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Hello guys, if you are preparing for system design interview and looking for common System deign problems and resources then you have come to the right place.

Earlier, I have shared best System design resources, as well as System design interview guide,  and common System design concepts and in this article to discuss one popular System deign problem — designing Twitter or X.com.

Designing a complex system like Twitter can be challenging, especially in a system design interview.

The biggest challenge is not the complexity but the time as you need to convince your interviewer in 40 minutes that you know your stuff and this can only be possible if you prepare well and follow a structured approach while answering such questions.

In this System design tutorial I will also give you a simple guide to help you structure and system design template (see below) to collect your thoughts and present a clear 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 ByteByteGo, Codemia.io, Design Guru, Exponent, Educative ,Bugfree.ai and udemy, which have many great System design courses

Similar, while answering System design questions you can also follow a System design template like this from DesignGurus.io 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 problem and solution.

How to System Design Twitter or X.com?

Designing a system like Twitter is a common scenario in system design interviews but if you want to practice this question from scratch you can start it on Codemia.io which is a Leetcode style platform for system design interviews.

It has more than 120+ system design problems and growing. It also provide editorial solution created by senior engineers from reputed company. It also got free questions and designing Twitter is one of them. You can access it here.

Now coming back to question itself, it’s a great way to showcase your understanding of large-scale distributed systems, as it involves various aspects such as handling massive user bases, ensuring high availability, and maintaining stability under heavy loads.

This solution guide will walk you through the process of designing Twitter, covering system requirements, capacity estimation, API design, database design, high-level design, request flow, detailed component design, trade-offs, and potential failure scenarios.

By the end of this guide, you’ll have a solid understanding of how to approach and present this design in an interview setting.

Twitter System Requirements

First thing you should get the requirements right and it starts with functional requirement.

Choose the one you are most familiar with if you are cloning a real app like buying stuff on Amazon or sending message on Facebook or Twitter.

Functional Requirements

To design a robust and user-friendly Twitter-like system, we need to outline the core functionalities.

• Users should be able to compose and share tweets, which is the primary function of the platform.

• This involves creating a new tweet, attaching optional media, and sharing it with their followers. Additionally, users should be able to follow other users to see their updates in their feed.

• This means managing a list of followed users and ensuring their tweets appear in the user’s timeline.

• Another essential feature is allowing users to favorite tweets, indicating their appreciation and potentially bookmarking these tweets for future reference.

Here are the key functional requirements for your reference:

1. Compose and Share Tweets: Users should be able to create and share tweets.

2. Follow Users: Users should be able to follow other users and see their updates.

3. Favorite Tweets: Users should be able to favorite tweets to show appreciation.

Non-Functional Requirements

For a platform with the scale of Twitter, non-functional requirements are crucial. Scalability is paramount as the system must handle a vast number of users, tweets, and interactions without degradation in performance.

• High availability ensures that the platform remains accessible and functional even during peak traffic times or in the event of hardware failures.

• Stability is another critical aspect, as the service must be reliable, with minimal downtime and consistent performance, even under high concurrency.

Here are the key non-functional requirements which you should mention during interview:

1. Scalability: The system should handle a large number of users, tweets, and interactions.

2. High Availability: The system should be available even under high traffic.

3. Stability: The system should be stable and accessible without frequent issues or downtime.

Capacity Estimation

Estimating the user base is the first step in understanding the scale of the system. For this design, let’s assume a user base of 500 million. This helps us gauge the expected load and the necessary infrastructure to support such a large number of users.

User Base

• Assume a user base of 500 million.

Traffic

To get a sense of the daily operations, we need to estimate the traffic. Assuming each user tweets once a day, we can expect 500 million tweets daily.

Additionally, if each user views 10 pages of their home feed per day, this results in substantial read operations.

Following relationships also add to the complexity, with each user following 100 others on average, leading to 50 billion follow relationships.

Lastly, if each user favorites 5 tweets daily, we have 2.5 billion favorite operations per day.

Here are the key traffic requirements you should consider or mention:

• Tweets: 500 million tweets per day (one tweet per user per day).

• Home Feed: Each user views 10 pages per day.

• Following: Each user follows 100 other users on average, leading to 50 billion follow relationships.

• Favorites: Each user favorites 5 tweets per day, leading to 2.5 billion favorites per day.

QPS (Queries Per Second)

Breaking down these operations into queries per second (QPS) helps us understand the real-time load.

For write operations, we calculate approximately 15k QPS, for read operations about 75k QPS, and for favorite operations around 30k QPS.

These numbers help in planning the necessary infrastructure and load balancing strategies.

• Write: 500M×23600×24≈15k\frac{500M \times 2}{3600 \times 24} \approx 15k3600×24500M×2​≈15k QPS

• Read: 500M×103600×24≈75k\frac{500M \times 10}{3600 \times 24} \approx 75k3600×24500M×10​≈75k QPS

• Favorites: 500M×53600×24≈30k\frac{500M \times 5}{3600 \times 24} \approx 30k3600×24500M×5​≈30k QPS

Data Size

Understanding the data size is crucial for storage planning. With 500 million tweets daily, and each tweet averaging 300 bytes after considering encoding, this totals to 140GB of new data daily, or 50TB annually.

For media content, if we assume it to be 100 times the size of tweets, it results in 10TB daily, or 4PB annually.

This estimation underscores the need for a distributed storage architecture.

• Tweets: 500 million tweets daily, each with 140 characters (300 bytes each). This totals 140GB per day, and 50TB per year.

• Media (Images/Videos): Assume 100 times the size of tweets, 10TB per day, and 4PB per year.

API Design

Now, let’s talk about the API Design which is is another important area in System design interviews:

Tweeting

APIs for tweeting need to handle the creation and posting of tweets efficiently. This involves capturing user information, tweet content, location data, and timestamp. Proper error handling and validation are essential to ensure a smooth user experience.

public Result postTweet(Long userId, String tweetText, String location, DateTime date);

Following

The following functionality requires APIs to manage relationships between users. This includes following and unfollowing users, ensuring data integrity, and updating the user’s following list promptly.

public Result followUser(Long userId, Long followedUserId);
public Result unfollowUser(Long userId, Long followedUserId);

Favorites

APIs for favorites allow users to like or unlike tweets. These operations should be efficient, with proper indexing and error handling to ensure quick updates and accurate count of favorites on each tweet.

public Result favoriteTweet(Long userId, Long tweetId);
public Result unfavoriteTweet(Long userId, Long tweetId);

The feed rendering API is crucial for fetching and displaying tweets from followed users. This requires efficient querying and pagination to ensure quick load times and a seamless user experience.

public Result getFeeds(Long userId, String location, int pageNo);

Here is a nice diagram showing key APIs using mermaid

Database Design

After API Design, let’s talk about Database design now:

Tables

The database design involves defining tables for users, tweets, and follower relationships.

The UserInfo table stores user details, the Tweets table handles tweet content and metadata, and the Follower table manages follow relationships.

Proper indexing is essential for fast lookups and updates.

• UserInfo Table

• userId (Primary Key)

• userName

• status

• otherProfile (avatar, age, etc.)

• Tweets Table

• tweetId (Primary Key)

• userId (Index)

• content

• postTime (Index)

• modifyTime

• status

• Follower Table

• userId

• followerId (Index)

• followedTime

And, here is a nice ERD diagram showing the relationship between these tables:

Storage

Choosing the right storage solutions is critical. For structured data like user profiles and tweets, MySQL is a good choice due to its support for complex queries and transactions.

For media storage, Amazon S3 offers scalable and cost-effective storage for images and videos.

• Use MySQL for structured data (users, tweets, follow relationships).

• Use Amazon S3 for media storage (images and videos).

High-Level Design

Let’s see the high level design first:

Client Layer

The client layer involves websites and apps sending requests to the server. These requests are distributed via load balancers to ensure even load distribution and high availability. Using a CDN for static files helps reduce latency and improve load times.

• Clients (websites/apps) send requests to the server.

• Requests are distributed via load balancers.

• Use a rate limiter to protect backend servers.

• Use a CDN for static files (images, videos).

Server Layer

The client layer involves websites and apps sending requests to the server. These requests are distributed via load balancers to ensure even load distribution and high availability.

Using a CDN for static files helps reduce latency and improve load times.

• Server clusters handle requests and different services:

• Tweet Service: Posting tweets.

• User Service: User registration and profile management.

• Follow Service: Following/unfollowing users.

• Home Feed Service: Rendering user feeds.

Data Layer

The data layer involves caching data with Redis to increase response speed and using MySQL for persistent storage with master-slave architecture to ensure consistency and availability. Amazon S3 is used for storing media files, ensuring scalability and durability.

• Use Redis for caching to increase response speed.

• Use MySQL with master-slave architecture for data consistency and availability.

• Use Amazon S3 for storing media files.

Request Flow

Explaining the request flow helps in understanding how different components interact. When a client sends a request, it first hits the load balancer, which distributes it to an appropriate server.

The server processes the request, updates the database, and caches the necessary data. For read requests, data is retrieved from the cache or database, and media files are fetched from the CDN, ensuring quick response times.

1. Client sends a request to the load balancer.

2. Load balancer distributes the request to a server.

3. Rate limiter checks the traffic.

4. Server processes the request and stores data in MySQL and Redis.

5. Media files are stored in CDN.

6. For read requests, the server retrieves files from CDN and data from Redis or MySQL.

Detailed Component Design

Now, let’s see the detailed component design and various software architecture components we can use to design twitter.

Load Balancer

Deploying multiple load balancers in a cluster ensures high availability and even load distribution.

Placing load balancers in different locations reduces latency for users, and using various algorithms like round-robin or least connections helps manage the load efficiently.

• Deploy multiple load balancers in a cluster.

• Place load balancers in different locations to reduce latency.

• Use algorithms like round-robin, least connections, or IP hash.

CDN

Using a CDN for static content reduces the load on the origin server and improves load times for users. Optimizing caching rules and adjusting TTL helps achieve higher cache hit ratios, ensuring content is served quickly.

• Use both pull and push caching approaches.

• Optimize caching rules and TTL to achieve higher cache hit ratios.

• Scale the system by adding more server nodes.

Redis

Using Redis for caching involves setting up a Redis cluster for scalability and employing master-slave replication for high availability. Sentinel monitors the cluster and handles failovers, ensuring the cache remains available even during node failures.

• Use Redis cluster for large-scale data.

• Employ master-slave replication for high availability.

• Use Sentinel to monitor the cluster and handle failovers.

MySQL

MySQL’s master-slave architecture supports high volume traffic and ensures data consistency through replication. Horizontal partitioning helps distribute the load across multiple servers, handling large datasets efficiently.

• Use master-slave architecture for high volume traffic.

• Use horizontal partitioning to handle more data.

Here is a nice diagram showing key component of Twitter:

Trade-offs and Tech Choices

This is probably the most important part for interviews, let’s see:

Database

Choosing MySQL over NoSQL is due to the need for complex queries and transaction support. While NoSQL offers schema flexibility, it lacks support for structured data and complex transactions, which are essential for Twitter’s business model.

• Chose MySQL over NoSQL due to the need for complex queries and transaction support.

Cache

Redis is preferred over Memcached due to its support for various data types and horizontal scaling. While Memcached is efficient for basic key-value storage, Redis offers advanced features and better scalability, making it suitable for large-scale systems.

• Chose Redis over Memcached for its advanced features and horizontal scaling.

Failure Scenarios and Bottlenecks

Hybrid Model

To handle users who follow many accounts, a hybrid model combining pull and push approaches can reduce latency. For users following many people, pushing new tweets reduces the load during feed aggregation, improving user experience.

• If a user follows many people, combine pull and push models to reduce latency.

Read Hotspot

Handling read hotspots, such as popular users with many followers, involves caching their tweets in Redis and using a cache-aside strategy for consistency.

Adding hot zones in Redis servers and using local caches can distribute the load, avoiding excessive calls to the same server.

• Cache hot data in Redis with a cache-aside strategy.

• Use local cache to handle high traffic.

Future Improvements

Future improvements include implementing a multi-region active-active strategy for disaster recovery and high availability.

Deploying service clusters and database clusters in multiple locations with automatic failover and load balancing ensures no single point of failure, maintaining service continuity and reliability.

• Implement a multi-region active-active strategy for disaster recovery and high availability.

• Continue optimizing caching, load balancing, and data storage strategies to handle future growth.

System Design Interviews Resources

And, here are curated list of best system design books, online courses, and practice websites which you can check to better prepare for System design interviews. Most of these courses also answer questions I have shared here.

1. DesignGuru’s Grokking System Design Course: An interactive learning platform with hands-on exercises and real-world scenarios to strengthen your system design skills.

2. Codemia.io : This is another great platform to practice System design problems for interviews. It got more than 120+ System design problems, many of them are free and also a proper structure to solve them.

