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Glossary.md

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shapes.add(new Rectangle()); // We can add a Rectangle, same reason
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}
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```
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1. 9 Clean Code Principles To Keep In Mind: [ref](https://blog.bytebytego.com/p/ep162-9-clean-code-principles-to)
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🔹 **Meaningful Names:** Use clear, descriptive names.
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🔹 **Small Functions:** Functions should do one thing well.
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🔹 **Avoid Duplicates:** **DRY** (Don’t Repeat Yourself).
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🔹 **Readable Code:** Code should be easy to read and understand.
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🔹 **Single Responsibility Principle:** One class or function should have only one reason to change.
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🔹 **Consistent Formatting:** Use a uniform style for indentation, spacing, etc.
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🔹 **Error Handling:** Handle errors gracefully and explicitly.
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🔹 **Write Tests:** Tests improve code reliability.
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🔹 **Refactor Regularly:** Keep improving code to avoid decay.
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1. Domain-Driven Design (DDD): [ref](https://blog.bytebytego.com/p/domain-driven-design-ddd-demystified)
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🔹 **Focus on the Core Domain:** Identify and model the essential part of the business.
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🔹 **Ubiquitous Language:** Create a shared language between developers and domain experts.
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🔹 **Bounded Contexts:** Divide the system into explicit boundaries where a model applies.
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🔹 **Entities and Value Objects:** Entities have identity; value objects are immutable.
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🔹 **Aggregates:** Group related entities to maintain consistency.
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🔹 **Domain Events:** Capture important business events explicitly.
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🔹 **Repositories:** Provide a way to retrieve and persist domain objects.
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🔹 **Continuous Collaboration:** Domain experts and developers work closely.

README.md

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- [Engineering blog](#engineering-blog)
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- [Computer Science Books](#computer-science-books)
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- **Section 3**: [Computer Science Papers](#computer-science-papers)
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- [Computer Science Papers Every Developers Should Read](#computer-science-papers-every-developers-should-read)
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- [Computer Science Papers Every Developers Should Read](#computer-science-papers-every-developers-should-read-ref)
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- [Distributed Systems!](#distributed-systems)
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- [25 Papers That Completely Transformed the Computer World](#25-papers-that-completely-transformed-the-computer-world)
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- [25 Papers That Completely Transformed the Computer World](#25-papers-that-completely-transformed-the-computer-world-ref)
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- **Section 4**: [Data Science (ML/NN)](#data-science-mlnn)
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- [Free e-books](#free-e-books)
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- [Free e-books](#free-ebooks-for-ml-data-science--ai-ref)
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- [GitHub](#github)
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- **Section 5**: [Terminology and Comparisons](#terminology-and-comparisons)
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## Computer Science Papers
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### Computer Science Papers Every Developers Should Read
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- [ref](https://x.com/milan_milanovic/status/1747167493553062398?s=20)
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### Computer Science Papers Every Developers Should Read: [ref](https://x.com/milan_milanovic/status/1747167493553062398?s=20)
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1. [On the Criteria To Be Used in Decomposing Systems into Modules (1972)](https://win.tue.nl/~wstomv/edu/2ip30/references/criteria_for_modularization.pdf): D.L. Parnas
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2. [An Axiomatic Basis for Computer Programming (1969)](http://sunnyday.mit.edu/16.355/Hoare-CACM-69.pdf): C.A.R. Hoare
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- Here's a reading list of 70+ Distributed Systems papers *mostly from conferences in just last 2 years*! [70+ Distributed Systems papers](https://docs.google.com/document/d/1XX5ksgLVnkPE-dfeVDZPlv5NMbgxJqWmi13YEQt1t1o/edit) [Jan 2024]
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### 25 Papers That Completely Transformed the Computer World
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- [ref](https://blog.bytebytego.com/p/ep111-my-favorite-10-books-for-software) [May 2024]
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### 25 Papers That Completely Transformed the Computer World: [ref](https://blog.bytebytego.com/p/ep111-my-favorite-10-books-for-software)
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1. [Dynamo](https://www.allthingsdistributed.com/files/amazon-dynamo-sosp2007.pdf): Amazon’s Highly Available Key Value Store
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2. [Google File System](https://static.googleusercontent.com/media/research.google.com/en//archive/gfs-sosp2003.pdf): Insights into a highly scalable file system
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## Data Science (ML/NN)
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### Free eBooks for ML, Data Science & AI > [ref](https://newsletter.theaiedge.io/p/30-free-machine-learning-e-books)
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### Free eBooks for ML, Data Science & AI: [ref](https://newsletter.theaiedge.io/p/30-free-machine-learning-e-books)
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##### Machine Learning & Deep Learning
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