What I'm Reading This Week (2024/10.13-10.19)

Table of Contents

• Trends
• What I’m Reading This Week (2024/10.13-10.19)
  • 1. SLUM: The Shadow Library Uptime Monitor
  • 2. kedro: A Development Framework for Team Data Science Projects
  • 3. Compare Performance and Ratings of Desktop or Laptop Processors, GPUs, or System-on-Chips (SoCs)
  • 4. Algorithms and Data Structures Implemented in Go for Beginners

Trends

This week, JD.com, a leading e-commerce company in China, faced a public relations crisis and a run on JD Finance due to their choice of controversial spokesperson “Yang Li”. Once the incident has fully unfolded, I will write a comprehensive report based on the materials I have continuously collected and followed up on.

What I’m Reading This Week (2024/10.13-10.19)

Good morning, this is the 3rd week of October 2024.

1. SLUM: The Shadow Library Uptime Monitor

• Monitors the operational status of shadow libraries worldwide.
• Greatness needs no further explanation.

2. kedro: A Development Framework for Team Data Science Projects

• Kedro is an open-source Python framework for creating reproducible, maintainable, and modular data science code.
• Key features:
  • Modular architecture, separating data processing, feature engineering, model training, and other steps into independent modules.
  • Data catalog, centrally managing read and write operations for various data sources.
  • Configuration management, using YAML files to manage project configurations for different environments.
  • Dependency management, using Directed Acyclic Graphs (DAG) to manage data processing workflows.
  • Version control, built-in versioning for datasets and ML models.
  • Visualization tools, providing data flow visualization.
• Suitable for building production-grade data science projects, standardizing team workflows, rapid prototyping, and large collaborative projects.
• Advantages include improving code quality, accelerating development and deployment, simplifying team collaboration, and facilitating project documentation.
• Particularly suitable for large-scale data science projects requiring long-term maintenance and team collaboration.

3. Compare Performance and Ratings of Desktop or Laptop Processors, GPUs, or System-on-Chips (SoCs)

• Users can select different processor models for comparison.
• Provides various performance metrics such as single-core performance, multi-core performance, gaming performance, etc.
• Data sources include well-known benchmarks like Geekbench, 3DMark, and others.

4. Algorithms and Data Structures Implemented in Go for Beginners

• In the computer world, my native language is Golang.
• Learning from algorithm tutorials written in foreign languages was really challenging at the time. Pseudocode can explain the process, but it made it painful for me to write actual code.