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  • 🍔🧠 Inside Apple Pay: The Secrets Behind 41 Million Daily Transactions

🍔🧠 Inside Apple Pay: The Secrets Behind 41 Million Daily Transactions

PLUS: 30 System Design Concepts 🧠, Simplifying MCP 📚, Visual Algorithms Cheat Sheet 📊

Author

Alex Zajac
March 31st, 2025 • Time-to-digest: 11 minutes

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Happy Monday! ☀️

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📚 Software Engineering Articles

🗞️ Tech and AI Trends

👨🏻‍💻 Coding Tip

  • torch.compile for fast inference with PyTorch

Time-to-digest: 5 minutes

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Apple Pay is handling 41 million secure transactions a day. That’s wild! Apple’s approach is strong because they don’t store your credit card info on the iPhone or their servers. That also means that you don’t need the internet to pay.

The challenge: 
Creating a payment system that's both super convenient (just tap!) and ultra-secure while working offline and protecting sensitive financial data.

Implementation highlights:

  • Credit card details are never stored on the iPhone or Apple servers. Instead, a unique Device Account Number (DAN) is generated and stored in a secure chip.

  • Biometric verification (Face ID/Touch ID) happens locally in the secure enclave, never leaving your device

  • Each transaction creates a unique one-time cryptogram combining DAN and transaction details, making replay attacks impossible

Results & Learnings:

  • Speed + Security: Apple Pay is almost frictionless because the crucial data is stored in specialized hardware and verified with short-lived cryptograms.

  • Zero Exposure: Your real card number stays hidden, minimizing risk of theft.

  • Works Anywhere: Works offline thanks to clever use of secure elements and NFC

Fun fact: Your iPhone is probably more secure than your physical wallet (unless your wallet is made of vibranium 😉)

LUC #79: Breaking Down Stateful vs Stateless Design

Plus, core principles of solution architecture design, 6 types of API testing explained, and how AI speeds up the software development cycle

System Design was HARD until I Learned these 30 Concepts

System Design can feel overwhelming especially when you're just starting out and don’t know where to begin.

How to Design a Good On-Call 🚨

Everything you need to know about rotations, with lessons from Netflix, Dropbox, Intercom, and Google.

MCP (Model Context Protocol): Simply explained in 5 minutes

What MCP is, how it can save you time, and how it works behind the scenes

What high performers know about doing hard things

The mindset, habits, and brain science behind pushing through

Selling isn’t just for sales: Why engineers and managers do it too

We will break down how important sales is for success in our roles as engineers and managers + 5 tips on how to become better at selling!

Turning 30: 5 Things My 20s Taught Me

Last weekend I turned thirty — the age many people seem to dread. Maybe surprisingly, I largely didn’t feel the existential angst that seems so common. Instead, I mostly felt thankful to see thirty at all, as well as a deep appreciation for all the important lessons that my twenties taught me. Today I’d like to share five of the most important lessons the last ten years of my life taught me.

Visual-Focused Algorithms Cheat Sheet

A visual-focused review of some key practical algorithms used in the real world.

ESSENTIAL (typescript zoom zoom)
Trends #7: TypeScript is getting 10x faster!

GITHUB REPO (awesome-llm-party)
awesome-llm-apps

ARTICLE (reacting to trends)
React Trends in 2025

ARTICLE (types are friends, not foes)
Don't Be Afraid Of Types

ARTICLE (react library secrets)
Common React libraries architecture

ARTICLE (github action detective)
Whose code am I running in GitHub Actions?

ARTICLE (next.js or next mess?)
You should know this before choosing Next.js

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Brief: Perplexity aims to rebuild TikTok in America with a transparent algorithm, enhanced AI infrastructure, and a focus on trustworthy content, transforming it into a platform for creativity and knowledge discovery.

Brief: DeepSeek's V3-0324 release enhances reasoning performance and tool-use capabilities, now available under the MIT License with open-source weights.

Brief: Amazon's new AI-driven 'Interests' feature helps users discover personalized products by continuously scanning for items that match their specific shopping needs.

Brief: Google unveils Gemini 2.5, showcasing advanced reasoning capabilities and top performance on benchmarks, setting a new standard in AI technology.

Brief: Open source developers are employing creative strategies to protect their work from AI crawlers, showcasing a blend of innovation and defiance in the face of automated threats.

This week’s coding challenge:

Build Your Own Shell

Real-world proficiency projects are designed for experienced engineers. Develop software craftsmanship by recreating popular devtools from scratch.

This week’s tip:

In Python, when using PyTorch for deep learning, you can leverage torch.compile() to optimize model performance by compiling the model into an optimized computation graph, reducing overhead and improving execution speed, especially for complex models.

Wen?

  • Performance-Critical Training: Speeds up training loops for large models like transformers or CNNs by reducing Python overhead and optimizing computation.

  • Inference Optimization: Enhances inference speed on production workloads, particularly when deploying models on resource-constrained environments.

  • Complex Graph Operations: Ideal for models with intricate layer interactions or dynamic control flow, where compilation can fuse operations efficiently.

"When one door of happiness closes, another opens, but often we look so long at the closed door that we do not see the one that has been opened for us."
Helen Keller

That’s it for today! ☀️

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Thanks for spending part of your Monday morning with Hungry Minds.
See you in a week — Alex.

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