Since most of the programmers often look for inspiration and resources to improve their coding skill, I decided to share some of the good books which can help them to improve their coding.

Since many universities, colleges, and training courses only teach programming languages but not the art of coding, it still remains one of the self-learned skills which many programmers acquired either in Job or working on real-world projects.

The internet has helped a lot to coders with several websites coming up to teach code, programming contest, helping to solve your programming interview questions and all, but IMHO, books are still vital for overall improvement.

In this article, I am going to share some of the great books written by both great authors and great programmers, which can certainly help you to write good code and become a better programmer. These are also the books that I wish I knew earlier because they have helped me a lot in improving my own coding skills.

It’s also a good chance that you might have read or seen this book before but just knowing is not enough. The real value comes from reading and adopting their suggestions.

For example, many developers know about Clean Code or Code Complete, two of the most recommended books to junior developers but very few of them have read them and even lesser people apply their teaching in the real world.

6 Books to Improve Coding and Refactoring Skill of Software Developers

I will be honest, Coding is an art, and like many arts, it takes a lot of practice, study, and self-discipline to become a good coder. In my childhood, I have read that “books are your best friend, keep them near to you,” and that has been proved absolutely correct in the world of Programming and Coding.

Programming is a challenging field with new development happening every day, and knowledge quickly becomes obsolete, but good coding skill and self-discipline is something, which will never get outdated and help you throughout your career.

Books have helped me a lot, and in this article, I am going to share 5 great books which will help you to improve your coding skill.

1. Working Effectively With Legacy Code

This is the one book I recommend to every Programmer who codes. Since development and maintenance are the primary jobs of software engineers, and bad code is hard to maintain, but sometimes you have no choice but to live with that, this book will help you how to work effectively with legacy code.

This is also from the Robert C. Martin Series, the same series where “Clean Code” and other good books like “Clean Coder” belongs.

If you need a course to combine with this book, I suggest Mosh Hamdani’s classic Learn The Art of Writing Clean Code course on Udemy. Examples are given in C# but the technique he shares is equally useful for Java developers.

2. Clean Code by Uncle Bob Martin

Clean code is one of the best books for java programmers, but any programmer can benefit from it. This book will help you to write better code. This book teaches you about code smell, function and data structure, object-oriented design principles, design patterns.

Uncle Bob Martin, who himself is a great programmer, has done a tremendous job of imparting his year of experience in simple words.

The title “Clean Code” aptly justifies advice, best practices given to the programmers in this book. If you have to choose just one book, then pick the clean code.

If you want, you can also combine this book with the Clean Code course by Maximillian Scwarzmuller on Udemy for more active learning.

3. Refactoring to Patterns by Joshua Kerievsky

This is one of the rare books where you will find the best combination of theory and practice. Refactoring is a process to make your working code more beautiful, and this book can help you there by leveraging already tried and tested patterns of the software development world.

This is one of the best books to learn how and when to use design patterns. Java developers have added advantage because examples are given in Java, but it’s not a big problem for C++, Scala, or Python developers because examples are easy to follow and can be understood by anyone who knows how to read the pseudo-code.

Java’s verbosity and English like language will also help a lot. And, if you need a course to combine with this book, I suggest you should check out Refactoring Java with the IntelliJIDEA course by James Richardson on Udemy.

This course will teach you how you cause Intellij IDEA’s powerful refactoring feature to create a clean code.

4. Refactoring: Improving the Design of Existing Code 2nd edition

Refactoring is a process of making a working code beautiful, refactoring helps to improve the design of working code. It is also one of the essential tricks of good programmers, more often than not good coders are also good at refactoring.

This book will teach you both the art and science of refactoring code. It doesn’t matter whether you are a Java programmer, C++ developer, or a Python developer, every programmer can benefit from this book.

This book is a collective effort of some of the best authors in the programming world. A list of authors includes Martin Fowler, Kent Beck, John Brant, William Opdyke, Don Roberts, and forward by Erich Gamma.

The 2nd edition of this book is filled with more examples and how you can also use refactoring in your test code. Also examples are now written in JavaScript but it should be easy to follow up even for Java and Python developers.

If you want, you can also combine this book with Refactoring to Design Pattern course on Udemy for better understanding.

5. Beautiful Code: Leading Programmers Explain How They Think

This is one of the great books to improve your coding skills because it offers you an opportunity to see how expert programmers approach a problem, how the written code and how do they solve the problem, and still able to keep their code beautiful.

This book is a collection of case studies that tells how those expert programmers, which includes, Brian Kernighan, Jon Bentley (author of Programming Pearls), Tim Bray, Karl Fogel, Michael Feathers (author of Working Effectively with Legacy Code), and many more great authors and programmers.

No matter which programming language you use for coding, like Java, C#, Python, or Golang, you will find something interesting in this book.

If you want, you can also combine this book with the Design Pattern in Java course on Dmitri Nesteruk to learn about the modern implementation of classic OOP design patterns.

6. Test Driven Development: By Example by Kent Beck

Kent Beck’s “Test Driven Development: By Example” is a classic in the field of software development, receiving a notable rating of 4.3 out of 5 stars from 534 reviews. In this foundational work, Beck introduces the concept of test-driven development (TDD) as a methodology aimed at eradicating fear in application development.

Beck argues that while a certain level of fear can be constructive, excessive fear leads to tentative, grumpy, and uncommunicative programmers.

By adopting TDD, programming teams can eliminate fear, fostering a positive work environment and enhancing their ability to tackle challenging tasks. TDD, as presented by Beck, emphasizes the continual testing and refactoring of code, leading to improved communication among team members and a proactive approach to handling constructive criticism.

The author teaches these principles through practical examples, providing programmers with a valuable resource to enhance the quality of their work while maintaining a collaborative and fearless development process. You can also combine this book with the Practical Test Driven Development for Java Programmers for better understanding.

His new book Tidy First?: A Personal Exercise in Empirical Software Design is though not as good as this one but if you want you can also check that one, it contains all the known best practices around coding and test driven development in a condensed form which is great for beginners but experience developer may already know that.

That’s all about some of the great books to improve coding skills. Both beginners and experienced programmers can benefit from these books. In fact, these are the best resource for expert beginners which has experience but lack the knowledge to support those experience.

It’s a real shame if you have 10 years of experience but cannot write good code, and believe me, it happens.

You might have read some of them already, but they are worth reading again. I have always learned new things while reading a good book like Clean Code twice.

They are must-read books for any Programmer who wants to become a good coder as well. They will also help you to do well in your Job, earn respect from peers and seniors, and also do well in job interviews.

If you are not writing code on a daily basis, not trying to improve, not introspecting then a number of years in the job will grow, but your programming experience will not. You will struggle to write good code and good unit tests, which is one of the essential traits of a good programmer.

It’s never is too late, read some of this book to get back on track, if you are not sure which one to start, just read the Clean Code.

Thank you for reading so far, if you have any book to add to this list of best coding and refactoring books, then please let me know via comments, and I’ll add them for everyone’s benefit.

P. S. - If you are looking for some Free Algorithms courses to improve your understanding of Data Structure and Algorithms, then you should also check the Java Data Structures & Algorithms + LEETCODE Exercises course on Udemy.

Hello guys, if you’re preparing for system design interviews, one thing is clear: brushing up on basic concepts won’t cut it anymore.

While its important to know System Basics like Caching, Replication, Rate limiting, API Gateway, Load Balancer, NoSQL vs SQL etc, today’s interviews are far more practical, and interviewers expect you to design real-world systems that are scalable, fault-tolerant, and performant.

Whether you’re aiming for top tech companies like Google, Meta, Amazon — or up-and-coming AI startups — mastering system design is non-negotiable, even for senior developers.

I know many friends who were confident enough to crack any System Design interviews and ignored preparation due to their busy life, only to find that it was a struggle during interview.

As someone who has reviewed dozens of System Design interview preparation courses and platforms over the years, like AlgoMonster, Exponent, ByteByteGo, Educative, Bugfree.ai, or DesignGurus.io I can say for sure that, practice makes you perfect. No matter how much you read, if you are not solving questions on your own, you will not master the necessary skills you need to crack those interview.

The good news? You don’t need to hunt around for scattered resources like articles, blogs, YouTube channels or Udemy courses.

One platform that stands out for system design prep is Codemia.io, Leetcode for System Design.

Codemia.io gives you:

1. Real-world system design questions

2. Clear explanations with free solutions for many problems

3. A diagramming editor to practice like real interviews

4. An ever-growing library curated by senior engineers

5. Editorial solutions created by experts

6. AI assisted learning and problem solving

7. Expert evaluation

All this makes Codemia.io a go-to resource for System Design interview prep and I have been using it for my interview preparation now.

In this post, I’ll share 20 must-solve system design problems, including free questions and solutions from Codemia.io that you should absolutely practice before your next interview.

They have both free and paid questions and I am going to share them both for you.

Top 5 Free System Design Interview Problems with Solutions

These are completely free to access, and they include detailed editorials, diagrams, and architecture breakdowns on Codemia.

1. Design a URL Shortening Service (Like TinyURL)

In this problem you will learn to implement hash-based URL shortening, handle collisions, and manage redirection at scale.

Here are both Functional and non-functional requirement

Functional:

1. Given a long URL, create an associated short URL.

2. Given a short URL, return the associated long URL.

Non-Functional:

1. Availability — This service has to be highly available. Especially functionality (2) (redirection).

2. Response time — Functionality (2) has to have low response time, e.g., less than 10ms. Functionality (1) (creating short URL) can takes more time — less than 10 seconds.

3. Scalability — We will get more and more requests to create short URLs, so the storage has to be highly scalable.

By the way, you should keep the requirements scope small. You only have 35–50 min in an interview. If you have a lot of requirements, you’d risk running out of time.

We could add other requirements like custom link. But we will start with a small set of requirements. Easier to expand later than shrink.]

You an further see the 📘 Solution once you have tried it yourself.

2. Design Twitter

In this problem you will design a messaging app like X with Follow/unfollow logic, timeline generation, tweet distribution, and fanout strategies explained.

Here are detailed functional and non-functional requirements for you:

Functional:

1. User can tweet (send) up to 140 character message.

2. User can follow other users.

3. User can like other users’ tweets.

4. User’s home feed will show tweets from the users they are following.

The home feed will show top K popular tweets, based on the number of likes they receive, and the number of the followers the author has.

Non-Functional:

1. Scalability. It will have to serve a very large population, e.g., 500M DAU.

2. Response time. User has to see tweets quickly. When user opens home feed, the first 10 tweets should show up within 500ms.

3. Availability.

See the 📘 Solution once you have tried your hand.

3. Design Ticketmaster

This is a great System design problem to learn Concurrency handling, seat locking, and avoiding double bookings.

Here are the detailed functional and non-functional requirements:

Functional:

User is able to:

1. Search for a theater by city or ZIP code.

2. After selecting a theater, search for movies shown the the theater.

3. After selecting a movie, select which show (e.g. show starting at 1PM, 3PM, 5PM, …)

4. After selecting a show, user is presented a list of available seats.

5. User can select the seats and finalize the booking.

Non-Functional:

We focus on the following aspects, as they are the most important:

1. Consistency — once a booking is made, it has to be respected. No double bookings.

2. Concurrency — multiple users are interacting with the service to view seats and book seats. Make sure system handles concurrent accesses and provide consistent bookings.

3. Scalability

4. Fault Tolerance

You can also refer the 📘 Solution once you have tried your hand.

4. Design a Parking Lot System

This is another free System Design problem on Codemia.io where you would design using Object-Oriented Design and handle both functional and non-functional requirement like

Functional:

1. User can reserve a parking spot.

2. User pays for the reservation.

3. User can park a car on the parking spot.

4. User can leave before the reservation time expires.

5. One common error case to handle is when a user makes a reservation, but fails to show up. In this case, we would charge for the first 24 hours..

Non-Functional:

1. Scalability. We are designing this for an international company who has 1000s of parking lots across nations.

2. Availability.

3. Consistency. Once a reservation is made, the parking spot must be available for the user. No double-booking.

Once you have tried it yourself you can also see the 📘 Solution here

Top 10 Hard System Design Problems for Interviews (Paid)

While these are paid on Codemia, they are worth every penny if you’re serious about mastering advanced system design.

5. Design Facebook Messenger

This problem is about designing a real-time chat, message delivery, synchronization across devices, and storage optimization.

Functional Requirements:

• Send/receive real-time text messages between users

• Support group chats with multiple participants

• Deliver messages across multiple devices with synchronization

Non-Functional Requirements:

• Message delivery latency < 100ms for real-time experience

• 99.9% uptime to ensure reliable communication

• Support 1 billion+ concurrent users with horizontal scaling

6. Design a Resource Allocation Service

This System design problem is perfect for scheduling systems like Uber, Lyft, or shared resource booking tools.

Functional Requirements:

• Match available resources (drivers/vehicles) to user requests

• Real-time tracking and status updates of resource location

• Handle booking confirmations and cancellations

Non-Functional Requirements:

• Response time < 3 seconds for resource matching

• 99.95% availability during peak hours

• Scale to handle 100K+ concurrent booking requests

7. Design a Fitness Tracking App

As part of this question you need to propose solution which can track steps, integrate GPS, sync across devices.

Functional Requirements:

• Track daily steps, distance, and calories burned

• GPS integration for route mapping and location tracking

• Sync fitness data across mobile, web, and wearable devices

Non-Functional Requirements:

• Battery optimization — minimal impact on device battery life

• Data accuracy within 95% for step counting and GPS tracking

• Offline capability — function without internet connectivity

8. Design a Weather Reporting System

Ingest and distribute real-time sensor data, cache weather forecasts, and scale APIs.

Functional Requirements:

• Ingest real-time weather data from multiple sensor sources

• Provide weather forecasts and current conditions via API

• Cache and serve weather data for millions of locations

Non-Functional Requirements:

• Data freshness — weather updates within 5 minutes of sensor reading

• API response time < 200ms for weather queries

• 99.99% availability for critical weather services

9. Design Pastebin

This System design problem can teach you about expiration logic, storing snippets, short link redirection.

Functional Requirements:

• Store and retrieve text snippets with unique URLs

• Support automatic expiration of pastes after specified time

• Generate short, shareable links for easy access

Non-Functional Requirements:

• Handle 1M+ paste creations per day

• Data durability — 99.999% reliability for stored pastes

• Low storage cost through compression and efficient data structures

10. Design a Nested Comments System

When you are asked this problem on interview, focus on tree structures, pagination, and moderation workflows.