3. ByteByteGo: A live book and course by Alex Xu for System design interview preparation. It contains all the content of System Design Interview book volume 1 and 2 and will be updated with volume 3 which is coming soon.

4. Exponent: A specialized site for interview prep especially for FAANG companies like Amazon and Google, They also have a great system design course and many other material which can help you crack FAAN interviews.

5. “System Design Interview” by Alex Xu: This book provides an in-depth exploration of system design concepts, strategies, and interview preparation tips.

6. “Designing Data-Intensive Applications” by Martin Kleppmann: A comprehensive guide that covers the principles and practices for designing scalable and reliable systems.

7. LeetCode System Design Tag: LeetCode is a popular platform for technical interview preparation. The System Design tag on LeetCode includes a variety of questions to practice.

8. “System Design Primer” on GitHub: A curated list of resources, including articles, books, and videos, to help you prepare for system design interviews.

9. Educative’s System Design Course: An interactive learning platform with hands-on exercises and real-world scenarios to strengthen your system design skills.

10. High Scalability Blog: A blog that features articles and case studies on the architecture of high-traffic websites and scalable systems.

11. YouTube Channels: Check out channels like “Gaurav Sen” and “Tech Dummies” for insightful videos on system design concepts and interview preparation.

image_credit — ByteByteGo

Remember to combine theoretical knowledge with practical application by working on real-world projects and participating in mock interviews. Continuous practice and learning will give you confidence for system design interviews.

Read more

Hello guys, It’s been hard way I approached learning System Design, especially for interviews.

When I started, I just went through reading blogs, Medium articles, joining courses, watching YouTube videos, reading books and whitepapers and what not.

While it did helped me to learn System Design, its certainly not the best or most structured way to learn System Design and I won’t recommend any people to do that way, especially if they have an interview coming up soon.

If I had to start my system design learning journey from scratch, I’d take a methodical, step-by-step approach to cover essential concepts, practical applications, and tradeoffs.

System design can feel overwhelming at first because of its vast scope, but breaking it down into smaller chunks helps and you will be surprised that its not that vast as it looks.

Below, I’ll outline how I’d structure my learning, focusing on key concepts, building blocks, databases, tradeoffs, and architectural patterns.

1. Start with the Core Concepts

System design fundamentals are built on understanding core concepts like scalability, availability, and fault tolerance.

These concepts are the foundation for designing systems that are efficient, reliable, and scalable.

• Scalability: Understand vertical vs. horizontal scaling and when to use each.

• Availability: Learn how to design systems with minimal downtime (e.g., active-active vs. active-passive setups).

• CAP Theorem: Explore the tradeoffs between consistency, availability, and partition tolerance in distributed systems.

• Latency vs. Throughput: Grasp the difference between low latency and high throughput, and how they impact user experience.

• Fault Tolerance: Study techniques like redundancy and replication to ensure systems continue functioning despite failures.

💡 Resources Tip : You can also checkout ByteByteGo, which has the best visual explanation for each of these System Design concepts.

2. Master the Building Blocks of System Design

Once the basics are clear, focus on the building blocks that form the backbone of system design.

• Content Delivery Network (CDN): Learn how CDNs reduce latency by caching content closer to users.

• Load Balancing: Understand load balancing algorithms like round robin, least connections, and IP hash.

• Caching: Dive into caching strategies, cache eviction policies, and distributed caching.

• Message Queues: Explore how queues decouple systems and enable asynchronous communication.

• Circuit Breakers: Study how they prevent cascading failures in distributed systems.

• API Gateway: Learn its role in managing APIs, rate limiting, and handling cross-origin resource sharing (CORS).

💡 Practical Step: Watch an interview focused System design course like Grokking Modern System Design for Software Engineers & Managers or Build a small project (e.g., a chat app) to see how load balancing, caching, and API gateways come together.

3. Dive into Databases

Databases are at the heart of every system. Knowing how to choose and design them is critical.

• SQL vs. NoSQL: Understand the differences and when to use each.

• Database Indexes: Learn how indexes speed up query performance.

• Database Sharding: Explore strategies for distributing data across multiple nodes.

• Replication and Scaling: Understand how to replicate databases for availability and scale them for performance.

• ACID Transactions: Learn the importance of atomicity, consistency, isolation, and durability.

💡 Practice Exercise: Design a schema for a large-scale e-commerce app and experiment with sharding and replication, you can also checkout Grokking the advanced System Design course on DesignGurus.io for more such topics.

4. Understand Key Tradeoffs

System design often involves making tradeoffs. Learning to evaluate them is crucial for building efficient systems.

• Strong vs. Eventual Consistency: Study the implications of each for real-world applications like banking systems and social media.

• Synchronous vs. Asynchronous Communication: Understand when to use synchronous APIs vs. asynchronous queues.

• Batch vs. Stream Processing: Learn the difference between systems like Apache Hadoop (batch) and Apache Kafka (stream).

• Stateful vs. Stateless Design: Explore how stateless systems scale better and why stateful systems are still essential in some cases.

💡 Resource Tip: Join a comprehensive course like “Grokking the System Design Interview” or Read real-world case studies from companies like Netflix and Google to see how they implement these concepts.

5. Study System Design Architectural Patterns

Architectural patterns provide blueprints for designing systems. They guide how components interact and scale.

• Client-Server Architecture: Learn the basic two-tier architecture and its limitations.

• Microservices Architecture: Understand how to break down monoliths into smaller, independently deployable services.

• Serverless Architecture: Explore event-driven architectures using AWS Lambda or Azure Functions.

• Event-Driven Architecture: Learn how events drive workflows in systems like e-commerce and IoT.

• Peer-to-Peer (P2P) Architecture: Study decentralized systems like torrents or blockchain.

💡 Project Idea: Design a system like Uber using a combination of event-driven and microservices architecture. You can practice such problems on codemia.io, a great place to practice system design.

6. Embrace Hands-On Learning

The best way to master system design is by building real-world systems. Start small and progressively increase the complexity of your designs.

• Basic Project: Design a URL shortening service (learn about hashing, databases, and scalability).

• Intermediate Project: Build a Twitter-like app (explore API design, rate limiting, and caching).

• Advanced Project: Design a ride-hailing service (focus on location-based systems, distributed databases, and fault tolerance).

💡 Mock Interviews: Participate in system design interviews on platforms like Pramp , Designgurus.io, or InterviewKickStart with peers to gain confidence.

7. Learn from Real-World Systems

Study architectures of systems like Netflix, Facebook, and Google Drive. Analyze how they handle challenges like scaling, high availability, and fault tolerance.

💡 Resource Tip: Check out YouTube channels like Tech Dummies and System Design Simplified for breakdowns of real-world architectures.

8. Use Structured Learning Resources

Lastly, supplement your learning with curated courses, books, and blogs.

• Books: “Designing Data-Intensive Applications” by Martin Kleppmann or “System Design Inside Guide” by Alex Xu

• Courses: Alex Xu’s ByteByteGo or Exponet System Design Course

• Blogs: Check out articles on Medium, GitHub, and educational platforms like Educative.io.

Read more

Hello guys, seven years ago, Alex Xu walked away from his job at Twitter with no clear plan for what came next. No flashy startup idea. No big funding round.

Just time — and a curiosity about how to help engineers prepare for one of the most intimidating parts of their careers: system design interviews.

What started as a personal project turned into something extraordinary. For 18 months, Alex poured his knowledge, research, and real-world experience into writing System Design Interview — An Insider’s Guide.

The book struck a nerve in the tech community. It didn’t just explain concepts — it made them clear, visual, and actionable. Software Engineers finally had a guide that could bridge the gap between theory and passing a real interview.

The success of the first book led to a sequel, System Design Interview — Volume 2, co-authored with another expert Sham Lam. That too became a bestseller. From there, the momentum didn’t stop.

Alex teamed up with brilliant engineers, data scientists, and AI specialists to create a complete library of interview preparation books — covering system design, machine learning systems, coding interview patterns, generative AI, and more.

Today, ByteByteGo’s book collection has grown to seven Amazon bestsellers — each designed to tackle a different, critical part of the modern technical interview process.

And now, Alex has taken it one step further. All these books, along with interactive coding practice, detailed visuals, and structured learning paths, are available in one place — the ByteByteGo platform.

This isn’t just a set of PDFs or videos. It’s a comprehensive preparation toolkit for cracking interviews at FAANG-level companies, fast-growing startups, or any tech role that demands deep problem-solving skills.

Whether you’re mastering distributed systems, designing machine learning pipelines, or refining your coding fundamentals, ByteByteGo gives you the skills, the structure, and the confidence to walk into any interview ready to win.

Whether you’re preparing for FAANG, startups, or top-tier tech interviews, ByteByteGo is fast becoming the go-to resource for serious developers.

You can also try it now with 50% OFF

As they are offering a rare 50% discount on their annual and lifetime plan.

My Favorite Books for Coding interviews in 2026

If you’re aiming to land a top-tier tech role in 2026, you can’t rely on just brushing up your coding skills a week before the interview.

Today’s hiring processes are tougher than ever — covering algorithms, data structures, system design, machine learning systems, object-oriented design, and even generative AI.

That’s where ByteByteGo shines. It’s not just a set of books — it’s an all-in-one interview preparation platform.

With a single subscription, you get access to all the best-selling ByteByteGo books, interactive coding practice, visual explanations, and a complete system design learning track.

It’s like having the ultimate interview prep library in your pocket. They are also offering a rare 50% discount on their annual and lifetime plan.

Anyway, here are the 7 must-read ByteByteGo books you can find inside the platform (and also on Amazon if you prefer physical copies).

1. System Design Interview — An Insider’s Guide by Alex Xu

This is the book that put ByteByteGo on the map. Alex Xu breaks down complex system design problems into clear, digestible frameworks.

With real-world examples like designing YouTube, Twitter, or a chat system, this guide is gold for mid-to-senior level interviews.

This was also the first book of Alex Xu I read after finding him on Twitter and since then I have read it a couple of times and recommended it to a countless number of folks.

This is also the go-to-book for System Design and you will find its reference everywhere from Reddit, LeetCode to HN and Facebook groups, and why not? It provide most structured way to prepare for System Design interview at that time.

Even when you are not preparing for interview, this book is a gold to improve your knowledge about designing distributed systems and key components of software architecture like cache, replication, database, and more.

Here is the link to get this book — System Design Interview — An Insider’s Guide by Alex Xu

2. System Design Interview — An Insider’s Guide: Volume 2

The sequel goes deeper into more advanced design challenges like distributed caching, notification systems, and news feeds. This book is actually better than first book due to obvious reasons.

This book is perfect for senior engineers and architects who want to master trade-offs and scalability.

Here is the link to get this book — System Design Interview — An Insider’s Guide: Volume 2

3. Machine Learning System Design Interview

With AI and ML dominating tech hiring, this book covers machine learning pipelines, model serving, data storage, and real-time inference. It’s an essential read for AI/ML engineers aiming at companies like Google, OpenAI, or Meta.

Here is the link to get this book — Machine Learning System Design Interview

4. Coding Interview Patterns: Nail Your Next Coding Interview

Instead of dumping 500+ random problems at you, this book focuses on patterns — repeatable approaches to solving algorithm challenges.

Once you master these, you can crack almost any coding interview question.

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

5. Generative AI System Design Interview

The newest addition to ByteByteGo’s library, this guide is perfect for 2026’s AI-driven job market. Learn how to design systems that power large language models, image generation, and conversational AI tools.

Here is the link to get this book — Generative AI System Design Interview

6. Object Oriented Design Interview: An Insider’s Guide

This is another awesome books for experienced developer to master OOP Design questions for interviews.

This book covers the principles, patterns, and real interview examples for OOD questions like designing a parking lot, a hotel booking system, or a movie ticket service.

A must-read for software engineers in product companies.

Here is the link to get this book — Object Oriented Design Interview: An Insider’s Guide

7. Mobile System Design Interview: An Insider’s Guide

The mobile world runs on systems that must be fast, battery-efficient, secure, and always online. This book teaches you how to design mobile architectures that scale — from messaging apps with millions of users to mobile-first e-commerce platforms.

You’ll learn how to handle offline-first design, push notifications, background tasks, caching strategies, and performance optimization.

Whether you’re preparing for a mobile architect role at a top tech company or aiming to stand out in a cross-platform engineering interview, this guide gives you the patterns, diagrams, and trade-off discussions you need to ace the mobile system design round.

Here is the link to get this book — Mobile System Design Interview: An Insider’s Guide

Why Its Better to Prepare with ByteByteGo Instead of Just Buying Books?

While you can buy these books individually, ByteByteGo’s subscription model gives you:

• All books in digital format (always updated to the latest editions)

• Interactive coding environments for practicing algorithms and patterns

• Visual explanations that turn abstract concepts into diagrams you can remember

• Mock interview questions with detailed solutions

• A guided learning path so you know exactly what to study and when

Instead of juggling PDFs, print copies, and random LeetCode problems, you get a structured, all-in-one preparation platform designed for modern technical interviews.

