U.S. Big Tech Frontend System Design in Practice: For Frontend Developers Who Don't Want to Remain Just Simple Implementers

The reason it is difficult for frontend engineers to increase their salary is not because they lack knowledge.

The problem is that many frontend developers
are still evaluated as "people who implement screens."

And in an era where AI writes code,
this evaluation is becoming increasingly dangerous.

Button implementation, card UI, API connection, CRUD screens, and simple state management are
already being rapidly caught up by AI.

In the future, the differentiator for frontend engineers will not be
“how quickly they can implement,”
but rather whether they can design complex product requirements into a system..

To reach a higher salary and a better position,
you must be able to decompose and design complex requirements into state, data flow, rendering strategies, performance, observability, and deployment structures.


This course trains your frontend system design thinking through actual Big Tech-style cases,
ranging from Threads feeds, Amazon shopping carts, Netflix streaming UI,
and Google Docs collaborative editing to Micro Frontends, performance optimization, and observability.

This course goes beyond simply implementing screens in frontend development; it covers how to structure and design complex service requirements from a frontend system design perspective.

Students will learn the mindset for designing large-scale UI architectures through cases such as Threads-style real-time feeds, Amazon-style product lists and shopping carts, Netflix/YouTube video streaming UIs, Google Docs collaborative editing UIs, SpaceX-style rocket part cost analysis platforms, and Figure AI-style robot operation dashboards.

In the course, you will learn step-by-step the core elements that must be considered in frontend system design, ranging from requirements analysis, rendering strategies, data flow, and state management to component structure, performance optimization, accessibility, security, testing, observability, and deployment strategies.

This course can be applied in fields such as US Big Tech, global startups, enterprise SaaS, real-time dashboards, operations consoles, observability platforms, and AI/robotics/space infrastructure UI. Beyond simple implementation skills, the goal is to develop the ability to design complex systems into interfaces that users can understand and operate.

The reason I planned this course is my belief that even in an era where AI writes more and more code, the ability to understand complex domains and translate requirements into design remains a powerful differentiator for developers—one that creates high added value.

This course was created to help students grow into frontend engineers who understand the entire product and system, rather than just developers who build screens, and to bring out the greatness within each student.

Recommended for these types of people

This is how you will change after taking the course

Before taking the course, your first thought when seeing a complex screen might be about the components.

But after taking the course, the questions you ask yourself will change first.

Instead of simply asking, "What component should I build?", you will start looking at it from the perspective of, "What structure should I design this product experience with so that it lasts long, operates quickly, and allows for tracking when problems arise?"

This difference creates the greatness and depth of the students, leading to roles and responsibilities where they can take on larger problems in practice.

What you will learn

1. This is a lecture that changes the evaluation standards for frontend developers.

If you are aiming for a higher salary and a better position as a frontend developer, simply having the ability to implement UIs faster is not enough.

The reason many frontend developers get stuck at a certain point in their growth is not because they lack skill.
It is because they are still seen by companies and interviewers as merely "someone who builds screens."

In an era where AI quickly generates components, forms, card UIs, and API connection code, this evaluation becomes even more dangerous.
With simple implementation skills alone, you will be more easily compared to others and pushed into lower-value roles.

A higher-level frontend engineer is not someone who simply builds screens, but someone who structures complex requirements.

You must be able to design everything from what states exist and where the source of truth for data lies, to which screens require SSR and which should be isolated with CSR, where race conditions occur, and which telemetry to use for tracking post-deployment issues.

This course was created specifically to meet those evaluation criteria.

This is not a lecture for memorizing more frontend knowledge, but a lecture that shifts your mindset so you can be evaluated as a frontend engineer who understands and designs the entire product and system.

2. Instead of memorizing theory, we understand it through reverse-engineering real-world service cases.

What matters in frontend system design is not knowing a lot of terminology.

What matters is that your questions change when you look at a complex screen.

Before the lecture, you might think like this.

“How should I divide this screen into components?”

But after the lecture, the questions change.

“What is the core user experience of this screen?”
“Which data must be absolutely accurate?”
“Which state is okay to be stale for a moment?”
“Is the source of truth for the state the server or the client?”
“How can we prevent slow responses from overwriting the latest UI?”
“What numbers should define performance?”
“If a problem occurs during operation, what data will be used to trace the cause?”

