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🍔🧠 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

🗞️ Tech and AI Trends

👨🏻‍💻 Coding Tip

  • Use Docker's --mount instead of -v for clearer volume management

Time-to-digest: 5 minutes

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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.

ARTICLE (docs-or-it-didnt-happen)
Why Documentation Fails Developers

GITHUB REPO (zoom-zoom-multiplayer)
Multiplayer at the speed of light

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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.

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.

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.

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.

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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See you in a week — Alex.

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