And now is the best time to get their annual or lifetime membership because they are offering a 50% discount.

Here is the link to get a discount — ByteByteGo 50% OFF

Conclusion

That’s all about the best books to prepare for coding interviews in 2026. If your goal in 2026 is to land a FAANG-level offer or a senior engineering role, you need to prepare smarter, not just harder.

The ByteByteGo platform is that smarter approach — combining world-class books, hands-on practice, and visual learning into one subscription.

You could spend hundreds buying these books separately, but with ByteByteGo, you get them all plus much more for a fraction of the price — and you can study anytime, anywhere.

Level up your coding interview prep today with ByteByteGo 🔗 Try it with 50% OFF →

All the best for your coding interviews. If you have any doubts or questions, feel free to ask in the comments.

P. S. — If you just want to do one thing at this moment, join ByteByteGo and start learning Coding patterns and System Design Concepts and practice coding interviews; you will thank me later. It’s one of the most comprehensive resources for coding interviews now.

Happy Independence Day to all US folks !!

35% off limited-time offer!

It’s been quite a while since I offered any discount on Substack and a lot of you reached out to me for discounts and 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 6 days!

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

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10. 40 Essential White Papers That Will Make You Great at System Design & Architecture

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

12. Top 10 Coding Questions from Junior level Java Interviews

13. Difference between Microservices and Monolithic Architecture

14. How SQL query works internally?

15. 24 Microservices Patterns for Experienced developers

16. The Complete Java Developer RoadMap

17. System Design Basics - Master Message Queues in Just 5 Minutes!

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Hello guys, Kubernetes is everywhere. Whether you're deploying microservices, running AI workloads, or managing cloud-native applications, chances are Kubernetes is running behind the scenes.

But many developers learn individual components like Pods, Deployments, Services, and Ingress without understanding why Kubernetes was created in the first place or how all these pieces fit together.

In this visual guide, our guest author, will tell you everything about K8, a developer needs to know. We'll start with the problem Kubernetes solves and then walk through each core component step by step, so you can finally see the complete picture instead of isolated concepts.

Kubernetes often feels overwhelming at first - not because it’s poorly designed, but because most explanations jump straight into YAML and commands.

This article takes a visual + conceptual approach, inspired by my new “the Kubernetes Visual Guide by Tech Fusionist”, to explain why Kubernetes exists, how it works internally, and how all major components fit together.

If you understand the mental model, Kubernetes becomes logical - even elegant.

1. The Container Revolution: How We Got Here?

Modern application deployment evolved through clear stages:

Monolith → Virtual Machines → Containers → Orchestration

Each stage solved one major problem but introduced a new one. Containers finally gave us speed, portability, and consistency - but managing them at scale created a new challenge.

Key insight:

Containers solved packaging. Kubernetes solved operations.

2. Before Containers: Deployment Chaos

Running applications directly on servers led to:

• Resource conflicts

• Environment mismatches

• Deployment failures

• Scaling nightmares

Every release felt risky. Stability depended on luck more than design.

Lesson:

Infrastructure without isolation never scales cleanly.

3. The Monolith Problem

Traditional monolithic applications were:

• Large and tightly coupled

• Hard to scale independently

• Risky to update

• Expensive to maintain

A single bug could bring the entire system down.

Lesson:

Big codebases don’t fail fast - they fail expensively.

4. Virtual Machines: The First Real Solution

Virtual Machines introduced isolation and stability, but at a cost:

• Heavy OS overhead

• Slow boot times

• Inefficient resource usage

They were powerful, but not agile.

Lesson:

VMs brought isolation, not velocity.

5. Docker Changed Everything

Docker revolutionized application delivery by introducing:

• ghtweight contain

  Liers

• Fast startup times

• Application + dependencies bundled together

Developers finally achieved true environment consistency.

Lesson:

“It works on my machine” stopped being an excuse.

6. Containers Are Great… Until Scale

Containers solved packaging - but at scale, teams asked:

• How do we auto-scale containers?

• What happens when containers crash?

• How do we manage networking?

• How do we deploy with zero downtime?

This is where Kubernetes enters.

Lesson:

Containers need a conductor.

7. Enter Kubernetes: The Captain of Containers

Kubernetes is a container orchestration platform that manages:

• Deployment

• Scaling

• Networking

• Self-healing

• Configuration

It doesn’t replace Docker - it coordinates containers across infrastructure.

Mental model:

Kubernetes is the operating system for distributed applications.

8. What Kubernetes Really Promises?

Kubernetes delivers on four core promises:

• Deploy once, run anywhere

• Automatic scaling

• Self-healing workloads

• Declarative configuration

You describe the desired state. Kubernetes continuously works to maintain it.

Lesson:

You declare intent. Kubernetes enforces reality.

Kubernetes Architecture: Master & Worker Nodes (How It All Works?)

At a high level, a Kubernetes cluster is split into two parts:

• Control Plane – Makes decisions

• Worker Nodes – Run applications

This separation is what enables Kubernetes to scale and self-heal.

Read more

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Hello guys, preparing for system design interviews can feel like climbing a mountain without a map.

Unlike coding interviews where you can gain confidence by practicing data structures and algorithms on platforms like AlgoMonster, Exponent, and LeetCode, system design questions demand a mix of breadth and depth, architecture principles, scalability patterns, trade-offs, and real-world application.

For me, this part of the interview loop was intimidating at first. I often felt lost in diagrams, unsure which concept to use where, and overwhelmed by the sheer vastness of distributed systems.

The turning point came when I started breaking the subject down into core concepts. Once I understood ideas like load balancing, caching, database sharding, CAP theorem, and message queues, everything else started to click into place.

Instead of memorizing solutions, I began recognizing patterns. That’s when I realized system design isn’t about giving a “perfect” architecture, but about reasoning through trade-offs with clarity.

What really accelerated my learning was leveraging structured resources. Books and visual explanations like ByteByteGo’s System Design Course made the hardest concepts digestible with diagrams and case studies.

I also explored platforms such as Codemia.io, Educative, and Bugfree.ai for hands-on interview prep and Exponent for mock interviews with engineers from top companies. Each helped me move from feeling clueless to confident, especially when facing open-ended system design questions at FAANG-level interviews.

In this article, I’ll share the 20 core concepts that completely changed how I approach system design interviews. Mastering these will save you from confusion, help you build better mental models, and make those tough whiteboard sessions a lot less scary.

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12 System Design Concepts Every Developer Should Learn

Here are the 12 important System Design patterns I learned and mastered by going through different System Design resources. Once you understand these concepts, half the battle is already one.

1. Load Balancing: The Traffic Director

Think of load balancers as smart traffic directors for your application. They distribute incoming requests across multiple servers to prevent any single server from becoming overwhelmed.

Key insight: There are different types , Layer 4 (transport layer) and Layer 7 (application layer). Layer 7 load balancers can make routing decisions based on content, while Layer 4 focus on IP and port information.

Real-world example: When you visit Amazon, a load balancer decides which of their thousands of servers will handle your request.

Here is a nice diagram from designgurus.io which explains the load balancer concept along with the API gateway, which we will see in a couple of seconds.

2. Horizontal vs Vertical Scaling: The Growth Strategies

• Vertical Scaling (Scale Up): Adding more power to existing machines

• Horizontal Scaling (Scale Out): Adding more machines to the pool

Game-changer moment: Understanding that horizontal scaling is almost always preferred for large systems because it’s more cost-effective and provides better fault tolerance.

Here is a visual guide from ByteByteGo which makes this concept crystal clear

3. Database Sharding: Divide and Conquer

Sharding splits your database across multiple machines. Each shard contains a subset of your data.

The breakthrough: Learning about sharding keys and how poor sharding strategies can create hotspots that defeat the entire purpose.

Example: Instagram shards user data based on user ID, ensuring even distribution across databases.

Here is another great visual from ByteByteGo which explains range-based sharding

4. Caching Strategies: The Speed Multiplier

Caching is storing frequently accessed data in fast storage. The key is understanding different caching patterns:

• Cache-aside (Lazy Loading): Application manages cache

• Write-through: Write to cache and database simultaneously

• Write-behind: Write to cache immediately, database later

Pro tip: The cache invalidation problem is one of the hardest problems in computer science. Master cache eviction policies (LRU, LFU, FIFO).

A picture is worth a thousand words, and this visual from ByteByteGo proves that, nicely explaining all the caching strategies a senior developer should be aware of.

If you like visual learning, I highly recommend you join ByteByteGo now, as they are offering 50% discount on their lifetime plan. I have taken that as it’s just 2.5 times of the annual plan, but it provides the most value.

5. Content Delivery Networks (CDN): Global Speed

CDNs cache your content at edge locations worldwide, reducing latency for users.

Aha moment: Realizing that CDNs don’t just cache static content --- modern CDNs can cache dynamic content and even run serverless functions at the edge.

6. Database Replication: The Backup Plan

• Master-Slave: One write node, multiple read nodes

• Master-Master: Multiple write nodes (more complex)

Critical insight: Understanding eventual consistency and how replication lag can affect your application logic.

7. Event-Driven Architecture: The Modern Approach

Systems communicate through events rather than direct calls. This creates loose coupling and better scalability.

Game-changer: Understanding that event sourcing can make your system audit-friendly and enable powerful debugging capabilities.

Read more

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Backend development might seem intimidating. There are frameworks, databases, APIs, authentication systems, deployment strategies, and countless architectural patterns.

But here’s the secret: Backend development is built on a small number of fundamental concepts.

Master those fundamentals, and everything else becomes logical extensions of them.

In this guide, I’ll walk you through every major concept in backend development — from HTTP requests to Kubernetes orchestration. Not as disconnected topics, but as building blocks that fit together into a complete system.

By the end, you’ll understand what every backend engineer needs to know in 2026.

Part 1: HTTP Fundamentals

Every backend system communicates through HTTP. Understanding this protocol is foundational.

HTTP Methods

HTTP methods define the action being performed:

• GET — Retrieve data (safe, idempotent)

• POST — Create data (not safe, not idempotent)

• PUT — Replace entire resource (idempotent)

• PATCH — Partially update resource (not idempotent)

• DELETE — Remove data (idempotent)

• HEAD — Like GET, but no response body

• OPTIONS — Describe communication options

Status Codes

Status codes communicate the result of a request:

2xx Success:

• 200 OK — Request successful

• 201 Created — Resource created

• 204 No Content — Success, no response body

3xx Redirection:

• 301 Moved Permanently — Permanent redirect

• 302 Found — Temporary redirect

• 304 Not Modified — Use cached version

4xx Client Error:

• 400 Bad Request — Invalid request

• 401 Unauthorized — Authentication required

• 403 Forbidden — Authenticated but not authorized

• 404 Not Found — Resource doesn’t exist

• 429 Too Many Requests — Rate limit exceeded

5xx Server Error:

• 500 Internal Server Error — Server crashed

• 502 Bad Gateway — Invalid upstream response

• 503 Service Unavailable — Server overloaded

Request and Response Headers

Headers provide metadata about HTTP messages:

Common Request Headers:

• Authorization — Authentication credentials

• Content-Type — Body data format (application/json, etc.)

• User-Agent — Client information

• Accept — Desired response format

• Cookie — Session data

• X-Forwarded-For — Original client IP

Common Response Headers:

• Content-Type — Response body format

• Set-Cookie — Session cookie

• Cache-Control — Caching instructions

• ETag — Resource version identifier

• Location — Redirect destination

• WWW-Authenticate — Authentication challenge

Here is a quick summary to remember these concepts:

Part 2: Authentication & Authorization

These are different concepts frequently confused:

Read more

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

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

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

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

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

But suddenly, none of that helped.

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

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

I failed that interview.

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

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

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

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

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

The Problem with Memorization

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

When you memorize “How to Design Twitter,” you learn:

• Use relational databases for users and tweets

• Use NoSQL for timelines

• Cache with Redis

• Use message queues for fanout

• Shard by user ID

• etc.

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

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

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

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

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

That’s what a framework gives you.

The Framework: 8 Steps to Structured Thinking

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

1. Clarify the Requirements

What: Ask clarifying questions before designing anything.

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

Questions to ask:

• What are the functional requirements? (What should the system do?)

• What are the non-functional requirements? (Scale, latency, consistency, availability?)

• Who are the users? What’s their usage pattern?

• What’s the expected traffic? Read/write ratio?

• Do we need strong consistency or eventual consistency?

• What’s acceptable latency?

Example: “Design a notification system”

• Clarify: Is this in-app notifications, push notifications, email, SMS, or all?

• What’s the scale? Millions of users?

• What’s the expected latency for notification delivery?

• Does every notification need to be delivered exactly once, or is some loss acceptable?

• Do we need real-time delivery or can we batch?

