• 52 Weeks of Cloud

  • Written by: Noah Gift
  • Podcast

52 Weeks of Cloud

Written by: Noah Gift
Listen for free

  • Summary

  • A weekly podcast on technical topics related to cloud computing including: MLOPs, LLMs, AWS, Azure, GCP, Multi-Cloud and Kubernetes.
    2021-2024 Pragmatic AI Labs
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Mathematics Science
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Episodes
  • Technical Architecture for Mobile Digital Independence

    Technical Architecture for Mobile Digital Independence
    Feb 23 2025
    Technical Architecture for Digital IndependenceCore Concept

    Smartphones represent a monolithic architecture that needs to be broken down into microservices for better digital independence.

    Authentication Strategy
    • Hardware security keys (YubiKey) replace mobile authenticators
      • USB-C insertion with button press
      • More convenient than SMS/app-based 2FA
      • Requires backup key strategy
    • Offline authentication options
      • Local encrypted SQLite password database
      • Air-gapped systems
      • Backup protocols
    Device Distribution Architecture
    • Core Components:
      • Dumbphone/flip phone for basic communication
      • Offline GPS device with downloadable maps
      • Utility Android tablet ($50-100) for specific apps
      • Linux workstation for development
    • Implementation:
      • SIM transfer protocols between carriers
      • Data isolation techniques
      • Offline-first approach
      • Device-specific use cases
    Data Strategy
    • Cloud Migration:
      • iCloud data extraction
      • Local storage solutions
      • Privacy-focused sync services
      • Encrypted remote storage with rsync
    • Linux Migration:
      • Open source advantages
      • Reduced system overhead
      • No commercial spyware
      • Powers 90% of global infrastructure
    Network Architecture
    • Distributed Connectivity:
      • Pay-as-you-go hotspots
      • Minimal data plan requirements
      • Improved security through isolation
    • Use Cases:
      • Offline maps for navigation
      • Batch downloading for podcasts
      • Home network sync for updates
      • Garage WiFi for car updates
    Cost Benefits
    • Standard smartphone setup: ~$5,000/year
      • iPhone upgrades
      • Data plans
      • Cloud services
    • Microservices approach:
      • Significantly reduced costs
      • Better concentration
      • Improved control
      • Enhanced privacy
    Key Takeaway

    Software engineering perspective suggests breaking monolithic mobile systems into optimized, offline-first microservices for better functionality and reduced dependency.

    🔥 Hot Course Offers:
    • 🤖 Master GenAI Engineering - Build Production AI Systems
    • 🦀 Learn Professional Rust - Industry-Grade Development
    • 📊 AWS AI & Analytics - Scale Your ML in Cloud
    • ⚡ Production GenAI on AWS - Deploy at Enterprise Scale
    • 🛠️ Rust DevOps Mastery - Automate Everything
    🚀 Level Up Your Career:
    • 💼 Production ML Program - Complete MLOps & Cloud Mastery
    • 🎯 Start Learning Now - Fast-Track Your ML Career
    • 🏢 Trusted by Fortune 500 Teams

    Learn end-to-end ML engineering from industry veterans at PAIML.COM
    Show more Show less
    10 mins
  • What I Cannot Create, I Do Not Understand

    What I Cannot Create, I Do Not Understand
    Feb 22 2025
    Feynman's Wisdom Applied to AI LearningBackground
    • Feynman helped create atomic bomb and investigated Challenger disaster
    • Challenger investigation revealed bureaucracy prioritized power over engineering solutions
    • Two key phrases found on his blackboard at death:
      • "What I cannot create, I do not understand"
      • "Know how to solve every problem that has been solved"
    Applied to Pragmatic AI Labs CoursesWhat I Cannot Create
    • Build token processor before using Bedrock
    • Implement basic embeddings before production models
    • Write minimal GPU kernels before CUDA libraries
    • Create raw model inference before frameworks
    • Deploy manual servers before cloud services
    Learning Solved Problems
    • Study successful AI architectures
    • Reimplement ML papers
    • Analyze deployment patterns
    • Master optimization techniques
    • Learn security boundaries
    Implementation Strategy
    • Build core concepts from scratch
    • Move to frameworks only after raw implementation
    • Break systems intentionally to understand them
    • Build instead of memorize
    • Ex: Build S3 bucket/Lambda vs. memorizing for certification
    Platform Support
    • Interactive labs available
    • Source code starter kits
    • Multiple languages: Python, Rust, SQL, Bash, Zig
    • Focus on first principles
    • Community-driven learning approach
    Key Takeaway

    Focus on understanding through creation, leveraging proven solutions as foundation for innovation.

