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Reading Tracker

Overview

This tracker is designed to record my continuous learning journey by reading books, attending presentations and talks, following blogs and articles, and studying papers.

The component:

Domain: Various domains, mostly coming from Technology and Programming

Type: Book, Course, Website, Presentation, Blogs, Article, Paper

Tracker

The table of content

Datetime Title [URL]
2025
2025-12-11 Passing Makefile arguments to a command,
2025-12-10 Nobody Writes Clean Code. We All Just Pretend
2025-12-11 Amethyst's Python Conference slides
2024
2024-03-25 Go to Redowan's Reflections: Annotating args and kwargs in Python
2023
2024-11-04 Sebastian Ramiez shared about: Keeping an Open Source Mind

Note: Datetime is in the format of YYYY-MM-DD

Resources

The content of Postman is so fresh and worst to try: https://blog.postman.com/tag/product-updates/

Blog: pawamoy

Microservices Pattern:

Book: Textbook | Object Oriented in Python. [https://python-textbok.readthedocs.io/en/latest/Introduction.html#]

Book: Textbook | Python 101 [https://python101.pythonlibrary.org/intro.html]

Book - Introduction to Machine Learning with Python

Idea: https://www.pythonsheets.com/notes/python-sqlalchemy.html

Research Portal: Intel https://www.intel.com/content/www/us/en/developer/topic-technology/overview.html

Book: Textbook | Advanced Python https://python-course.eu/

Blog: OWASP Cheat Sheet Series: https://cheatsheetseries.owasp.org/index.html

Book

Course Status Level Expected Time Reference Link
[1] Docker basic concept Beginner 4 hours https://devopswithdocker.com/
[2] CS 329S: Machine Learning Systems Design Intermediate 2 weeks https://stanford-cs329s.github.io/syllabus.html
[3] Coursera: Data Engineering, Big Data, and Machine Learning on GCP Specialization Advanced 2 months https://www.coursera.org/specializations/gcp-data-machine-learning

  • Python Pandas Learn:

  • Pandas — Effective Python for Data Scientists