Vibe Coding in Practice at Big Tech: How Frontend Engineers Can Avoid Being Replaced in the AI Era

Introduction: Vibe Coding Has Become the New Normal at Big Tech

For a long time, frontend developers focused their attention on traditional technical topics like framework selection, performance optimization, and engineering infrastructure. But today, an unavoidable topic is reshaping the entire industry — Vibe Coding (AI-assisted programming).

According to a frontend interviewer at a major tech company who shared his experience on Bilibili, whether frontend or backend, a large number of real development scenarios have already deeply integrated AI. This interviewer offered a rather striking judgment: junior and intermediate frontend development work will gradually be entirely replaced by AI. This isn't fearmongering — it's a reality already unfolding. If you still lack even the mindset for programming with AI, then at this juncture, you've indeed fallen behind.

This article will unfold around three progressively deeper interview questions about Vibe Coding at big tech companies, helping you understand the complete capability map — from tool selection, to complex product development, to rapid full-stack delivery.

What Is Vibe Coding?

Vibe Coding is a programming paradigm with AI as the core collaborator. Developers no longer hand-write every line of code; instead, they describe requirements, intent, and architecture in natural language, and large models generate, iterate, and refine the code. The developer's role shifts from "coder" to "conductor" and "reviewer."

The Origins and Paradigm Evolution of Vibe Coding

The term Vibe Coding was coined by OpenAI co-founder Andrej Karpathy on social media in early 2025. He used the term to describe a development experience of being "fully immersed in the vibe of AI-generated code, barely writing any code by hand." This concept quickly resonated across the industry because it precisely captured the fundamental change in how developers work after the leap in large model capabilities.

From a technical evolution standpoint, AI programming has gone through three stages: The first stage was rule-based code completion (such as early IntelliSense, which provided candidates through static lexical and syntactic analysis — essentially a rule engine); The second stage was intelligent completion based on statistical models (such as Tabnine, which used RNNs/early Transformers to learn statistical patterns from code repositories, capable of capturing the probability distribution of local code, but limited by the long-range dependency problem of sequence modeling, making it difficult to understand cross-file semantic relationships); The third stage is natural language programming driven by large language models built on the Transformer architecture.

The core innovation of the Transformer — the Self-Attention mechanism — enables the model to directly compute relevance weights between any two positions in a sequence, rather than relying on step-by-step propagation of hidden states like RNNs (whose gradients decay exponentially with sequence length, leading to the "long-range forgetting" phenomenon). Specifically, self-attention performs fully parallel computation over the input sequence via three matrices — Query, Key, and Value: each token maps itself into a Query vector and maps all other tokens into Key vectors, computes attention weights through dot-product similarity, and then performs a weighted sum over all tokens' Value vectors, allowing each token to simultaneously "perceive" information from all other positions in the sequence. This property enables the model to simultaneously "see" the dependencies between a function definition and its call site, or between an interface declaration and its implementation details, when handling ultra-long code contexts — this is the technical foundation for its ability to understand contextual requirements and generate complete functions and even modules. The collaboration granularity between developers and AI has risen from "line" to "system," a result of three generations of leapfrog technical evolution.

The pace at which this paradigm has spread has exceeded many people's expectations. From early code completion (such as GitHub Copilot) to today's Agent-type tools capable of handling complex tasks (such as Claude Code, Codex, and Cursor), AI programming is evolving from an auxiliary tool into a true productivity mainstay. Understanding this shift is the prerequisite for navigating the new wave of technological change.

Interview Question One: Tool Selection and Taming Code Chaos

Which AI Programming Models and Tools Have You Used?

The first level of the interview examines basic awareness: In your past projects, have you practiced Vibe Coding? Which models and tools do you know?

Current mainstream AI programming tools have formed a fairly clear hierarchy. At the tool layer, IDEs or command-line tools like Cursor, Claude Code, and Codex have become daily choices for big-tech developers; at the model layer, the Composer capability of the Claude series, the GPT series, and others each have their own advantages in code generation quality. Being able to clearly explain how you combine and use these tools in real projects is the first step to standing out.

Technical Differences Among Mainstream AI Programming Tools

Current mainstream AI programming tools differ significantly in their technical architecture, and understanding these differences is the foundation for reasonable tool selection.

