 best resources, courses, books, and interactive guides that will teach you how to build intelligent AI agents from the ground up.

These resources come from trusted platforms like Udemy, Coursera,, DataCamp, Frontend Masters, Udacity, and ZTM Academy** as well as popular books like The Agentic AI Bible by Thomas R. Caldwell

12 Best Online Resources to learn Agentic AI in 2026

Without any further ado, here are the 10 best resources you can use to learn and master Agentic AI in 2026 and learn to build production-quality AI agents in no time. These resources include both books and online courses

1. The Complete Agentic AI Engineering Course (2026) Udemy

If you want one comprehensive, project-based course to master Agentic AI, this is it. The course takes you from the basics of AI agents to building 8 real-world projects using tools like OpenAI Agents SDK, CrewAI, LangGraph, AutoGen, and MCP.

Key Highlights:

Career Digital Twin: Deploy an agent that represents you to potential employers.
SDR Agent: Build a sales automation agent that sends professional emails.
Deep Research: Create a multi-agent research team for any topic.
Stock Picker Agent: Automate investment analysis with CrewAI.
Capstone Trading Floor: Deploy 4 autonomous trading agents powered by MCP servers.

[Bestseller | 4.7 ⭐ (14,746 ratings) | 114,822 students]

Why Take This Course:\ It's one of the most practical and up-to-date Agentic AI resources available. Perfect for engineers who learn best by building.

Here is the link to join this course--- The Complete Agentic AI Engineering Course

2. Agentic AI Nanodegree --- Udacity

Why it's the most comprehensive option: If you want to go all-in on Agentic AI, the Udacity Nanodegree is the most rigorous, career-focused program available. At 53 hours and rated 4.8/5 by 51 reviewers, it's not just a course - it's a complete specialization.

This intermediate program takes you from advanced prompting (Chain-of-Thought, ReAct) through sophisticated multi-agent orchestration.

You'll build a portfolio of real projects: a multi-agent travel planner, an AI-powered project manager, and a fully automated sales system. These are portfolio pieces that actually impress employers.

Time commitment: 53 hours (roughly 6--8 weeks at 8--10 hours/week)

Career value: Nanodegree credential is recognized by employers. This is more valuable than a single course certificate.

Join the program: Agentic AI Nanodegree

3. IBM RAG and Agentic AI Professional Certificate --- Coursera

This isn't a basic course. It covers Retrieval-Augmented Generation (RAG) pipelines, enterprise AI architecture, and how to build production-ready Agentic systems at scale. You'll understand how Fortune 500 companies approach Agentic AI deployment.

What sets it apart:

IBM Skills Network backing (enterprise credibility)
Focus on RAG architectures for knowledge-intensive agents
Enterprise-scale deployment patterns
IBM Watson integrations
Recognized professional certificate

Certificate value: Adds credibility to your LinkedIn profile and resume. IBM credentials carry weight.

Here is the link to join this AI certificate --- IBM RAG and Agentic AI Professional Certificate

Pro tip: If you plan to take multiple Coursera courses, consider Coursera Plus (~$399/year) for unlimited access to 10,000+ courses and certificates. They are offering 50% discount now so you can get it for just $199.

4. Build AI Agents and Multi-Agent Systems with CrewAI Educative

CrewAI is one of the hottest frameworks for designing goal-driven agents, and this Educative course is the best way to learn it.

Here is why you should join this course:

Design structured agent roles and collaborative teams.
Integrate LLMs into real-world tasks.
Automate workflows with Python.

Rating: 4.5 ⭐ | Level: Intermediate | Duration: 2h 15min

Here is the link to join this course - Build AI Agents and Multi-Agent Systems with CrewAI

5. Agentic System Design--- Educative

If you want to learn how to architect large-scale agentic systems, this course delivers a deep dive into system design for multi-agent environments.

Here are the key features of this interactive Agentic AI course:

System-level thinking for complex agents.
Advanced tools: RAG, vector databases, LangChain.
Real-world case studies for applied learning.

Rating: 4.7 ⭐ | Level: Advanced | Duration: 6h

Here is the link to join this course - Agentic System Design

By the way, you need an Educative Unlimited subscription to access these courses, and the best thing is that you can get it now for 50% discount as Educative is offering 55% discount on annual plans on Educative Premium Plus. I highly recommend you get this plan while the sale is on.

6. Agentic AI and AI Agents: A Primer for Leaders--- Vanderbilt University (Coursera)

If you're a manager, product leader, or founder looking to understand the strategic impact of Agentic AI, this Coursera course is perfect.