Functional Requirements:

• Support hierarchical comment threads with unlimited nesting

• Paginate comments for performance on large threads

• Implement comment moderation and spam filtering

Non-Functional Requirements:

• Load comment trees in < 300ms for good user experience

• Support 10K+ comments per thread without performance degradation

• Scalable architecture to handle viral content with millions of comments

11. Design Online Presence Indicator

Track real-time status updates, efficient updates via websockets, and availability caching.

Functional Requirements:

• Track and display real-time user online/offline status

• Update presence across all user sessions and devices

• Provide last-seen timestamps for offline users

Non-Functional Requirements:

• Status updates propagated within 1–2 seconds via WebSockets

• Minimize bandwidth usage for presence updates

• 99.9% accuracy in presence detection

12. Design a Vending Machine System

This problem was asked to me on J P Morgan interview and its about Inventory management, embedded system integration, payment gateway design.

Functional Requirements:

• Track inventory levels and product availability

• Process payments through multiple methods (cash, card, mobile)

• Dispense products and provide change/receipts

Non-Functional Requirements:

• 99.9% uptime for payment processing

• Real-time inventory synchronization across all machines

• Secure payment processing with PCI compliance

13. Design a Tagging Service

Design a solution which support Tag hierarchy, search performance, and recommendation systems.

Functional Requirements:

• Create hierarchical tag structures and relationships

• Support fast tag-based search and filtering

• Generate personalized tag recommendations

Non-Functional Requirements:

• Search response time < 100ms for tag queries

• Support billions of tags with efficient indexing

• Auto-complete suggestions within 50ms

14. Design Craigslist

Design a Craiglist like system which can categorized listings, search index, and ad posting flows.

Functional Requirements:

• Post classified ads in categorized sections

• Search and filter listings by location, category, price

• Support user communication through anonymous email relay

Non-Functional Requirements:

• Handle 50M+ listings with fast search performance

• 99.5% uptime for ad posting and browsing

• Search results returned within 500ms

15. Design Multi-Device Screenshot Capture System

You need to design a solution where you can manage screenshots from remote devices, sync, and store them securely.

Functional Requirements:

• Capture screenshots from remote devices on-demand

• Sync and store screenshots across cloud storage

• Provide secure access controls for screenshot viewing

Non-Functional Requirements:

• Screenshot capture latency < 5 seconds

• Secure transmission with end-to-end encryption

• Support 1000+ concurrent device connections

16. Design a Network Path Analyzer

Design a System to track, simulate, and monitor multi-hop network paths.

Functional Requirements:

• Trace and analyze multi-hop network paths between endpoints

• Simulate network conditions and monitor path performance

• Generate network topology visualizations

Non-Functional Requirements:

• Real-time path analysis with < 100ms latency

• Support monitoring 10K+ network paths simultaneously

• 99.9% accuracy in path detection and metrics

17. Design an Employee Swap System

You need to design a system like a job board within your org — matching roles with available employees.

Functional Requirements:

• Match employees with available positions based on skills

• Handle swap requests and approval workflows

• Maintain employee profiles and role requirements

Non-Functional Requirements:

• Matching algorithm response time < 2 seconds

• Support 100K+ employee profiles with efficient search

• 99.5% system availability during business hours

18. Design a Conference Room Booking System

Can you design a system with calendar integration, booking slots, room conflicts, and availability display.

Functional Requirements:

• Book meeting rooms with time slot management

• Integrate with calendar systems (Outlook, Google Calendar)

• Handle booking conflicts and room availability checks

Non-Functional Requirements:

• Booking confirmation within 1 second

• 99.9% accuracy in conflict detection

• Support 1000+ concurrent booking requests

19. Design a Video View Count System

You need to design a system to optimize counting at high scale, prevent fraudulent views, aggregate with accuracy.

Functional Requirements:

• Count and aggregate video views in real-time

• Detect and prevent fraudulent/bot views

• Provide view analytics and reporting dashboards

Non-Functional Requirements:

• Handle 1M+ views per second with accurate counting

• 99.99% data consistency for view counts

• Anti-fraud detection with < 1% false positive rate

20. Design an API Rate Limiter

Design a System to implement sliding window or token bucket algorithm at scale for rate-limiting APIs.

Functional Requirements:

• Implement rate limiting using sliding window or token bucket algorithms

• Support per-user, per-API, and global rate limits

• Provide rate limit status and remaining quota information

Non-Functional Requirements:

• Rate limiting decision in < 1ms

• 99.99% availability for rate limiting service

• Scale to handle 1M+ API requests per second

21. Design a Resource Management System (OOD)✨ Bonus

This one is anObject-Oriented Design problem to prepare for FAANG and tier-1 companies. Includes class diagram practice.

Functional Requirements:

• Allocate and deallocate system resources (CPU, memory, storage)

• Track resource usage and availability in real-time

• Implement resource scheduling and priority management

Non-Functional Requirements:

• Resource allocation latency < 10ms

• Support 10K+ concurrent resource requests

• 99.99% accuracy in resource tracking and allocation

Why Codemia.io Is My Go-To System Design Interview Prep Platform?

I have read books, tried many courses and while all of them provide value the most value I have got from practicing the problem my myself and using AI for assistant.

Also most platforms just give you a PDF solution or a video. But Codemia.io gives you:

1. Real-world, open-ended problems

2. Rich editorials with diagrams and best practices

3. A built-in diagramming tool to practice visually

4. Discussion board, solution submissions, and design patterns

5. AI Assistance on both problem solving and evolution.

Whether you’re a beginner or preparing for Staff Engineer interviews, Codemia.io has questions from simple CRUD systems to large-scale distributed architectures.

They also have this nice System Design roadmap where you can click the topic and practice the related System Design problems

For example, when you click Artificial Intelligence you can see these problems to practice

💡 Pro Tip: Start with the free problems + editorials. Once you’re comfortable, invest in the premium ones to tackle the more complex scenarios.

Conclusion

That’s all about Top 20 System Design Problem you can practice on Codemia.io, online to hone your software design skills. These questions also touch based different topics like handling load, handling concurrency etc.

System Design isn’t just an interview topic anymore — it’s a must-have skill for modern software engineers. Nowadays, your ability to design scalable systems can determine whether you land that Staff Engineer, SDE III, or Senior Software Engineer role.

With Codemia.io, you get a structured way to learn, free questions to practice, and a real-world environment to simulate interviews.

So, bookmark this list, tackle one problem a week, and you’ll be amazed at how fast your system design confidence grows.

Ready to get started? Check out Codemia.io System Design Library and pick your first problem today!

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

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

Hello guys, if you are passionate about AI tools and follow the developments in AI world then you may have come across names like Cloud Code and Claude AI. If not, don’t worry, I will introduce these tools to you today and also share affordable and best Udemy courses to learn them.

Claude Code and Claude AI are among the fastest-evolving tools in the AI developer stack. Created by Anthropic, Claude stands out for its safety-first design, natural language understanding, and real-world coding capabilities.

In 2026, Claude Code has become a powerful interface for developers who want to write production-ready code using conversational prompts or automate workflows with AI agents.

But mastering Claude Code isn’t just about chatting with an AI. It’s about learning to use it effectively — — to build, test, deploy, and even automate full-stack applications using cutting-edge AI tooling.

If you want to learn Claude Code and looking for online courses then you have come to the right place as today, I am going to share best online courses to learn Claude Code and Claude AI in 2026.

Whether you’re a beginner, a startup builder, or an automation geek, these top-rated courses will help you unlock Claude’s full potential.

Btw, if you are new to AI and ChatGPT/Claude world then I also suggest you to invest time on learning prompts as they help you to get best from these AI models.

If you need a resource, The Complete Prompt Engineering for AI Bootcamp (2026) course on Udemy is great one to start with . It will teach you practical coding skills for working professionally with AI, including GPT-4, Stable Diffusion, and GitHub Copilot.

5 Best Udemy Courses to learn Claude AI and Claude Code in 2026

Without any further ado, here are the top 5 Udemy courses to learn Claude Code and Claude AI in 2026. The list include courses with covers both genera AI gent Claud AI and specialized agent like Claude Code which can seriously help you in coding and software development tasks.

1. Claude Code: Building Faster with AI, from Prototype to Prod

Hot & New | 608 students | Instructor: Frank Kane

This course is created by none other than Frank Kane, ex Amazon and one of the best instructor on Udemy when it comes to clarity.

This hands-on course teaches you how to use Claude Code right from your terminal to build production-grade software. You’ll learn by doing — — creating an online radio station app using Claude AI and AI agents, all from the command line.

What you’ll learn:

• Using Claude Code to build software directly from prompts

• AI-assisted development workflows

• Building, testing, and deploying without writing boilerplate manually

Here is the link to join this course — — Claude Code: Building Faster with AI, from Prototype to Prod

2. The Complete AI Coding Course (2026) — Cursor, Claude Code

Bestseller | 5,343 students

This is the ultimate vibe coding course if you’re interested in using Claude Code alongside Cursor, ChatGPT, and Replit. It walks you through full-stack development where you co-code with AI agents and iterate from idea to MVP fast.

What you’ll learn:

• Claude Code in action for full-stack projects

• Integrating with other tools like Cursor and v0.dev

• Building real-world applications with minimal code

Here is the link to join this course — — The Complete AI Coding Course (2026) — — Cursor, Claude Code

3. MCP: Build Agents with Claude, Cursor, Flowise, Python & n8n

Bestseller | 741 students

If you’re diving into automation and agent workflows, this is the best course to understand how Claude and other models work within the Model Context Protocol (MCP). You’ll also integrate with LangChain and Flowise for orchestration.

What you’ll learn:

• Automating with Claude, n8n, and Flowise

• Understanding Model Context Protocol (MCP)

• Server/client prompt pipelines and tool integration

Here is the link to join this course — -Build Agents with Claude, Cursor, Flowise, Python & n8n

4. The Complete Agentic AI Engineering Course (2026)

Bestseller | 69,333 students

Even though this course covers multiple agent frameworks, Claude is a key component. You’ll build 8 agentic apps using Claude, AutoGen, CrewAI, and LangGraph — — making this course ideal for developers seeking depth.

What you’ll learn:

• Build Claude-powered autonomous agents

• Use LangGraph, CrewAI, and AutoGen to structure complex workflows

• 8 projects from productivity to automation

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

5. Claude AI: The AI Assistant You’ll Actually Use

Role Play | 216 students

This practical course helps you get the most out of Claude AI, not just as a coder but as a business user, researcher, or creator. You’ll explore prompt strategies, writing help, and how to turn Claude into your go-to assistant.

What you’ll learn:

• Claude AI for research, writing, coding, and problem solving

• Use Claude in role-play and productivity workflows

• Difference between Claude Free vs Pro

Here is the link to join this course — Claude AI: The AI Assistant You’ll Actually Use

Bonus: Claude AI Pro — The Ultimate Course

Role Play | 538 students

This course takes you deeper into Claude’s Pro version and how to apply it creatively. You’ll learn how to use Claude for content, workflows, prompt design, and practical decision-making — ideal for entrepreneurs and creators.

Here is the link to join this course — Claude AI Pro— The Ultimate Course

Final Thoughts

That’s all about the best Udemy courses to learn Claude Code and Claude AI Agent in 2026. Claude Code is quickly becoming a staple for developers who want to build faster, smarter, and with fewer barriers.

Whether you’re interested in full-stack app development, automating workflows, or building AI agents, these courses offer the guidance you need to level up in 2026.

With Claude’s powerful natural language capabilities and Anthropic’s vision of safe, steerable AI, learning how to build with Claude today means staying ahead of the curve tomorrow.

By the way, if you want to join multiple courses on Udemy then you can also checkout Udemy’s Personal Plan, where you get access to best of Udemy’s 11000+ courses for a monthly fee of $30.

If you want to join multiple courses then Udemy Personal Plan is actually a better deal. You can also try for free for 7 days to get a feel of it.

So, what are you waiting for? Pick a course, start learning, and join the AI revolution!

Happy Learning!

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

👉 Read smart. Build fast. Stay ahead.

P. S. — You can also join a course like LLM Engineering: Master AI, Large Language Models & Agents to get some hands-on experience on building RAG based chatbot and learning LLM by watching.

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Hello guys, its well know that Algorithms are language agnostic, and any programmer worth their salt should be able to convert them to code in their programming language of choice.

Unfortunately, I have come across several programmers who are REALLY good at programming languages like Java or Python, few knows minor details of API and language intricacies but have very poor knowledge of the fundamentals of Algorithms and Data Structures.

Just ask them to implement any popular sorting algorithms like quicksort or merge sort, and they will fall apart.

If you expect them to know more advanced and sophisticated algorithms like String processing algorithms, graph algorithms, tree traversal, or greedy algorithms, be ready to check on Interviews; otherwise, you might end up with some surprises.

Once, I come across a perfect candidate for a core Java senior developer role, He was excellent in Java, multi-threading but his data structure and algorithm skill were penniless to his experience and caliber.

When I asked him why he didn’t spend time brushing his algorithm and problem-solving skill before coming to the interview?

His excuse was, “those algorithms are just for meetings and never really used in practical coding. I have never used them in my 6 years of Java development career. “

He was somewhat right; you don’t need to implement a hash table in Java; you can always use the HashMap class or Dictionary in Python for that sense, but he failed to recognize the more long-term improvement algorithm and data structure do in improving programming skill.

Algorithms are tools for developing programming solving skills and coding sense, which is required to convert a user requirement into a line of code, also known as a computer program.

They are also all around, if you use Facebook, have you ever thought about how they find your friends so quickly? Or how does Netflix recommends the movie you want to watch?

Those are just a couple of examples of Machine Learning Algorithms, which are taking Algorithms to another level.

Before we start with the books, I have an exciting news to share with you all. This week, all the Datacamp courses are free, I mean completely free because they are running a Free access week and all their courses, career tracks and certifications are free this week.

If you’ve been waiting for the perfect opportunity to learn Data Science, AI, Machine Learning, Python, SQL, Data Engineering, or Business Intelligence, this is probably the best deal you’ll see all year.

DataCamp is running its Free Access Week from June 1st to June 7th, 2026, giving everyone free access to almost their entire learning platform.

The best part?