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

2. Estimate the Scale

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

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

Key metrics:

• Daily Active Users (DAU)

• Requests per second (QPS)

• Data storage needed

• Bandwidth requirements

Example: For a notification system with 1 billion users:

• If 10% are active daily = 100 million DAU

• If each active user generates 5 notifications = 500 million notifications/day

• 500M / 86400 seconds ≈ 5,787 QPS

• This tells you: you need a system that handles thousands of notifications per second

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

3. Define the Core APIs

What: Write the API contracts for the system.

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

Example: For a notification system:

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

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

4. Design the Data Model

What: Define database schema and data structures.

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

Example: For notifications:

Users table:
- userId (PK)
- username
- email
- preferences

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

Now you’re thinking about:

• How do you query user preferences efficiently?

• How do you find unread notifications for a user?

• How do you handle delivery status?

The data model reveals design requirements.

5. Build the High-Level Architecture

What: Sketch the major components and how they interact.

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

Example: High-level architecture for notifications:

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

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

6. Identify Bottlenecks

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

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

Example: For notifications, bottlenecks might be:

• Database writes when millions of notifications created simultaneously

• Message queue throughput

• Delivery worker capacity

• External API rate limits

Identifying bottlenecks reveals where you need optimization.

7. Discuss Trade-Offs

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

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

Example:

• Why Kafka for the queue? Durability (survives failures), high throughput, replay capability. Trade-off: complexity vs. simpler queue.

• Why eventual consistency for preferences? Users don’t need real-time preference updates. This allows caching and reduces database load. Trade-off: brief window where old preferences are used.

• Why shard by userId? Distributes load evenly. Trade-off: cross-user queries become complex.

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

8. Evolve the Design Based on Constraints

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

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

Example: Interviewer adds: “Delivery must be guaranteed. No lost notifications.”

Now you need to:

• Add delivery status tracking (Step 4 — update data model)

• Add retry logic and dead-letter queues (Step 5 — architecture)

• Discuss idempotency for retries (Step 7 — trade-offs)

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

Walking Through a Complete Example: Notification System

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

1. Clarify Requirements

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

• Functional: We need to send notifications to users through multiple channels: in-app, push, email

• Non-functional: 1 billion users, 10% daily active. Need to handle 5,000+ notifications per second

• Consistency: Eventual consistency is fine. Users don’t need real-time preference updates

• Latency: In-app notifications should appear within seconds. Email can be minutes

• Delivery: Best effort initially. We can improve to guaranteed delivery later”

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

2. Estimate Scale

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

• If each generates 5 notifications/day = 500M notifications/day

• 500M / 86,400 = ~5,787 QPS baseline

• But peaks could be 5–10x, so we need to handle 30,000+ QPS

• Storage: 500M notifications/day × 365 days × 500 bytes ≈ 90TB/year

• This tells me we need: distributed database, queuing system, and batch processing”

3. Define Core APIs

POST /notifications/create
  - userId: string
  - title: string
  - body: string
  - channels: [in-app, push, email]
  - metadata: object

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

4. Design Data Model

Users:
- userId (UUID, PK)
- email
- phone
- createdAt

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

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

UserPreferences:
- userId (PK)
- pushEnabled
- emailEnabled
- smsEnabled
- quietHours
- unsubscribedCategories (array)
- updatedAt

NotificationQueue:
- queueId (UUID)
- notificationId (FK)
- status (pending, processing, completed, failed)
- createdAt
- processedAt

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

5. Build High-Level Architecture

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

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

6. Identify Bottlenecks

“The bottlenecks are:

1. Database writes: Creating 5,000+ notifications/second. Solution: Shard by userId, use write-optimized database

2. Kafka throughput: Need to handle 5,000+ msgs/sec. Solution: Partition by userId, scale consumer groups

3. Delivery workers: Must keep up with Kafka output. Solution: Auto-scale based on queue depth

4. External API limits: Firebase, email services have rate limits. Solution: Queue locally, respect rate limits, retry backoff

5. Storage growth: 90TB/year. Solution: Archive old notifications, tiering strategy”

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

7. Discuss Trade-Offs

“Important trade-offs:

Message Queue (Kafka vs RabbitMQ):

• Chose Kafka: High throughput (5K+ msgs/sec), persistent (survives worker crashes), partition support

• Trade-off: More complex to operate than RabbitMQ

Eventual Consistency vs Strong Consistency:

• Chose eventual: Preferences cached in workers, updated every 5 minutes

• Trade-off: Users might see outdated preferences briefly. Worth it for 10x less database load

Shard by userId vs Shard by Time:

• Chose userId: Distributes load evenly, straightforward

• Trade-off: Some queries (all notifications in time range) become complex

Best-Effort Delivery vs Guaranteed:

• Starting with best-effort: Simpler, sufficient for most notifications

• To upgrade: Add deduplication, idempotency, longer retries. Tradeoff: Added complexity

These aren’t random choices. Each is explicitly reasoned.”

8. Evolve on Feedback

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

“Then we need:

1. Idempotency keys (Step 3): Each notification gets a unique key. Retries with same key are idempotent.

2. Deduplication (Step 5): If a notification is retried, we don’t create duplicates.

3. Longer retention (Step 4): Keep delivery logs longer for audit trails.

4. Checkpoints (Step 5): Mark notifications as delivered only after confirmation.

5. Dead-letter queue (Step 5): Send undeliverable notifications to a queue for manual review.

We still use the same core architecture. We’re just adding layers of reliability.”

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

Why This Framework Works

This framework works because:

1. It’s systematic — You follow the same steps for every problem. Consistency reduces panic.

2. It’s independent of technologies — You clarify requirements before choosing technologies. You’re not forcing Kafka into every problem.

3. It’s flexible — When requirements change, you know which step to revisit. New requirement? Update clarifications → re-estimate → adjust architecture.

4. It’s interviewable — Interviewers see clear thinking, not pattern matching. When they push back, you can adjust confidently.

5. It’s complete — You think about requirements, scale, APIs, data, architecture, bottlenecks, and trade-offs. Nothing is forgotten.

Building Intuition Through Practice

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

I practiced this framework on problems like:

• Design a Simple URL Shortening Service (TinyURL) — Simple requirements, teaches API design

• Design Twitter — Complex scaling, feeds, fanout

• Design TiketMaster — Time-sensitive, high concurrency, inventory management

• Design an API Rate Limiter — Small problem, teaches precision

• Design Pastebin — File storage, sharing, expiration

• Design a Video View Count System — High-frequency writes, eventual consistency

• Design Craigslist — Search, filtering, inventory

• Design a Nested Comments System — Hierarchical data, efficiency

• Design an Online Presence Indicator Service — Real-time updates, scale

• Design a Tagging Service — Performance, caching, consistency

• Design a Fitness Tracking App — Mobile-first, time-series data

• Design a Weather Reporting System — Data aggregation, multiple sources

• Design a Multi-Device Screenshot Capture System — Distributed processing, file handling

• Design a Conference Room Booking System — Scheduling, conflicts, availability

• Design an Employee Swap System — Complex state machine, transactions

Plus object-oriented design problems like:

• Design a Parking Lot System — Teaches low-level design

• Design an Elevator System — State machines, coordination

• Design a Vending Machine System — State, inventory, transactions

• Design a Resource Management System — Allocation, scheduling

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

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

The Path from Theory to Confidence

This is the journey:

1. Week 1: Watch YouTube videos, feel confident about technologies

2. Week 2–3: Practice 5 problems, realize you don’t have a system

3. Week 4–6: Practice 10 more problems, start seeing patterns

4. Week 7–10: Practice 10 more, the framework crystallizes

5. Week 11+: You trust the framework. New problems don’t panic you

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

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

Where to Practice This Framework

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

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

• 120+ system design problems across difficulty levels (Easy, Medium, Hard)

• Expert solutions for each problem, showing how professionals think

• Interactive design tools to sketch architecture while practicing

• AI-powered feedback that evaluates your design against best practices

• Difficulty progression (Easy → Medium → Hard) so you build gradually

• Company tags so you practice problems actually asked at FAANG

For structure, they also offer:

• Free course: Tackling System Design Interview Problems — Teaches the fundamentals

• System Design Fundamentals — Deeper dive into core concepts

• Mock interviews — Simulate real interview pressure

• Object-Oriented Design section — For low-level design interviews

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

Final Thoughts

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

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

The framework gives you that system. Use it:

1. Clarify before you build

2. Estimate to understand scale

3. Define APIs to clarify contracts

4. Design data to reveal problems

5. Build architecture only after understanding the problem

6. Identify bottlenecks explicitly

7. Discuss trade-offs to show reasoning

8. Evolve smoothly when requirements change

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

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

Good luck. You’ve got this.

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

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

Hello friends! Prime Day is finally here, and this year it’s bigger than ever.

Amazon has officially moved Prime Day to June 23–26, 2026, making it a 4-day event instead of the traditional 2-day sale.

This shopping event is exclusive for Prime members and gives them exclusive access to millions of deals across various categories, including electronics, home goods, fashion, and more.

Prime members get access to millions of deals across electronics, laptops, developer tools, home office equipment, books, and productivity gear.

Though, if you are not a prime member you can sign here, its a great program which not only give you exclusive deals but also free deliveries, quick deliveries and full access to Amazon Prime video, one of the best OTP platform for watching shows and movies after Netflix.

As developers, AI engineers, cloud architects, cybersecurity professionals, and content creators, we spend thousands of hours every year in front of our computers. That’s why I believe investing in the tools you use every day is one of the best investments you can make.

In the AI era, having the right hardware matters more than ever.

Whether you’re:

• Running AI coding assistants like ChatGPT, Claude, GitHub Copilot, Cursor, or OpenClaw

• Working with Docker and Kubernetes

• Experimenting with local LLMs

• Building side projects

• Creating technical content

• Preparing for coding interviews

Prime Day is one of the best opportunities to upgrade your setup without paying full price.

Disclosure: Some links are affiliate links and I may earn a commission at no extra cost to you. If you purchase through these links, thank you for supporting my work.

12 Things Every Developer Should Buy During Prime Day 2026

Without any further ado, here is my prime day wish list which any developer, AI engineer, Software engineer can also copy as most of the items are for developers like me.

1. Ultrawide Monitor

If there is one upgrade that can instantly boost your productivity, it’s an ultrawide monitor.

Modern development involves multiple windows simultaneously:

• IDE

• Terminal

• Browser

• Logs

• AI chat windows

• Documentation

An ultrawide monitor lets you keep everything visible without constantly switching tabs.

I still recommend:

SAMSUNG 32” Odyssey G55C Series QHD 1000R Curved Gaming Monitor, 1ms(MPRT), HDR10, 165Hz, AMD Radeon FreeSync, Eye Care, Glare Free, Sharp Resolution LS32CG550ENXZA

It’s one of the best productivity monitors for developers and often receives significant Prime Day discounts.

2. Mechanical Keyboard

Developers type millions of characters every year.

A quality mechanical keyboard can improve:

• Typing comfort

• Speed

• Accuracy

• Overall coding experience

If you’re still using the same keyboard you bought years ago, Prime Day is a great excuse to upgrade.

My budget recommendation remains:

MageGee Portable15% OFF on Prime day Deal Mechanical Gaming Keyboard

3. Ergonomic Chair

Your code quality won’t improve with a new chair.

Your health will.

After years of software development, I’ve learned that comfort directly impacts productivity.

My recommendation:

GABRYLLY Ergonomic Mesh Office Chair

A proper ergonomic chair is one of the few purchases that pays dividends every single day.

4. Noise-Canceling Headphones

Sometimes the biggest productivity boost isn’t faster hardware.

It’s fewer distractions.

If you work remotely, travel frequently, or share your workspace, noise-canceling headphones are a game changer.

Premium option:

Bose QuietComfort Noise Cancelling Headphones

Budget option:

Soundcore Anker Life Q20 Hybrid Active Noise Cancelling Headphones

5. External SSD

AI projects, Docker images, virtual machines, and local models consume storage quickly.

A fast external SSD is one of those tools you don’t appreciate until you need it.

My favorite:

SanDisk 2TB Extreme Portable SSD

Fast, reliable, and perfect for backups, project storage, and transferring large files.

6. Standing Desk

One of the best upgrades for developers who work from home.

I don’t code while standing all day, but I do use standing mode for:

• Meetings

• Reading

• Documentation

• Emails

• Research

My recommendation:

ErGear Height Adjustable Electric Standing Desk

7. Wireless Mouse

Simple.

Affordable.

Reliable.

I’ve been using Logitech mice for years.

Logitech M185 Wireless Mouse

One of the best budget productivity accessories you can buy.

8. Apple MacBook Neo — Best Budget Laptop for Developers

One of the most interesting products for developers this year is the new MacBook Neo.

For:

• Students

• Junior developers

• Content creators

• AI enthusiasts

it’s arguably the best entry-level Mac Apple has ever released.