This course does not make you memorize difficult theories first.

It starts with real-world service examples such as the Threads feed, Amazon shopping cart, Netflix streaming UI, Google Docs collaborative editing, Micro Frontend, and Observability.

We first analyze the product experience from the user's perspective, then explain in reverse why state management, data flow, rendering strategies, performance optimization, and observability are necessary to make that experience stable.

Therefore, students will develop the ability to explain why such structures are necessary, going beyond simply "how to implement" them.

3. In the era where AI writes code, frontend is shifting toward Agentic UI

AI can already write a significant amount of frontend code.

Buttons, forms, card UIs, CRUD screens, API call code, and basic styling are being created faster and faster.

Therefore, simple implementation skills alone may weaken a frontend developer's competitive edge.

But there is an even bigger change.

AI is not just changing the way we develop; it is changing the UI structure of the product itself.

In traditional UI, users performed every step themselves.

They entered search terms, selected filters, checked the results, clicked buttons, and moved to the next screen.

But in Agentic UI, the user states the goal first.

"Analyze this problem."
"Compare this data."
"Process this task automatically."
"Report these results and recommend the next action."

Then, the AI interprets the goal, selects the necessary data and tools, and executes multiple steps.

The role of the frontend becomes even more important here.

You must show what the AI is doing.
Intermediate results must be expressed in a way that the user can understand.
Dangerous actions must require user approval.
Failed tool calls must be recoverable.
When AI results are uncertain, they should not be finalized as is, but presented in a reviewable state.

This lecture also covers Agentic UI design for these AI-native product experiences.

We will look into how to model Agent run states, how to display tool-calling UIs, how to handle streaming responses, how to design user approval flows, and how to represent AI-generated results through verifiable UIs.

In the AI era, the work of a frontend engineer is not decreasing, but rather changing.

Simple implementation may become more automated,
but the ability to design complex product experiences where AI and users work together is becoming increasingly important.

In line with those changes, this course helps frontend engineers grow beyond being simple UI implementers into engineers who design AI-native product experiences.

4. Train in frontend system design through real-world Big Tech-style cases

This course does not just explain abstract concepts.

We train frontend system design based on complex UI problems frequently encountered in real-world services.

In a Threads-style feed, it's not just about rendering a list of posts; it covers infinite scroll, deduplication, optimistic reactions, stale responses, real-time updates, and preserving the scroll position.

Amazon-style commerce doesn't end with just creating product lists and shopping carts.
It covers everything from guest cart merging, price changes, inventory validation, payment unknown states, idempotencyKey, to hydration mismatch.

In Netflix/YouTube-style streaming UIs, it doesn't just end at using the <video> tag.
It handles buffering, playback status, network quality, quality switching, and disaster recovery as state models.

In a Google Docs-style collaborative editing UI, it's not just about creating an input field; it's about handling the balance between local editing states and server synchronization, conflict resolution, real-time updates, latency, and consistency.

In Micro Frontends, you don't just learn how to split an app.
We also look at organizational deployment units, team responsibilities, the boundaries between shell and remote, shared dependencies, and design system consistency.

Through these cases, students will learn frontend system design not as mere theory, but as a mindset for solving real-world service problems.

5. State management is taught not as a library tutorial, but as a way to design states.

In this lecture, we will cover how to model complex UI states using the X-M method, going beyond the limitations of boolean state combinations.

The X-M (the "X-" will not be disclosed here) method is one of the approaches I discussed with Indian colleagues during a Big Tech fellowship program and while auditing a course at one of the UC universities through an acquaintance. I decided to add it to the curriculum because I believe it will be a useful method for documentation, collaboration, and design within a company.

But more importantly, it is about “what should be considered as state.”

A like button might not end with just a simple isLiked.
A shopping cart quantity might not end with just a simple quantity.
A payment status might not end with just simple loading, success, and error states.

In actual services, states are more complex.

There is the state the user first saw, the state the client shows optimistically, the state confirmed by the server, the state that must be rolled back upon failure, and the unknown state where the result is not yet certain.

Students will learn not just "how to use state management libraries," but how to design complex product experiences into predictable state flows.