1. No credit card required

2. Access to hundreds of premium courses

3. Career tracks and certifications included

4. Learn at your own pace for a full week

If you’ve ever wanted to try DataCampbut weren’t sure whether it’s worth the subscription, this is your chance to explore everything completely free.

10 DSA Books Every Software Engineering Should Read

Another gold tip to those who think that Algorithms are Data Structures is for those who want to work in Amazon, Google, Facebook, Intel, or Microsoft; remember it is the only skill that is timeless, of course, apart from UNIX, SQL, and C.

Programming languages come and go, but the core of programming, which is algorithm and data structure remains the same.

If you know how a hash table works, then you can use their implementation in any programming language like HashMap from JDK, Dictionary in Python, or HashMap from the C++ boost library.

So, if you are serious about programming and realizing now that algorithms and data structure is not optional, here are some of the great books to learn algorithms.

Some of you might have already read them before, but they are worth reading again.

1. Introduction to Algorithms 4th Edition by Thomas H. Corman

This is one of the most popular algorithm books, but be aware that it contains a heavy dose of theory.

The current edition of this book is 4th Edition, and I strongly suggest that every programmer should have this on their bookshelf, but only for short reading and references.

This book is also commonly referred to as CLRS (named after its authors Cormen, Leiserson, Rivest, and Stein), is a seminal textbook in the field of computer science.

Now in its fourth edition, this comprehensive and authoritative resource provides an in-depth exploration of fundamental algorithms and data structures.

This book is also the gold standard in data structure and algorithms but It’s not possible to finish this book in one sitting, and some of you may find it difficult to read as well.

In that case, you can combine your learning with an online course like Data Structures and Algorithms: Deep Dive Using Java along with this book.

This is like the best of both worlds, you learn basic Algorithms quickly in an online course, and then you further cement that knowledge by going through the book, which would make more sense to you now that you have gone through a class already.

Another reason I recommend this book as one of the first books on algorithms is because of its language agnostic and accompanied by lectures here

2. Grokking Algorithms 2nd Edition by Aditya Bhargava

This is one of the best book to learn about Algorithms in Python. When I got bored by reading Algorithms and Introduction to Algorithms book, I started reading this book and it was quite refreshing.

Starting with the size of the book, this one is quite lean and clean compared to other algorithms book which encouraged me to reading.

Then I hooked into Aditya’s diagram and explanation and real world examples which I can related to, like how does Facebook will find my profile when I login.

Their chapter on Dynamic Programming and Knapsack problem is another gem as before that I never really understood the dynamic programming but this book makes using Dynamic programming really easy, just like an extension of recursion and divide and conquer approach.

All these things makes this book an essential book on Algorithms particularly for Python developers. In fact, one of the best Algorithms book on Python.

But if you ask this is one of the most readable book a complex topic like data structure, algorithms and dynamic programming. I highly recommend it and other books from Aditya Bhargava.

By the way, If you also struggle with Dynamic programming like me, you can also check the Grokking Dynamic Programming Patterns for Coding Interview course on DesignGurus.io, this was another resource I used a lot while preparing for Dynamic programming question for coding interviews. It’s also part of their all course bundle which I mentioned before.

3. Algorithms by Robert Sedgewick & Kevin Wayne

This was my preferred resource on algorithms for a long time, it still is, but now I see it less often than before. You will learn lots of background on the algorithm, and nowadays, even specific versions of this book are available for different programming languages like Java and C++.

There’s also a couple of free Coursera online courses for this book, Algorithms Part 1 and Algorithms Part 2, which nicely complement this book. It’s excellent. It’s also my top recommendation to Java programmers for learning algorithms.

Believe it or not, but if you already know a programming language, then seeing an example of an algorithm on that programming language than others also reduces the learning curve. You can also read the 4th Edition of this book online for free here.

4. The Algorithm Design Manual 3rd Edition by Steve S. Skiena

This is another excellent book on computer algorithms that go over a ton of algorithms with a lot of code as well.

What I especially liked about the book is where he actually gives examples of where he used the algorithms (or variations thereof) in practice; it really helps you see the class(es) of problems that a particular algorithm (or family of algorithms) can be used for.

The code is in C, but it’s not very esoteric, and it’s easy to follow. I had also been out of school for a while, and this helped me get up to speed quite quickly on several graph algorithms.

I’ve had this for almost 10 years now and still look at it from time to time.

One notable feature of this book is the inclusion of “war stories”** that provide real-world insights into algorithm design and problem-solving.

The book is often praised for its practicality, emphasizing the importance of intuition and problem-solving skills in addition to formal algorithmic analysis.

It is also widely used in academic settings and serves as a valuable resource for anyone seeking a deeper understanding of algorithmic principles and their practical applications.

5. Coding Interview Patterns: Nail Your Next Coding Interview

This is one of the newest book I read on Data structures and algorithms, I read this book during holiday period and really loved it.

This book is created by Alex Xu, creator of ByteByteGo and author of famous System Design Interview --- Insider guide in collaboration with Shaun Gunawardane.

This book will teach you coding patterns like two pointers, sliding window, fast and slow pointer which you can use to solve 100s of Leetcode problems.

While the idea of coding interview pattern is not new and I already know them having gone through courses like Grokking the Coding Patterns from DesignGurus.io, this book bring them together with more real world examples then you will find elsewhere.

There are 101 real coding interview problems with detailed solutions and most importantly 1000+ diagrams which makes learning really easy. Just because of those diagrams value of the book increased multi-fold.

In short, “Coding Interview Patterns,” is a great book to start your coding interview pattern and master key patterns needed to ace your next coding interview.

Here is the link to get this book - Coding Interview Patterns: Nail Your Next Coding Interview

6. Algorithm for Interviews

Algorithm for Interview by Adnan Aziz is a must-read book on algorithms, written in terms of keeping programming interviews in mind.

The cover itself shows how unusual the book could be if you look at the image carefully on the cover is drawn with thumbnails of famous people, and the book explains how you can develop such algorithms. Interesting, isn’t it?

Well, I like this book because of its approach and objective, sometimes learning the same thing with different objects helps to understand it better.

You can also combine this book with the Grokking the Coding Interview: Patterns for Coding Questions, an excellent interactive course from DesignGurus.io to learn essential coding patterns like sliding window, merge interval, fast and slow pointers, etc that can be used to solve 100+ Leetcode problems.

And, if you like these Grokking interview courses from DesignGuru, you can get all of them for a big discount by purchasing their All course bundle. This bundle include their most popular Grokking courses. You can also use discount code GURU to get 30% discount.

7. Algorithm in Nutshell

O’Reilly’s Algorithms, in a Nutshell, is an excellent book to learn programming algorithms, especially for Java programmers.

It describes the algorithms with a focus on implementing them and without heavy mathematics used in classic books on algorithms.

All algorithms are presented in pattern form, with a motivation to use them, pictures and pseudo-code giving a high-level overview, and working code (in C, C++, Java, and Ruby).

They also have benchmarks to provide proof of the theoretical performance of the algorithms. In short, one of the best books to learn algorithms for programmers.

8. Algorithm Design by Kleinberg & Tardos

This is actually the second-best book in Algorithms after Thomas Cormon’s Introduction to Algorithms.

It’s not really an introduction to algorithms and is more suited to experienced programmers.

It’s more about algorithm design for developers familiar with the basic algorithms.

You should start with the Introduction of Algorithm book or Algorithms by Robert Sedgewick and then continue with this book.

9. Introduction to Algorithms: A Creative Approach*

Introduction to Algorithms: A Creative Approach By Udi Manber is another excellent book for self-study as it is full of hundreds of problems and examples.

This book is designed to enhance the reader’s problem-solving abilities and understanding of the principles behind algorithm design, which will help you to develop your Problem solving and Coding skills.

10. The Design and Analysis of Algorithms

This is another great cook on computer algorithms and deserves a place on a programmer’s shelf.

Once you’ve gone through the Coursera class on Algorithms and one of the intro books, you can read this book for studying advanced topics in algorithms.

11. Data Structures and Algorithms. Aho, Ullman & Hopcroft [Bonus 1)

Another good intro book on algorithms and data structures. A lovely and bright book and any programmer who doesn’t like the heavy use of Mathematics on the algorithm will appreciate this book.

Btw, if you find this book challenging to read, which is what some of my readers complain about, then you can also take a look at the Grokking Algorithms by Aditya Bhargava, one of the easiest and exciting books on Algorithms for beginners.

12. Python Algorithms: Mastering Basic Algorithms in the Python Language (Bonus 2)

This book is designed for Python programmers. Magnus Lie Hetland is also the author of one of the popular introductory Python books, Beginning Python.

As I have told that algorithms are language-independent; learning python algorithms doesn’t mean you cannot implement them in Java or C++, but if you already know Python, then this is an excellent book to learn computer algorithms.

This book also gives a lot of focus on Graph Algorithms, which are very useful in solving real-world problems.

That’s all about Top 5 Algorithm books every programmer should read. I agree that algorithms are a complex topic, and it’s not easy to understand them in one reading; in that case, I suggest reading the same book twice.

Also, just reading is not enough, try to implement them in a programming language you love. It doesn’t hurt writing your own ArrayList, HashMap, or a tree-based Map implementation.

The effect of learning Algorithms is not immediately visible, but you will notice a subtle improvement in your thinking, solution building, and code quality over time.

Btw, if you are interested in an online course on algorithms, I suggest you check out Data Structures and Algorithms: Deep Dive Using Java on Udemy.

It’s not a free course, but you can buy in just under $10 on several of Udemy flash sales, which happens every few days.

At last, there are two types of programmers, one who understands the algorithm and one who doesn’t.

Remember, each of these books represents a treasure trove of knowledge that can transform your approach to software engineering. So pick one today and start your journey toward mastery!

All the best with your learning journey !!

Hello friends, 2026 has gone rather quickly and with new AI advancements and tools it's the perfect time to see where are you going with this new Vibe coding era.

As a Java developer, the biggest challenge you face is keeping your skills up-to-date. Technology evolves at an astonishing pace, frameworks change, cloud becomes the default, and now AI is reshaping software development.

If you're not actively learning, you're falling behind.

Over the past couple of years, we’ve seen major releases like Java 25, Spring Boot 4, Spring Framework 7, Spring Security 7, and more recently, exciting additions like Spring AI and LangChain4j, which bring AI capabilities directly into Java applications.

If you’re wondering what to focus on in 2026, here’s a curated list of essential topics every modern Java developer should explore.

Building AI Agents Without Leaving the Java Ecosystem

AI is moving fast, but most examples are still Python-first.

Meanwhile, many enterprise teams are trying to integrate AI into systems that are already built in Java.

The challenge isn’t building a demo. It’s building AI applications that are reliable, observable, and ready for production.

Join , LangChain4J contributor, iSAQB Software Architect, and international speaker, for a hands-on workshop on building reliable AI agents in Java with evaluation, guardrails, and observability built in.

🚨 Live Hands-on Workshop: Building Reliable AI Agents with Java and LangChain4J

This hands-on workshop is designed for Java developers, backend engineers, architects, and technical leads who want to understand how production-ready AI systems are actually built within enterprise Java environments.

What You’ll Learn

· Build AI agents in Java using LangChain4J

· Move from deterministic pipelines to agent-based systems

· Add routing, decision-making, and guardrails

· Evaluate and validate agent behaviour

· Debug AI systems using observability and tracing

· Understand how production-ready AI applications are structured

No prior AI or LLM experience is required.

As more organizations move from AI prototypes to real-world applications, reliability and observability are becoming essential engineering skills. This workshop gives Java teams a practical path to building AI systems that can be measured, monitored, and trusted.

Here is the link to register → Building Reliable AI Agents with Java and LangChain4J

🎟️ You can also use the code JAVA50 for an exclusive 50% discount

17 Things Java Developers Should Learn in 2026

Without any further ado, here are the 15 things Java programmers can learn in 2026 to level up their tech skills and boost their career.

1. Explore Spring AI and LangChain4j

AI is everywhere, and Spring has embraced it. Spring AI and LangChain4j make it easy to build LLM-powered apps using familiar Spring constructs.

If you’re curious about building intelligent assistants, retrieval-augmented generation (RAG) apps, or AI-enhanced features using Java, these are must-learn tools.

And, if you learn better with live coding and workshops then you can also join Building Reliable AI Agents with Java and LangChain4J workshop by , LangChain4J contributor, iSAQB Software Architect, and international speaker, for a hands-on workshop on building reliable AI agents in Java with evaluation, guardrails, and observability built in.

What to learn:

• Spring AI + OpenAI integration

• LangChain4j for orchestrating LLM pipelines

• Embeddings, vector search, and RAG apps in Java

• Prompt engineering basics for Java developers

Pro tip - Focus on OpenAI API integration and if you need resources, Spring AI: Beginner to Guru by John Thompson is nice course to start with on Udemy.

2. System Design and Architecture (Top Priority)

System design is no longer just for senior engineers. Even mid level roles now expect candidates to design scalable, reliable systems and explain tradeoffs clearly.

Java developers should learn how to design APIs, databases, caching strategies, messaging systems, and fault tolerant architectures.

Understanding scalability, availability, consistency, and performance is essential for both interviews and real production work.

And, if you need resource, I highly recommend you to join ByteByteGo (50% OFF) and Codemia.io (60% OFF), two of my favorite resources which can be used to learn theory as well as practice System Design concepts. For mock interviews, Exponent is another good option.

3. Upgrade to Java 25 (LTS)

Java 25 is the current Long-Term Support (LTS) release, and it's time to adopt it. It brings pattern matching, record patterns, virtual threads (Project Loom), structured concurrency, and many other enhancements.

If you're still on Java 8 or 11, or 17this is your cue to catch up. Java 21 is feature-rich and production-ready.

And, if you want to learn Java in depth and need resource, I recommend going through Java Specialists Superpack by Dr. Heinz Kabutz is a legend in the Java community. This isn’t a course, it’s advanced consulting disguised as education.

Pro Tip - Don’t upgrade to JDK 17 or 21, if you haven’t done so, go for Java 25

4. Master Spring Boot 4 and Spring Framework 7

Spring Framework 7 and Spring Boot 4, both generally available as of late 2026, introduce significant enhancements focused on modernization, performance, and developer experience.

Key new features include built-in API versioning, enhanced GraalVM native image support, and a Java 17+ baseline

If you need a course to learn Java and Spring together, I recommend going through this Both Java + Spring Boot from Basics to Advanced course on Udemy. It’s affordable and updated course.

