Directly design 30 systems from Netflix, Tesla, Meta, and Tinder within 6 weeks,
This is a course where you can complete everything, including an open-source portfolio, all at once.
1. Experience designing 30 real-world system designs from Big Tech
- Netflix, Tesla FSD, Meta, Spotify, CI/CD pipelines, etc.
- Practice distributed systems, DB, cache, MQ, disaster recovery, and scaling at an actual interview level
2. Full design from your own service architecture to DB and scaling
- SLO Quantification → Requirements Definition → Architecture → Data Model → Scaling Plan
- Templates will be provided for the final project so that it can be included in your portfolio/resume.
3. Contribute a PR to an open-source AI coding agent currently in operation
Download to the Rust-based CLI agent (Snailer)
Send at least one or more PRs, and
Upon merging your first PR, you will receive Collaborator permissions + practical OSS experience that you can include on your resume.
Silicon Valley Interview Revealed
I conducted an interview at Stanford with the author of the AI newsletter 'Noonoji', who is an alumnus of JoCoding. We discuss how I am applying the system design from this lecture to the startup I am currently running in Silicon Valley.
👉 Full EO Article Interview: The Secrets of Irreplaceable People Learned in Silicon Valley (Developer Edition)
Surviving in Silicon Valley: The Story of a Developer Who Grew from Junior to Senior in Just One Year https://eopla.net/magazines/35688
Threads
System Design Secrets of Silicon Valley Big Tech. Learn distributed systems, DB, scaling, and interview preparation in a practical way using cases like Tesla FSD, Netflix streaming, and Meta infrastructure.
Finally, you will design your own service and complete the entire process from SLO quantification → architecture → data model → scaling.
Outstanding works will be featured as references on Thread (3.8K+) / Substack (420+), serving as powerful evidence for your portfolio/resume. If you wish, your LinkedIn/resume link can be included → providing direct evidence to recruiters.
Through my experience interviewing with various US Big Tech companies such as Apple, Meta, Tesla, and Snapchat, I deeply realized that global companies in the US are groups that handle terabytes or more of data while constantly obsessing over the user. Consequently, the barriers for these US global companies to evaluate resumes from 1,000 or even 10,000+ applicants have risen, making algorithm and system design capabilities more critical than ever.
Additionally, those currently working on side projects alone will be able to take the initiative in determining what kind of system design is needed to create a strong portfolio.
I have created this hands-on lecture based on real-world systems, drawing from my experience receiving small-group training from engineers at various Big Tech companies like Apple, Google, Netflix, Meta, and OpenAI. This included pair programming and discussion-based classes with talent educated in the US, India, and China, covering curricula such as system design, algorithms, frontend, DB design, and OOD design, as well as my experience interviewing with companies like Apple, Tesla, and Meta. My vision and goal for this lecture is to share this with you so that, amidst the current IT war between the US and China for AI supremacy, you can use this course as a stepping stone. I want to empower you to become engineers who don't just follow recipes, but truly understand the core essence and can implement new things, standing shoulder-to-shoulder with engineers from the US and China.
- Global Service Design
- Netflix, Tesla FSD, Spotify, CI/CD pipelines, etc.
Based on real-world cases, we will dive deep into "how it was designed and what trade-offs were chosen."
- Database & Object-Oriented Design
- RDBMS vs NoSQL, sharding/indexing, cache strategies, and OOD design principles
Practice with real-world examples.
- Machine Learning System Design (NEW)
- Using a Tesla FSD-style pipeline (Training → Deployment → Observation) as an example
Explains the structure of deploying ML models into actual production.
- Large-scale traffic scaling & disaster recovery design
- LB, CDN, MQ, Redis/Cache, Blob(S3), WebSocket, Proxy, Telemetry, etc.
Structure it to a level that can be explained in an interview.
- Continuous Q&A / 1 year of additional learning materials support
- You can freely ask questions about system design even after completing the course.
- Free 1-year membership for scenario and algorithm content to prepare for interviews at FAANG and US/domestic companies
Provides actual interview questions and strategies for FAANG, US AI companies, and top Korean tech companies (Naver, Kakao, Line, Coupang, Baemin, Danggeun, Toss).
Providing practical, scenario-based system design and algorithm content
Recommended for the following people
- Those who are preparing for a job or career change in Korea and want to reach the Silicon Valley Big Tech system design interview level
- Job seekers/junior developers who lack actual service design/operation & open-source contribution experience to include on their resumes
- Those preparing for a Lead/Architect position where they must design a new service from scratch
- Those who have only thought, "I want to try applying to a US/global company at least once," but haven't been able to take action.
- Those who want to challenge an algorithm Leetcode rating of 1800+ - Free 1-year membership for special learning materials promotion provided
There are moments like this when preparing for interviews.
You can solve the problems to some extent, but for some reason, your explanations get tangled and your designs falter during the actual interview.
The newly added Code Craft Interviews is a feature designed to help you practice OOP / API-centric problems in an actual interview flow, rather than simple LeetCode-style problems that might appear in real big tech interviews.
It is not just a method of submitting the correct code.
You will be trained to think and answer in the following order, just like in a real interview.