• Cursor: An AI-native IDE built on top of VS Code. Its core advantage lies in deep integration of codebase context (Codebase Indexing). Under the hood, it builds a Vector Embedding index of the local codebase — encoding code snippets into high-dimensional vectors and storing them in a local vector database (such as Qdrant or an in-house store). When the user enters a prompt, it uses semantic retrieval algorithms like cosine similarity to find the most relevant code snippets and automatically injects them into the model's context window, enabling the model to understand project structure across files. This technique is essentially the engineering implementation of Retrieval-Augmented Generation (RAG) in the IDE scenario: the core idea of RAG is to decouple "retrieving relevant information from an external knowledge base" from "model generation" into two steps — retrieve first, then generate — effectively overcoming the limitation of knowledge frozen in model parameters, enabling it to perceive the real-time changing state of the codebase without cramming the entire project into the context (which would exhaust the token budget and introduce large amounts of noise). This mechanism is especially well suited for large engineering scenarios, significantly reducing "context blind spots" when AI generates code.

• Claude Code: A command-line Agent tool released by Anthropic, powered by the Claude 3.x series models, excelling at long-context understanding (supporting a 200K token context window, far exceeding most competitors — roughly equivalent to the reading capacity of 150,000 lines of code). Its Agent mode supports autonomously executing terminal commands, reading and writing files, and calling external tools, performing exceptionally well on complex, multi-file refactoring tasks. A 200K context window means it can simultaneously "read through" the entire source code of a mid-sized project in a single conversation and then provide cross-file refactoring suggestions — this complements Cursor's RAG retrieval strategy: the former relies on "reading everything," the latter on "precise retrieval," each suited to projects of different scale and scenarios.

• OpenAI Codex: A specialized optimization of the GPT series models for the code domain. It once served as the underlying model for GitHub Copilot and has mature performance in code continuation and single-file generation scenarios. The new-generation Codex CLI now supports Agent mode, capable of autonomously running code in a sandbox environment and self-correcting based on the results.

Notably, these tools are evolving toward being "Agentic" — shifting from passive completion to active planning, execution, and self-correction. The core of Agent mode is the "Think–Act–Observe" loop (the ReAct framework, Reasoning + Acting): the model first performs chain-of-thought reasoning on the task, generating a plan for the next action; then executes the specific action (Acting), such as running Shell commands, calling APIs, or reading/writing the file system; finally observes the execution result (Observation), feeding terminal output, error messages, and so on back into the context, triggering the next round of reasoning. This autonomous "Think–Act–Observe" loop enables the model not only to generate code but also to execute code, read errors, and self-correct, forming a truly closed-loop automation. It's a qualitative leap in tool capability and a key dimension to consider when selecting tools.

How to Solve the Code Chaos Problem in AI Programming?

This is a core pain point every developer using AI programming inevitably faces. AI-generated code often suffers from loose structure, inconsistent naming, duplicated logic, and lost context. As project scale grows, "code chaos" rapidly accumulates into technical debt.

The Technical Root Causes of AI Code Chaos

The fundamental reason AI-generated code becomes "chaotic" lies in the working mechanism of large language models: LLMs generate token sequences based on probabilistic prediction, and their output is subject to the hard limit of the Context Window — content outside the window effectively doesn't exist for the model. As the number of conversation turns increases, the early context the model can "see" gets replaced by new content in a "sliding window"-like manner (more precisely, it's truncated after exceeding the maximum token count), causing architectural conventions established earlier to be "forgotten," and later-generated code to diverge from earlier code in naming conventions, design patterns, and error-handling approaches.

The deeper reason is that LLMs are inherently Stateless — each inference starts fresh from the given context, with no persistent "memory" across sessions. The model doesn't maintain a continuously evolving "project mental model" in its mind like a human engineer; all the information it can rely on is limited to the content within the context window of the current request. Software engineering, however, precisely requires the codebase to maintain strong consistency over the time dimension. This structural contradiction between "model statelessness" and "engineering strong consistency" is the fundamental source of "AI code chaos," and the core challenge that any AI programming tool must compensate for at the engineering level.

