🍔🧠 Netflix's 3-Tier Cache System That Powers 700M Hours Daily
PLUS: Caching Explained in 5 Minutes 🗝️, AI Agents Starter Kit 🧰, Time Management for Engineers ⏳
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📚 Software Engineering Articles
Master PostgreSQL with this internal architecture deep dive
Netflix reveals how they accurately track eBPF flows
Microsoft releases beginner-friendly AI agents guide
Learn every caching strategy in just 5 minutes
Discover traits of exceptional programmers from real experiences
🗞️ Tech and AI Trends
Meta unveils Llama 4, pushing multimodal AI boundaries
ChatGPT now has persistent memory across all conversations
Samsung's AI robot Ballie arrives with Gemini integration
👨🏻💻 Coding Tip
Use Docker's
--mountinstead of-vfor clearer volume management
How Netflix delivers seamless streaming to 301M+ subscribers
Netflix powers 15% of global internet traffic and streams 94 billion hours of content globally. Their sophisticated distributed system architecture enables instant, uninterrupted video delivery to millions of concurrent viewers across 190 countries through cloud services and custom content delivery networks.
The challenge: Building a globally distributed system that maintains sub-second latency and high reliability while streaming HD video to millions of concurrent users, handling 15% of global internet traffic.
Implementation highlights:
Two-plane architecture: Control plane on AWS for browsing/recommendations, data plane (Open Connect) for video delivery
Custom CDN network: Purpose-built Open Connect servers placed directly within ISP networks
Microservices at scale: 700+ services enabling independent scaling and deployment
Chaos engineering: Tools like Chaos Monkey randomly break things to ensure resilience
Sophisticated data pipeline: Real-time analytics processing millions of events/second
Results and learnings:
Global reach: Successfully streams to 301M+ subscribers across 190 countries
Reliability: Maintains 99.99% availability despite massive scale
Optimization: Reduced bandwidth needs by 20-40% through per-title encoding
Netflix's architecture shows how thoughtful system design can solve incredibly complex technical challenges a massive scale. Their willingness to build custom solutions rather than accept off-the-shelf limitations demonstrates how architecture can be a competitive advantage.
Remember: Even Netflix's system isn't perfect. Just ask anyone who tried to watch the Tyson vs Paul fight! Sometimes the best architectures still buffer.
How PostgreSQL Works: Internal Architecture Explained
By
and
How to develop a Senior mindset
By
How to Test Software 🔍
By
and
3 Steps to Weather Tough Tech Markets
By
Time Management 101
By
Building Robust React Apps with Zustand and Immer
A pro-argument for Zustand and simmer (that I support 200%)
Every Caching Strategy Explained in 5 Minutes
Awesome visuals and explanation for caching strategies
The Best Programmers I Know
An opinionated guide to becoming a tier-1 programmer :)
ARTICLE (docs-or-it-didnt-happen)
Why Documentation Fails Developers
GITHUB REPO (ai-for-noobs)
10 Lessons to Get Started Building AI Agents
GITHUB REPO (zoom-zoom-multiplayer)
Multiplayer at the speed of light
ARTICLE (js-ninja-tricks)
Hiding elements that require JavaScript without JavaScript
ARTICLE (css-wizardry)
Item Flow, Part 1: A new unified concept for layout
ARTICLE (db-drama)
Database Protocols Are Underwhelming
ARTICLE (ai-test-fightclub)
Comparing AI Unit Test Generators on TypeScript: Tusk vs. Cursor Agent vs. Claude Code
ARTICLE (postgres-go-brrr)
PostgreSQL BM25 Full-Text Search: Speed Up Performance with These Tips
ARTICLE (netflix-spy-mode)
How Netflix Accurately Attributes eBPF Flow Logs
Want to reach 180,000+ engineers?
Let’s work together! Whether it’s your product, service, or event, we’d love to help you connect with this awesome community.
Brief: Meta launches Llama 4 Scout (17B params, 10M-token context) and Llama 4 Maverick (beats GPT-4o/Gemini 2.0), both open-weight and multimodal, while teasing the training of Llama 4 Behemoth, a 288B-parameter powerhouse.
🎨 Midjourney Unveils V7 Alpha: Smarter, Faster, and More Personalized AI Art (4 min)
Brief: Midjourney's V7 Alpha introduces model personalization by default, Draft Mode for rapid iteration, and Turbo/Relax modes, offering higher-quality textures, better coherence, and 10x faster rendering for AI-generated art.
⚡ Google Unveils "Ironwood" Chip to Slash AI's Hidden Inference Costs (3 min)
Brief: Google’s new Ironwood TPU shifts focus from training to AI inference, tackling soaring costs with double performance per watt and 6x more memory than its predecessor.
🤖 Samsung’s Ballie Gets Gemini AI Upgrade, Launching Next Month (2 min)
Brief: Samsung’s Ballie rolling companion will debut Gemini AI integration in May, promising smarter home automation and voice-controlled tasks in its latest revival.
🧠 ChatGPT Now Remembers All Past Chats by Default — Here’s What It Means (4 min)
Brief: OpenAI upgrades ChatGPT’s memory to reference past conversations by default, raising privacy concerns but enabling more personalized responses for Plus and Pro users.
This week’s coding challenge:
This week’s tip:
Use Docker's --mount flag instead of -v for explicit and more maintainable volume declarations. The --mount syntax is more verbose but self-documenting and supports additional options like bind-propagation and readonly.
Wen?
Microservice deployments: Clearly document volume requirements in deployment scripts with explicit mount options.
Shared development environments: Prevent accidental writes with readonly flags and ensure consistent mount behavior across teams.
Production systems: Better control over volume properties and easier debugging of mount-related issues.
“If you don't read the newspaper, you're uninformed. If you read the newspaper, you're misinformed.”
Mark Twain
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