SDE & AI @Amazon | Simplifying software engineering, system design, and generative AI | Building Hungry Minds: the newsletter to stay ahead in software 👇

Tell me about the last time you felt growing as a software engineer. Really.

𝙽𝚞𝚕𝚕𝙿𝚘𝚒𝚗𝚝𝚎𝚛𝙴𝚡𝚌𝚎𝚙𝚝𝚒𝚘𝚗? Here's why:

We all face information overload as software engineers. 
There's too much content and too little time.
You have to stay relevant, but it's hard.

Right now, you're either working or taking a break.

You are hoping to get a dopamine shot from scrolling your feed.

You can find:

💡 A practical tip to implement
📚 An inspirational quote
😆 Something funny

And then what?

→ Scroll past it? 
→ Forward it blindly to a friend?
→ Forget about it in the bookmarks?

The harsh truth: nothing.

What you consume passively doesn't make you learn.

How do I know?
It was the same for me for years.

How do you stay updated with AI, stay relevant in the job market, and continuously grow?

That's the problem I set out to solve with Hungry Minds - a free weekly newsletter to help software engineers learn and stay updated, without the noise.

I'm doing that by:

1. Writing to you daily on LinkedIn to 40K+ followers and over 5M impressions last year
2. Compressing 1 week of software deep dives and trends in 1 free newsletter issue
3. Sharing my thoughts and learnings being promoted to SDE II in 1.5y at Amazon

If that sounds interesting, check out Hungry Minds or DM me! I'm always down for a chat.

Save 50+ hours of your week here: 𝗵𝘁𝘁𝗽𝘀://𝗵𝘂𝗻𝗴𝗿𝘆𝗺𝗶𝗻𝗱𝘀.𝗱𝗲𝘃 (6K+ engineers from startups to big tech are reading already)

DISCLAIMER: Opinions expressed are solely my own and do not express the views or opinions of my employer.

My career background:

I'm currently an L5 software engineer at Amazon in Madrid, working on core Search CX and Personalization. 
In 2022/2023, I delivered 2 successful billion-scale projects with multiple teams across different time zones.

I have worked with multiple code languages (although this doesn't make a difference), and with tech stack in the lines of AWS CDK, React applications, scheduling systems, backends with GraphQL, and simple AI applications.

My education background:

- I finished a Master's in Machine Learning and AI with l'Ecole Polytechnique and IPP
- I completed a Master's in Computer Science and Data Science with ESILV Paris
- I got a Master's in business development and marketing with EMLV Paris

Alexandre Zajac

𝗖𝗼𝗱𝗲 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗡𝗢𝗧 𝗔𝗯𝗼𝘂𝘁:


→ One senior gate-keeping all the knowledge. 

Code documentation is ALL about:

→ Sharing knowledge so the whole team knows how the system works.

1-person docs = single point of failure.

Whole-team docs = redundancy + knowledge multiplication.

Remember:
You NEED documentation to manage a system through people and time.


How do you manage docs effectively?


P.S: Documente my personal interview cheatsheet: https://lnkd.in/d_eCGEJR

SDE & AI @Amazon | Building Hungry Minds to 1M+ | Daily Posts on Software Engineering, System Design, and AI ⚡

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10+1 System Design Patterns Every Engineer Should Know in 2026:


___
btw, my personal interview cheatsheet: https://lnkd.in/d_eCGEJR
___


0.💡Load Balancer: 

Distributes incoming traffic across multiple servers to ensure no single server gets overwhelmed.

➡️ Useful when: You're scaling web applications or ensuring high availability.

1.💡CDN (Content Delivery Network):

Caches content at edge locations worldwide, reducing latency by serving users from the nearest node.

➡️ Useful when: You're serving static assets globally or streaming media at scale.

2.💡Message Queue:

Decouples services by letting them communicate asynchronously through a queue (like RabbitMQ or Kafka).

➡️ Useful when: You need reliable communication between microservices or handling traffic spikes.

3.💡Rate Limiter:

Controls the number of requests a user or service can make within a time window.

➡️ Useful when: You're protecting APIs from abuse or managing resource allocation.