The engineering community has already summarized several systematic countermeasures:

1. Rule solidification: Use configuration files like .cursorrules and CLAUDE.md to solidify architectural conventions (such as naming conventions, directory structure, forbidden APIs, and error-handling patterns) into the model's System Prompt, ensuring every conversation follows unified standards. This essentially transforms "tacit knowledge" into "explicit constraints." These files are automatically injected by the tool at the very front of the context on every request, carrying the highest attention weight (the model pays significantly more attention to the beginning of a prompt than to the middle — the "primacy and recency effect"), effectively providing the AI with a continuously effective "project constitution" so that no matter which turn the conversation has reached, the foundational conventions are always present;
2. Divide-and-conquer strategy: Break large tasks into independent small task units to generate separately, then integrate them manually, avoiding the consistency degradation caused by context window dilution;
3. AI self-review: Introduce AI code review, letting the model perform consistency checks on the code it generated, forming a "generate–review–correct" closed loop.

The essence of this methodology is "using engineering means to constrain the randomness of AI," taming chaos at the source. Effective countermeasures include: clear rule constraints, modular progressive generation, manual review and refactoring intervention, and leveraging AI itself for code review. Mastering this "governance" methodology is the watershed determining whether Vibe Coding can truly be used in production environments.

Interview Question Two: Architecture Design and Quality Assurance for Complex Products

After solving problems at the tool and model layers, mid-to-senior interviews will probe further: If you were to use Vibe Coding to build a complex product like Feishu's multi-dimensional tables or collaborative documents, how would you complete the foundational development? How would you ensure code quality and performance?

The core of this level is architectural thinking. Products like Feishu's multi-dimensional tables and online collaborative documents involve complex data structures, real-time collaboration, and large-scale rendering performance optimization — hardcore challenges that simply cannot be accomplished by letting AI "wing it."

CRDT/OT Algorithms and the Technical Complexity of Collaborative Editing

The reason products like Feishu's multi-dimensional tables and online collaborative documents are a touchstone for architectural ability lies in the core difficulty of real-time collaboration technology. The industry currently mainly adopts two algorithms to solve the conflict problem of multiple people editing simultaneously:

• OT (Operational Transformation): The technical approach adopted by Google Docs, which guarantees eventual consistency by mathematically transforming concurrent operations. Its core idea is: when two users' operations produce concurrent conflicts under network latency, the server uses a Transform Function to adjust the operations into mutually compatible forms before applying them. For example, if user A inserts the character "X" at position 5 while user B deletes the character at position 3, the server needs to transform A's operation into "insert X at position 4" to keep both sides consistent. OT is mathematically provably correct in two-party concurrent scenarios, but in multi-party (three or more) concurrent scenarios, the compositional design of transform functions becomes extremely complex — historically, numerous papers have pointed out correctness flaws in certain OT implementations (such as the failure of the Jupiter protocol under specific concurrent sequences). Engineering implementation is exceptionally difficult, and Google invested years of research into it;

• CRDT (Conflict-free Replicated Data Type): A more modern approach, adopted by products like Figma, Linear, and Notion. Its core idea is to design special data structures (such as the RGA sequence algorithm, or Logoot/LSEQ tree-based position encoding), assigning each operation a globally unique logical timestamp (usually based on Lamport clocks — an incrementing logical counter, or vector clocks — a vector recording the operation count of each node, used to precisely determine concurrency relationships), so that the merge result of operations in any order satisfies commutativity (independent of operation order) and idempotency (repeated application doesn't change the result), eliminating conflicts at the mathematical level, without requiring the server to perform complex operation transformation coordination, and also naturally supporting offline editing scenarios (local operations can be staged and automatically merged upon reconnection). The cost is that CRDT data structures typically consume more memory than ordinary data structures (because they need to retain tombstone markers of deleted nodes to maintain causal consistency), requiring periodic Compaction/GC optimization in very large document scenarios.

In addition, collaborative documents also involve engineering challenges such as WebSocket long-connection management, operation serialization and compression (such as using MessagePack or Protobuf instead of JSON to reduce bandwidth), multi-user cursor synchronization, offline caching (IndexedDB), and operation replay upon reconnection. The in-depth requirements in these areas are precisely what pure AI-generated code cannot directly cover, requiring engineers to have clear architectural guidance capabilities.

True capability is demonstrated by: whether you can break down complex requirements into clear module boundaries, whether you can provide AI with accurate architectural guidance, and whether you can perform performance tuning on top of AI-generated code (such as virtual scrolling, incremental rendering, and state management optimization). AI handles implementation details, humans handle architectural decisions and quality control — this is precisely the irreplaceable value of mid-to-senior engineers.