Pro Tip - Master Spring Ecosystem as its one of the most used framework in Java world.

5. Refine Your DevOps Skills

Modern Java developers are expected to own more than just code. You should know how your applications are built, tested, deployed, and monitored.

What to learn:

• Docker and Kubernetes

• CI/CD with GitHub Actions or Jenkins

• Infrastructure as Code (IaC) with Terraform

• Observability: logs, traces, metrics with Prometheus, Grafana, Elastic Stack

And, if you need resource, I suggest going through DevOps Beginners to Advanced with Projects course on Udemy. This is a hands-on course to learn and master DevOps Skills.

6. Learn Git Inside Out

What is the best time to learn Git, well it was a couple of years ago but if you haven’t then now. No more excuses — Git is non-negotiable. Go beyond basic commands and learn:

• Branching strategies (Git Flow, trunk-based)

• Interactive rebase

• Resolving complex merge conflicts

• Cherry-picking and reflog

If you need a resource, I recommend Version Control with Git by Atlassian, the company behind BitBucket, JIRA, and Confluence on Coursera.

7. Master REST APIs & GraphQL

APIs are at the heart of every backend. Spring Boot makes building RESTful services easy, but don’t forget:

• API design principles (versioning, pagination, HATEOAS)

• Security with OAuth2 and JWT

• Rate limiting and caching

• Explore GraphQL with Spring for flexible querying

And, if you need a course, I recommend REST API vs GraphQL vs gRPC - The Complete Guide by Memi Lavi on Udmey. It’s affordable, comprehensive and up-to-date.

8. Get Strong in Testing (JUnit 5, Mockito, Testcontainers)

Modern testing goes beyond writing a few unit tests. Learn:

• JUnit 5 features like parameterized tests, tagging, nested tests

• Mockito, AssertJ, and Hamcrest

• Testcontainers for integration testing with real databases

• Contract testing with Spring Cloud Contract

And, if you need a resource, you can join Testing Java: JUnit 5, Mockito, Testcontainers, REST Assured course on Udemy, it covers all three essential libraries for testing Java applications.

9. Build Microservices with Spring Boot + Spring Cloud

Monoliths are fine — until you need scale and agility. Microservices, when done right, offer flexibility.

What to learn:

• Spring Cloud Config, Eureka, and Gateway

• Resilience patterns with Resilience4j

• Distributed tracing with Zipkin or Jaeger

• Service-to-service authentication with OAuth2

I also recommend you to learn essential Microservices patterns like CQRS, Circuit-breaker, Saga etc, most of them are covered in Grokking Microservices Design Patterns course on DesignGurus.io.

10. Use Containers Daily (Docker + Kubernetes)

Containers are the new standard for packaging and running apps. Kubernetes is the de facto orchestration platform.

What to learn:

• Build efficient Dockerfiles

• Use multi-stage builds

• Helm, Kustomize, and Kubernetes manifests

• Deploy Java apps to Kubernetes with Skaffold or Tilt

And, if you need a course, join Docker Mastery: with Kubernetes +Swarm from a Docker Captain. The course is also full of quizzes and exercises as well as hands-on practice. You will also learn best practices for making Dockerfiles and Compose files.

11. Get Cloud-Native (AWS, Azure, GCP)

Understanding how your app runs on the cloud is essential.

Start with AWS, then learn:

• Deploying Java apps to Elastic Beanstalk, ECS, or EKS

• Serverless Java with AWS Lambda

• Cloud-native database choices: DynamoDB, Aurora, RDS

• Observability with CloudWatch and X-Ray

And, if you need a course, join AWS From Zero to Hero - The Complete Guide by Memi Lavi on Udemy. It’s a great course for developers to learn about Cloud Computing with AWS in mind.

12. Learn Modern Frontend Basics (Optional but Useful)

Full-stack awareness helps. Learn basics of:

• React or Angular

• API consumption via fetch/axios

• Component-driven UI architecture

• Material UI / Tailwind CSS

Not mandatory for backend-focused devs, but a nice bonus.

13. Understand Event-Driven Architecture (Kafka, RabbitMQ)

Building responsive, loosely coupled systems often involves messaging.

What to learn:

• Apache Kafka basics

• Spring Kafka

• Event sourcing and CQRS

• Async communication between microservices

I also recommend checking out Event Driven Architecture — — The Complete Guide course on Udmey to learn how to use Kafka, RabbitMQ and other messaging tools to build a event driven system.

14. Understand Concurrency & Multithreading in Java

Even with virtual threads, understanding concurrency is key. If you are new to multithreading and concurrency I suggest you to first go through with Michael Pogrebinsky’s Java Multithreading, Concurrency, and Performance Optimization .

What to cover:

• Threads, synchronization, and the Java memory model

• Executors, ForkJoinPool, CompletableFuture

• Virtual threads (Project Loom)

• Structured concurrency

And if you want to test your knowledge of Java multithreading and concurrency topics for interviews then I highly recommend you to try out Java Multithreading for Senior Engineering Interviews course on Educative, one of my personal favorite.

15. Security First

Security is non-optional and as a Java Developer you should spend some time and energy mastering it.

Learn:

• Spring Security 7 (OAuth2, OIDC, JWT)

• API security best practices

• Secure coding: XSS, CSRF, SQL injection prevention

• Secrets management with Vault or AWS Secrets Manager

If you need a resource, you can checkout Spring Security Zero to Master along with JWT, OAUTH2 course on Udemy. I found it very engaging and informative.

16. Learn Prompt Engineering + LLM APIs

With LLMs becoming part of application logic, Java developers must understand how to:

• Call LLM APIs (OpenAI, Cohere, Claude)

• Write clear and robust prompts

• Use tools like LangChain4j, Spring AI, or even gRPC-based AI inference endpoints

If you are new to Prompt Engineer, I highly recommend you to start with Generative AI: Prompt Engineering Basics on Coursera. It’s a great course to learn essential Prompting techniques to efficiently interactive with LLMs like ChatGPT and Claude. It’s also a highly popular course.

17. Bonus: Learn AI Tools (GitHub CoPilot, ChatGPT, CodeRabbit)

AI tools should be top of your list if you don’t want to left behind in this era of Vibe Coding.

If you don’t know, Vibe coding is a new approach to software development where users primarily use AI, specifically Large Language Models (LLMs), to generate code based on natural language prompts. Instead of manually writing code.

You describe the desired functionality to the AI, which then generates the code. The role of the programmers now shifts to guiding, testing, and refining the AI-generated code.

At bare minimum you should be well versed in using ChatGPT and GitHub CoPilot but I highly recommend you to explore tools like Replit and Cursor as well as code review tools like CodeRabbit, which can not only make your Code review more effective and robust but also create learning opportunity for junior developers and free up time of your senior developers.

If you need a course, you can start with Complete ChatGPT Prompt Engineering Course on Udemy for ChatGPT.

By using these tools you can easily improve your productivity to 2X to 4X time and remember

AI will not replace you but the developers using AI will !!

Now that we have make note of AI tools time to go back to traditional stuff which is bread and butter of Java developers.

Conclusion

That’s all in this post about 15 Things Java developer should learn in 2026. As I said, 2026 is going to be a huge year for AI and cloud-native development. As a Java developer, you don’t have to pivot to data science or switch to Python — Java is evolving to embrace this new era.

The key is to stay current, build real projects, and continuously improve. Pick 3–5 items from this list and go deep in the first half of the year. Then expand.

If you want more structured guidance, join newsletters, follow modern Java-focused blogs, and take a few curated courses from platforms like Udemy, Educative, and Coursera.

Let’s make 2026 your best year yet as a Java developer.

I also want to remind you about our New Year Offer which is running now. Make sure you redeem it by this Friday.

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Hello Devs, if you have worked as a Software Engineer, Developer, or Programmer, you may know that, as software engineers, continuous learning is not just a goal—it’s a necessity.

From one task to the next, from one project to the next, there is always learning required. Many times you need to learn a new programming language or framework, or library, or coding and designing.

And, when it comes to learning, nothing beats a book.

Whether you’re looking to refine your coding practices, master system design, or gain a deeper understanding of software architecture, the right books can offer insights and techniques to elevate your skills.

I know that we learn from our experience, and books are a great way to learn from other people’s experiences.

There is a limit to how much you can learn from your own experience, but if you start learning from other folks’ experiences, then there is no limit.

And, books give a glimpse of how experts think, what understanding they have of certain topics that matter most for software engineers and developers.

In the last few articles, I have shared many popular system design questions like API Gateway vs Load Balancer and Horizontal vs Vertical Scaling, Forward proxy vs reverse proxy as well as 50 system design problems and today I am going to share the 10 books you can read to become a better software engineer.

This article features ten essential books every software engineer should read, divided into five categories: General Advice, Coding, Software Architecture, Design Patterns, and Data Structures & Algorithms.

So what are we waiting for? Let’s start

10 Books Every Software Engineer and Developer Must Read

Without any further ado, here are the best books any Software engineer, developer, or programmer can read.

We will start with general advice for programmers and then finish with Data Structures and Algorithms, one topic that separates amateurs from professionals in the programming field.

General Advice

1. The Pragmatic Programmer. This classic by Andrew Hunt and David Thomas provides actionable advice for software developers, from debugging techniques to career growth. It emphasizes the importance of being adaptable and continuously refining your craft.

2. Code Complete (2nd Edition) Written by Steve McConnell, this book is often hailed as a comprehensive guide to software construction.

It provides proven practices for writing robust, maintainable code and improving your software craftsmanship. This book was first recommended to me by our tech lead, who had a tradition of giving this book to every new joiner in the company.

Coding

Now, let’s see a few coding books that every software engineer should read

1. Clean Code 2nd Edition
   By Robert C. Martin (”Uncle Bob”), this book is a manifesto for writing readable, efficient, and maintainable code. It provides practical examples of bad code versus clean code and teaches you the mindset of a skilled programmer.

2. Refactoring: Improving the Design of Existing Code
   This book by Martin Fowler is essential for anyone looking to improve their existing codebase without introducing new bugs. It’s a step-by-step guide to transforming bad code into good code while preserving functionality.

Software Architecture

1. Designing Data-Intensive Applications 2nd Edition
   By Martin Kleppmann, this book dives deep into the systems that power modern software applications. It’s an indispensable resource for understanding distributed systems, data pipelines, and scalability.

2. System Design Interview (Volumes 1 & 2)
   Designed to prepare you for system design interviews, these books by Alex Xu break down complex systems into simple, digestible components. They’re packed with real-world examples and diagrams to help you master system design.

Design Patterns

1. Design Patterns: Elements of Reusable Object-Oriented Software
   This seminal work by Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides (the “Gang of Four”) introduces 23 foundational design patterns. It’s a must-read for understanding object-oriented programming and software design.

2. Domain-Driven Design: Tackling Complexity in the Heart of Software
   By Eric Evans, this book explores techniques to handle complex business domains in software development. It emphasizes collaboration between developers and domain experts to create intuitive, effective systems.

Data Structures & Algorithms

1. Introduction to Algorithms
   Known as the “CLRS book,” this comprehensive guide covers a wide range of algorithms and data structures, complete with mathematical rigor. It’s an essential resource for both beginners and seasoned developers.

2. Cracking the Coding Interview
   By Gayle Laakmann McDowell, this book is the ultimate resource for technical interviews. It provides 189 programming questions and solutions, along with strategies to tackle them effectively.

Why These Programming and Development Books Matter?

This is a good question: why should you read these books? There are better things you can do, like watching Squid Games season 2 on Netflix or binge-watching YouTube videos, so why should one read these books?

Well, these ten books collectively address the most critical aspects of software engineering.

From honing your coding skills and understanding architectural design to mastering data structures and algorithms, they prepare you for real-world challenges and interviews alike.

Just reading these books will expand your knowledge base, and even if you don’t get 100%, you will be a much better engineer than without reading these books.

If you ask me, I have read most of them, not page to page, but most of it, and I can say that every time I read a book, I come up with more knowledge and wisdom, even on topics that I thought I knew very well.

If you want to just read one book, start with Clean Code, one of my favorite books on coding. I learned a lot about coding as an art from this book.

It’s the one that takes you from HelloWorld programmer to a professional, well, at least it made that impact for me.

How to Get Started? Which Book Should You Read First

This is a difficult part, especially if you are presented with a list of 10 books.

I would suggest starting by identifying the areas where you want to improve.

If you’re aiming for cleaner code, dive into Clean Code or Refactoring: Improving the Design of Existing Code.

Preparing for interviews? Focus on “Cracking the Coding Interview” and System Design Interview (Volumes 1 & 2)

If you’re venturing into software architecture, Design Patterns: Elements of Reusable Object-Oriented Software is a must-read.

Remember, each of these books represents a treasure trove of knowledge that can transform your approach to software engineering. So pick one today and start your journey toward mastery!

All the best with your learning journey !!

Other Book Recommendations you may like

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Hello guys, if you have been designing systems in last few years then you may have noticed that modern applications don’t constantly ask other systems for updates anymore. Instead, they rely on Webhooks — a push-based communication mechanism where one system instantly notifies another when an event happens.

From payment confirmations and GitHub actions to Slack notifications and Stripe events, webhooks power real-time asynchronous communication across modern distributed systems.

In this article, Sahil Sarwar, our guest author and a passionate Software Engineer, we’ll explain how webhooks work, why they are more efficient than polling, their architecture, common challenges like retries and security, and how companies use them to build scalable event-driven systems

No frameworks. No assumptions. Just pure systems thinking.

If you remember, Sahil has been here before and shared his experience on sticky sessions, caching , and magic behind one click checkout before. With that, over to Sahil Sarwar to take over to the rest of the article.

This will be the start of a 3-part series on systems that seem familiar, which can be used interchangeably, but are quite different in how they are fundamentally designed.

For today’s post, we will look at Webhooks.

Let’s try to understand WHAT they are, and WHY we need them.

How to get updates from an async process?

Let’s consider a scenario, and I believe it might be a common one.

You are working on your new app, and you want to integrate payments into it. So, you use Stripe for payments.

Every time a user wants to pay, you call a Stripe API, and it returns a result as a confirmation of the payment, if it suceeded or failed.

Now, to truly understand what webhooks are, let’s forget about Stripe for now. Let’s assume we are building something like Stripe, and let’s look at a problem that our client might face while calling our gateway.