Apple MacBook Neo

Excellent battery life.

Silent operation.

Great display.

Perfect for learning programming and AI development.

9. Apple MacBook Pro — Best Laptop for Serious Developers

If coding is your profession and not just a hobby, this is the machine I recommend most.

The MacBook Pro excels at:

• Docker

• Kubernetes

• AI workflows

• Video editing

• Running multiple development environments

MacBook Pro

In the age of AI agents and AI-assisted development, powerful hardware saves a tremendous amount of time.

10. Apple Mac Mini — Best Value Computer for Developers

If I had to pick one Prime Day purchase this year, it might actually be this.

Apple Mac Mini

Pair it with:

• A good monitor

• Mechanical keyboard

• External SSD

and you’ll have a fantastic development workstation.

For many developers, it offers the best performance-per-dollar available today.

11. Coding & System Design Books

Prime Day is also a great opportunity to build your technical library.

A few books I recommend repeatedly:

Clean Code

System Design Interview — An Insider’s Guide

Both remain among the most popular books for software engineers.

12. Portable Monitor

One of the most underrated productivity upgrades.

A portable monitor is perfect for:

• Travel

• Conferences

• Hybrid work

• Coffee shop coding sessions

I recommend:

KYY Portable Monitor 15.6-inch FHD

My Top 5 Prime Day Picks for Developers in 2026

If I had to prioritize, here’s what I’d buy:

1 Mac Mini

Best performance per dollar.

2 Ultrawide Monitor

Huge productivity boost.

3 External SSD

Essential for AI and development workflows.

4 Mechanical Keyboard

You’ll use it every day.

5 Noise-Canceling Headphones

Massive focus improvement.

Final Thoughts

Amazon Prime Day 2026 officially runs from June 23 through June 26, making it the longest Prime Day event yet. Amazon has confirmed millions of deals across electronics, computing devices, home office equipment, and productivity tools.

As developers, we spend most of our day using the same tools repeatedly.

The best investments are often the ones that:

• Save time

• Improve focus

• Reduce friction

• Make learning easier

A faster machine, a better monitor, or a more comfortable workspace can easily pay for itself many times over.

Browse all current Prime Day offers here:

Happy Prime Day shopping!

P.S. Prime Day 2026 ends on June 26, and many of the best deals sell out early. Amazon is also rotating deals throughout the event, so it’s worth checking back regularly.

P.P.S. If you’re entering the AI era and can only upgrade one thing, I’d strongly consider either a Mac Mini or a MacBook Pro. AI-assisted development is quickly becoming the new normal, and having capable hardware makes a huge difference.

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Hello everyone — the Agentic AI wave is here, and it’s not slowing down. Multi-agent systems. Tool-calling. Autonomous reasoning loops. LLMs that don’t just respond — they act.

But here’s the problem: most content on agentic AI is fragmented tutorials, half-baked demos, or hype-driven blog posts.

What you need is depth.

Frameworks that survive beyond the next model release. Thinking built by people who’ve shipped real autonomous systems.

After reading 20+ AI and LLM books over the past two years, I’ve identified the 5 essential books every engineer serious about Agentic AI should own. These aren’t philosophy books. These are operational guides written by practitioners.

Why Agentic AI Demands Better Books?

Prompt engineering taught you how to chat with AI.

Agentic AI teaches AI to do things — plan multi-step tasks, use tools, collaborate with other agents, recover from failures, and execute goals with minimal human hand-holding.

That’s a fundamentally different discipline. And it demands a fundamentally different reading list.

The 5 Essential Books for Agentic AI (Organized by Focus)

1. AI Engineering by Chip Huyen — Start Here

Why it matters for agentic AI:

You can’t build reliable autonomous agents without understanding the systems they run on. Chip Huyen’s book gives you exactly that — the full AI engineering stack, including the agent architectures and deployment patterns that make agentic systems viable in production.

What you’ll learn:

• The real AI engineering stack (data pipelines, model versioning, deployment, monitoring)

• Evaluation frameworks — essential for measuring agent reliability

• Deployment patterns for production AI, including autonomous pipelines

• The complete AI product lifecycle

Why Chip Huyen: Former researcher at Netflix, core developer at NVIDIA (NeMo framework), Stanford ML lecturer. She’s built the systems she writes about.

Best for: Anyone who wants to build agents that actually stay alive in production

Reading time: 8–10 hours

2. The LLM Engineer’s Handbook by Paul Iusztin and Maxime Labonne — The LLM Operations Manual

Why it matters for agentic AI:

Agents live on top of LLMs. If you don’t understand the LLM layer deeply — fine-tuning, RAG, evaluation, cost — your agents will be brittle, expensive, and unreliable. This book closes that gap. It reads like an operations manual: here’s what you do, here’s why it works, here’s what breaks.

What you’ll learn:

• RAG patterns at scale (the backbone of most agentic memory systems)

• Fine-tuning strategies and when to use them

• Evaluation frameworks for measuring LLM and agent performance

• Production cost optimization for LLM APIs

• Modular system design for composing autonomous workflows

Why the authors matter: Paul Iusztin and Maxime Labonne have built LLM applications at scale. The agent-relevant sections alone are worth the price of the book.

Best for: Engineers building RAG-backed agents or multi-step LLM pipelines

Reading time: 6–8 hours

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

Why it matters for agentic AI:

Anyone can build a demo agent. The hard part is shipping one. Scaling it. Keeping it running at 3 AM when it starts looping or burning through your API budget. This book treats deployment as a first-class engineering problem.

What you’ll learn:

• Deployment architecture for LLM-powered systems

• Scaling LLM inference for production agents

• Monitoring agent behavior and detecting drift

• Cost management strategies at scale

• Real-world deployment failures and how to prevent them

Real-world focus: The authors have deployed LLMs at scale through Towards AI. They know where agent systems break because they’ve been there.

Best for: Engineers moving an agent from demo to production

Reading time: 7–9 hours

4. Building Agentic AI Systems by Anjanava Biswas and Wrick Talukdar — The Definitive Agent Engineering Guide

Why this is the core agentic AI book:

This is the one book on this list that is entirely about agents. Not LLMs in general, not AI systems broadly — autonomous AI agents specifically. It covers the full architecture: how agents reason, plan, use tools, coordinate with each other, and operate safely.

What you’ll learn:

• Agent architecture and design patterns (coordinator, worker, delegator)

• Reasoning patterns: ReAct, chain-of-thought, self-reflection

• Multi-step planning and decision-making under uncertainty

• Tool use and external API integration

• Multi-agent orchestration and collaboration

• Trust, safety, and ethics in autonomous AI systems

• Real-world applications across automation, finance, and healthcare

Why it stands out: The authors are working AI architects building enterprise agentic systems. This is a practitioner’s book written by people who’ve hit the same walls you’re about to hit.

Best for: Engineers building the next generation of autonomous AI products

Reading time: 8–10 hours

5. Agentic Artificial Intelligence by Pascal Bornet et al. — The Strategic and Technical Complete Picture

Why this rounds out the list:

Named a Forbes Top 10 Tech Book, this is the most comprehensive treatment of agentic AI available today — co-authored by 11 practitioners from institutions including NUS, Babson, Northeastern, and Microsoft Research. Where the other books focus on engineering mechanics, this one addresses the full scope: from agent origins in RPA, to present-day pilots, to long-term business and societal impact.

What you’ll learn:

• The full agentic AI landscape from historical roots to current state

• How to identify, plan, and scale agentic projects inside organizations

• Why most agentic pilots fail to reach production (and how to avoid that)

• Business transformation frameworks for autonomous AI deployment

• Governance, ethics, and risk management for agent systems

• The compounding advantage of early agentic adoption

Why the authors matter: This is a rare collaboration across academia and industry — co-authored by Tom Davenport (Babson/MIT), Pascal Bornet, and 9 other recognized leaders in AI and automation. It brings both technical depth and strategic clarity.

Best for: Tech leads, architects, and anyone responsible for agentic AI at an organizational level

Reading time: 12–15 hours (dense at 550 pages — use it as a reference)

Recommended Reading Order

If you have 1 month: Start with Building Agentic AI Systems (the core), then AI Engineering for production context.

If you have 3 months:

1. AI Engineering (foundation)

2. The LLM Engineer’s Handbook (LLM layer)

3. Building Agentic AI Systems (agents proper)

If you have 6 months (ideal):

1. AI Engineering

2. The LLM Engineer’s Handbook

3. Building LLMs for Production

4. Building Agentic AI Systems

5. Agentic Artificial Intelligence

Books vs. Content: The Real Truth

The agent space moves fast. But the fundamentals of reasoning, planning, tool use, and multi-agent coordination don’t change when a new model drops. That’s why books win here.

Books give you:

• Thinking frameworks that outlast any single model

• Depth on why things work, not just how to copy code

• Time to reflect — reading engages you in ways video doesn’t

• Timeless principles that survive the next API version

After reading, build something:

• An agent that can browse the web and summarize research

• A multi-agent pipeline that breaks down and executes a complex task

• A RAG-backed agent over your own data

• An agentic workflow with tool use and memory

Books give you the mental model. Projects reveal what actually works.

Final Word

The engineers who figure out agentic AI in 2026 are going to have a significant edge — in interviews, in their codebases, and in their careers. Static LLMs are already table stakes. Autonomous systems that reason and act are where the real leverage is.

Start with one book this week. The list above is your map.

Pick it up. Build something. That’s how you win.

P.S. — If you want hands-on agentic AI training to go alongside these books, check out the Agentic AI Engineering course by Paul Iusztin — the author of book #2 on this list. One of the most practical courses available right now.

Hello Devs, if you have prepared for coding interviews, then you know how daunting it can be. Apart from the regular work you do, you need to spend a considerable amount of time practicing data structure and algorithm problems just for the interview.

I have done that, but more often than not, it’s either miss or hit.

If you have practiced coding problems like how to reverse a linked list, how to find the longest substring with given characters, then the interview can be a breeze. All you need to do is act as if you are solving that question for the first time. However, if you get an unknown question, then good luck to you.

I knew my interview prep approach wasn’t foolproof and needed improvement.

After solving numerous problems, I noticed certain tricks or patterns that I could apply repeatedly — for example, how the Two-Pointer technique on a linked list can help find the middle element or detect a cycle.

I didn’t know that these are essential coding patterns until I came across Algomonster, and ByteByteGo 101, this course teaches you 15+coding patterns that can be used to solve thousands of LeetCode problems.

That’s the first time I came across this terminology, and I also learned many other patterns I didn’t even know.

Just knowing that helped me a lot in my coding interview prep, and also to many of my readers who thanked and appreciated me that I told them about these patterns and this course.

In this article, I am going to share 15 essential coding interview patterns you can use to solve 100+ coding problems on Leetcode. Many of them are also covered in Algomonster), in greater depth.

Now, you don’t need to blindly solve many coding problems just to get your hands on it. Instead, learn these patterns and then start using them to solve problems.

To make your preparation structured and effective, I also recommend ByteByteGo 101 and Grokking Advanced Coding Patterns for Interviews from Designgurus.io. This course covers more advanced coding patterns you need for interviews and integrates interactive practice.

Educative also just rolled out AI-powered Personalized Interview Preparation Plan.

Think of it as a custom prep roadmap, tailored to your strengths and gaps.

15 Coding Patterns to Crack Tech Interviews

Without any further ado, here are the 15 essential coding patterns you should master, along with 2- 3 Leetcode problems to practice for each. These patterns are also quite common on FAANG and Big Tech interviews, as analysed by Algomonster.com, one of the popular websites for coding interview prep from ex-Google engineers.

1. Two Pointers

Two Pointers is a versatile pattern that involves using two pointers to traverse an array or linked list efficiently.

This was the first coding pattern I learned, and it works remarkably well on solving linked list and array-based problems like finding the middle elements of the list or finding the kth element from the end of the list.

Key Concepts:

• Optimize problems involving pairs, such as sum or difference.

• Avoid nested loops to reduce time complexity.

LeetCode Problems:

1. Two Sum II --- Input Array Is Sorted (167)

2. Remove Duplicates from Sorted Array (26)

3. Move Zeroes (283)

2. Prefix Sum

Prefix Sum is a powerful technique for optimizing range queries in arrays. By preprocessing cumulative sums, you can solve range-based problems efficiently.

This coding pattern is also very important for solving array-based problems like Subarray Sum Equals K on Leetcode.

Key Concepts:

• Precompute cumulative sums for quick access.

• Use for range queries like subarray sums.

Leetcode Problems:

1. Range Sum Query --- Immutable (303)

2. Subarray Sum Equals K (560)

3. Minimum Value to Get Positive Step by Step Sum (1413)

3. Sliding Window

Sliding Window is a powerful pattern to optimize problems involving contiguous subarrays. This problem can be tough to understand at first, but once you solve a couple of problems, you will appreciate its simplicity.

Key Concepts:

• Maintain a window over part of the array.