6. Performance optimization is treated as a system requirement, not just a set of tips

Frontend performance optimization is not simply a matter of lazy loading images or splitting code.

In actual services, you must first define which user experiences will be guaranteed by which metrics.

On a product detail page, LCP may be important.
In an autocomplete search bar, Time to First Suggestion may be important.
In a real-time feed, scroll stability and deduplication may be important.
In a video UI, buffering rate and playback recovery time may be important.
In a collaborative editing UI, input latency and synchronization delay may be important.

In this course, we do not treat performance vaguely as just "making it fast."

We will examine which performance metrics are important for specific screens, which rendering strategies should be selected, which states and data flows create performance bottlenecks, and even how to observe the performance of actual users after deployment.

Therefore, students will develop the ability to design performance based on product requirements, rather than just learning simple optimization tips.

7. Learn frontend design that considers both operations and observability

Critical frontend issues in practice do not only occur before deployment.

Rather, the real problems occur after deployment.

Buttons may not work only in specific browsers.
Payment status may fall into an unknown state in certain network environments.
The shopping cart rollback rate may suddenly increase in a specific version.
As search responses slow down, stale responses may increase.
Hydration mismatch may occur only on specific pages.

At this point, simply saying "I have integrated an error tracking tool" is not enough.

Frontend engineers must be able to design which events to collect, which state transitions to record, and which metrics indicate issues in the user experience.

In this course, observability is not viewed as a simple implementation like installing Sentry.

We view the frontend system as an operable structure through metrics such as RUM, error tracking, telemetry contract, state transition logs, rollback rate, stale response ignored count, and payment unknown rate.

With this perspective, a frontend developer can grow beyond being a mere implementer into an engineer who takes responsibility for quality even after deployment.

The person who created this course

• Silicon Valley Survivor | US Snail

  Based on experience and know-how accumulated at the forefront of the Global Tech Scene, I present a path for non-majors to overcome technical barriers and become masters of their business.

  • Current) Founder of a Silicon Valley AI Coding Agent Startup

    • Operating self-developed AI tool 'Snailer CLI' (13K+ downloads)

    • Selected for the Google for Startups Program

  • Former) Career as an Engineer at U.S. Big Tech and Promising Startups

    • Reached the final stage at Amazon, but gave it up to start a business.

    • Silicon Valley AI Fintech Startup Engineer

    • OpenAI / Meta / Apple / Adobe / Amazon Full-Stack Fellowship

    • Domestic search engine portal, fintech development

    • AI Startup AR/B2B/SDK Development

  • Proven Educational Capabilities

    • Dual major in Computer Science and Business Administration at a 4-year university in Seoul, with extensive entrepreneurial experience.

    • Produced 1,500+ cumulative students, with 4.4K+ followers on Threads and 460+ on Substack

    • Threads: https://www.threads.com/@rich_l_2024?igshid=NTc4MTIwNjQ2YQ==

    • Substack: https://open.substack.com/pub/1234373801

Section 3 Case Study Update Plan

Section 3 is currently private and under sequential production; it will be composed of case studies applying what has been learned so far to actual U.S. Big Tech service examples.

The direction currently being prepared is as follows.

• Meta Threads-style real-time social feed frontend system design

• Instagram image feed and story design

• Amazon online commerce product listing and shopping cart design

• Netflix/YouTube video streaming UI design

• Slack/Messenger real-time chat design

• Google Docs/Sheets Collaborative Editing Design

• X.com service feed system design

• Uber/Lyft-style real-time location-based design

• SpaceX-style Rocket Part Cost Analysis Platform Design

• Starlink / SpaceX Observability Control Plane Design

• Designing a Figure AI-style humanoid robot operation dashboard

• Space Data Center / Orbital Data Center Management Console Design

• Rocket Internal Systems Monitoring Front-end Design

The goal of Section 3 is not simply to show that "you can build these kinds of services," but to train you on how to break down and analyze actual complex services in terms of requirements, architecture, data models, interfaces, performance, observability, and error handling.

Therefore, each case will be covered in greater depth from a frontend system design perspective rather than focusing on simple implementation.

I am currently producing them sequentially and plan to release the lectures as they are completed. Thank you for waiting.

Do you have any questions?

Notes before taking the course