Taught by Dr. Jules White, a top instructor in AI systems.
Covers the fundamentals of AI agents and how they fit into business workflows.
Over 57,000 enrolled learners.

*Here is the link to join this course * Agentic AI and AI Agents: A Primer for Leaders Vanderbilt University

7. AI Agents and Agentic AI in Python: Powered by Generative AI Coursera Specialization

Another excellent Coursera track for developers who want a hands-on introduction to AI agents in Python.

Here are key skills you will learn in this course --- -

Build resilient AI agents using GenAI frameworks.
Develop end-to-end agentic workflows.
Future-proof your Agentic AI skills.

Here is the link to check this specialization on Coursera AI Agents and Agentic AI in Python

By the way, if you are planning to join multiple Coursera courses or specializations, then consider taking a Coursera Plus subscription which provides you unlimited access to their most popular courses, specializations, professional certificates, and guided projects.

It costs around $399 per year but is worth it because you get access to more than 10000+ courses and projects, and you can also get unlimited certificates.

8. The Agentic AI Bible --- Book

Written by Thomas R. Caldwell, author of The AI Engineering Bible, this book is the most comprehensive written reference on Agentic AI.

Why Read:

Covers both theory and practice.
Explains agent orchestration, tools, and real-world patterns.
Great for engineers and AI strategists alike.

It's also got great reviews and ratings, which show a lot about the usefulness of this book.

*Here is the link to get this book *The Agentic AI Bible

9. Building Agentic AI Systems --- Book

If you prefer a technical, hands-on book, this is an excellent choice.\ It walks you through:

Building autonomous agents step by step.
Designing multi-agent ecosystems.
Real-world deployment patterns.

I had also recommended this book in the past when I wrote about 5 must-read AI engineering books, and I am recommending it here again because it's a great resource for anyone who wants to build AI agents in 2026.

Here is the link to get this book Building Agentic AI Systems

10. Designing Agentic Systems with LangChain --- DataCamp

This interactive short course is ideal for intermediate learners who want to design custom LangChain agents from scratch.

Key Skills:

Build LangChain-powered agents.
Create structured workflows for advanced use cases.
Understand key tools and vector databases.

Here is the link to join this course --- Designing Agentic Systems with LangChain

By the way, you need a Datacamp plan to access this course. They have different plans, like standard, professional, and premium, which allow access to all projects. I generally recommend the standard plan because it is right-priced and you get access to all the essentials to grow your data skills.

11. Complete Agentic AI Bootcamp with LangGraph and LangChain - Udemy

If you specifically want to master LangGraph and LangChain, two of the most popular frameworks for multi-agent systems, this bootcamp is a perfect fit.

In this udemy course, you will learn to build autonomous apps with real-world workflows, including task orchestration, knowledge retrieval, and goal-driven execution.

Along the way, you will learn crucial skills like building AI agents using LangGraph, including creating workflows, managing agent state, memory, and event-driven behavior.

You will also develop and deploy multi-agent collaborative systems that can communicate, reason, and solve complex problems together.

**Here is the link to join this course Complete Agentic AI Bootcamp with LangGraph and LangChain

12. Learn Agentic AI --- Build Multi-Agent Automation Workflows --- Udemy

This course is ideal if you want to focus on multi-agent automation workflows. Using AutoGen and MCP, you'll build six agents capable of handling real-world automation tasks.

Here are the key skills you will learn in this course:

Build collaborative AI teams.
Create self-improving agentic workflows.
Deploy agents for end-to-end automation.

Talking about social proof, more than a thousand students have already joined this course.

**Here is the link to join this course Learn Agentic AI Build Multi-Agent Automation Workflows

Bonus Agentic AI Learning Resources

Apart from this, there are a few more resources you can explore on bootcamp-style sites like Frontend Masters and ZTM Academy:

Build an AI Agent from Scratch Frontend Masters

2. Build AI Agents with CrewAI on ZTM Academy. If you are looking for a bootcamp-style course to learn how to build AI Agents, then this course is for you.

Final Thoughts

That's all about the best resources to learn Agentic AI in 2026. The era of Agentic AI is just beginning, and the opportunities are limitless. Whether you want to launch AI products, automate workflows, or secure a top AI engineering job, these 10 resources give you everything you need to become an Agentic AI expert in 2026.

If you prefer a single course that combines everything, projects, tools, and cutting-edge frameworks, start with the Complete Agentic AI Engineering Course (2026). It's the fastest way to gain job-ready skills while building real-world projects. It's also very affordable.

The future belongs to those who can design intelligent, autonomous systems, and 2026 is the perfect year to start mastering Agentic AI.