Especially, as the title suggests — “how to get updates from an async process?”

Consider the design below —

Here are the steps that might happen —

1. Our PaymentService calls a Stripe endpoint with the necessary information for payment.

2. Now, Stripe on their end might have some queue for all the payment requests that they get, and they process them asynchronously (we will get in deep into this later in this article).

3. Now, the payment is processed, and we want the result from Stripe. What do we do?

Let’s look at a couple of different ways of getting the information from an external vendor, like Stripe (for an async request).

Pull-Based Approach

If Stripe has that information, then maybe our PaymentService can call Stripe’s API to get the latest information on whether that request is completed or not.

We can keep an exponential retry on a request, and keep calling Stripe until we get a confirmation from them that a payment is completed.

But I think it consumes a lot of resources. Every few seconds, our service has to make an external call, wait for their response, and the response can’t even be valuable.

On the other hand, Stripe’s API will probably get flooded with so many requests from their clients, asking them the information for their payment.

It’s not scalable in this case.

Push-Based Approach

Instead of PaymentService calling Stripe again and again to check the status, what if it does the following —

1. Tell Stripe — “whenever you are ready with my payment request, just let me know”

2. Now, Stripe can process the payment async, and whenever it’s done, it can call our PaymentService, telling us the details of the payment

This is clean. None of the servers is loaded with frequent status check calls.

How do we do something like this?

Well, that’s exactly what we are looking at today.

Building Webhooks

But achieving something, at the scale of Stripe, is a different thing altogether. There are a lot of moving parts that need to be handled when designing a webhook-based system that works on a global scale.

But let’s think about it, one system at a time.

Client registering the webhook call

That’s the first step.

Let’s say we have an endpoint — https://api.stripe.com/v1/payment

And we have asked our clients to do a POST call on this endpoint with some parameters. Let’s look at what those parameters could be —

1. Payment info (amount to charge and a few other details)

2. Client ID (to specify which client wants to get notified after the event)

3. Callback URL (which endpoint should Stripe call once the payment is done)

Ingress

Everything begins here. The core product (like Stripe Payments) emits internal events to a shared message bus, typically Kafka or a message queue.

There are a few properties of this:

• Each event = small object (event_id, type, payload, timestamp).

• Stored in a topic partitioned by tenant or event type.

• Ordered delivery per tenant (e.g., all events for one customer go to the same partition).

Payment service
 ├─→ Kafka topic: payments.events
 │       - key: merchant_id
 │       - value: {”type”: “payment.succeeded”, ...}
 ├─→ Kafka topic: user.events
 │       - key: merchant_id
 │       - value: {”type”: “user.created”, ...}

Event router/subscription service

Now, we need some kind of way to find who to notify about an event. And that’s what we do here.

Every merchant registers their callback_url and their event_type And that’s what we store here in this DB.

merchant_id | event_type    | callback_url             | secret | status
----------------------------------------------------------------
m_123  | payment.succeeded  | https://storeA.com/webhook | ... active 
m_124  | *                  | https://storeB.com/hook    | ... active

When the router consumes from the event bus:

1. It looks up all subscribers for that event type.

2. Enqueues one “delivery job” per subscriber, some kind of a delivery queue.

The delivery queue (Kafka) can now fan out across multiple subscribers; it decouples event generation from delivery.

So, in this way, one payment success event could become 10 delivery jobs (if 10 subscribers exist).

[Event Router] consumes Kafka topic → writes to Delivery Queue
   Event {type: payment.succeeded, merchant: m_123}
       ⤷ Delivery Job 1 (m_123, https://a.com)
       ⤷ Delivery Job 2 (m_999, https://b.com)

Dispatcher workers (the delivery layer)

This layer is the one that makes sure that the process status is sent back to the webhook clients.

Each worker process:

• Pulls jobs from the delivery queue.

• Sends the event via HTTP POST.

• Records result (success, failure, latency).

• Retries failed jobs with exponential backoff

DispatcherWorker:
  1. job ← queue.pop()
  2. payload ← fetch(event_id)
  3. headers ← build_signature_headers(payload, secret)
  4. resp ← http.post(callback_url, json=payload, headers=headers, timeout=5s)
  5. if success: mark_delivered()
     else: enqueue_retry(job)

Retries

But we can’t just retry a failed job blindly. If we do so, we might DDoS ourselves. We need to design the retry carefully.

So we can maybe separate the main queue and retry queue, with different timeouts.

We can do something like this —

• Retry #1 → after 1 min

• Retry #2 → after 5 min

• Retry #3 → after 15 min

• Retry #4 → after 1 hr

• Max retries → 24 hr, then dead-letter queue.

So, let’s look at how we have designed it till now.

Something to Note

The whole design relies on event driven system, and when we are in a world of distributed systems and queues, we are dealing with at least once semantics. And for that, we need to handle idempotency at the consumer level.

The last few of my posts deal with idempotent systems. You can take a look at these posts for reference —

Back-pressure and blocked consumer problem

If one of the consumers is slow, i.e., if its callback endpoint is slow, it could block our whole pipeline, and the dispatcher would keep retrying.

What to do to handle this? We can’t let one blocked consumer block our entire flow.

Let’s look at a few of the options we have —

Tenant Sharding

We can partition the delivery queue by tenant or region. Something like this —

delivery_queue_m123
delivery_queue_m124
...

Each worker pool processes a subset of these tenants; this prevents one slow customer from affecting others.

Rate limits per client

We can apply a configurable delivery concurrency per tenant (something like 5 in-flight requests max at a time).

Circuit breakers

If a tenant’s endpoint fails N times in a row, we can pause deliveries for that tenant for a cooldown period (it can be configured per tenant).

Design

This design captures the tradeoff between a huge number of events and delivery fairness.
A monolithic system with a “just-send-HTTP” approach will collapse under backpressure, retries, and latency spikes.

What Stripe, GitHub, etc. have done is essentially build a mini pub/sub system on top of HTTP, with —

• Persistent queues

• Isolation boundaries

• Delivery semantics

That’s what makes it production-grade.

Share Brain Bytes & Binary

Looking Back…

At the surface, a webhook feels simple — one service calling another.
But underneath, it’s a quiet agreement between systems: “When something happens, I’ll tell you.”

It’s trust, made machine-readable.
Retries, backoffs, and queues aren’t just technical constructs — they’re the system’s way of keeping its promise even when the world can break apart.

That’s what makes distributed systems so human sometimes, it’s that everything is asynchronous, everything can fail, and yet we keep finding ways to communicate, to notify, to stay consistent in the face of uncertainty.

In the next part, we’ll look at a different kind of communication — one where the connection never really closes: Server-Sent Events (SSE).

A Message

I’ve been thinking a lot about how writers monetize their content in different ways, including paywalls. That’s not what I want for this space.

Everything I write here will stay free; that’s important to me. At the same time, writing takes time, solitude, and energy.

If you have ever felt that my work helped you in some way, and you want to support it, you can do that here —

Buy me Coffee

References

1. https://beeceptor.com/docs/webhook-feature-design

2. https://docs.stripe.com/webhooks?locale=en-GB

Other AI and System Design Articles you may like

Just wanted to send a quick note that our Memorial Day SALE is ending today, in few hours. If you always wanted to get paid subscription, now is the time as you will lock 35% discount rate forever.

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35% off limited-time offer!

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Thank you for supporting Javarevisited Newsletter and me over the years, its been more than 14 years since I am writing Java related article !!

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Free Gift

As promised, here is a free gift 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 have nice time with friends and family this memorial day !!

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Final Thoughts

Memorial Day is more than just a holiday — it’s an opportunity to invest in yourself and your career at unbeatable prices.

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Happy learning! 🚀

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Happy Memorial Day in advance !!

35% off limited-time offer!

In the spirit of celebration I am giving you the limited-time offer of 35% off forever for becoming a paid subscriber. The offer is valid for the next 2 days!

Instead of paying 50$ / year, you pay 32.5$ / year (only 3$ / month)!

Get 35% off forever

Here are the benefits you unlock with a paid subscription:

1. Get access to paid subscribers posts.

2. Full archive

Many expense it with team's learning budget

Get 35% off forever

Here are the top paid subscriber only article you can read after redeeming this special offer:

1. What is Rate Limiter?

2. Scaling to Millions: The Secret Behind NGINX’s Concurrent Connection Handling

3. System Design Basics - Database Scaling

4. The Complete Java Developer RoadMap

5. 15 Side Hussle for Software Engineers to Create Multiple Income Streams

6. What Java Developer Should Learn in 2026

7. Beat 90% of Candidates: Software Engineer’s Fast-Track Coding Interview Guide

8. How to grow financially as Software Engineer? 5 Tips

9. 100+ Free Courses for Developers to Learn Java, Spring Boot, and Microservices

10. Top 10 Coding Questions from Junior level Java Interviews

11. Difference between Microservices and Monolithic Architecture

12. How SQL Query works?

13. 24 Microservices Patterns for Experienced Developers

14. 15 Best Places to Prepare for System Design interview

15. 12 Multithreading and Concurrency Questions for Interviews

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Thank you for supporting Javarevisited Newsletter and me over the years, its been more than 14 years since I am writing Java related article !!

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Free Gift

As promised, here is a free gift 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 have nice time with friends and family this memorial day !!

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Hello guys, if you are on X then you may have seen how brutal was Meta’s layoff a couple of days ago. They made a record profit and yet let go more than 10% of thier work force.

Meta began notifying roughly 8,000 employees this week that they are being laid off, the first wave of a major restructuring the company has framed as necessary to fund its push into artificial intelligence. T

his isn’t just another tech layoff headline—it’s a reality check.

If you work in tech, regardless of where, this should concern you.

Here’s what happened, why it matters, and what you should do about it right now.

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The Scale of the Impact

The cuts amount to about 10% of the company’s workforce, and Meta had just under 80,000 employees at the end of March. But that’s only half the story.

Separately, upward of 7,000 workers will be redirected into newly created AI-focused teams.

This tells you something critical: Meta isn’t just cutting—it’s restructuring entirely around AI.

Why This Matters to You (Even If You’re Not at Meta)?

Meta CEO Mark Zuckerberg told employees in a memo that the company’s decision to lay off 8,000 employees is necessary because “success isn’t a given” in the fierce and competitive space of artificial intelligence.

Think about that.

If “success isn’t a given” at a company with Meta’s resources, what does that mean for smaller companies? For startups? For your job security?

So far in 2026, there have been almost 110,000 layoffs at 137 tech companies. Meta is just the latest domino.

The Urgent Lessons from Meta’s Layoff

Here are a couple of things one can learn from this layoff:

Lesson #1: Being at a “Safe” Company Is a Myth

Meta is one of the most profitable tech companies in the world. They just have record breaking profit.

Yet here we are.

The hard truth: No company is safe. No position is permanent.

Lesson #2: You Have 2-4 Weeks, Not 2-4 Months

When layoffs hit, you don’t get months to prepare. You get days.

Many of the 8,000 Meta employees are now scrambling to interview. If you’re not ready now, you’ll be panicking then.

Being “interview ready” means:

• DSA mastery — You can solve coding problems confidently under pressure

• System design fluency — You understand architecture decisions and tradeoffs

• Clear communication — You can articulate your experience effectively

• Mental preparedness — You’re not learning from scratch during crisis mode

Lesson #3: Interview Prep Is Non-Negotiable

This is where most developers fail. They assume they’ll have time to prepare when a layoff happens.

They’re wrong.

The best interview prep resources to get ready now:

If you want structured DSA practice:

• AlgoMonster — Covers all major coding patterns with a clear roadmap

• LeetCode — Thousands of problems, company-specific question banks

For system design (critical for mid-level and above roles):

• ByteByteGo — The clearest system design explanations available. Seriously, this is the gold standard.

• DesignGurus — Interview-focused system design prep with real scenarios

• Educative — Hands-on labs with cloud environments included

For live interview practice:

• Exponent — Mock interviews with ex-FAANG interviewers

• Codemia — Coding practice with AI feedback

For debugging and deeper coding skills:

• BugFree.ai — AI-powered debugging practice to catch mistakes faster

If you prefer structured course formats:

• System Design Masterclass (Udemy) — Comprehensive and affordable

• ZTM Academy — Beginner to advanced developer tracks

For deep, lasting understanding:

• Designing Data-Intensive Applications — The bible of system design

• System Design Interview: An Insider’s Guide — Real patterns from real interviews

The key: Start today. Treat this like gym training, not a crash course before the interview.

Lesson #4: Meta Shows Why System Design Matters More Than Ever

Here’s what’s interesting about Meta’s layoffs: affected workers include those on the company’s integrity team – the group in charge of removing malicious content and hate speech – as well as members of the company’s cybersecurity teams and content design division.

Notice what’s happening? Non-engineering roles are being cut. Pure coding roles can be done by AI or engineers who understand system-level thinking.

The future belongs to engineers who understand:

• How systems scale?

• How to design for reliability?

• How to think at an architectural level?

This is why system design is no longer optional. It’s mandatory. Even for junior engineers.

Platforms like ByteByteGo and DesignGurus focus on exactly this. If you’re only grinding DSA problems, you’re preparing for 2015, not 2026.

Lesson #5: Build Your Safety Net Now

While interview prep is critical, it’s just one piece:

Diversify your income:

• Build a GitHub portfolio (showcase real projects)

• Start a side project or freelance work

• Consider teaching (YouTube, courses, blogging)

• Explore affiliate income or SaaS ideas

Even an extra $500/month:

• Reduces financial anxiety

• Gives you options during transitions

• Buys you time to find the right next role

Increase your visibility:

• Post on LinkedIn about what you’re learning

• Share technical insights on Twitter/X

• Contribute to open source

• Write about system design concepts

Strengthen your network:

• Reconnect with former colleagues (even if you’ve been away)

• Add people to LinkedIn before you need them

• Engage meaningfully in tech communities

• Remember: referrals beat cold applications every time

Lesson #6: This Is the AI-Driven Era of Efficiency

Zuckerberg said executives “do not expect other companywide layoffs this year” but another potential round is expected in August, followed by another during the fall.

The message is clear: The restructuring isn’t over. This is the new normal.

Companies are:

• Replacing roles with AI

• Focusing on high-impact engineering

• Cutting overhead aggressively

• Investing everything into AI/ML

If you’re not learning how to work with AI, you’re falling behind. Fast.