• Expand or contract the window as needed.

Leetcode Problems:

1. Longest Substring Without Repeating Characters (3)

2. Maximum Sum of Subarray of Size K (643)

3. Minimum Window Substring (76)

You can further see Algomonster.com‘s article and explanation on the Sliding Window to understand this pattern better.

4. Fast & Slow Pointers

Fast & Slow Pointers (also called Floyd’s Cycle Detection) are commonly used for cycle detection in Linked Lists. This pattern is also known as the tortoise and hare pattern.

Key Concepts:

• Use two pointers moving at different speeds.

• Detect cycles or meeting points in a sequence.

Leetcode Problems:

1. Linked List Cycle (141)

2. Find the Duplicate Number (287)

3. Happy Number (202)

5. LinkedList In-Place Reversal

This pattern focuses on reversing portions of a Linked List without extra space. In-place algorithms can be used when you have memory constraints.

Key Concepts:

• Reverse a Linked List iteratively or recursively.

• Solve problems requiring sublist reversal.

LeetCode Problems:

1. Reverse Linked List (206)

2. Reverse Linked List II (92)

3. Swap Nodes in Pairs (24)

6. Monotonic Stack

The Monotonic Stack is a structured stack that maintains elements in sorted order (increasing or decreasing).

Key Concepts:

• Solve problems involving the next greater or smaller element.

LeetCode Problems:

1. Daily Temperatures (739)

2. Next Greater Element I (496)

3. Largest Rectangle in Histogram (84)

Though, when it comes to mastering this pattern, I found this flow chart on Algomonster.com and it’s really great. It makes when to use this pattern a cakewalk if you know the concepts. I have used this site, and it’s really great for anyone preparing for coding interviews.

7. Top ‘K’ Elements

This pattern focuses on efficiently finding the top K largest, smallest, or most frequent elements. You can use this pattern to solve problems like how to find the Kth largest element in a given array.

You can also find many questions based on these patterns on Educative-99, where you will get a chance to solve 99 selected questions instead of 2800 Leetcode problems.

Key Concepts:

• Use heaps (priority queues) or sorting.

LeetCode Problems:

1. Top K Frequent Elements (347)

2. Kth Largest Element in an Array (215)

3. Find K Pairs with Smallest Sums (373)

8. Overlapping Intervals

This pattern deals with problems involving intervals (e.g., time ranges, numeric ranges) where you need to detect, merge, or manipulate overlapping segments. It often requires sorting intervals by start time.

Key Idea: Sort intervals, then iterate to check for overlaps, merge them, or count conflicts based on problem constraints.

When to Use: Scheduling problems, resource allocation, or any scenario with start/end pairs.

Common Problems:
- Merge Intervals: Combine overlapping intervals into a single range.
- Non-Overlapping Intervals: Remove the minimum intervals to make the rest non-overlapping.
- Meeting Rooms: Determine if meetings conflict or how many rooms are needed.

Key Concepts:

• Sort intervals and merge based on conditions.

LeetCode Problems:

1. Merge Intervals (56)

2. Insert Interval (57)

3. Meeting Rooms II (253)

image_credit --- Educative.io

9. Modified Binary Search

Modified Binary Search optimizes searching in sorted or rotated arrays.

Key Concepts:

• Apply binary search creatively for custom conditions.

LeetCode Problems:

1. Binary Search (704)

2. Search in Rotated Sorted Array (33)

3. Find Peak Element (162)

10. Binary Tree Traversal

This is not a pattern but an essential binary tree concept, which is presented as a pattern. Basically, you need to learn various traversal techniques, such as in-order, pre-order, and post-order, to solve tree problems.

Another key thing to remember is that with the inorder traversal, you can print the list in a sorted order. Not many developers know this,s but it’s a very good thing to remember.

LeetCode Problems:

1. Binary Tree Inorder Traversal (94)

2. Maximum Depth of Binary Tree (104)

Here is a nice diagram which shows different ways to traverse a binary tree like preorder, indorder and postorder

11. Depth-First Search (DFS)

DFS explores tree or graph nodes as deeply as possible before backtracking. In other words, all the nodes in one branch are explored before starting another branch.

Leetcode Problems:

1. Path Sum (112)

2. Number of Islands (200)

12. Breadth-First Search (BFS)

BFS explores nodes level by level, often implemented with a queue. This pattern is used to solve binary tree-related patterns, and it is also known as level-order traversal because it traverses all nodes in one level before moving to the next level.

LeetCode Problems:

1. Binary Tree Level Order Traversal (102)

Also, here is a nice diagram which explains the difference between breadth first and depth first search clearly

13. Matrix Traversal

Matrix Traversal involves navigating a 2D array (matrix) to solve problems like searching, counting paths, or collecting elements. Common approaches include depth-first search (DFS), breadth-first search (BFS), or iterative traversal.

Key Idea: Systematically visit matrix cells (rows/columns) while handling boundaries and tracking visited cells.

When to Use: Problems involving grids, such as finding paths, connected components, or specific patterns in a matrix.

Common Problems:
- Number of Islands: Count distinct landmasses in a grid (1s = land, 0s = water).
- Spiral Matrix: Return elements in spiral order.
- Flood Fill: Change a region’s color starting from a given cell.

LeetCode Problems:

1. Flood Fill (733)

14. Backtracking

Backtracking is a recursive algorithmic pattern for solving problems by exploring all possible solutions incrementally and abandoning paths that don’t lead to a valid solution.

It’s like navigating a maze: you try a path, backtrack if it’s a dead end, and try another.

• Key Idea: Build a solution step-by-step, and if it violates constraints, undo steps (backtrack) and try a different path.

• When to Use: Problems requiring all possible combinations, permutations, or solutions, often with constraints (e.g., puzzles, graph traversals).

• Common Problems:

  • N-Queens: Place N queens on an NxN chessboard so none attack each other.

  • Subsets: Generate all subsets of a set.

  • Sudoku Solver: Fill a 9x9 grid following Sudoku rules.

LeetCode Problems:

1. Subset (78)

Backtracking is a go-to for combinatorial problems in interviews. It tests recursion, state management, and problem decomposition—key skills for Java developers.

15. Dynamic Programming Patterns

Dynamic Programming focuses on breaking problems into subproblems and solving them optimally, and you can develop patterns by solving problems like the knapsack problem.

LeetCode Problems:

1. Climbing Stairs (70)

2. Longest Increasing Subsequence (300)

I also suggest that you go through Grokking Dynamic Programming Patterns for Coding Interviews on Educative to better understand these Dynamic programming patterns.

Top 6 Resources to Crack Coding Interviews

While I have mentioned the resource along with the articles, here is the summary of the top 6 resources I have used for coding interview preparation apart from popular books like Cracking the Coding Interview by Gayle Mcdowell and Coding Interview Patterns by Alex Xu and Shaun Gunawardane

Here are the top resources for coding interview prep, capturing their essence and value for developers in 2026:

1. AlgoMonster

   • Focused, pattern-based, efficient, and structured preparation.

2. ByteByteGo

   • Comprehensive, covers coding patterns, system design, oop design

3. Educative

   • Interactive, text-based, in-depth

4. DesignGurus

   • Structured, pattern-focused, expert-led

5. Udemy

   • Affordable, video-based, diverse

6. ZTM Academy

   • Practical, project-driven, career-focused

7. Bugfree.ai

   • Covers both System Design and DSA, provides mock interviews and AI AI-powered platform

8. LeetCode

   • Comprehensive, practice-heavy, community-driven

And, if you prefer to read books, then Coding interview pattern book by Alex Xu and Shaun Gunawardane is another great book to learn coding interview patterns.

You will also learn 24 patterns there, and then you can combine these patterns with consistent LeetCode practice, and you’ll be well-prepared to land your dream job!

That’s it about the 15 essential Coding Interview Patterns for interviews. You must master these 15 patterns if you want to crack the interview and get the offer you want.

Preparing for coding interviews can be daunting, but focusing on essential problem-solving and coding patterns can make it significantly easier.

Instead of learning solutions to individual problems, mastering these patterns will help you tackle various coding challenges effectively.

You can also start with the Educative-99**, for a structured preparation. Here you will get a chance to solve 99 selected questions instead of 2800 Leetcode problems. This will also help you to master the above coding patterns better.

Hello guys, when it comes to preparing for coding interviews, LeetCode has been the go-to platform for years. It’s vast, it’s challenging, and it covers nearly every problem you can imagine.

But let’s be honest — many developers eventually hit a wall with LeetCode.

Endless grinding without structure can make you feel stuck, even if you’ve solved hundreds of problems.

That’s where AlgoMonster comes in.

Designed by former Google engineers, AlgoMonster takes a structured, data-driven approach to mastering coding interviews.

Instead of throwing random problems at you, it helps you learn by patterns — the same way top candidates prepare to crack FAANG interviews efficiently.

Patterns are a structured way to learn and solve coding problems. Once you understand a pattern, you can solve any question based upon that and most importantly you will start seeing patterns in questions and problems.

But, this only happens if you know what are common patterns, how they work and how to use them.

AlgoMonster gives you everything you need to master Patterns, right from the which patterns are most important for interview to even company based coverage.

Here is one such example:

To start with you can pick the easy patterns which have high ROI and then you move on to difficult ones which separate average to experts.

This pattern based learning is what makes AlgoMonster a golden resource for FAANG/MAANG interview preparation.

And, If you want to join, now is the perfect time because they are offering 50% discount on their annual plan, I have the same and I highly recommend to any developer who are preparing for coding interviews or just want to get better at problem solving, particularly solving FAANG level coding problems.

Which Coding Patterns to Master?

Mastering coding patterns is the most efficient way to prepare for technical interviews. Instead of memorizing solutions, you learn to identify the underlying structure of a problem.

Here are 15 essential coding patterns with brief explanations and examples.

1. Two Pointers

Use two references (pointers) to traverse an array or string, usually moving from both ends toward the middle or at different speeds to find a pair or subarray.

• Example: Finding a pair in a sorted array that sums to a target value.

2. Fast and Slow Pointers

Also known as the “Hare & Tortoise” algorithm. Two pointers move through a data structure at different speeds.

• Example: Detecting a cycle in a linked list.

3. Sliding Window

Maintain a subset of elements (the “window”) that satisfies a condition, sliding it across the data structure.

• Example: Finding the maximum sum of a subarray of size k.

4. Merge Intervals

Used to deal with overlapping intervals. Sort the intervals by start time and then merge or compare them.

• Example: Merging overlapping meeting times.

5. Cyclic Sort

Used when you are given an array containing numbers in a range (e.g., 1 to n) and need to place them in their correct index.

• Example: Finding the missing number in an array of 1 to n.

6. In-place Reversal of a Linked List

Reversing links between nodes without using extra space (O(1) space complexity).

• Example: Reversing a sub-list from position m to n.

7. Tree Breadth-First Search (BFS)

Traverse a tree level-by-level using a queue.

• Example: Calculating the average value of each level in a binary tree.

8. Tree Depth-First Search (DFS)

Traverse a tree using recursion (pre-order, in-order, or post-order).

• Example: Checking if a tree has a path with a given sum.

9. Two Heaps

Maintain two heaps (a min-heap and a max-heap) to track elements in two halves.

• Example: Finding the median of a streaming dataset.

10. Subsets

Used for problems requiring combinations or permutations of a set.

• Example: Generating all possible subsets of a given set.

11. Modified Binary Search

Searching in a sorted array or matrix, or finding boundaries.

• Example: Searching for a target in a rotated sorted array.

12. Top ‘K’ Elements

Use a heap to keep track of the largest or smallest k elements.

• Example: Finding the kth largest element in an array.

13. K-way Merge

Merge multiple sorted arrays into one sorted result.

• Example: Merging k sorted linked lists.

14. Topological Sort

Used for ordering elements in a Directed Acyclic Graph (DAG) where some tasks must be completed before others.

• Example: Determining the order of tasks given dependencies.

15. Dynamic Programming (0/1 Knapsack)

Breaking problems into subproblems and storing results to avoid redundant calculations.

• Example: Determining the maximum value that can fit into a knapsack with limited capacity.

This pattern based learning is what makes AlgoMonster a golden resource for FAANG/MAANG interview preparation.

And, If you want to join, now is the perfect time because they are offering 50% discount on their annual plan, I have the same and I highly recommend to any developer who are preparing for coding interviews or just want to get better at problem solving, particularly solving FAANG level coding problems.

Here is the link to learn more — 50% discount on Algomonster

What About LeetCode?

LeetCode is like a massive library. You can search by tags, difficulty, or companies and start solving problems right away. It’s great for volume practice — especially once you’re familiar with patterns and just want to fine-tune your speed or accuracy.

However, its biggest drawback is the lack of structure.

Beginners often find themselves solving problems randomly without understanding underlying principles such as two-pointers, sliding windows, binary search, or dynamic programming transitions.