Requirements Gathering
API Design
Happy Path Implementation
Validation / Error Handling
Tests
Explaining Trade-offs
In other words, it is not "practice for solving problems," but rather
practice for thinking and explaining like a good engineer in front of an interviewer.
The problems are based on real-world scenarios in the style of DoorDash, Uber, Netflix, Spotify, Amazon, and Meta,
and are designed so that you can practice in Python, Java, Kotlin, or C++.
In addition to Code Craft Interviews, we also provide features such as
AI resume analysis reports, personalized position recommendations, algorithm solution code analysis,
and a feature to learn the design phase with AI by directly drawing diagrams for system design problems.
It is especially suitable for these types of people.
Those who can solve algorithm problems to some extent but struggle with practical coding interviews
Those who feel burdened by interviews where good code, design, and explanatory skills are all evaluated together
Those who want to train the thought process necessary for actual passing, going beyond simple problem-solving.
Rather than just being a service for solving many problems, I wanted to create a training environment where you can practice in a way that actually leads to passing.
And for those who purchased the April lecture,
we plan to prioritize providing a 1-month free trial benefit.
Coupon codes for April purchasers will be sent out in bulk via email during May.
Developers & Students
Developers/university students preparing for employment/career moves at big tech/global or domestic companies
Lead Developers & Architects
Leads/Architects who need to design new services from scratch
I'm curious about the design process.
Those who want to prove differentiated system design capabilities in interviews
- In interviews, you can define SLOs numerically and explain your design while pinpointing potential failure or bottleneck points.
- Based on more than 30 practical system design examples, you will be able to answer scalability and reliability design questions by incorporating trade-offs.
- You can document your own service from requirements → architecture → DB design → scaling plan and include it in your portfolio.
- Actual PR links/evidence of contributing to an open-source AI coding agent with 8.6K+ cumulative downloads
You can include this on your resume and LinkedIn.
- If desired, excellent designs will be featured as references on my Threads, Substack, and community channels, allowing them to be used in your resume portfolio.
Design your own service from start to finish as the final course project.
Outstanding designs will be featured as official references.
(We will ensure you can add this to your resume or portfolio)
Upon agreement, on the Thread channel (4.4K+) and Substack (paid subscription newsletter 460+)
It will be introduced as an excellent design reference.
If desired, your LinkedIn/resume link will also be promoted. (Including subscriber communities from major corporations such as Kakao and CJ)
Update: Silicon Valley Practical Application & Open Source Track
- Current cumulative downloads 13K+ - Currently operating a Rust-based CLI AI coding agent (Claude Code/Codex workflow).
- We are releasing the agent as a Public Repo so that students can make open-source contributions.
- An Open Source Contribution Guide lecture has also been added.
[1.25/2026 Update]
- We have released the Rust library for token compression used in AI coding agents as open source. This library is applied to actual services, and contributions from students are merged so that they can be reflected in portfolios or resumes. Below is the GitHub repository.
[3/2026 Update]
The Snailer multi-model coding agent is being released and distributed as an open-source GUI desktop app for Mac, serving over 8.6K+ users.
Career Benefits
- Once your first PR is merged, you will be granted Collaborator permissions (branch protection
, PR required) granted → Examples for resume/portfolio entries provided
- Secure demonstrable evidence (documents, diagrams, OSS contributions) that directly leads to employment or career transitions.
Please feel free to leave your questions in the Q&A. Your designs may even be cited as actual references!
I listened to the lecture carefully. The technical content was explained in a friendly and easy-to-understand way, so it felt like a skilled senior was teaching me. Personally, I had a few regrets. First, the sound was too low and the sound quality was not good, so it was difficult to listen to the lecture. There was a lot of explanation about the API specifications, but I felt it was unnecessary for me. Instead, I wish there had been a more in-depth explanation of system design methodology and scaling. In such cases, what components can be considered, what are the advantages and disadvantages of each (although it was explained in Section 2), and what problems and improvements might arise during actual use. Despite this, it was a lecture that I learned a lot from. The instructor said that he would continue to update the lecture and lecture materials, and that he would improve the lecture quality by using a high-quality microphone in the future, so I look forward to it.
It was great to learn how to design systems to solve complex problems through various corporate cases! It was fun to learn concepts with examples, and I could understand in which situations the concepts would be used. Visualizing the design with Excalidraw was also a great help in learning! Thank you for the great lecture.
This is the first time I've had a good design lecture in Korea. The content is really good. Please record the next lecture with the audio a little louder. I look forward to other good lectures. Be healthy and happy.
The lecture content is really good, but... you say "uh" too much, so it's really distracting to listen to the lecture.
The subject of the lecture itself is very good, but on the contrary, the lecture as a whole feels very unrefined. The specific details are as follows: - The lecture lacks unity as it goes from the first to the last lecture. You will think, why didn't you do it this way before? - It is a raw lecture with no editing at all. There are quite a few places where the speaker stutters or says "uh" or where the audio is blank for more than 5 seconds. When the audio is blank, you check to see if the network is disconnected, which distracts you from concentrating. - In particular, in the Meta System Design section, there is a constant buzzing sound throughout the lecture, and the instructor's voice sounds a little distorted. At least the Meta section should be re-uploaded. - Also, as a minor point, you can sometimes hear Bluetooth connection sounds during the lecture.
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