🍔🧠 Inside Apple Pay: The Secrets Behind 41 Million Daily Transactions
PLUS: 30 System Design Concepts 🧠, Simplifying MCP 📚, Visual Algorithms Cheat Sheet 📊
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📚 Software Engineering Articles
Breaking down stateful vs stateless design for better architecture decisions
Master system design with these 30 essential concepts
Learn to build a robust on-call process that won't burn out teams
CPU architecture concepts every developer must understand
Critical insights before choosing Next.js for your project
🗞️ Tech and AI Trends
Gemini 2.5 release brings groundbreaking improvements to Google's AI
Open source developers fight back against AI crawlers
Amazon's AI-powered shopping introduces a personalized interests feature
👨🏻💻 Coding Tip
torch.compilefor fast inference with PyTorchTime-to-digest: 5 minutes
How Apple Pay Handles 41 Million Transactions a Day Securely 💸
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 👀)
Breaking Down Stateful vs Stateless Design
System Design was HARD until I Learned these 30 Concepts
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How to Design a Good On-Call 🚨
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and
MCP (Model Context Protocol): Simply explained in 5 minutes
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What high performers know about doing hard things
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Turning 30: 5 Things My 20s Taught Me
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Visual-Focused Algorithms Cheat Sheet
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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 (cpu knows best)
Hardware-Aware Coding: CPU Architecture Concepts Every Developer Should Know
ARTICLE (ai: the new sidekick)
AI for Software Engineering is Just Another “Paradigm Shift”
ARTICLE (react library secrets)
Common React libraries architecture
ARTICLE (github action detective)
Whose code am I running in GitHub Actions?
ARTICLE (editor speedrun)
How we made our rich text editor load faster
ARTICLE (next.js or next mess?)
You should know this before choosing Next.js
Want to reach 170,000+ engineers?
Let’s work together! Whether it’s your product, service, or event, we’d love to help you connect with this awesome community.
📱 Rebuilding TikTok: A Vision for an American Future (4 min)
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.
🚀 DeepSeek Unveils V3-0324 with Enhanced Reasoning and Open-Source Models (2 min)
Brief: DeepSeek's V3-0324 release enhances reasoning performance and tool-use capabilities, now available under the MIT License with open-source weights.
🛍️ Amazon Unveils AI 'Interests' Feature to Personalize Shopping Experience (3 min)
Brief: Amazon's new AI-driven 'Interests' feature helps users discover personalized products by continuously scanning for items that match their specific shopping needs.
🚀 Gemini 2.5 Launches with Enhanced AI Reasoning and Performance (3 min)
Brief: Google unveils Gemini 2.5, showcasing advanced reasoning capabilities and top performance on benchmarks, setting a new standard in AI technology.
🛡️ Open Source Developers Combat AI Crawlers with Ingenuity and Resolve (3 min)
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:
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 d
“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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