You learn by repetition, but not always by insight.

If you’re self-motivated and already know your weak points, LeetCode is a powerful tool. But if you’re struggling to find a systematic roadmap, AlgoMonster might be a better fit.

Why is AlgoMonster different?

AlgoMonster focuses on understanding patterns before practice. Instead of just solving problems, it walks you through why each approach works, the patterns behind it, and how to apply those patterns across multiple problem types.

Here’s what makes it stand out:

• Pattern-Based Learning — Every question belongs to a specific pattern (like BFS, DFS, Sliding Window, etc.), making it easier to generalize solutions.

• Interactive Explanations — You can visualize problem-solving steps, making complex concepts easier to grasp.

• Progress Tracking — AlgoMonster tracks your mastery level by topic, helping you focus on weak areas.

• Company-Specific Problems — It includes questions asked by Google, Amazon, Meta, and Microsoft, so you can prepare strategically.

• Time Efficiency — You don’t need to grind 500+ LeetCode questions. AlgoMonster focuses on 150–200 core patterns that appear repeatedly.

For example, their Monster 50 list is an excellent curated set of must-practice problems that build your pattern intuition faster than random LeetCode practice.

My Favorite Coding Problem Lists for Quick Practice

Let’s be honest, LeetCode is huge, over 3000+ problems, even the most dedicated ones will not be able to complete it in years, so if you really want to crack interviews in a limited time, you need to choose the list of questions that covers most of the key concepts, that’s where lists like ByteByteGo 101 and Monster 50 by AlgoMonster come into the picture.

They provide the most structured way to prepare based upon coding patterns, and once you know the pattern, you can solve a lot more problems than without knowing them.

If you’re serious about coding interviews, here are some of the best curated problem lists I use daily:

• ByteByteGo 101 Coding Patterns — Great for understanding both coding and system design patterns.

• Monster 50 by AlgoMonster — A concise, structured list to master the top interview patterns.

• Educative-99 — Deep, text-based guided problems with clear explanations.

• Blind 75 by NeetCode — The community’s gold-standard problem list, great for brushing up before interviews.

If you are in a rush, solve Monster 50 by AlgoMonster, but if you want more thorough practice, then ByteByteGo 101 is your best bet. It also gives you practice with patterns, and you will solve 101 patterns and learn 19 coding interview patterns like two pointers, sliding window, prefix sum, etc., along the way.

And, if you want to join ByteByteGo, now is the best time because they are giving 50% discount on their lifetime plan, which is probably the best resource for coding interview preparation in 2026, covering both coding questions as well as System Design questions

Here is the link — Join ByteByteGo with 50% discount

When to Choose LeetCode over AlgoMonster? or Vice-Versa?

Leetcode is best for both beginners and experienced coders who are looking for volume and lots of practice. Most suitable for beginners because they don’t have experience, and grinding LeetCode can give them a lot of confidence.

They also have time on hand, so LeetCode is the best platform for them.

For experienced developers, time is limited as they need to balance both work and coding interview practice; that’s why I feel a structured platform like AlgoMonster or ByteByteGo is probably a better choice for them.

In general, if you already know the main patterns and just want to practice hundreds of variations, LeetCode remains unbeatable.

But if you want to learn patterns efficiently, improve your problem-solving intuition, and save time before interviews, AlgoMonster is the smarter choice.

Here is also the full comparison of AlgoMonster with LeetCode and NeetCode, two of the popular coding interview platforms:

Final Thoughts

In 2026, coding interviews are not just about solving problems — they’re about solving them systematically and explaining your reasoning clearly. AlgoMonster helps you do exactly that.

Whether you’re preparing for your first technical interview or targeting FAANG-level roles, AlgoMonster gives you a structured, efficient roadmap to success.

Combine it with curated resources like ByteByteGo 101, Monster 50, and Blind 75 — and you’ll have a complete system for coding interview mastery.

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

P. S. — If you are serious about getting into FAANG companies and want to leave no stone unturned then I also suggest you to join Algomonster for DSA and DesignGurus.io for System Design, and start practicing mock interviews on Exponent. This is the perfect recipe to crack coding interviews in quick time

Hello friends, AI Engineering is probably the hottest skill in tech right now, particularly with Agentic AI skills. There is also huge gap between number of openings and skills AI professionals who know more than just using AI tools.

Every company, both big and small, wants developers who can:

• Build AI apps

• Work with LLMs

• Create AI agents

• Use RAG pipelines

• Fine-tune workflows with tools like ChatGPT and Claude

But here’s the problem, its a relatively new skills and not many people know how to teach it.

Most AI courses are either:

• Too theoretical

• Too academic

• Or just another “prompt engineering” tutorial

I wanted something practical but also which have a bit more depth than AI courses on Udemy.

Something that actually teaches:

• How modern AI systems work?

• How to build production-ready AI apps?

• And how to become an AI Engineer instead of just an AI user?

That’s when I found the Full Stack AI Engineering Course by by Louis-François Bouchard and Louie Peters, author of popular Building LLMs for Production book by and

And honestly? This is one of the best AI Engineering courses I’ve taken.

Why I Decided to Try This AI Engineering Course?

Over the last year, I’ve been experimenting heavily with:

• ChatGPT

• Claude

• GitHub Copilot

At first, I was just using them for coding help.

But eventually I realized:

The real opportunity is not just using AI tools. It’s building AI-powered applications.

That means learning:

• LLMs

• Vector databases

• RAG

• AI agents

• Prompt engineering

• AI system architecture

And most tutorials online barely scratch the surface.

So I started looking for a proper structured course.

That’s when I came across the Full Stack AI Engineering Course.

What Makes This Course Different?

The biggest difference?

👉 It’s built for developers.

Not researchers.
Not mathematicians.
Not PhD students.

This course focuses on:

• Building real AI applications

• Practical workflows

• Production-ready concepts

Which is exactly what most developers need in 2026.

Here is the link to join this awesome course - Full Stack AI Engineering Course.

What You will Learn?

The course covers a lot of modern AI Engineering topics, including:

• Large Language Models (LLMs)

• RAG (Retrieval-Augmented Generation)

• AI Agents

• Prompt Engineering

• Vector Databases

• LangChain-style workflows

• AI App Development

• Full Stack AI Systems

And the best part?

👉 It doesn’t feel overly theoretical.

You actually learn how these systems are built.

Here is the link to join this course — Full Stack AI Engineering

My Favorite Part — Building RAG AI Tutor

This was probably the most exciting section for me.

AI Agents and Agentic AI are becoming a huge trend right now.

Instead of simple chatbots, you build systems that can:

• Reason

• Use tools

• Retrieve information

• Execute workflows

• Automate tasks

And this course explains the concepts in a very developer-friendly way. Though for more comprehensive learning on Agentic AI , I also recommend Paul Iustzin’s Agentic AI Engineering course on same platform.

If you don’t know, Paul is author of one of the most popular book on LLM Engineering, The LLM Engineer’s Handbook, if you haven’t read it yet, its definitely worth reading.

One more thing I really liked about this course is that its very beginner-friendly…

But gradually moves into more advanced topics.

So:

• Beginners won’t feel lost

• Experienced developers won’t get bored

That balance is surprisingly rare.

Why This Matters in 2026?

We’re entering a phase where AI is becoming part of every software product.

Developers who understand:

• AI workflows

• LLM architectures

• AI system design

will have a massive advantage.

Just like cloud computing changed software engineering 10 years ago…

AI Engineering is changing it again and this course can help you acquire all the necessary AI skills to survive next decade.

Here is the link to join this course — Full Stack AI Engineering

Who Should Take This Course?

I think this course is excellent for:

• Software Developers

• Backend Engineers

• Full Stack Developers

• AI Enthusiasts

• Students exploring AI Engineering

• Anyone wanting to build AI-powered products

Especially if you want to move beyond:

“Just using ChatGPT and Claude ”

and actually learn how AI systems are built.

Is Full Stack AI Engineering Course by Louis-François Bouchard Worth It?

For me? Absolutely yes. This course is not just comprehensive and well structured but also very practical.

What I liked most:

• Practical approach

• Modern AI topics

• Clear explanations

• Real-world focus

• Beginner → advanced structure

There’s a lot of AI hype online right now.

But this course actually teaches useful skills.

Here is the link to join this course — Full Stack AI Engineering

Final Thoughts

Most developers know AI is important.

But very few are taking the time to properly learn AI Engineering.

That gap is going to matter a lot over the next few years.

If you want a structured and practical way to learn:

👉 I highly recommend checking out the Full Stack AI Engineering Course.

It’s one of the few courses I’ve taken recently that genuinely felt modern, practical, and immediately useful.

P. S. — If you want to do just one thing now, I suggest go ahead and join Full Stack AI Engineering Course you will thank me later.

MyClaw is not another AI chatbot. It’s the 24/7 AI assistant that gets better with you.

Most AI tools give you a blank chatbox and wish you good luck. MyClaw is different — it arrives pre-loaded with expertise for your specific role, connects to the apps you already use (Telegram or WhatsApp), and runs tasks on autopilot while you sleep.

The difference:

• ChatGPT waits for you to ask. MyClaw sends you the answer before you wake up.

• ChatGPT forgets you every session. MyClaw remembers your preferences, your clients, your writing style.

• ChatGPT lives in a browser tab. MyClaw lives in your pocket.

This is the difference between AI that talks and AI that works.

Try Now

Hello folks, last year, we have shared The Complete AI and LLM Engineering Roadmap: From Beginner to Expert and you guys loved it, while most of the article is still valid and very much applicable in 2026, I thought to share an updated article for anyone who wants to become an AI Engineer in 2026.

AI is everywhere now and you cannot ignore or postpone AI anymore and software engineers are rushing to become AI Engineers.

If you are also one who wants to learn AI Engineering or want to become an AI engineer then you have come to the right place.

While you can find a lot of information and resources on becoming an AI engineer online, for example, every week, a new video claims you can become an AI Engineer in 3 months.

Let’s be honest, that’s not how this works.

AI engineering is one of the most exciting careers in tech today, but it’s also a multi-year craft that combines software engineering, machine learning, and real-world system design.

The good news? You don’t need to master everything on day one to start building useful AI applications.

This guide breaks down a realistic roadmap — what to learn, when to learn it, and how to grow from beginner to professional AI engineer. Along the way, I have shared resources you can take like AI Engineer learning track and Associate AI Engineer for Data Scientists track from Datacamp.

While there are many places you can go for structured learning, I often choose Datacamp because of their byte sized lesson and effective curriculum.

First, What Does an AI Engineer Actually Do?

AI Engineers are application builders, not primarily model researchers.

Instead of training models from scratch, AI engineers:

• Build apps on top of pre-trained foundation models (GPT, Claude, Llama, etc.)

• Use prompt engineering, RAG, and fine-tuning

• Focus on deployment, scalability, evaluation, and optimization

• Handle security, data pipelines, and user feedback loops

In short:

The 2026 AI Engineering RoadMap

Here is the roadmap you can follow in 2026 to become an AI Engineer. If you are looking for shortcuts then this roadmap is not for you.

I am sorry but you cannot become an AI Engineer in 3 months or even 6 months.

You need to put time and effort to gain all the skills required to get a job as an AI Engineer in today’s market.

Stage 1 — Build Your Foundations

Before touching LLMs, you need core skills, this is where most people struggle as they are not fancy. They are neither easy and require determination and hard work.

Here are the core skills you should revise/learn/master to become an AI Engineer in 2026

1.1 Math (Conceptual Level)

You don’t need a PhD, but you should understand:

• Probability

• Statistics

• Basic linear algebra (vectors, matrices)

In particular Matrix multiplication which is the basis of how these large language model and generative AI work.

1.2. Python Programming

AI engineering runs on Python. You should be comfortable writing clean, production-style code.

1.3 Software Engineering Basics

If you are a junior engineer who directly wants to get into AI engineering, then you should learn software engineering basics first like:

• Git & version control

• APIs

• Command line

• How services communicate

1.4 Core ML Concepts

This is the most important part for anyone who want to become an AI Engineer. Even if you don’t train models, you must understand:

• Supervised vs. unsupervised learning

• Overfitting vs. underfitting

• Evaluation metrics

In short, AI engineering sits on top of software engineering, not instead of it.

If you need a course to learn more of the core skills, I highly recommend you to join the Artificial Intelligence A-Z: Learn How to Build an AI on Udemy. It’s a great course to build fundamentals.

Stage 2 — Start Using AI in Real Applications

Now the fun begins.

1. Learn AI APIs

Use services like OpenAI or other LLM APIs to build features without worrying about model training.

2. Master Prompt Engineering

Well-designed prompts = dramatically better results. This becomes a daily skill.

3. Build RAG Applications

Retrieval-Augmented Generation (RAG) connects LLMs to your own data using:

• Embeddings

• Vector databases

This is what makes AI apps actually useful.