4.💡Circuit Breaker:

Prevents cascading failures by stopping requests to a failing service and allowing it time to recover.

➡️ Useful when: You're building resilient microservices that depend on external APIs.

5.💡Database Sharding:

Splits a database into smaller pieces (shards) distributed across multiple servers.

➡️ Useful when: Your database is too large for a single server or you need horizontal scaling.

6.💡Caching Layer:

Stores frequently accessed data in memory (Redis, Memcached) to reduce database load.

➡️ Useful when: You're optimizing read-heavy applications or reducing latency.

7.💡API Gateway:

A single entry point that handles routing, authentication, and rate limiting for all your microservices.

➡️ Useful when: You're managing multiple services and need centralized control.

8.💡Reverse Proxy:

Sits in front of servers and forwards client requests, adding security, caching, and load balancing.

➡️ Useful when: You need SSL termination, compression, or hiding server architecture.

9.💡Event-Driven Architecture:

Services react to events rather than direct calls, enabling loose coupling and real-time processing.

➡️ Useful when: Building real-time analytics, notifications, or audit logging systems.

10.💡CQRS:

Separates read and write operations into different models, optimizing each for its specific purpose.

➡️ Useful when: You have complex domains with different read/write performance needs.


What did I miss?

Solving system design problems is HARD.

1 mega-template to solve any problem:


Approaching system design problems can be harder than coding problems.

For one reason: ambiguity.

13 concepts to use when solving a system design problem:


0. Load Balancer:

A load balancer efficiently distributes incoming network traffic across multiple servers to ensure no single server bears too much load.

1. API Gateway:

An API Gateway acts as a single entry point for all client requests, providing a unified interface to various microservices.

2. Cache:

Caching stores recently accessed data for speedy retrieval, reducing the load on databases and improving response times.

3. CDN:

A Content Delivery Network (CDN) stores static content closer to users to minimize latency and speed up content delivery.

4. Distributed File Storage:

Distributed file storage ensures data is redundantly stored across multiple locations for fault tolerance and easy access.

5. Search Index:

Using a search index like Elasticsearch allows for scalable and fast search capabilities across large datasets.

6. Notification Service:

A notification service handles the delivery of messages or alerts to users across different platforms and devices.

7. Data Processing Systems:

Data processing systems like Hadoop and Spark analyze large volumes of data, enabling scalable and complex computational operations.

8. Distributed Scheduler: 

A distributed scheduler orchestrates and manages the execution of distributed tasks across various computing resources.

9. Metadata Server:

The metadata server stores information about the structure of data, enabling efficient access and management.

10. Block Server:

Block servers manage the storage blocks in distributed file systems, which helps in data replication and retrieval.

11. Distributed Logging:

Distributed logging collects logs from various services in a centralized manner for monitoring and troubleshooting.

12. Replication and Partitioning:

Replication ensures data availability and durability, while partitioning helps in scaling databases by distributing data across different nodes.


Reference (aff.):

🔥 Grokking the system design interview: 
https://lnkd.in/dcMM8sZM


What did I miss?


~~~

👉🏻 Join 50,001+ software engineers getting curated system design deep dives, trends, and tools (it's free):
➔ https://lnkd.in/dkJiiBnf

~~~

If you found this valuable:

👨🏼‍💻 Follow Alexandre Zajac
🔖 Bookmark this post for later
♻️ Repost to help someone in your network

You are what you eat.

10 brain foods to grow as an engineer:


0. Why Clock Synchronization Makes Engineers Lose Sleep:
► https://lnkd.in/gkDbWARS

1. How ChatGPT Actually Stays on Track (by Neo):
► https://lnkd.in/ggNMHDuV

2. How to Build Internal AI Agents That Actually Work:
► https://lnkd.in/gkJKyQnm

3. Scaling LLMs to Larger Codebases:
► https://lnkd.in/gWwfibJQ

4. Connection Pooling Finally Explained in 6 Minutes (by Nikki):
► https://lnkd.in/gkx6eNSG

5. 5 Engineering Dogmas It's Time to Retire (by Anton):
► https://lnkd.in/gYTdTmap

6. The System That Eliminates Interruptions for 10x Engineers (by Francisco):
► https://lnkd.in/g65_i6jf

7. Why Logging Sucks (And Everyone Knows It):
► https://loggingsucks.com/

8. Internal Dev Tooling at Meta (from Gergely):
► https://lnkd.in/g_A_BxFd

9. The Future of Software Development Is Software Developers:
► https://lnkd.in/gvzpK2CM


I am packaging the other deep dives and trends in today's issue of Hungry Minds.