Here are few resources to learn key AI skills:

1. AI Engineer Agentic Track: The Complete Agent & MCP Course Udemy

2. IBM RAG and Agentic AI Professional Certificate — Coursera

3. Build AI Agents with CrewAI — ZTM Academy

4. Designing Agentic Systems with LangChain — DataCamp

5. Build an AI Agent from Scratch — Frontend Masters

6. Agentic AI Nanodegree — Udacity

7.Full Stack AI Engineering - Towards AI

What You Should Do This Week?

1. Pick one interview prep platform and commit (ByteByteGo for system design, AlgoMonster for DSA, Exponent for mock interviews)

2. Start a 30-day interview prep sprint — Treat it like your job depends on it (because in 2026, it does)

3. Update your LinkedIn and GitHub — Make yourself findable when opportunities come

4. Reach out to 5 former colleagues — Just a “Hey, thinking of you, let’s catch up” message

5. Define your side income goal — Even $500/month changes the game, I have shared 20+ side hustles for devlelopers here:

The Bottom Line

Meta’s layoff isn’t tragic because it happened to Meta. It’s tragic because it confirms what we’ve all suspected:

In 2026, job security is a myth. Skill security is real.

The developers who will thrive aren’t the ones who panic when layoffs hit. They’re the ones who were ready.

So instead of waiting for the next headline:

• Master DSA thoroughly

• Build system design thinking

• Increase your visibility

• Strengthen your network

• Diversify your income

The best time to prepare for a layoff was 6 months ago. The second-best time is today.

P.S. — If you’re currently affected by layoffs and preparing for interviews, resources like ByteByteGo (use code JALJAD for 10% off), DesignGurus, and Exponent have helped thousands of engineers land their next roles. Many also offer layoff support pricing. Don’t hesitate to reach out to their communities—most are incredibly supportive during transitions like this.

You can also join my newsletter today for 30% OFF now.

Your next opportunity is closer than you think. But only if you’re ready for it.

In the chaos of trying to keep up with the latest tools, most engineers miss the fundamentals. And that’s where you lose the race.

After reading 30+ AI and LLM books in last 2 years, I’ve identified the 10 that every serious AI engineer should own. These aren’t coffee table books about AI philosophy. These are operational manuals written by practitioners who’ve shipped real systems to production.

I’m talking about people who’ve worked at Netflix, NVIDIA, Anthropic, and Google. People who’ve scaled LLM systems to millions of users. People who know what actually works when the stakes are real.

Agentic AI Engineering Course by Paul Iustzin (aff)

If you’re serious about Agentic AI, this is the course you’ve been waiting for.

Authored by Paul Iusztin (bestselling LLM Engineer’s Handbook author), the Agentic AI Engineering course takes you from fundamentals to production-ready agents with real-world projects, evaluation frameworks, and deployment pipelines.

Perfect for engineers who want to move beyond demos.

Here is the link to join this course - Agentic AI Engineering (aff) by Paul Iusztin

Why Books Still Win in 2026?

Before I dive into the list, let me be clear about something: books beat online courses for learning depth.

Here’s why:

• Depth: Books go deep. They don’t skip the hard parts. They teach you to think, not just execute.

• Permanence: A book from 2024 will teach you fundamentals that are still true in 2026. Online courses are outdated by month 6.

• Author expertise: These books are written by people with 10-20+ years of experience. Not YouTubers with 10K subscribers.

• Offline learning: You can read on the plane, on the train, without WiFi. Try that with a Udemy course.

• Retention: Reading complex ideas forces your brain to engage. Passively watching videos? Not the same.

The engineers who read deeply are the ones who move fast and think clearly.

The 10 Essential Books (Organized by Career Stage)

Now let’s see the books, which I have organized by career stage.

Foundation Layer: Understand What AI Engineering Actually Is

1. AI Engineering by Chip Huyen — Start Here

Why it’s the foundation:

Most people think AI engineering is just about models. Chip Huyen shows you it’s actually about systems—data pipelines, model versioning, deployment, monitoring, and scaling.

This book answers the question that most courses won’t: “What does a real AI engineering stack look like?”

What you’ll learn:

• The difference between ML engineering and AI engineering (it matters)

• How to build data pipelines that actually work

• Model versioning and reproducibility

• Deployment patterns for production AI

• Monitoring and maintaining AI systems

• The complete AI product lifecycle

Why Chip Huyen: She’s worked as a researcher at Netflix, was a core developer at NVIDIA building NeMo (NVIDIA’s GenAI framework), and taught ML at Stanford. She’s built the systems she’s writing about.

Best for: Anyone transitioning into AI engineering, people who want the comprehensive picture

Reading time: 8-10 hours (dense, but worth it)

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

2. Designing Machine Learning Systems by Chip Huyen — The Operational Reality

Get this book

Why two books from Chip:

“AI Engineering” shows you the big picture. This one teaches you how to actually design and operate ML systems in the real world—where data drifts, models fail, and you have to retrain at 2 AM.

What you’ll learn:

• Handling data drift and distribution shift

• Model retraining strategies

• A/B testing in production

• Debugging failed models

• Cost optimization

• Real-world constraints (budgets, latency, accuracy trade-offs)

Why it’s crucial: Most books ignore the operational reality. This one embraces it.

Best for: Engineers building systems that need to stay alive in production

Reading time: 8-10 hours

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

LLM Engineering Layer: Master Building with Large Language Models

3. The LLM Engineering Handbook by Paul Iusztin and Maxime Labonne — The LLM Operations Manual

Get this book

Why this is the LLM bible:

Created by , creator of Agentic AI Engineering course on Towards AI, this is the most practical LLM engineering book I’ve read. It’s written like an operations manual—here’s what you do, here’s why it works, here’s what breaks.

What you’ll learn:

• Prompt engineering at scale

• Fine-tuning strategies and when to use them

• Retrieval-Augmented Generation (RAG) patterns

• Evaluation frameworks (how to measure LLM performance)

• Production patterns for LLM applications

• Cost optimization for LLM APIs

Why the authors matter: and have built LLM applications at scale. They’re not theorizing—they’re sharing what actually worked.

Best for: Developers building LLM-powered products, anyone shipping RAG systems

Reading time: 6-8 hours

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

4. Building LLMs for Production by Louis-François Bouchard and Louie Peters — The Shipping Guide

Get this book

Why this book is survival manual:

You can build an LLM. But can you ship it? Deploy it? Scale it? Maintain it? This book shows you how.

What you’ll learn:

• Fine-tuning LLMs for specific domains

• Deployment architecture for LLMs

• Scaling LLM inference

• Managing costs at scale

• Monitoring LLM behavior

• Real deployment challenges and solutions

Real-world focus: The authors , founder of Towards AI, are writing from the trenches. They’ve deployed LLMs. They know where things break.

Best for: Engineers shipping LLM products, anyone moving from prototype to production

Reading time: 7-9 hours

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 — The Deep Dive

Get this book

Why you need to understand the foundation:

Sebastian Raschka is a legend. This book teaches you how transformers actually work—from first principles, with code in PyTorch.

What you’ll learn:

• Transformer architecture from scratch

• Tokenization and embeddings

• Attention mechanisms (the core of modern LLMs)

• Training techniques and optimization

• Fine-tuning strategies

• How to read and understand LLM papers

Why it matters: You don’t need to build LLMs from scratch in production. But understanding how they work makes you 10x more effective when debugging, optimizing, or fine-tuning.

Best for: Engineers who want to understand LLMs at the code level, not just API users

Reading time: 12-15 hours (complex, technical)

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

Practical Implementation Layer: Build and Deploy

6. Hands-On Large Language Models: Language Understanding and Generation — The Practical Guide

Get this book

Why hands-on learning accelerates mastery:

Jay Alammar and Maarten Grootendorst are among the most respected voices in NLP. This book teaches you to build with LLMs using modern tools.

What you’ll learn:

• Working with Hugging Face Transformers

• LangChain for building LLM applications

• Fine-tuning for specific tasks

• Evaluation and metrics

• Real-world applications

• Production considerations

Why these authors: They’ve taught thousands of people NLP. They know what sticks and what doesn’t.

Best for: Developers building LLM applications, data scientists implementing NLP

Reading time: 8-10 hours

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 — Master the Interface

Get this book

Why prompt engineering is an engineering discipline:

Most people think prompt engineering is guessing and checking. It’s not. It’s a discipline with patterns, principles, and proven techniques.

What you’ll learn:

• Prompt patterns that work consistently

• Few-shot prompting techniques

• Chain-of-thought reasoning

• Structured prompting

• Evaluation and testing prompts

• Advanced techniques from researchers

Why it matters: Good prompting can improve results by 50%+. Bad prompting wastes API costs and time.

Authors: John Berryman and Albert Ziegler—practitioners who’ve worked with LLMs at scale

Best for: Anyone building AI products, engineers working with OpenAI/Claude/open-source models

Reading time: 4-6 hours

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

8. Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs — Future-Proof Your AI Strategy

Get this book

Why you need future-proofing:

AI models change. But prompt engineering principles don’t. This book teaches principles that work across GPT, Claude, open-source models, and whatever comes next.

What you’ll learn:

• Prompt patterns for text generation

• Image prompt engineering (DALL-E, Midjourney)

• Code generation prompting

• Robust prompting for production

• Business applications

• Testing and evaluation

Why it’s future-proof: The patterns taught here have survived multiple model generations. That’s the test of real knowledge.

Authors: James Phoenix and Mike Taylor—both recognized in the prompt engineering community

Best for: Product teams, non-technical people building with AI, anyone shipping AI features

Reading time: 5-7 hours

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

Advanced Layer: Build Autonomous Systems

9. Building Agentic AI Systems: Create Intelligent, Autonomous AI Agents that can Reason, Plan, and Adapt — The Next Frontier

Get this book

Why agentic AI is the future:

Prompt engineering taught ChatGPT to chat. Agentic AI is teaching AI to act—to make decisions, take actions, coordinate with other agents.

What you’ll learn:

• Agent architecture and design

• Reasoning patterns (ReAct, chain-of-thought)

• Planning and decision-making

• Tool use and API integration

• Multi-agent systems

• Building systems like AutoGPT, BabyAGI, LangGraph agents

Why it matters: Static LLMs are cool. Autonomous agents that can reason and act? That’s where the real value is.

Authors: Anjanava Biswas and Wrick Talukdar—both working on agentic AI systems

Best for: Engineers building the next generation of AI products, researchers in agentic systems

Reading time: 8-10 hours

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

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

Get this book

Why this is the “encyclopedia”:

If you’re leading AI implementation at your organization or building systems from scratch, this book covers the entire lifecycle.

What you’ll learn:

• Architecture for production AI systems

• Infrastructure and deployment

• MLOps and monitoring

• Cost optimization

• Security and compliance

• Scaling to millions of users

• Leading AI projects

Scope: Unlike other books that focus on one area (LLMs, operations, agents), this covers the complete picture.

Author: Thomas R. Caldwell—experienced in shipping AI systems at scale

Best for: Tech leads, architects, CTOs, anyone responsible for AI implementation

Reading time: 10-12 hours

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

The Recommended Reading Order

If you have 1 month:

1. AI Engineering by Chip Huyen (foundation)

2. The LLM Engineering Handbook (LLM focus)

If you have 3 months:

1. AI Engineering

2. Designing Machine Learning Systems

3. The LLM Engineering Handbook

4. Prompt Engineering for LLMs

If you have 6 months (ideal for career transition):

1. AI Engineering

2. Designing Machine Learning Systems

3. The LLM Engineering Handbook

4. Building LLMs for Production

5. Build a Large Language Model (from Scratch)

6. Hands-On Large Language Models

7. Prompt Engineering for LLMs

8. Building Agentic AI Systems

If you want complete mastery (1 year plan): Read all 10 books in this order:

1. AI Engineering (foundation)

2. Designing Machine Learning Systems (operations)

3. The LLM Engineering Handbook (LLM basics)

4. Prompt Engineering for LLMs (practical)

5. Build a Large Language Model from Scratch (deep dive)

6. Building LLMs for Production (shipping)

7. Hands-On Large Language Models (implementation)

8. Prompt Engineering for Generative AI (future-proofing)

9. Building Agentic AI Systems (next frontier)

10. The AI Engineering Bible (complete picture)

Books vs. Courses: The Real Truth

Books give you:

• Depth of understanding (not just surface knowledge)

• Thinking frameworks (not just code recipes)

• Timeless principles (not trendy techniques)

• Time to reflect (reading forces engagement)

• Offline learning (freedom from WiFi)

Courses give you:

• Hands-on practice (essential for retention)

• Instructor interaction (Q&A support)

• Structured pacing (deadlines help)

• Visual/interactive learning (sometimes clearer)

• Immediate community (peer discussion)

The winning strategy: Read these books (gain frameworks), then take hands-on courses (practice the frameworks).

The Next Step: Books + Practice

Reading is powerful. But building is essential.

After reading these books, pair them with hands-on projects:

• Build a RAG-based chatbot over your own data

• Fine-tune an open-source LLM on a custom dataset

• Deploy an LLM application to production

• Build an agentic AI system that solves a real problem

• Implement prompt engineering improvements on a product

Books teach you what’s possible. Projects teach you what actually works.

Final Word

The AI landscape is chaotic. New tools drop weekly. But the fundamentals stay constant.

Engineers who master these 10 books will be able to:

• Design AI systems that actually scale

• Build LLM applications that work in production

• Understand and optimize every layer of the stack

• Lead AI implementation at their organization

• Stay relevant no matter what new models/tools emerge

This is your 2026 reading list. Start with one book this week. Finish one book per month. In 10 months, you’ll have the depth of knowledge that separates great AI engineers from good ones.

The difference will show in your code, your designs, your interviews, and your career trajectory.

Pick up book #1 today. You won’t regret it.

Hello guys, if you are preparing for coding Interviews to get a job on FAANG companies like Google, Microsoft, Meta, Apple, and Amazon then you may know that how important is to crack them on first attempt.

It’s not easy even to get a call as they are hard to come by and you will only get a couple of chances in your lifetime so it does make sense to give your best shot on first attempt and mock interviews plays an important role on that.

In the past, I have shared best System Design Books, System Design Questions and best Places to learn System Design and courses, where I mentioned about Mock interviews and you guys asked me how to find the right people for Mock interviews?

So, here I am going to share 7 of my favorite platform where you can find right expert for Coding and System Design interviews,.

