Backend Design Basics for AI - SpringBoot SNS Edition

Hello, students of AI-Powered Backend Design Fundamentals - SpringBoot SNS Edition!

Hello, students of AI Backend Design Basics - SpringBoot SNS Edition!

Today, I've brought a few tips to help you learn from the course more effectively.

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As mentioned in the course introduction, this course isn't about copying code line by line, but rather understanding "why was it designed this way?" Try implementing it yourself with an AI agent, but critically review the code generated by AI, asking yourself "Is this really well-written?"

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Key points to consider for effective learning from the lecture

Points to consider for effectively taking the course

1) Data Characteristics-Based Decision Making

The characteristics of session data (temporary, high read/write),
the characteristics of post data (permanent, complex queries),
the characteristics of media data (large files, frequent access),
the characteristics of timeline data (derived data, ultra-fast reads).

When selecting storage like in the example above, I recommend developing a sense for prioritizing data characteristics and choosing the appropriate storage accordingly.

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2) Trade-off Analysis

Every technical choice has pros and cons. Soft Delete allows recovery but makes queries complex, and Fanout on Write is fast for reads but costly for writes.
Please learn while understanding what you gain and what you give up.
And always keep this record. When you look back at this document after time passes, the accumulated decision records become a valuable asset.

Please study while understanding. And always keep this record. When you look at this document after years have passed, the accumulated decision records will become a valuable asset.

3) Scalable Architecture

It's good to start simple and expand when needed. It's best to avoid over-engineering.
However, here we'll also practice expanding. You need to learn how to scale as well. Please learn while appropriately balancing simplicity and complexity.

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4) Actively Utilize AI Agents

Try implementing what you learned in the lecture directly with an AI agent. You can make specific requests like "Create a follow service and API using Follow and FollowCount entities" or "Write logic to increase follow count using Atomic Update." Leave repetitive tasks (boilerplate code, test code) to AI, and focus on judging "whether this code is correct" and "if there's a better way." The more you use AI, the faster your skills will improve.

Review and Practice (Mission⭐️⭐️⭐️⭐️⭐️) Without review, memory retention rate is ~20-30% one day after learning, but with proper spaced repetition review, it can be maintained at over 70%

Review and Practice (Mission⭐️⭐️⭐️⭐️⭐️)

Research shows that without review, memory retention drops to ~20-30% one day after learning, but with properly spaced repetition reviews, it can be maintained at over 70%.
It's helpful to write down the "why?" of each chapter in your notes and try drawing the diagrams yourself.

There are research findings showing that retention can be maintained at 70% or higher with spaced repetition. It's good to write down the "why" of each chapter in your notes and try drawing diagrams yourself.

Additionally, I strongly encourage you to complete the missions in each chapter and build an SNS service (user, authentication, follow, post, media, timeline services) yourself. I highly recommend this.
When you build it yourself, unlike simply watching videos, the process of writing code directly, encountering errors, and connecting the flow will allow backend design to become internalized as experience rather than just knowledge, and this will become your valuable asset.

Through the process of writing code directly, encountering errors, and connecting the flow, backend design will become ingrained as experience rather than just knowledge, and this will become your valuable asset.

Through the process of writing code directly, encountering errors, and connecting the flow, backend design becomes internalized as experience rather than just knowledge, and this will become your valuable asset.

Please leave a review

I put a lot of time and effort into creating this course. Your sincere and detailed reviews are a great help to other students, essential for improving the course, and a huge source of encouragement to the instructor. ❤

If you didn't like the course, I'd appreciate it if you could contact me first before leaving a review. If you let me know what parts were unsatisfactory, I'll do my best to improve them or provide additional materials and help you.
My email: apiece.dev.ai@gmail.com

Please let me know if there are any areas you're not satisfied with, and I'll do my best to improve them, provide additional materials, or assist you in any way I can. My email: apiece.dev.ai@gmail.com

Thank you.

A Piece

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