Thanks a lot for reading this article so far. If you like these Agentic AI resources, then please share them with your friends and colleagues. If you have any feedback or questions, please drop a note.

**P. S. **--- If you prefer reading books and watching courses and doing projects then you should start with the The Agentic AI Bibleby Thomas R. Caldwell, author of another popular book, The AI Engineering Bible, great for general AI introduction, both of his books are gem for AI Engineering.

How AlgoMonster Helped Me Master DSA and Crack Coding Interviews

Soma — Wed, 15 Apr 2026 14:57:45 +0000

Disclosure: This post includes affiliate links; I may receive compensation if you purchase products or services from the different links provided in this article.

Hello Deves, let me be brutally honest with you.

After 6 months of regularly grinding LeetCode problems, I thought I was ready for coding interviews, and why not? I read blogs, write blogs, and have a decent number of years of experience.

I was dead wrong.

Because when I walked into my first FAANG interview in years, and they asked me:

"Find all anagrams of a string in another string"

I panicked.

Sure, I had solved 100+ LeetCode problems. I knew basic algorithms. I could implement bubble sort in my sleep. Wrote an application that is running on production, and I even had ideas about two-pointers pattern and knew a few others.

But I had no idea this was a sliding-window pattern problem. I didn't recognize that it was similar to dozens of other problems I'd already solved.

I started with a brute force approach.

I mentioned nested loops.

Then spent 35 minutes trying to optimize something that should have taken 15 minutes max.

I didn't crack the interview.

That's when I realized something crucial: solving random problems isn't the same as understanding algorithmic patterns.

I was memorizing solutions instead of learning to recognize when to apply specific techniques.

I needed a systematic approach to pattern recognition, not just more practice problems.

That's when I discovered AlgoMonster, and it completely transformed how I approached coding interview preparation.

Here's how this platform took me from randomly grinding problems to systematically recognizing patterns, and ultimately cracking multiple interviews at both FAANG and other Big Tech companies.

1. First, I realized random problem-solving wasn't Working

Most people (including me) approach DSA preparation all wrong:

Solve problems randomly on LeetCode
Look up solutions when stuck
Move to the next problem
Hope to see similar problems in interviews

This used to work, and I have cracked multiple interviews inthe past by following just this method, but it doesn't work anymore.

Things have evolved, and now more and more companies are using platforms like HackerRank, Codility, CodingGame, and CoderPad to test candidates' algorithmic and DSA skills, where you need to solve real questions online with test cases, which not just test for input but also for performance.

But here's what I learned the hard way: coding interviews test pattern recognition, not memorization.

The same 15--20 patterns appear in 80% of coding interview problems:

Two pointers
Sliding window
Fast and slow pointers
Binary search variations
Tree traversal patterns
Dynamic programming templates
Backtracking frameworks

Random grinding wasn't teaching me these patterns.

I was solving each problem in isolation instead of understanding the underlying algorithmic thinking.

That's exactly what AlgoMonster fixed for me.

2. I Discovered the Power of Pattern-Based Learning

When I first logged into AlgoMonster, their approach was completely different from every other platform I'd tried.

Instead of throwing random problems at me, they organized everything around patterns:

a) Pattern Identification First

Before solving any problem, AlgoMonstertaught me to ask:

What type of data structure does this involve?
What's the constraint or optimization goal?
Does this match a known algorithmic pattern?

b) Template-Based Solutions

Each pattern came with a clear template:

Two Pointers Pattern Template:

left, right = 0, len(array) - 1\
while left < right:\
    if condition_met:\
        # process result\
        left += 1\
        right -= 1\
    elif need_to_expand_search:\
        left += 1\
    else:\
        right -= 1

Sliding Window Pattern Template:

left = 0\
for right in range(len(array)):\
# expand window\
add array[right] to window

while window_invalid:\

    # contract window\

    remove array[left] from window\

    left += 1

  
  
  process current window

c) Systematic Problem Progression

Instead of random difficulty jumps, problems were ordered to build pattern recognition gradually:

Basic pattern problems --- learn the template
Slight variations --- adapt the template
Combined patterns --- use multiple techniques
Advanced applications --- complex real-world scenarios

This systematic approach made everything click.

I wasn't just solving problems --- I was building a mental framework for algorithmic thinking.

They have also shared a nice chart of all top coding Patterns from FAANG interview so that you put your effort where you expect maximum gain in a short amount of time.

3. I Learned Through Their Unique Three-Step Method

AlgoMonster's problem-solving interface was unlike anything I'd seen before.