I once interviewed for Google Bangalore but couldn’t make it after first few rounds and one thing I learned from that experience is that FAANG interviews (Google, Amazon) are very different from Wall street and Big investment banks like Goldman Sachs or JP Morgan interviews and regular Software companies.

At that time, even though I prepared with all the material available online but I missed a trick or two by not doing mock interviews. Had I done the real like mock interviews I would have found my shortcomings earlier and probably worked on them better.

I may be a bit over-confident that time as I thought I was well prepared because of my experience and I write a popular Java blog and that cost me a big opportunity but you can learn from my experience (not repeat it) and always give your best shot on first time itself.

Now, let’s see what is Mock interviews and how they can help you prepare better for your technical interviews.

The Power of Mock Interviews

Preparing for technical interviews is a crucial aspect of landing your dream job in the competitive world of software engineering. Mock interviews provide a simulated experience, allowing you to hone your skills and receive valuable feedback.

While doing LeetCode and solving System Design problems are very important part of any tech interview preparation, I also recommend you to go for a couple of mock interviews before the D-Day.

This will help you to find your strong and weak areas before interview and give you some time to improve upon them and improve your chances on real interviews.

But, the big question is finding right people for Mock interviews?

Because it’s the people with whom you do mock interviews matter, if you do mock interviews with someone no experience of FAANG interviews then its completely useless, hence you should spend some time to find the right people for mock interviews.

If you are wondering where can you get authentic Mock interviews for System design and Coding interviews then don’t worry. In this article, I am going to share three outstanding platforms that offer top-notch mock interviews for system design and coding.

1. Codemia.io

I first found Codemia.io when I was looking for System design practice problem similar to LeetCode and when I found it, I was overjoyed that I found the LeetCode for System Design.

Since then Codemia.io has come a long way, earlier they used to have only System Design practice problem but now they have expanded and provides DSA practice problems as well as OOP Design Practice problems.

Their most recent addition, a mock interview is a real game changer because it allows you to do peer mock interview at your choose schedule with real people.

All you need to do is:

1. Add your available times or browse slots created by others.

2. Book a slot that works for you. You’ll be matched with the creator.

3. Join the video call at the scheduled time. Take turns interviewing each other.

That’s it. its that simple but very effective.

I tried this recently and I must say that I learned more on those 90-minutes than the two weeks of study.

You may think that you know the answer of that system design question and can rehearse in mind but you will be surprised how difficult it is to articulate when real interview. That’s where these mock interviews helps.

If you are serious about your FAANG interviews, I highly recommend doing mock interviews and codemia.io is one of the best place to start with.

Here is the link to book your first mock interview.

2. Exponent: Elevate Your Skills with Expert Guidance

Exponent is another my go-to platform for System design interview preparation, expert coaching, mock interviews, and more.

It not only have a great Software engineer interview course but also allow you to connect with industry experts to practice mock interviews, review your resume, or create a study plan tailored to your needs.

Key Features:

• Expert Coaching: Connect with professionals in your field for mock interviews, resume reviews, or personalized study plans.

• Discounted 5-Session Package: Save with a discounted package of five sessions, allowing you to use them at your convenience.

• Tailored Matching: Get matched with the right coach based on your needs and goals after purchasing sessions.

If you ask me, Exponent is another affordable and qualified place where you can get good Mock interview experience. You can purchase sessions now and elevate your skills at your own convenience.

Here is the link to join Exponent — Exponent Mock interview

3. DesignGurus.io

When it comes to System design interview, DesignGurus doesn’t need any introduction. They have two of the best System design course like Grokking the System Design interview and Grokking the advanced System design interview as well a many coding interview courses.

But many people doesn’t know that they also provide an excellent Mock interview service. DesignGuru sets itself apart by offering personalized and in-depth mock interviews conducted by experienced FAANG engineers and authors of the renowned ‘Grokking’ courses like Arslan Ahmad

How DesignGurus Mock interview Works:

• Purchase and Schedule: You can first purchase mock interviews or 1-on-1 sessions using this link and schedule them according to your availability.

• Meet Your Interviewer: Engage in fully anonymous virtual sessions with experienced FAANG engineers for either coding or system design interviews.

• Get Personalized Feedback: Receive detailed feedback highlighting strengths and areas for improvement, along with a customized learning plan for a competitive edge.

Specialized Interviews Offered:

DesignGurus.io offers two types of Mock interviews, one is focused on coding interviews and other is focused on System design interview, you can take one or both depending upon your budget and preparation need.

• Coding Mock Interview: Practice real coding problems from interviews, making mistakes, learning, and improving in a supportive environment.

• System Design Mock Interview: Tackle real system design questions, learning how to design robust, scalable systems and communicate your thought process effectively.

Here is the link to join the mock interview — Purchase a Mock interview

Post-Interview Benefits:

• Personalized and in-depth feedback.

• Customized learning material for continuous improvement.

Overall DesignGurus.io offers a great opportunity at minimal cost to see where you are with your preparation of big FAANG interview. Unlock the door to your dream job by scheduling your mock interview with Design Guru today.

Here is also few tips from DesignGuru.io on how to answer System design questions on interviews:

4. Bugfree.ai — Best AI-Powered Platform for System Design Mock Interviews

Bugfree.ai is a specialized interview preparation platform that positions itself as the “LeetCode for System Design”. It offers 150+ real System Design questions and AI-powered mock interviews that closely simulate FAANG-level interviews.

With a strong focus on System Design, Object-Oriented Design (OOD), and Behavioral interviews, Bugfree.ai has become a favorite among developers preparing for senior and staff roles.

Why It Stands Out for System Design:

• Realistic AI Mock Interviews that ask intelligent follow-up questions, challenge your design decisions, and probe deeper into trade-offs.

• Instant, detailed feedback on your solution structure, communication, diagram clarity, and technical depth.

• Large library of 150+ curated System Design questions from top companies with step-by-step solutions.

• Support for diagramming and interactive practice during mocks.

• Personalized scenarios based on your target company and role.

Users especially love how the AI adapts to their responses and provides structured feedback similar to real interviewers — helping them improve both technical depth and communication skills.

Best For: Developers who want focused, high-quality System Design mock interviews with realistic pressure and immediate feedback.

Try it here: Bugfree.ai Mock Interviews (Free trials available)

5. Interviewkickstart.com

This is another great resource for mock interviews on System Design. This site provides mock interviews with real FAANG experts.

It’s one of the best place to experience real-world mock interviews led by FAANG+ hiring managers.

They offer two packages, Essential Pack and Elite pack. First one is single mock interview with full support while other one is multiple mocks with comprehensive guidance like Resume review and LinkedIn optimization.

1. Personalized written feedback after the interview

2. Progress tracking tools

3. Access to curated prep guides and practice questions

4. One-on-one session with FAANG+ experts

5. Skill gap analysis, target role insights, and salary benchmarks

6. 3 Mock Interviews

The best thing about this platform is that interview is conducted by real FAANG experts and you also get lifetime access to all interview recordings and progress tools.

If you are preparing for FAANG interviews then this is a great resource for you. After all FAANG chances are hard to come by and you should everything in your hand to prepare for it.

I suggest you to go for elite package, its bit expensive but nothing compared to your FAANG chance. They are also offering 30% discount now

Here is the link to join — Interviewkickstart.com

6. Meetapro: Your Gateway to Real-Life Success

Meetapro, is another revolutionary platform in the technical interview preparation space, it acts as your personal guide to success. I didn’t know about this awesome place until a few weeks ago which is shame.

Think of it as the Airbnb for mock interviews and career coaching, connecting job seekers with seasoned interviewers from FAANG and other top-tier companies.

Why Choose Meetapro?

• Affordability: Meetapro boasts an average session cost significantly lower than other platforms, making it accessible for all aspiring candidates.

• Expert Interviewers: Benefit from the wisdom of veterans who bring years of experience, offering feedback that truly makes a difference in real-life interviews.

• Direct Communication: The platform facilitates direct communication between job seekers and interviewers, fostering a personalized and effective preparation environment.

• All-in-One Tools: Equip yourself with essential tools for effective interview sessions, including the option to record and review your performance.

You can seize the opportunity to enhance your interview skills and move closer to your dream job with Meetapro.

Here is the link to join and learn more — Meetapro mock interviews

7. Educative Mock Interviews — Best AI-Powered Mock Interview Platform

If you’re looking for realistic, on-demand practice with instant feedback, Educative’s Mock Interview feature is one of the strongest options available in 2026.

Designed specifically for technical interviews, it offers personalized mock sessions across Coding Interviews, System Design, and MAANG+ Interviews.

With over 18,000 mock interviews already conducted, it has quickly become a go-to platform for many developers preparing for senior and staff-level roles.

Why It Stands Out for System Design:

• AI Interviewer that feels surprisingly human — it understands your answers and follows up with probing questions like a real interviewer.

• Interactive diagramming workspace — you can actually draw system diagrams during the session.

• Deep dive analysis with performance breakdowns on System Architecture, Technical Design, Trade-off Analysis, Communication, and more.

• Tailored questions based on your target role (SDE-2, SDE-3, Engineering Manager, etc.).

• Instant, actionable feedback highlighting your strengths and critical gaps.

Many users mention that the AI feels more patient and structured than some human coaches, making it especially valuable for practicing System Design explanations and trade-off discussions.

Best For: Developers who want unlimited practice without scheduling stress, especially those preparing for FAANG-level System Design interviews.

Try it here: Educative Mock Interviews (Free interviews available)

Conclusion

That’s all about the 7 best places for System design and Coding Mock interviews in 2026. While its important to prepare using LeetCode and online courses. Mock interviews are the secret weapon in your arsenal for acing technical interviews.

Whether you choose Codemia.io, DesignGuru, Educative.io, or Exponent, each platform offers a unique and effective approach to help you sharpen your skills, receive valuable feedback, and confidently step into your dream job. Don’t just prepare; prepare with purpose.

Other 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:

• System Design Interview Course on ByteByteGo by Alex Wu

• CodeKarle’s System Design Interview Course on Udemy

• Grokking the System Design Interview on DesignGuru

• Mastering the System Design Interview by Frank Kane

• Software Design and Architecture Specialization [Coursera]

• System Design Interview course and Mock Interview by Exponent

• Web Application & Software Architecture 101 [Educative.io]

• Pragmatic System Design [Udemy Course]

• Grokking Modern System Design for Software Engineers & Managers

Thanks for reading this article so far. If you like this System design Mock interview platforms and websites then please share it with your friends and colleagues who are preparing for tech interviews. If you have any questions or feedback then please drop a note.

All the best with your interviews.

P. S. — If you want to do just one thing right now, I suggest schedule your mock interview on DesignGurus.io with Arslan Ahmad and you can thank me later.

If you’re serious about Agentic AI, this is the course you’ve been waiting for.

Authored by Paul Iusztin (bestselling LLM Engineer’s Handbook author), the Agentic AI Engineering course takes you from fundamentals to production-ready agents with real-world projects, evaluation frameworks, and deployment pipelines.

Perfect for engineers who want to move beyond demos.

Here is the link to join this course - Agentic AI Engineering (aff) by

Hello guys, if you’re a senior developer looking to deepen your expertise and stay ahead of the curve in this AI era, then reading is non-negotiable.

It’s the single most important thing to keep yourself up to date and to remember whatever you have learned.

While you definitely cannot beat AI when it comes to knowledge, you can learn how to make the best use of AI and combine that with our years of experience in System Design and Software architecture, and then you can definitely future-proof your career.

I regularly read blog posts, case studies, documentation, and books.

While reading is not easy and most of the time you will feel bored or sleepy, there are a few books that you want to read from start to end, and I am going to share such books for your 2026 reading.

These five books represent the essential knowledge every engineering leader should have in their arsenal, whether you’re architecting systems, optimizing performance, or navigating the AI landscape.

5 Must-Read Books for Backend Developers in 2026

Last year, I read close to 50 books; these are my top 5 picks to learn advanced software engineering skills and in-demand technologies this holiday.

Instead of books, I have divided them into skills you will learn by reading those books. This will help you to quickly find which book is worth your time.

1. Head First Software Architecture: A Learner’s Guide to Architectural Thinking

Architecture is the language of senior engineers. Head First Software Architecture: A Learner’s Guide to Architectural Thinking makes architectural patterns accessible without oversimplifying them.

If you’re transitioning into leadership or want to strengthen your foundational knowledge, this book forces you to think visually and systematically about system design.

2. Software Architecture: The Hard Parts by Neal Ford and Mark Richards

Once you’ve got the fundamentals down, Software Architecture: The Hard Parts by Neal Ford and Mark Richards is where you’ll find real wisdom.

It doesn’t tell you the “best” architecture, but it teaches you how to evaluate trade-offs in distributed systems.

This is required reading for anyone making architectural decisions that impact hundreds of engineers and millions of users.

3. Latency: Reduce delay in software systems by Pekka Enberg

Performance is Non-Negotiable. A senior engineer knows that performance isn’t a feature; it’s a discipline. Latency: Reduce delay in software systems by Pekka Enberg tackles the hidden costs of latency that most engineers overlook.

From CPU cache behavior to distributed system delays, this book equips you with the insights needed to build systems that don’t just work --- they fly.

Whether you’re optimizing critical paths or debugging mysterious slowdowns, this knowledge becomes invaluable.

4. AI Engineering: Building Applications with Foundation Models

AI engineering is no longer optional for senior engineers, it’s foundational. AI Engineering: Building Applications with Foundation Models bridges the gap between AI research and production systems.

It teaches you how to think about foundation models as infrastructure, not magic, which is exactly the mindset senior engineers need.

5. LLM Engineer’s Handbook

For deeper production expertise, LLM Engineer’s Handbook covers the practical challenges you’ll face: prompt engineering at scale, managing model costs, handling latency, and building reliable systems around LLMs. This handbook is your guide to staying relevant as AI transforms software engineering.

For active learning, you can also join his new course, the Agentic AI Engineering that takes you from fundamentals to production-ready agents with real-world projects, evaluation frameworks, and deployment pipelines.

Why Backend Developers Should Read these books?

If you have been reading news, then its not that bad for senior engineer while its mostly junior engineer who is facing AI’s wrath if you are somewhere in middle, then its high time that you jump the ship towards senior.

Senior Backend engineers don’t just write code, they shape systems, influence architecture, and guide teams.

