Thursday, March 12, 2026

I Found LeetCode Alternative for Coding Interview and Its Awesome

LeetCode vs. AlgoMonster? Which One Should You Use for Coding Interviews?

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.

I solved more than 100 problems on LeetCode but never able to develop the confidence I needed to solve any unknown or unseen problems on the interview. 

Even if they make the question a bit longer, I struggled to find out how to solve it, that's where AlgoMonster's pattern based approach helped me a lot. 

I not only learned the key patterns like Two pointers, fast and slow pointers, sliding window and prefix sum but also learned how to spot them on coding interview problems and apply them. 

Learning patterns for solving coding patterns is certainly a better approach then grinding LeetCode endlessely and AlgoMonster is probably the best platform to master those coding patterns and develop the instinct to spot them on real coding interview questions. 

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

How LeetCode Works?

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 like two pointers, sliding window, 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 AlgoMonster is 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

Let’s be honest LeetCode is huge, over 3000+ problems, even the most dedicated ones will not be able to complete it on years.

If you really want to crack interviews in a limited time, you need to choose the list of questions which covers most of the key concepts, that’s where list like ByteByteGo 101 and Monster 50 by AlgoMonster comes into picture.

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

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

If you are in rush, solve Monster 50 by AlgoMonster but if you want more through practice then ByteByteGo 101 is your best bet. 

It also gives you practice by 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 suitalbe for beginners because they don’t have experience and grinding LeetCode can give them a lot of confidence.

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

For experienced developer, 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 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.

System Design · Coding · Behavioral · Machine Learning Interviews

Everything you need to take your system design skill to the next level

bytebytego.com

Other Programming and Interview Articles you may like

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

Tech Interview Preparation — System Design, Coding & Behavioral Courses | Design Gurus

Ace system design, coding, and behavioral interviews with Design Gurus’ all-in-one prep platform. Access expert-led…

designgurus.org

    By javin paul at March 12, 2026 0 comments
    Labels: Algomonster , Coding Interviews , Coding problems , data structure and algorithm , LeetCode , online resources

    I Tried 15+ LLMOps Courses on Udemy: Here are My Top 5 Recommendations for 2026

    I Tried 15+ LLMOps Courses on Udemy: Here are My Top 5 Recommendations
    credit — medium.com

    Hello friends, Large Language Models (LLMs) are redefining what’s possible with AI, but deploying them in real-world systems is where the real challenge begins.

    That’s where LLMOps comes in — — the discipline of operationalizing LLMs at scale, managing everything from fine-tuning and optimization to versioning, monitoring, cost control, and serving in production.

    It’s MLOps on steroids, built for the unique needs of foundation models.

    In 2026, the demand for AI engineers and ML practitioners who can not only fine-tune but also deploy and manage LLMs in production has exploded.

    Whether you’re building your first GPT-based app or trying to get Llama 3 running efficiently with quantization on GPU clusters, these Udemy courses will equip you with the right tools.

    If you’re serious about AI engineering and don’t want to be left behind as models grow more powerful and infrastructure grows more complex, this is your starting point.

    If you want to learn LLMOps in 2026 and looking for best online resources then you have come to the right place.

    Earlier, I have shared best AI and Machine Learning courses, and Gen AI and LLM courses and today I am going to share best online courses from Udemy to learn LlamaIndex in 2026.

    While books like AI Engineering by Chip Huyen and The LLM Engineering Handbook by Paul Iusztin and Maxime Labonne are a good starting point, but if you really want to gain confidence, nothing beats learning by doing — — and that’s where these Udemy courses shine.

    AI Engineering: Building Applications with Foundation Models

    AI Engineering: Building Applications with Foundation Models [Huyen, Chip] on Amazon.com. *FREE* shipping on qualifying…

    www.amazon.com

    LLM Engineer's Handbook: Master the art of engineering large language models from concept to…

    LLM Engineer's Handbook: Master the art of engineering large language models from concept to production [Iusztin, Paul…

    www.amazon.com

    6 Best Udemy Courses to Learn LLMOps in 2026

    Without any further ado, here are the best online courses you can join on Udemy to learn how to deploy large language models in production also known as LLMOps.

    1. Deploying LLMs: A Practical Guide to LLMOps in Production

    This is one of the most current and comprehensive guides specifically focused on LLMOps.

    The course explores model deployment using Llama 3, GPT, LoRA, AWQ, GPTQ, and production-ready practices with Ray, MLflow, and Flash Attention.

    You’ll learn how to manage compute costs, optimize model loading, and implement scalable deployment patterns.

    If you want to get serious about deploying open-source models or fine-tuned LLMs at scale, start here.

    Here is the link to join this course — — Deploying LLMs: A Practical Guide to LLMOps in Production

    2. 2026 Deploy ML Model in Production with FastAPI and Docker

    HuggingFace Transformers, FastAPI, Docker, and AWS — — this course combines them all.