Every problem followed the same three-step process:

Step 1: Pattern Recognition

Before writing any code, I had to identify:

Which algorithmic pattern applies
Why is this pattern appropriate
What the high-level approach should be

Example: "Find the longest substring without repeating characters"

My pattern analysis:

This is a sliding window problem
We need to expand the window while characters are unique
Contract when we find duplicates
Track the maximum window size

Here is what it looks like on AlgoMonster website:

Once you choose the correct pattern, you can see whether your choice is correct or not and why that particular pattern is the correct choice.

For example, when you choose the "Sliding Window" pattern to solve the "Longest Substring Without Repeating Characters", you can see that it validates your choice with reasoning

"Correct! The Sliding Window pattern is ideal for this problem. It allows us to efficiently check substrings without recomputing every possible combination, keeping track of the current substring in one pass through the string."

Once you find the pattern, they also share a code template which you can use to solve any problem that is based on this pattern, for example, the"sliding-window" pattern in our case:

You can even choose the pattern in any programming language of your choice. This really helped me because while I am a Java developer and I prefer Java, I can also see how it's done in Python.

Step 2: Implementation

Only after identifying the pattern and learning about the template could I start coding.

The platform provided:

Language-specific templates
Inline hints when I got stuck
Real-time syntax checking
Test cases to validate my solution

This is a really great feature to practice coding problems online because you can type code online without installing any IDE or tools on your machine.

You don't need to worry about test cases as they provide comprehensive test cases to test your solution for all edge cases as well as for performance.

They even allow an interface to test your solution for a custom input, which is great for debugging purposes.

Here is what it looks like in Algomonster platform:

Step 3: Complexity Analysis

After solving the problem, we are not done. I had to analyze:

Time complexity and why
Space complexity and why
Potential optimizations
Edge cases to consider

This three-step process trained my brain to think like an interviewer evaluates candidates.

Pattern recognition → Clean implementation → Complexity analysis

They also share a list of the top 50 coding questions that are often asked on interviews, along with Blind 75 and other lists similar to ByteByteGo 101, which you can use for practice.

In fact, practicing these questions is completely free on the AlgoMonster website. All you need to do is create a free account and start practicing.

📢 Get AlgoMonster 50% OFF here → https://algomonster.io

4. I Mastered Company-Specific Question Patterns

Another huge advantage of AlgoMonster was their extensive company-specific question bank.

What I discovered about different companies:

Google Patterns:

Heavy focus on tree and graph algorithms
Complex string manipulation problems
Mathematical optimization challenges
Multi-step logical reasoning

Amazon Patterns:

Array and string manipulation
Two pointers and sliding window
Basic dynamic programming
Simple tree traversals

Meta Patterns:

Graph-based social network problems
Real-time data processing scenarios
Optimization under constraints
System design coding challenges

Microsoft Patterns:

Classic computer science problems
String and array fundamentals
Recursive problem solving
Clean, readable code emphasis

Instead of preparing generically, I could focus on patterns specific to my target companies.

This targeted approach saved me months of unfocused practice.

By the way, the company-specific question bank is only available inside paid subscription, which I highly recommend to get yo,u and they are offering a whopping 50% discount now, which makes the Algomonster subscription even more valuable and worth it

📢 Get AlgoMonster 50% OFF here → https://algomonster.io

5. I Practiced With Their SpeedRun Feature

AlgoMonster's Speedrun feature is designed to help you go through as many questions as quickly as possible. It is the third step of the 3-step system.

Instead of writing code for each problem, you will be given a multiple choice related to the techniques and templates used to solve the problem.

This cuts down the time to go through many problems significantly.

Why Speedrun?

**Train your intuition.\ **In a real interview, you will be spending at least half the time identifying the algorithm to solve the question and explaining your approach to the interviewer.
**Reinforces your learning.\ **Going through a few more problems after learning the patterns and templates reinforces what you've learned. Seeing many problems helps train your "intuition" when faced with a new problem.
Increase your luck.\ There are only so many problems out there. The more you see, the greater the chance you may encounter a problem at a real interview. You never know!
Most importantly, it saves you time! You can go through many more problems in a shorter amount of time.

6. I Built Confidence Through Systematic Progress Tracking

AlgoMonster's progress tracking was incredibly motivating.

What they tracked:

Patterns mastered vs. patterns remaining
Problems solved by difficulty and the company
Average solving time by pattern type
Success rate on first attempts
Improvement trends over time

My 4-month progress:

Month 1: Basic patterns (Two Pointers, Sliding Window, Binary Search)
Month 2: Tree and Graph algorithms
Month 3: Dynamic Programming and advanced patterns
Month 4: Company-specific problem sets and mock interviews

Seeing measurable improvement kept me motivated during difficult periods.