    🔥 Hot Course Offers:
    • 🤖 Master GenAI Engineering - Build Production AI Systems
    • 🦀 Learn Professional Rust - Industry-Grade Development
    • 📊 AWS AI & Analytics - Scale Your ML in Cloud
    • ⚡ Production GenAI on AWS - Deploy at Enterprise Scale
    • 🛠️ Rust DevOps Mastery - Automate Everything
    🚀 Level Up Your Career:
    • 💼 Production ML Program - Complete MLOps & Cloud Mastery
    • 🎯 Start Learning Now - Fast-Track Your ML Career
    • 🏢 Trusted by Fortune 500 Teams

    Learn end-to-end ML engineering from industry veterans at PAIML.COM
    Show more Show less
    5 mins
  • Rise of Microcontainers

    Rise of Microcontainers
    Feb 21 2025
    The Rise of Micro-Containers: When Less is More

    Podcast Episode Notes

    Opening (0:00 - 0:40)
    • Introduction to micro-containers: containers under 100KB
    • Contrast with typical Python containers (5GB+)
    • Languages enabling micro-containers: Rust, Zig, Go
    Zig Code Example (0:40 - 1:10)// 16KB HTTP server exampleconst std = @import("std");pub fn main() !void { var server = try std.net.StreamServer.init(.{}); defer server.deinit(); try server.listen(try std.net.Address.parseIp("0.0.0.0", 8080)); while (true) { const conn = try server.accept(); try handleRequest(conn); }}Key Use Cases Discussed (1:10 - 5:55)1. Edge IoT (1:14)
    • ESP32 with 4MB flash constraints
    • Temperature sensor example: 60KB total with MQTT
    • A/B firmware updates within 2MB limit
    2. WASM Integration (2:37)
    • Millisecond-loading micro-frontends
    • Component isolation per container
    • Zero initialization overhead for routing
    3. Serverless Performance (3:11)
    • Traditional: 300ms cold start
    • Micro-container: 50ms start
    • Direct memory mapping benefits
    4. Security Benefits (3:38)
    • No shell = no injection surface
    • Single binary audit scope
    • Zero trust architecture approach
    5. Embedded Linux (3:58)
    • Raspberry Pi (512MB RAM) use case
    • 50+ concurrent services under 50KB each
    • Home automation applications
    6. CI/CD Improvements (4:19)
    • Base image: 300MB → 20KB
    • 10-15x faster pipelines
    • Reduced bandwidth costs
    7. Mesh Networks (4:40)
    • P2P container distribution
    • Minimal bandwidth requirements
    • Resilient to network partitions
    8. FPGA Integration (5:05)
    • Bitstream wrapper containers
    • Algorithm switching efficiency
    • Hardware-software bridge
    9. Unikernel Comparison (5:30)
    • Container vs specialized OS
    • Security model differences
    • Performance considerations
    10. Cost Analysis (5:41)
    • Lambda container: 140MB vs 50KB
    • 2800x storage reduction
    • Cold start cost implications
    Closing Thoughts (6:06 - 7:21)
    • Historical context: Solaris containers in 2000s
    • New paradigm: thinking in kilobytes
    • Scratch container benefits
    • Future of minimal containerization
    Technical Implementation Note// Example of stripped Zig binary for scratch containerconst builtin = @import("builtin");pub fn main() void { // No stdlib import needed asm volatile ("syscall" :: [syscall] "{rax}" (1), // write [fd] "{rdi}" (1), // stdout [buf] "{rsi}" ("ok\n"), [count] "{rdx}" (3) );}

    Episode Duration: 7:21

    🔥 Hot Course Offers:
    • 🤖 Master GenAI Engineering - Build Production AI Systems
    • 🦀 Learn Professional Rust - Industry-Grade Development
    • 📊 AWS AI & Analytics - Scale Your ML in Cloud
    • ⚡ Production GenAI on AWS - Deploy at Enterprise Scale
    • 🛠️ Rust DevOps Mastery - Automate Everything
    🚀 Level Up Your Career:
    • 💼 Production ML Program - Complete MLOps & Cloud Mastery
    • 🎯 Start Learning Now - Fast-Track Your ML Career
    • 🏢 Trusted by Fortune 500 Teams

    Learn end-to-end ML engineering from industry veterans at PAIML.COM
    Show more Show less
    7 mins

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