Join 50,001+ software engineers from big tech to startups here: https://lnkd.in/dkJiiBnf


What are you reading this week?


#softwareengineering #systemdesign #programming

Krutam Hathi is an ML enthusiast who is always curious about how systems scale. He believes that everything happens for good, and he's always looking for opportunities to collaborate with others. Currently, working as a Software Engineer at Microsoft Hyderabad, where he has worked on three different projects over the span of a year. 

One of the projects, Digital Tech Link, is an application that brings global IT support to users' palms. He built it from scratch using C# and Azure, and it's now used by approximately 500K Microsoft employees.

He has also authored a publication on the identification of performance improvement approaches of Virtual Labs experiment using Google Analytics. 

If you're interested in collaborating or just want to chat around, feel free to DM.

Krutam Hathi

I help brands go viral without dancing on instagram! SDE@Cohesity | Ex-Microsoft

Ever seen a monkey scratching its back?
That’s basically me every winter.

Severely dry skin. Constant itching. Moisture gone by noon.
I tried everything - Instagram DIY hacks, random creams, coconut oil (yes, that trick). Nothing really worked.

Then we planned a trip to Switzerland. Sub-zero temperatures. Snow everywhere. And I was genuinely worried if Indian winters mess up my skin, Swiss winters would destroy it.

My wife suggested we try sunscreen and moisturiser from @Aqualogica for both of us. Honestly, I didn’t expect much. But here’s the surprising part.

Ten days of snow, long walks in freezing weather and
👉 zero itching
👉 zero dryness
👉 just… normal skin

For the first time, winter wasn’t uncomfortable.

Sharing this simply because when something works this reliably, it deserves to be talked about.

Kudos to the team at Aqualogica™ and Ghazal Alagh for building products that actually show up when it matters.

Curious - anyone else battle winter skin? What’s worked for you?

𝗞𝗮𝗿𝗻𝗮 𝘄𝗮𝘀 𝗯𝗲𝘁𝘁𝗲𝗿 𝘁𝗵𝗮𝗻 𝗔𝗿𝗷𝘂𝗻𝗮 𝗮𝘁 𝗮𝗿𝗰𝗵𝗲𝗿𝘆. 𝗬𝗲𝘁 𝗔𝗿𝗷𝘂𝗻𝗮 𝗸𝗶𝗹𝗹𝗲𝗱 𝗞𝗮𝗿𝗻𝗮.

I grew up hearing Mahabharata stories. But this one truth hit me differently as I got older.

Karna had the skill. The dedication. He trained harder than anyone. Overcame every disadvantage life threw at him. He was a better archer than Arjuna.

But he lost.

Not because he was not a good warrior, but he chose the wrong side.

Here's the lesson I keep coming back to.

In every aspect of life, there will be people better than you. Smarter. More skilled. More talented. More connected.

That's just reality.

But skill alone doesn't determine outcomes. Your choices do. The side you stand on. The values you hold. The things you refuse to compromise.

Karna had everything except dharma.

Arjuna wasn't perfect either. But he had Krishna by him and Krishna is where dharma is. That made all the difference.

Focus on doing what's right. Because in the long run, dharma wins. Not always immediately. Not always obviously. But eventually.

GPT can totally replace software engineers.

Build a life your body can actually live.

We chase promotions, raises, titles, rarely considering the cost. All-nighters that wreck sleep, desk work that ruins posture, stress that shows up as exhaustion.

I always prioritized other stuff over my health. Until last year.

Started being physically active in 2025. Felt the difference immediately. Less stress. More energy. Actually enjoyed my life more.