Instead of feeling lost in an endless sea of problems, I had clear milestones and achievement markers.

To start with, I highly recommend solving problems in their Monster 50 questions and track your progress there.

7. I Applied Pattern Recognition in Real Interviews

The real test came during actual coding interviews.

How pattern-based thinking transformed my performance:

Before AlgoMonster:

Interviewer: "Find all anagrams of a string in another string" My brain: "Uh... nested loops? HashMap maybe? Let me think..."

After AlgoMonster:

Interviewer: "Find all anagrams of a string in another string." My brain: "This is sliding window + character frequency tracking. I need to maintain a window of the anagram length and compare character counts."

Interview at one of the Big Investment Banks

Problem: "Design a data structure for autocomplete."
My approach: Immediately recognized as Trie + DFS pattern
Result: Solved in 20 minutes with optimal complexity
Feedback: "Excellent pattern recognition and clean implementation."

Interview at a Product-Based Company on the AI space

Problem: "Find shortest path in binary matrix."
My approach: BFS pattern with coordinate tracking
Result: Solved with multiple optimizations discussed
Feedback: "Strong algorithmic thinking and systematic approach."

8. I Started Teaching Others Using Pattern-Based Approach

The ultimate test of understanding is teaching others.

How I helped colleagues using AlgoMonster's methodology:

For Junior Developers:

Taught them to identify patterns before coding
Showed them how to build mental templates
Emphasized the importance of complexity analysis

For Interview Preparation Groups:

Led mock interview sessions using pattern recognition
Created study guides organized by algorithmic patterns
Shared company-specific pattern insights

Every teaching session reinforced my own pattern recognition skills.

When someone asked "How do I solve this binary search problem?" I could explain:

Why it's a binary search pattern
What the template looks like
How to adapt it for different constraints
What are the complexity implications

My Results After 4 Months with AlgoMonster

Interview Performance:

Coding interviews passed: 12 out of 14
Offers received: from both banks and a product-based company
Average problem-solving time: Reduced by 60%
First-attempt success rate: Improved from 30% to 85%

Specific Improvements:

Could identify patterns within 2--3 minutes
Solved medium problems in 15--20 minutes consistently
Handled follow-up optimization questions confidently
Wrote cleaner, more readable interview code
Analyzed time/space complexity accurately every time

Career Impact:

Interview confidence: Complete transformation from anxiety to excitement
Technical leadership: Now lead algorithm discussions in team meetings

The Investment That Transformed My Career

While Algomonster is not cheap, especially if you compare it to coding interview courses on Udemy, it's still not very expensive, unlike those coding interview bootcamps.

It's very much affordable, and when you compare the salary hike and opportunities you get, it's a no-brainer.

AlgoMonster is currently offering 50% off their annual and lifetime plans.

What you get:

Complete pattern-based curriculum covering all major algorithmic patterns
100+ problems organized by patterns and companies
Interactive coding environment with real-time feedback
Company-specific question banks for targeted preparation
Progress tracking and performance analytics
Mock interview simulation tools
Lifetime access option for continuous learning

ROI Analysis:

My salary increase: $180,000+
Time saved vs random grinding: 6+ months
Interview success rate improvement: From 30% to 85%+
Confidence boost: Immeasurable

If you think it makes sense, here is the link toget an AlgoMonster PRO lifetime membership for $459, that's what I have because I know its long time investment and totally worth it.

Lifetime plans are great because they provide the best value, and you will be using these platforms for the rest of your career.

Why AlgoMonster Worked When LeetCode Grinding Failed?

If you ask me, LeetCode teaches you to solve individual problems.

AlgoMonster teaches you to think algorithmically.

Traditional Approach (LeetCode grinding):

Random problem selection
Solution memorization
No systematic pattern learning
Isolated problem-solving
Overwhelming volume without direction

AlgoMonster's Pattern-Based Approach:

Systematic pattern identification
Template-based problem solving
Gradual complexity building
Connected algorithmic thinking
Focused, efficient learning path

The difference is like learning vocabulary words randomly vs. learning grammar patterns that apply to thousands of sentences.

My Recommendation

If you're serious about coding interviews, especially at top tech companies, random LeetCode grinding is inefficient and overwhelming.

You need:

Systematic pattern recognition training
Template-based problem-solving approaches
Company-specific preparation strategies
Realistic interview simulation practice
Measurable progress tracking

AlgoMonsterprovides all of this in one comprehensive platform.

Is it worth the investment?

If you're targeting even one coding interview at a major tech company, absolutely yes.