These five books represent the critical knowledge areas: architectural decision-making, performance optimization, and AI engineering.

They’re not quick reads, but they’re investments in your career that pay dividends for years.

The engineers who read these books will be the ones designing the systems of 2026 and beyond. Make sure you’re one of them.

All the best !!

Other Post you may like

Codemia.io is a hands-on system design learning platform that helps you practice designing real systems step-by-step. They have now launched Agentic AI Design courses and Practice Problems to better learn Agentic AI Design and prepare for Interviews.

It gives you challenges like design a RAG-Based Q&A Agent, Design an AI Travel Planning Agent, Design a Personal Finance Agent, Design a Code Generation Agent and also provide editorial solution of many of these problem.

If you are learning Agentic AI Design then you can use Codemia.io to solve real problems and learn what it takes to explain your solution on real interview. Their platform is both AI powered and give you tools to architect and explain you solution.

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Hello guys, if you are preparing for System design interview and don’t want to leave any chance then apart from joining courses, reading books and case studies, you can also read white papers from companies like Google and AWS to learn about the architecture of complex real system.

In today’s highly distributed technology world, understanding system design is crucial for building robust, scalable, and efficient systems.

White papers are an excellent resource for learning, as they provide in-depth technical insights, real-world case studies, and best practices from industry leaders and today I am going to share 40 best white papers you can read to take your System design interview preparation to next level .

Whether you’re an aspiring system architect, a seasoned developer, or a technology enthusiast, these 40 must-read white papers will enrich your understanding of system design.

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

Similar, while answering System design questions you can also follow a System design template like this from DesignGuru to articulate your answer better in a limited time. Following this template is actually one of the best thing you can do to start your preparation for any system design interview.

Now, let’s jump into the best white papers you can read to learn Software design better.

40 Best White Papers for Learn Software Architecture and System Design

Here are 40 must-read research papers you can read you understand the key concepts of system design, software architecture and prepare for your interview.

From basic distributed systems to the latest industry trends, these papers cover a lot of useful concepts. Whether you’re new to system design or a pro, these papers will give you the knowledge and skills you need to excel in your interview and career.

1. Google File System

Author: Sanjay Ghemawat, Howard Gobioff, Shun-Tak Leung
Summary: This foundational paper describes the Google File System (GFS), a scalable distributed file system for large distributed data-intensive applications.
Link: https://research.google/pubs/pub51/

After reading this paper if you want to test your system design skill by actually designing a distributed file system then you can also checkout Codemia.io, a new, Leetcode style website where you can practice System design problem online.

2. MapReduce: Simplified Data Processing on Large Clusters

Author: Jeffrey Dean, Sanjay Ghemawat
Summary: This white paper is an essential read on the MapReduce programming model that enables processing vast amounts of data across many machines.
Link: https://research.google/pubs/pub62/

3. Dynamo: Amazon’s Highly Available Key-Value Store

Author: Giuseppe DeCandia et al.
Summary: In this research paper from AWS, you will learn about Dynamo, Amazon’s key-value store designed for high availability and scalability, used to manage the state of various services.
Link: https://allthingsdistributed.com/files/amazon-dynamo-sosp2007.pdf

4. Bigtable: A Distributed Storage System for Structured Data

Author: Fay Chang et al.
Summary: This paper details Bigtable, Google’s distributed storage system for managing structured data designed to scale to a very large size.
Link: https://research.google/pubs/pub27898/

5. The Chubby Lock Service for Loosely-Coupled Distributed Systems

Author: Mike Burrows
Summary: This paper presents Chubby, a lock service for loosely-coupled distributed systems designed to manage coarse-grained locks.
Link: https://research.google/pubs/pub27897/

6. Paxos Made Simple

Author: Leslie Lamport
Summary: A simplified explanation of the Paxos consensus algorithm, which is foundational for understanding distributed systems and achieving consensus.
Link: https://research.microsoft.com/en-us/um/people/lamport/pubs/paxos-simple.pdf

7. Raft Consensus Algorithm

Author: Diego Ongaro, John Ousterhout
Summary: An approachable and understandable consensus algorithm designed as an alternative to Paxos, providing better understandability and manageability.
Link: https://ramcloud.stanford.edu/raft.pdf

8. Spanner: Google’s Globally-Distributed Database

Author: James C. Corbett et al.
Summary: This paper introduces Spanner, Google’s scalable, multi-version, globally-distributed, and synchronously-replicated database.
Link: https://research.google/pubs/pub39966/

9. The Log-Structured Merge-Tree (LSM-Tree)

Author: Patrick O’Neil et al.
Summary: The LSM-Tree paper introduces a method for improving write performance in databases, which is crucial for high-write systems.
Link: https://cs.umb.edu/~poneil/lsmtree.pdf

10. Kafka: A Distributed Messaging System for Log Processing

Author: Jay Kreps et al.
Summary: This paper describes Kafka, a distributed messaging system that is highly scalable and fault-tolerant, widely used for real-time data pipelines.
Link: https://storageconference.org/2011/Papers/19.Kreps.pdf

11. Cassandra — A Decentralized Structured Storage System

Author: Avinash Lakshman, Prashant Malik
Summary: This paper introduces Cassandra, a decentralized storage system designed to handle large amounts of data across many commodity servers.
Link: https://www.cs.cornell.edu/projects/ladis2009/papers/lakshman-ladis2009.pdf

12. Mesos: A Platform for Fine-Grained Resource Sharing in the Data Center

Author: Benjamin Hindman et al.
Summary: Learn about Apache Mesos, a resource management platform that allows multiple distributed systems to efficiently share cluster resources.
Link: https://people.eecs.berkeley.edu/~alig/papers/mesos.pdf

13. The CAP Theorem

Author: Eric Brewer
Summary: This white paper introduces the CAP Theorem, which states that it is impossible for a distributed data store to simultaneously provide consistency, availability, and partition tolerance.
Link: https://www.cs.cornell.edu/courses/cs7412/2013sp/papers/decisive.pdf

14. The Tail at Scale

Author: Jeffrey Dean, Luiz André Barroso
Summary: This paper discusses the phenomenon of long latency tails in large-scale services and how to mitigate their effects.
Link: https://research.google/pubs/pub40801/

15. The End-to-End Argument in System Design

Author: Jerome H. Saltzer, David P. Reed, David D. Clark
Summary: A seminal paper that introduces the end-to-end argument, a principle in system design that helps in deciding where to place functions in a networked system.
Link: https://web.mit.edu/Saltzer/www/publications/endtoend/endtoend.pdf

16. FAWN: A Fast Array of Wimpy Nodes

Author: David G. Andersen et al.
Summary: FAWN presents an architecture that uses a cluster of low-power nodes to provide efficient, scalable, and reliable storage.
Link: https://www.cs.cmu.edu/~dga/papers/fawn-sosp09.pdf

17. The Zebra Copying Garbage Collector

Author: Bill McCloskey et al.
Summary: This paper discusses the Zebra garbage collector, which provides a copying collector optimized for high throughput and low pause times.
Link: https://research.fb.com/wp-content/uploads/2016/11/the-zebra-copying-garbage-collector.pdf

18. The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines

Author: Luiz André Barroso, Urs Hölzle
Summary: This paper introduces the concept of warehouse-scale computing and discusses the design of datacenters that function as single massive computers.
Link: https://research.google/pubs/archive/281200.pdf

19. Pregel: A System for Large-Scale Graph Processing

Author: Grzegorz Malewicz et al.
Summary: Pregel is a system designed by Google for processing large-scale graphs efficiently using a vertex-centric model.
Link: https://research.google/pubs/archive/36721.pdf

20. SEDA: An Architecture for Well-Conditioned, Scalable Internet Services

Author: Matt Welsh et al.
Summary: This paper presents SEDA (Staged Event-Driven Architecture), a framework for building scalable and well-conditioned internet services.
Link: https://people.eecs.berkeley.edu/~dawnsong/teaching/spring08/Papers/seda-sosp01.pdf

21. TreadMarks: Distributed Shared Memory on Standard Workstations and Operating Systems

Author: Pete Keleher et al.
Summary: TreadMarks is a distributed shared memory system that allows processes on different machines to share memory efficiently.
Link: https://www.cs.rochester.edu/u/scott/papers/1994_TreadMarks_TR536.pdf

22. The SWIM Gossip Protocol

Author: I. Gupta et al.
Summary: This paper describes the SWIM protocol, a scalable, weakly-consistent, infection-style process group membership protocol.
Link: https://cs.brown.edu/~irina/papers/swim.pdf

23. CORFU: A Shared Log Design for Flash Clusters

Author: Dahlia Malkhi et al.
Summary: CORFU introduces a scalable shared log design that leverages the properties of flash storage to provide high throughput and low latency.
Link: https://research.cs.wisc.edu/wind/Publications/corfusosp2012.pdf

24. Dapper: A Large-Scale Distributed Systems Tracing Infrastructure

Author: Benjamin H. Sigelman et al.
Summary: This paper presents Dapper, Google’s large-scale distributed systems tracing infrastructure for monitoring and diagnosing complex systems.
Link: https://research.google/pubs/archive/36356.pdf

25. ZooKeeper: Wait-Free Coordination for Internet-Scale Systems

Author: Patrick Hunt et al.
Summary: ZooKeeper is a coordination service for distributed applications, providing primitives such as configuration maintenance, synchronization, and naming.
Link: https://www.usenix.org/legacy/event/atc10/tech/full_papers/Hunt.pdf

26. Ceph: A Scalable, High-Performance Distributed File System

Author: Sage A. Weil et al.
Summary: Ceph is a distributed file system that provides high performance, reliability, and scalability, designed for a wide range of storage applications.
Link: https://www.ssrc.ucsc.edu/Papers/weil-atc06.pdf

27. Consul: A Distributed, Highly Available Service Discovery and Configuration System

Author: HashiCorp
Summary: This white paper details Consul, a distributed service discovery and configuration system that provides an easy way to find and configure services in large-scale systems.
Link: https://www.consul.io/docs/whitepaper/consul-whitepaper.pdf

28. The Raft Consensus Algorithm

Author: Diego Ongaro, John Ousterhout
Summary: An approachable and understandable consensus algorithm designed as an alternative to Paxos, providing better understandability and manageability.
Link: https://raft.github.io/raft.pdf

29. Amazon Aurora: Design Considerations for High Throughput Cloud-Native Relational Databases

Author: Anurag Gupta et al.
Summary: This paper discusses the design considerations behind Amazon Aurora, a high throughput cloud-native relational database.
Link: https://www.allthingsdistributed.com/files/p1041-gupta.pdf

30. Snowflake: A Self-Tuning, Elastic Cloud Data Warehouse

Author: Marcin Zukowski et al.
Summary: Snowflake presents a cloud-native data warehousing solution that automatically optimizes for performance and scalability.
Link: https://www.cidrdb.org/cidr2015/Papers/CIDR15_Paper28.pdf

31. Kubernetes: Up and Running

Author: Joe Beda et al.
Summary: This white paper introduces Kubernetes, an open-source platform designed to automate deploying, scaling, and operating application containers.
Link: https://kubernetes.io/docs/concepts/overview/what-is-kubernetes/

32. GFS: Evolution on Fast-Forward

Author: Sean Quinlan, Sean Myrick
Summary: This paper discusses the evolution of the Google File System (GFS) and its impact on large-scale data processing.
Link: https://www.usenix.org/system/files/conference/atc15/atc15-paper-quinlan.pdf

33. Borg, Omega, and Kubernetes

Author: Brendan Burns et al.
Summary: This paper examines the relationship between Borg, Omega, and Kubernetes, providing insights into the evolution of cluster management systems at Google.
Link: https://storage.googleapis.com/pub-tools-public-publication-data/pdf/44843.pdf

34. In Search of an Understandable Consensus Algorithm

Author: Diego Ongaro, John Ousterhout
Summary: This paper presents the Raft consensus algorithm, designed to be more understandable than Paxos while providing similar functionality.
Link: https://raft.github.io/raft.pdf

35. Distributing and Querying the “Big Data” with Apache Hive

Author: Ashish Thusoo et al.
Summary: This paper discusses Apache Hive, a data warehouse infrastructure built on top of Hadoop for providing data summarization, query, and analysis.
Link: https://www.vldb.org/pvldb/vol2/vldb09-938.pdf

36. Cassandra: A Decentralized Structured Storage System

Author: Avinash Lakshman, Prashant Malik
Summary: This paper introduces Cassandra, a decentralized storage system designed to handle large amounts of data across many commodity servers.
Link: https://www.cs.cornell.edu/projects/ladis2009/papers/lakshman-ladis2009.pdf

37. The Chubby Lock Service for Loosely-Coupled Distributed Systems

Author: Mike Burrows
Summary: This paper presents Chubby, a lock service for loosely-coupled distributed systems designed to manage coarse-grained locks.
Link: https://research.google/pubs/pub27897/

38. The Google File System

Author: Sanjay Ghemawat, Howard Gobioff, Shun-Tak Leung
Summary: This foundational paper describes the Google File System (GFS), a scalable distributed file system for large distributed data-intensive applications.
Link: https://research.google/pubs/pub51/

39. MapReduce: Simplified Data Processing on Large Clusters

Author: Jeffrey Dean, Sanjay Ghemawat
Summary: An essential read on the MapReduce programming model that enables processing vast amounts of data across many machines.
Link: https://research.google/pubs/pub62/

40. Dynamo: Amazon’s Highly Available Key-Value Store

Author: Giuseppe DeCandia et al.
Summary: Learn about Dynamo, Amazon’s key-value store designed for high availability and scalability, used to manage the state of various services.
Link: https://allthingsdistributed.com/files/amazon-dynamo-sosp2007.pdf

That’s all about the 40 best white papers or research paper you can read to learn System Design and Software architecture better. This list represents a selection of must-read white papers on system design.

Each paper provides unique insights and practical knowledge that are invaluable for anyone involved in building and managing complex systems.

By reading and understanding these papers, you will be well-prepared for your system design interview and have the knowledge and skills necessary to excel in your career.

Thanks for reading this article so far. If you like these system design interview tips then please share with your friends and colleagues. If you have any questions feel free to ask in comments.

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Hello guys, cracking interviews at FAANG and other top-tier tech companies in 2026 requires more than just grinding LeetCode.

Interviews today test coding fundamentals, system design depth, behavioral clarity, and communication skills — often across multiple rounds.

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