    You’ll deploy ViT, BERT, and TinyBERT models in real-world cloud environments. The focus is on packaging and serving models in a secure and scalable way.

    Even though it’s not LLM-specific, the techniques covered here apply directly to building reliable backend services for LLM applications.

    Here is the link to join this course — — 2026 Deploy ML Model in Production with FastAPI and Docker

    3. LLMOps Masterclass 2026 —  Generative AI, MLOps, AIOps

    If you’re looking to understand how LLMOps fits within MLOps and AIOps, this is your course. It provides a broader perspective on managing generative AI systems beyond just deployment.

    You’ll get hands-on experience deploying HuggingFace and OpenAI models with a focus on monitoring, cost optimization, and automation pipelines. A must if you want to think beyond one-off deployments.

    Here is the link to join this course — — LLMOps Masterclass 2026 — Generative AI, MLOps, AIOps

    4. Complete MLOps Bootcamp With 10+ End To End ML Projects

    Students: 22,612 (Bestseller)

    Why take it: If you prefer project-based learning, this bootcamp delivers 10+ end-to-end real-world machine learning projects — — from data prep and training to deployment and automation.

    While LLMs are not the only focus, the course builds your foundational MLOps skills, which are essential before moving to LLMOps. It’s a strong fit for engineers transitioning into AI infrastructure roles.

    Here is the link to join this course — — Complete MLOps Bootcamp With 10+ End To End ML Projects

    5. Azure AI Studio (AI Foundry): Prompt Flow, LLMOps & RAG

    Students: 2,271\ Why take this course: If you work in a Microsoft Azure environment, this course is for you. It focuses on Prompt Flow, RAG (Retrieval-Augmented Generation), and other Azure-native LLMOps tools.

    It covers model evaluation, content safety, and LLMOps workflows in Azure AI Studio, making it a good option for enterprise engineers or teams deploying AI apps inside Microsoft’s cloud ecosystem.

    Here is the link to join this course — — Azure AI Studio (AI Foundry): Prompt Flow, LLMOps & RAG

    6. Deploying AI & Machine Learning Models for Business | Python

    Students: 9,902

    Why take it: This course focuses on business-ready model deployment. It shows how to build ML, deep learning, and NLP applications and wrap them with Docker containers for real-world deployment.

    Although not LLM-centric, it’s highly relevant for engineers who need to deploy LLM pipelines as part of broader AI workflows — — especially useful for Python developers coming from a traditional ML background.

    Here is the link to join this course — — Deploying AI & Machine Learning Models for Business | Python

    Why Learn LLMOps in 2026?

    Language models have gone from research tools to production-critical systems. But deploying them isn’t as simple as calling an API. LLMs are compute-hungry, dynamic, and often need custom datasets, fine-tuning, and orchestration.

    As organizations adopt them across search, chatbots, agents, and more, LLMOps becomes essential to ensure:

    • Scalability without breaking the bank
    • Monitoring to avoid hallucinations or failures
    • Version control for fine-tuned checkpoints
    • Security and compliance for enterprise use
    • Toolchain integration with platforms like Ray, LangChain, MLFlow, Azure, HuggingFace, etc.

    Companies are actively hiring LLMOps engineers and specialists to manage this complexity. If you want to future-proof your career in AI, investing in LLMOps is one of the smartest decisions you can make this year. year.

    That’s all about the top 6 Udemy courses to learn LLMOps in 2026. Mastering LLMOps and learning how to deploy language models in production isn’t just a nice-to-have skill anymore — — it’s essential for anyone serious about working with AI at scale.

    The courses we’ve explored offer hands-on guidance, real-world projects, and the technical depth you need to bridge the gap between experimentation and production.

    Whether you’re deploying models with FastAPI, fine-tuning LLaMA 3, or integrating with Azure AI Studio, these resources equip you to build reliable, efficient, and scalable AI systems.

    Invest the time to learn these tools properly — — you’ll thank yourself when your models move seamlessly from prototype to production.

    By the way, if you want to join multiple course on Udemy, its may be worth getting a Udemy Personal Plan, which will give instant access of more than 11,000 top quality Udemy courses for just $30 a month.

    If you got a lot of time and want to save money, Udemy Personal Plan will be perfect for you.

    Other AI, LLM, and Machine Learning resources you may like

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

    P. S. — — If you want to learn from books and looking for best AI and LLM Books then I highly recommend you to read AI Engineering by Chip Huyen and The LLM Engineering Handbook by Paul Iusztin and Maxime Labonne, both of them are great books and my personal favorites. They are also highly recommend on Redditt and HN.

    AI Engineering: Building Applications with Foundation Models

    AI Engineering: Building Applications with Foundation Models [Huyen, Chip] on Amazon.com. *FREE* shipping on qualifying…

    www.amazon.com

      By javin paul at March 12, 2026 0 comments
      Labels: artificial intelligence , courses , DevOps , LLM , LLMOps , Udemy
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