The salary difference between passing and failing that interview is typically $100,000-$200,000+.

With their current 50% discount, there's never been a better time to invest in systematic algorithmic thinking.

If you're a long-term learner, mentor, or planning to revisit interviews every 1--2 years, the Pro Lifetime at $459 is 100% worth it --- especially with the included coaching.

You can also learn more about AlgoMonster plan here

Start Your Pattern-Based DSA Journey Today

Stop grinding random problems hoping to get lucky.

Start learning algorithmic patterns that apply to thousands of problems.

One pattern at a time. One template at a time. One successful interview at a time.

The difference between random preparation and systematic pattern learning is the difference between hoping for the best and confidently expecting success.

All the best for your interview.

I Tried Both LeetCode and ByteByteGo — Here’s What I Found?

Soma — Sat, 04 Apr 2026 08:31:57 +0000

Disclosure: This post includes affiliate links; I may receive compensation if you purchase products or services from the different links provided in this article.

Hello guys, preparing for a technical interview in 2026 looks very different from what it did a few years ago.

With companies increasingly expecting engineers to demonstrate both coding proficiency and system design thinking, the tools you choose for your preparation can make or break your success.

Two names dominate the interview prep world --- LeetCode and ByteByteGo.

LeetCode has been the go-to platform for coding problem practice for years. At the same time, ByteByteGo has emerged as a comprehensive, pattern-based learning ecosystem for mastering the why behind coding and design.

If you're wondering which one gives you a real edge in cracking FAANG-level interviews, this in-depth comparison will help you decide.

By the way, if you are in a hurry and want to know which one to join then I suggest go and join ByteByteGo, it's now the complete platform for technical and coding interviews, covering coding problems, system design, OOP design, and ML System Design.

They are also offering a rare 50% discount now on lifetime plan. I got the same one, and I highly recommend it to any software engineer looking for a chance or a break into FAANG or big investment banks.

Here is the link to join ByteByteGo now --- 50% discount on ByteByteGo

Let's start with what each of these two popular platforms offers in terms of preparing for tech interviews, particularly FAANG interviews.

LeetCode: The Problem-Solving Giant

LeetCode is the most well-known coding practice platform, offering thousands of problems categorized by difficulty, topic, and company.

It's a great place to build problem-solving stamina through consistent repetition and pattern recognition.

You can filter problems by tags (like "Dynamic Programming" or "Binary Tree"), simulate interviews, and discuss solutions with a huge community of developers.

However, LeetCode is primarily a practice-driven platform - it helps you grind problems but doesn't focus deeply on teaching the underlying concepts or system-level thinking.

ByteByteGo: The All-in-One Interview Learning Platform

ByteByteGo takes a very different approach. Founded by Alex Xu (author of System Design Interview --- An Insider's Guide) and Sahn Lam, it's built to help developers understand and apply concepts, not just memorize solutions.

While it covers coding interview patterns, its real strength lies in being a complete technical interview preparation platform - with rich visual explanations, structured patterns, and lessons that go far beyond coding problems.

In fact, ByteByteGo covers:

Coding interview patterns and exercises (with visual solutions)
System Design for interviews (from small to large-scale architecture)
OOP Design Patterns
Machine Learning System Design
Generative AI System Design
Mobile App System Design

And right now, they're offering a massive 50% OFF Lifetime Plan --- which gives you unlimited access to all these modules forever. It's a one-time investment that replaces multiple subscriptions from other sites.

👉 Check out the ByteByteGo Lifetime Plan here

The biggest difference between ByteByteGo and LeetCode lies in approach.

LeetCode focuses on quantity. You get thousands of problems to solve, which helps build confidence but often leads to "pattern fatigue." You might solve 500 problems but still struggle to recognize the underlying pattern in a new one.
ByteByteGo, on the other hand, focuses on quality and structure. It organizes problems into coding patterns - Sliding Window, Two Pointers, BFS/DFS, Dynamic Programming, etc. Each pattern is broken down visually and conceptually before you solve practice problems.

This pattern-based approach trains you to recognize problem archetypes instantly --- exactly what senior interviewers expect during coding rounds.

If you're short on time and want to learn strategically, ByteByteGo gives you a much better return on effort.

In fact, they also give you a curated list of 101 problems known as ByteByteGo 101, which is based upon the 19 most popular coding interview patterns like two pointers, sliding window, prefix sum, hashset, and hashmap. I highly recommend that you solve that to build the expertise in quick time.

Here is the link --- Join ByteByteGo now for 50% OFF

This is where ByteByteGo truly shines.