4. Build Simple Projects

I am big fan of project based learning as that’s where true learning happens. You should start with small and fun projects like:

• Chatbots

• Content generators

• Document Q&A tools

• Simple automation apps

In short, Projects > theory at this stage and you must build as many as short, medium or large project as you can.

Too much reading and you will forget everything, too little reading and you will always look back, so just create a right balance between reading and actually building stuff.

And, if you need a resource, Oxford Artificial Intelligence Programme is a great one to start with.

Stage 3 — Go Beyond the Basics

Once you’ve built multiple projects, you’ll naturally want deeper understanding.

3.1 Understand How LLMs Work

• Transformer architecture

• Attention mechanisms

• Embeddings

You don’t need to be a researcher, but you should know what’s happening under the hood. For this thing I recommend reading books like The LLM Engineer’s handbook, it covers all these concepts in depth.

3.2 Advanced RAG Systems

If you truly want to excel as an AI Engineer not just want those certification and knowledge then you should move beyond simple setups instead you should:

• Better chunking strategies

• Smarter retrieval methods

• Embedding optimization

These things will help you grow faster, much faster if you are the right place and right time as more and more companies are building their AI strategies to make full use of all the data they have collected over the years.

3.3. Fine-Tuning & Model Selection

This is another thing which can help you to really showcase your AI skills and put you into category of senior AI engineer which are in huge demand right now.

You should at least try to learn:

• When to fine-tune vs. prompt engineer

• Cost vs. performance tradeoffs

• Model licensing considerations

While fine-tuning and model selection is not an easy task, most of the companies are looking for engineers who can not just create AI models but make them work with their data in the most optimal way.

And, if you need a resource to mater LLM, I highly recommend you to checkout the Generative AI with Large Language Models course on Coursera. It’s created in collaboration with AWS and DeepLearning.AI and worth every penny.

Stage 4 — Think Like a Production AI Engineer

This is where you level up from “builder” to “professional.” You have heard about end-to-end, this is what it means. You are not just familiar with software but also hardware and infra which powers it.

Once you have gone through previous stages and build enough skill and confidence to call yourself AI engineer, these are things I want you to focus on

4. 1. Deployment & Infrastructure

• Docker

• Cloud platforms (AWS, GCP, Azure)

• Monitoring & logging

4.2. Evaluation Systems

You must measure:

• Hallucinations

• Bias

• Response quality

AI engineering is not just “it works on my laptop.” or “works on my mobile” but it must work for everyone.

4.3. Inference Optimization

This is another advanced topic which AI Engineer should learn. Inference optimization is about making models cheaper and faster using:

• Quantization

• Distillation

• Efficient serving architectures

4.4 AI Agents

Now, the hottest thing of 2026, the AI agents. You should try to build systems that:

• Use tools

• Maintain context

• Break down complex tasks

4.5 Security, Privacy & Ethics

While whatever we learn so far its important but you cannot use it on production if you don’t know how to handle security and privacy.

You should at least learn to defend against:

• Prompt injection

• Data leakage

• Misuse of AI systems

And, if you need a resource, I highly recommend the The Complete Agentic AI Engineering Course from Udemy. It’s one of the must have course for AI Engineers.

How to Learn These AI Skills?

Now, the big question is how will you learn these essential AI skills? While I have share courses along the way focused on each stage but here I will share the main resource I would use to learn these skills for beginners.

I personally like to choose training which not just cover theory but also full of quizzes, exercises and projects, and If you want a guided path instead of random tutorials, structured tracks can help a lot.

While there are many places you can go for structured learning, I often choose Datacamp because of their byte sized lesson and effective curriculum.

For developers, Datacamp’s AI Engineer learning track covers APIs, LLM apps, LangChain, and vector databases. It also prepares you for the professional certification like AI Engineer for Developers Associate Certification

On the other hand, If you come from a data science or ML background, then you can explore Associate AI Engineer for Data Scientists track on Datacamp as it focuses more on working with foundation models and MLOps.

These paths are great because they combine theory + hands-on AI application building, which is exactly what this role requires.

The Realistic Timeline (No Hype)

Here’s what the journey typically looks like:

1. Foundations + first apps — ~6 months (part-time)

2. Advanced concepts — 6–12 more months

3. Professional-level skills — 1–2 years

4. Senior AI engineer — 3–5 additional years

So yes — this is a 3–6 year journey if you start from scratch.

But here’s the key:

1. You can start building and even working with AI much earlier

2. You don’t need to “know everything” before you start

Another great thing is that if you are already a software engineer then you already know many stuff including fundamentals and traditional software engineering topics and skills.

Final Thoughts

That’s all about this 2026 AI Engineer Roadmap. AI engineering isn’t a 90-day sprint. It’s a long-term, high-reward career path that blends coding, systems thinking, and applied AI.

If you:

• Focus on real projects

• Learn tools step by step

• Use structured learning when needed

• Stay consistent

You’ll be far ahead of people chasing shortcuts.

The future doesn’t belong to people who talk about AI. It belongs to those who build with it.

Other AI and LLM Engineering articles you may like

Hello guys, Agentic AI, systems of autonomous agents that plan, act and coordinate is shaping up to be one of the most important trends in AI development.

Frameworks that enable multi-agent orchestration, tool integration, memory, reasoning and collaboration are now becoming critical skills for engineers and developers.

But before that, shout out to for his amazing Agentic AI Engineering Course, If you’re serious about Agentic AI, this is the course you’ve been waiting for.

This 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

And, now, here are 10 frameworks you should be familiar with in 2026 and for each one, I’ve added a recommended Udemy courses to get you up to speed.

1. AutoGen (Microsoft)

An open-source, multi-agent framework from Microsoft designed for scalable agent systems, inter-agent communication and orchestration.

Recommended course: Building AI Agents & Agentic AI Systems via Microsoft AutoGen

Why: Hands-on with AutoGen, ideal for engineers wanting to build real agent workflows.

2. CrewAI

Designed to orchestrate teams (or “crews”) of agents, CrewAI simplifies multi-agent collaboration, tool use and large-scale agentic systems.

Recommended course: The Complete Agentic AI Engineering Course (2025)

Why: Covers both CrewAI and AutoGen, great for beginners-to-intermediate looking at multi-agent frameworks.

3. LangChain

While originally more workflow-oriented, LangChain increasingly supports agentic AI patterns (tool + agent orchestration, memory, chains).

LangChain allows developers to quickly integrate Large Language Models (LLMs) like GPT into applications, enabling features such as natural language understanding, text generation, and AI-powered automation.

It simplifies the complex processes of model integration and accelerates AI app development, making it a go-to framework for creating LLM-powered applications.

Recommended course: LangChain — Develop LLM Powered Applications with LangChain

4. LangGraph

A graph-based framework for modelling multi-agent workflows and dependencies, suited for complexity and scale.

LangGraph builds on LangChain’s capabilities and introduces the concept of AI agents. These agents can autonomously carry out tasks, making decisions based on user input and real-time data.

This makes LangGraph essential for developing more interactive, autonomous systems that can manage workflows or even communicate with other systems.

Recommended course: LangGraph Mastery: Develop LLM Agents with LangGraph

5. LlamaIndex (formerly GPT-Index)

LlamaIndex provides a powerful way to bridge the gap between LLMs and your data. While large models like GPT-4 or LLaMA are incredibly capable, they don’t know your company’s internal documents, private datasets, or custom knowledge bases.

LlamaIndex helps you create pipelines to ingest, index, query, and retrieve data, allowing LLMs to reason over your specific information.

This framework simplifies Retrieval-Augmented Generation (RAG) architectures, making it easier to enhance accuracy, reduce hallucinations, and add context-awareness to your AI applications.

With LlamaIndex, you can rapidly build tools like custom chatbots, search engines, or AI agents that work on your own data — and do it at scale.

Not purely an “agentic” framework but increasingly used in agent workflows for retrieval, memory, tool integration.

Recommended course: LlamaIndex Develop LLM powered apps (Legacy, V0.8.48)

6. Hugging Face Transformers Agents

Hugging Face’s Transformers library provides a simple and powerful API for accessing over 100,000 pre-trained models for tasks like text classification, question answering, translation, summarization, and more.

With seamless support for TensorFlow, PyTorch, and JAX, it has become the go-to toolkit for developers, data scientists, and researchers working in NLP and beyond.

The agentic extension of Hugging Face’s library, enabling agents that use transformer models in complex workflows.

Recommended course — Learn Hugging Face Bootcamp

7. Semantic Kernel (Microsoft)

Microsoft’s Semantic Kernel (SK) is one of the most enterprise-ready frameworks for building agentic AI systems. It allows developers to easily connect large language models (LLMs) like GPT-4 with real-world tools, data sources, and APIs.

What sets SK apart is its focus on tool-enabled LLMs, function calling, semantic memory, and planning/chaining mechanisms, which make it ideal for orchestrating autonomous workflows in business environments.

It integrates deeply with the .NET ecosystem, Python, and even JavaScript — allowing both backend and full-stack engineers to prototype and deploy intelligent agents quickly.

If you’re working on enterprise applications where AI needs to reason, plan, and act across multiple services, Semantic Kernel is a must-learn framework in 2026.

Here’s a great Udemy course to start: Mastering Semantic Kernel by Creating Projects

8. RASA (Agentic Conversational Agents)

RASA has long been a powerhouse for building contextual chatbots and conversational AI, but in recent years, it has evolved into a more agentic conversational framework.

With RASA Pro and its new open-source components, you can now integrate LLMs, external APIs, and reasoning layers to create hybrid agents by combining deterministic dialogue management with LLM-driven intelligence.

It remains a top choice for teams building customer service agents, voice bots, or AI assistants that must maintain consistent state and memory across interactions.

Developers can also extend RASA pipelines with retrieval-augmented generation (RAG) and tool execution to make agents more autonomous.

And, if you need a course, a good starting point is: The Complete Course of Rasa Chatbot

9. Atomic Agents

Atomic Agents is an emerging open-source framework designed for decentralized, multi-agent systems. Unlike traditional centralized frameworks, it enables many agents — each with specialized goals — to coordinate tasks in a distributed fashion.

This architecture is particularly useful for complex environments like simulation, large-scale automation, and research collaborations where agents must communicate and negotiate.

The focus on decentralization makes Atomic Agents highly scalable and resilient, positioning it as a promising player in the evolution of agentic AI.

Developers exploring Web3, blockchain, or distributed computing can benefit significantly from learning how Atomic Agents work.

Learn more here: Build GenAI & Multi-Agent Systems Tools for Software Testing

10. Botpress (Agentic Platform)

Botpress has transitioned from a traditional chatbot builder into a full-fledged agentic AI platform. The latest versions allow you to build agents that reason, call tools, and orchestrate multi-step workflows — all within a no-code or low-code interface.

It supports advanced integrations with APIs, databases, and vector stores, allowing you to combine structured logic with LLM-based decision-making.

For organizations and teams looking to deploy scalable, secure AI agents without heavy engineering overhead, Botpress offers the perfect balance of accessibility and sophistication.

Its open-source foundation and plugin ecosystem make it ideal for developers and non-developers alike.

Why This Matters in 2026?

Agentic AI is no longer academic — it is moving into mainstream applications: automation workflows, tool-enabled assistants, and autonomous decision-making systems.

Understanding these frameworks gives you an edge as the AI ecosystem shifts from single-model prompts to multi-agent orchestration, collaboration, and deployment.

Choosing the Right Framework

• For multi-agent orchestration: AutoGen, CrewAI, LangGraph

• For tool-enabled agents & workflows: LangChain, Semantic Kernel

• For distributed/decentralized agents: Atomic Agents, Botpress

• For retrieval/memory-centric agents: LlamaIndex, Hugging Face Agents

Pick the framework that aligns with your goal, stack, and use case — then follow up with one of the Udemy courses listed to deepen your skill.

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 for reading this article so far. If you find these Udemy Courses for learning Microsoft Autogen framework for building AI Agents then please share with your friends and colleagues. If you have any questions or feedback, then please drop a note.

P. S. — If you are a complete beginner on Agentic AI then I also recommend you to first go through a comprehensive course like The Complete Agentic AI Engineering Course, I highly recommend that to anyone who want to start with Agentic AI in 2026, and if you need books you can also check my previous article.

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 to Learn AI And LLM Engineering (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

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

2. 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

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

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 Paul Iusztin , 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: Paul Iusztin and Maxime Labonne 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

He has also created an awesome course on Agentic AI Engineering, which is very relevant for today, I highly recommend you to check that out as well, here is the link Agentic AI Engineering (aff) by Paul Iusztin

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 Louis-François Bouchard , 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

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

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

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

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

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

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

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

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

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

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.

P. S. - If you are into Agentic AI, I highly recommend you to checkout Agentic AI Engineering by , author of LLM Engineer’s handbook and one of the authority when it comes to teaching AI and LLM Engineering.

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