So I took a resolution to be fitter in 2026.

Then last week, something unexpected arrived: an invitation to the Man Matters Active Club An actual note from the founders that said, "Progress isn't loud. It's consistent."

That line landed.

I've tried fitness routines before and never stuck with them. Too extreme, too disconnected from real life. This felt different, thoughtful and practical. Their Super blend nutrition powder had 25gms of protein and vitamins and minerals, quite useful when you want all of the things in one shake. 

This isn't about gym-bro culture. It's about building something sustainable.

Fitness isn't separate from success. It's what makes success sustainable. You can earn more, but if your body's breaking down, what are you really winning?

My goal for 2026: hit the gym three times a week. Not to chase a look, but so my body can support the life I'm building.

Money matters. Health matters just as much. They're partners, not competitors.

So yes, I'm joining the Active Club. Small habits. Building a routine that actually sticks.

Tagging a few people who should join this: Akash Keshri Ashish Misal

What's stopping you from prioritizing fitness? Time, energy, something else?

#ManMattersActiveClub #Fitness #Health #2026Goals #Wellness

𝗧𝗿𝗶𝗲𝗱 𝗶𝗻𝘀𝗲𝗿𝘁𝗶𝗻𝗴 𝟭𝟬 𝗺𝗶𝗹𝗹𝗶𝗼𝗻 𝗿𝗲𝗰𝗼𝗿𝗱𝘀 𝗶𝗻𝘁𝗼 𝗮 𝗣𝗼𝘀𝘁𝗴𝗿𝗲𝗦𝗤𝗟 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲. 𝗧𝗼𝗼𝗸 𝗳𝗼𝗿𝗲𝘃𝗲𝗿. 𝗧𝗵𝗲𝗻 𝘁𝗿𝗶𝗲𝗱 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝗶𝗻 𝗖𝗮𝘀𝘀𝗮𝗻𝗱𝗿𝗮. 𝗗𝗼𝗻𝗲 𝗶𝗻 𝗺𝗶𝗻𝘂𝘁𝗲𝘀.

Here's what I didn't understand back then.

PostgreSQL uses B-Trees. Every write updates data in place. Lots of random disk I/O. Great for reads. Slow for massive writes.

Cassandra uses LSM Trees (Log-Structured Merge Trees). Completely different approach.

Instead of updating data in place, LSM Trees do this:

* Buffer writes in memory.
* Write to disk sequentially (super fast)
* Merge data later in the background.

Basically, they convert random writes into fast sequential writes.

That's why databases like Cassandra, HBase, RocksDB handle massive write loads so well. Logs. Metrics. Events. Anything write-heavy.

But here's the trade-off: writes are blazing fast, but reads get complex. LSM trees require Bloom filters, compaction, and heavy caching to maintain efficient reads.

In many ways, SQL vs NoSQL comes down to B-Tree vs LSM Tree. Different data structures for different workloads.

Once I understood this, database choices became way clearer.

Not "which database is best?" but "which database fits my workload?"

Write-heavy (logs, analytics, time-series)? LSM-based databases. Read-heavy with complex queries? B-Tree databases.

We’re amazing at spotting what’s broken.

Twitter threads, Reddit rants, dinner table debates.
India is basically one giant feedback form beacuse India wants to grow.

But to get that groth, we need companies solving for what matters. 

For the foudner community, it is choosing a problem that deserves your next 5 years.
That’s the insight I loved in Razorpay’s Fix My Itch.

Instead of throwing more “startup ideas” at founders,
They built a living, breathing bank of real problems.

The thought is sharp, but what really stood out to me is the execution.

The way it’s designed, structured, and brought together makes it feel less like a genuine tool for builders.
Payments were once an itch. Razorpay scratched it.
Logistics was an itch. Delhivery scratched it.

The next wave of startups will come from founders who start here.

Worth exploring if you’re figuring out what to build:
https://lnkd.in/ddKveZmb

Founders, I’m curious - how did you know your problem was the right one?

In the below cpp code, will we hit a race condition in function B?

Assuming functionA is being called by multiple threads and not just one.

Comment your answer.

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