LeetCode does not cover system design in any structured way --- it's entirely focused on algorithmic problems. If you're preparing for mid-level, senior, or staff-level roles, that's a huge gap.

ByteByteGo, in contrast, offers an entire ecosystem of visual, in-depth system design content, including:

Classic System Design Topics --- caching, load balancing, messaging queues, databases, and scalability.
Case Studies --- designing systems like Twitter, Netflix, Instagram, and Uber.
Emerging Topics --- LLM-based systems, AI architectures, and mobile system design.
OOP and Microservices Design Patterns --- for better modular design thinking.

Each topic is paired with visual explanations, diagrams, and scenario-based Q&A --- making it easier to retain and apply the concepts during real interviews.

Here is an example of that, where ByteByteGo explained how to design a Notification Push system

It's no surprise that many engineers say ByteByteGo feels like "LeetCode + Educative + Exponent + DesignGuru in one place."

Now, let's compare the learning experience between these two stalwarts, ByteByteGo and LeetCode:

LeetCode offers text-based questions and community-driven solutions. It's efficient for repetition but can be hard to grasp for visual learners.
ByteByteGo excels in visual storytelling --- every concept, from caching layers to load balancers, is explained with clean diagrams and intuitive visuals.

This makes it far easier to understand complex distributed systems, a must-have skill for system design interviews in 2026, especially if you're targeting backend, SDE-II+, or architect roles.

The visual guides really make it easy to understand complex concepts. They also have many System Design cheat sheets like this to quickly remember key System Design concepts before interviews

credit --- ByteByteGo

Now, let's compare the most important thing, the cost and the value proposition. Which one is more affordable, and which one provides more value? ByteByteGo or LeetCode?

LeetCode

Model: Free, but LeetCode Premium is Subscription-based
Price Range: $39 per month to $299 per year
Best For: Practicing coding problems and accessing company-wide questions and premium content.

ByteByteGo

Model: Annual or One-time Lifetime Plan
Current Offer: 50% OFF
Best For: Complete interview preparation --- covers coding, system design, OOP design, ML system design, GenAI system design, and mobile system design

Verdict:

If you're preparing for the long term or want resources beyond coding practice, ByteByteGo Lifetime Plan is a far better investment. It's more affordable over time and provides a structured, all-in-one roadmap for mastering both coding and design interviews.

👉 Claim the 50% OFF Lifetime Deal on ByteByteGo here

You cannot learn alone, and even if you try, you will get better results with communities and friends. When it comes to Community and Support, LeetCode has a massive advantage.

LeetCode has a massive open community --- millions of users post solutions, discuss ideas, and share interview experiences. However, the discussions vary widely in quality.
They also have many subreddits dedicated to LeetCode questions and people solving leetcode problems, where you can look for buddies as well.
ByteByteGo offers a curated, high-signal learning community with regular content updates and expert-led insights.
Since the platform is run by real FAANG engineers, the guidance and feedback are more precise and up-to-date with actual interview trends.

Here's the short answer:

If your goal is to practice coding problems daily, LeetCode is still one of the best free resources.
But if you want to understand the patterns, master system design, and prepare comprehensively for FAANG-level interviews in 2026, ByteByteGo is the better investment.

It's not just a platform - it's a structured learning path covering everything from data structures to AI system design.

And with their current 50% OFF Lifetime Plan, it's arguably the best deal available right now for serious developers.

Here is the link --- Join ByteByteGo now with 50% Discount

The difference between a good engineer and a great one often comes down to how deeply they understand the "why" behind what they build.

LeetCode will sharpen your algorithmic problem-solving muscles. But ByteByteGo will train you to think like an engineer, design like an architect, and communicate like a senior developer --- all critical skills to land top-tier tech jobs in 2026.

If you're looking for one platform that does it all --- coding, design, and AI engineering --- ByteByteGo is the clear winner.

Thanks for reading this article so far. If you like this article, then please share it with your friends and colleagues. If you have any questions or feedback, please drop a note.

P. S. --- If you just want to do one thing at this moment, go join ByteByteGo and start learning System Design and Coding Interview concepts, you will thank me later. Make it a goal for the new year, and you will surely be a better Software Engineer.

I found Leetcode for System Design, and it's Awesome

Soma — Sun, 29 Mar 2026 04:55:27 +0000

Disclosure: This post includes affiliate links; I may receive compensation if you purchase products or services from the different links provided in this article.

credit- codemia.io

While LeetCode is the go-to platform for DSA, system design has always been a challenge.

These are great resources to learn fundamentals, go through case studies, and understand the theory part of the System design, but LeetCode-style practice is one thing that is missing - until now.