Interview Question Three: The Expert-Level Challenge of One-Hour Full-Stack Delivery

The most striking is the third, expert-level question. In the past, on-site tests were often algorithm problems, but the format has now completely changed: You're given one hour to complete the full-stack development of a core feature — from frontend to backend, to database design, to the entire pipeline.

In the past, this was almost an impossible task. But in the AI era, with tools like Claude Code, Codex, or Cursor, combined with mainstream large models, and with a global perspective and architectural thinking, completing the full-stack development of a core feature within one hour has indeed become possible.

The Logic of Tech Stack Selection for Rapid Full-Stack Delivery

"One-hour full-stack delivery" places extremely high demands on technology selection, requiring a precise balance between development efficiency and system completeness. The efficient full-stack technology combination currently recognized in the industry typically includes:

Layer	Recommended Choice	Core Advantage
Language	TypeScript	Unified types across frontend and backend, eliminating type conversion costs
Frontend Framework	Next.js / Remix	Built-in SSR/API Routes, reducing configuration overhead
ORM	Prisma / Drizzle	Type-safe database operations, high AI generation quality
Database	PostgreSQL	Feature-complete, ample AI training corpus coverage
BaaS	Supabase / PlanetScale	Compresses database operations and authentication system setup time

The deeper logic of this combination is: choosing a tech stack with high AI training data coverage and convention-over-configuration can significantly improve the accuracy and usability of AI-generated code.

Take Prisma as an example: its declarative Schema syntax (defining data models, field types, relationships, and indexes in a DSL-like manner in the schema.prisma file) is highly structured and semantically clear, with hundreds of thousands of open-source project examples on GitHub, appearing extensively in large models' training corpora. This means AI can accurately understand Prisma's syntax conventions and generate correct database model definitions and type-safe query code (the Prisma Client automatically generates complete TypeScript type definitions based on the Schema), with a significantly lower error rate than having AI hand-write raw SQL, and the generated code integrates directly with the TypeScript type system, requiring almost no manual verification of type issues. This combination of "high training coverage + strong type constraints" is the core reason to choose Prisma over lightweight ORMs in AI-assisted programming scenarios.

Meanwhile, BaaS (Backend as a Service) platforms like Supabase come with built-in PostgreSQL database hosting, Row-Level Security (RLS) access control (defining "which types of users can access which rows" directly at the database layer through policy expressions, without needing to write permission logic at the application layer), an Auth authentication system (supporting email/password, OAuth, Magic Link, and other methods), and real-time subscription capabilities (based on PostgreSQL's Logical Replication mechanism — parsing the database's WAL log into row-level change events and pushing them to clients via WebSocket to achieve real-time synchronization of table data). Developers only need to invoke these capabilities through the Supabase SDK, compressing backend infrastructure that would originally take days to build — authentication systems, permission systems, real-time push — down to minutes of configuration, bringing one-hour full-stack delivery from theory to practice. Choosing such "convention-over-configuration" platforms essentially transfers the complexity of architectural decisions to the platform, allowing developers to focus their cognitive bandwidth on the business logic itself.

The interviewer gave a typical example: using this entire Vibe Coding AI toolchain to develop the full-stack application features of a "Feishu collaborative table" within one hour. What this question truly screens for is not typing speed, but whether you can command AI to complete end-to-end delivery — from requirement understanding, technology selection, and database modeling, to frontend-backend integration, accelerating throughout with AI while always steering the ship yourself.

Conclusion: From the Replaced to the Conductor

The assertion that "junior and intermediate frontend developers will be entirely replaced by AI" reveals a cruel but clear trend: pure code implementation ability is depreciating, while architectural thinking, global perspective, and AI collaboration ability are rapidly appreciating.

For developers, rather than being anxious about being replaced, it's better to complete the role transformation as early as possible:

• From writing code to reviewing code: Master the governance methodology for AI-generated code;
• From module development to system architecture: Cultivate the architectural ability to decompose complex products;
• From single-point skills to full-stack vision: Acquire the comprehensive ability to command AI for end-to-end delivery.

Vibe Coding is not about eliminating programmers, but about eliminating programmers who "only know how to write code." Mastering this new way of working is the most reliable moat of this era.