I recently found Codemia.io, and I must say, it feels like the LeetCode for System Design.

If you've struggled with structuring your system design answers, getting real feedback, or knowing whether your approach is correct, Codemia.io is a game-changer.

They not just have the biggest collection of System Design and OOP Design problems for practice, they also have a free System design course called Tackling System Design Interview Problems, which is a great free resource to learn essential System Design concepts.

It's a short course with 2 hours of content, but powerful and also has quizzes to test your skills. Here are all the key System design topics you can learn on this free course:

Now, let's check out how Codemia.io can help you to prepare better for your System design and OOP Design interview, and why I think it's like Leetcode for System design.

Most system design resources today are long, text-heavy articles or expensive courses. The problem?

No hands-on practice - Reading about system design isn't enough; you need to actively design solutions.
No structured progression --- Unlike DSA, where problems are categorized by difficulty, system design prep often feels random and overwhelming.
No evaluation or expert feedback --- You don't just need an answer; you need to know why one design is better than another.

This is where Codemia.io changes the game.

Instead of just giving you theory-heavy content, Codemia.io provides an interactive, structured, and guided approach to system design. Here's what I loved:

1. Real-World System Design Problems

Codemia.io has over 120+ system design problems, each with a structured approach similar to how LeetCode presents DSA problems.

For example, consider the classic "Design a URL Shortening Service" problem.

You start with:
1. Functional and Non-Functional Requirements
2. Capacity Estimation
3. Database Schema and API Design
4. High-Level & Low-Level Design

It guides you step by step so you don't feel lost.

Bonus: You also get access to a drawing board where you can sketch your architecture, just like in a real system design interview.

And most importantly, they also share expert solutions for most of the problems to see how an expert should answer that question. For example, here is the solution of how to design a URL shortening service, and it's also available for free

2. Difficulty-Based Categorization

Just like LeetCode, both System design and object-oriented design problems are categorized as:

Easy --- Foundational concepts like Load Balancing, Caching, Message Queues
Medium --- Scenarios like Rate Limiting, Search Autocomplete, Real-Time Chat Systems
Hard --- Complex designs like Distributed Databases, Cloud Storage, and Microservices Architecture

3. AI-Powered Hints & Evaluations

One of the best features is AI-assisted guidance. You get to chat with the coach --- the AI agent that will help you with the question and solution.

1. If you get stuck, AI provides hints --- just like LeetCode's hint system.

2. If you submit a design proposal, AI evaluates it against industry best practices.

3. You get automated feedback on scalability, fault tolerance, and efficiency.

This bridges the gap between self-learning and expert mentorship.

Beyond System Design: Object-Oriented Design & AI Interviews

Codemia.io goes beyond just system design. It also covers Object-Oriented Design (OOD) Questions. If you don't know, Object-Oriented Design is a common low-level design (LLD) round at FAANG.

Here are OOP problems you can solve on codemia.io to get some practice:

Classic problems like Parking Lot, ATM Machine, and Hotel Booking System
Step-by-step UML diagrams and class structure explanations
Comparison of different design patterns

If you struggle with OOD interviews, this is a goldmine.

Here is also all the OOP design problems available on Codemia.io

What point to mention, what should you discuss, tradeoffs, and so much more.

So far, we have compared the content, but if we talk about the platform, codemia.io has been especially designed for online practice, particularly for design questions.

Then there is community, and you can also see the solutions of other folks to learn from them. I love this feature as it allows me to see what other folks are thinking and how they solve that problem. This comparative analysis has helped me a lot to learn key system design concepts.

Overall, I think codemia.io is a great platform to prepare for System design and OOP design interviews. You may find courses, but it's hard to find a platform like this.

I highly recommend it for anyone preparing for System Design and Tech interviews. And, if you want to join now, you can also use the discount code PROMOTION20 to get 20% off now.

Here is the link --- 20% OFf on Codemia.io

Final words

If you're preparing for FAANG system design interviews, Codemia.io is a must-try. It solves the biggest problems in system design prep:

Structured learning path (instead of random articles)
Real-world hands-on practice (instead of just reading)
Expert evaluation & AI feedback (so you know what to improve)
Mock interviews to simulate real conditions

I'm still exploring Codemia.io, but what I've seen so far is truly impressive.

If you've tried it, let me know what you liked or didn't like.

Thanks for reading this article so far. If you like this article, then please share it with your friends and colleagues. If you have any questions, feel free to ask in the comments.

P. S. - They are also offering 20% discount now, and you can get access for just $59 for one year, the original price is $120 so you have a chance to save $60 and also learn System design better.