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đź“ť Riley Brown Blog

🎥 We Made Claude Code Build Lovable in 75 Minutes (With No Code)

⏱️ Duration: 83:00
đź”— Watch on YouTube

đź“š Video Chapters (16 chapters):

Overview

This video provides a step-by-step walkthrough of building a "Lovable" clone—a
website that builds websites—using Claude Code and its SDK, with a focus on
“vibe coding” (rapid, AI-powered development). Across 16 chapters, the
presenters demonstrate every stage: from setting up Claude Code, to integrating
its SDK, to handling complex infrastructure like isolated code execution
environments, culminating in a functional clone that mirrors the features of
Lovable. The chapters build sequentially, each introducing new technical and
conceptual challenges, and together illustrate both the power and current
limitations of AI-driven coding tools.

Chapter-by-Chapter Deep Dive

Introduction & Setup (00:00)

Core Concepts:
The video sets the stage by introducing Claude Code as a potentially superior
alternative to Cursor for AI-assisted coding. Khan Zang, an expert in AI coding
tools, will attempt to build a full application—a Lovable clone—using Claude
Code in one session.

Key Insights:
- Claude Code is being tested for end-to-end application development.
- The main goal is to see if it can autonomously build a web app.

Actionable Advice:
- The viewer will learn how to set up Claude Code and use its SDK.

Connection to Overall Theme:
Establishes the challenge and outlines the journey: using Claude Code to build a
real product from scratch.

Explaining Claude Code and SDK (00:31)

Core Concepts:
An in-depth explanation of what Claude Code is: an AI coding assistant that
operates via the command line interface (CLI), distinct from cursor-based tools.

Key Insights:
- Claude Code is designed primarily for coding, unlike generalist LLMs.
- Developers can build wrappers (custom interfaces/tools) using the Claude Code SDK.

Actionable Advice:
- Opportunities exist to develop and monetize wrappers around Claude Code, similar to ChatGPT wrappers.

Connection to Overall Theme:
Frames Claude Code as both a developer tool and a foundation for building more
complex coding agents.

Starting the Lovable Clone Build (02:11)

Core Concepts:
The project begins with setting up a fresh environment in Cursor for the Lovable
clone, using Claude Code.

Key Insights:
- Claude Code can proactively start building based on file/project names.
- The tool is eager—sometimes overly so—requiring user intervention to control.

Actionable Advice:
- Set up a standard development environment and learn to manage Claude Code’s auto-initiated actions.

Examples:
- Claude Code recognized the "lovable clone" project name and began suggesting full-stack scaffolding.

Connection:
Initiates the hands-on experiment, highlighting the tool’s capabilities and
quirks.

Memory and Configuration Setup (04:04)

Core Concepts:
Explains project vs. user memory in Claude Code, introducing markdown-based
memory storage that informs the AI’s context.

Key Insights:
- Project memory enables persistent context for specific tasks, improving coherence.
- Proper configuration is critical for controlling Claude Code’s actions and permissions.

Actionable Advice:
- Use project memory for project-specific instructions.
- Use user memory for global, recurring preferences.

Connection:
Demonstrates best practices in context management—a recurring theme for
effective AI code generation.

Using Plan Mode and SDK Docs (05:51)

Core Concepts:
Leverages “plan mode” to guide Claude Code using documentation and structured
prompts.

Key Insights:
- Claude Code can use online resources but needs direction.
- Scoping tasks (breaking them down) improves outcomes.

Actionable Advice:
- Direct Claude Code to focus on SDK-based code generation.
- Limit project scope to manageable chunks.

Examples:
- Feeding the Claude Code SDK documentation to the tool and specifying backend-oriented objectives.

Connection:
Highlights the importance of guiding AI tools with clear scope and resources.

Generating a Simple Function (07:00)

Core Concepts:
Tasking Claude Code to generate a basic function: accept a prompt and generate
code accordingly.

Key Insights:
- Simplicity in requirements yields more predictable AI outputs.
- Permissions and configuration remain central.

Actionable Advice:
- Request raw code snippets rather than complex, multi-step outputs.

Examples:
- Requesting a simple TypeScript function that takes a prompt and interacts with Claude Code SDK.

Connection:
Demonstrates iterative, lean development with AI assistance.

Executing with Claude Code (09:00)

Core Concepts:
Runs the generated function, focusing on backend execution before UI
development.

Key Insights:
- Claude Code’s knowledge of its own SDK is imperfect; online research fills gaps.
- AI-generated pseudocode needs human review and iterative refinement.

Actionable Advice:
- Test simple functions in isolation before integrating into a larger app.

Connection:
Continues the foundational, backend-first approach—building and validating
codegen logic.

Building a Tic Tac Toe Game (11:00)

Core Concepts:
Tests the code generation function by prompting it to build a simple tic-tac-toe
HTML game.

Key Insights:
- Permissions and file-writing issues can block progress.
- Real-time feedback and iterative debugging are essential.

Actionable Advice:
- Isolate risky code generation (discusses Docker and containers).
- Use HTML for quick, low-overhead prototyping.

Examples:
- Successfully generates and plays tic-tac-toe, validating core functionality.

Connection:
First tangible proof that the codegen loop works, setting the stage for more
complex integration.

Front-End UI Build for Lovable Clone (16:33)

Core Concepts:
Moves from backend logic to frontend development, aiming to replicate Lovable’s
UI.

Key Insights:
- Claude Code can use image inputs (screenshots) for UI cloning.
- Prompt specificity (e.g., gradients, button icons) improves UI fidelity.

Actionable Advice:
- Provide visual references and detailed UI requirements to the AI.
- Iterate on design elements through targeted prompts.

Connection:
Bridges backend codegen with user-facing interfaces, emphasizing full-stack
workflows.

Connecting Front-End with Backend Function (21:24)

Core Concepts:
Integrates the front end with the backend function, enabling prompt-driven code
generation from the UI.

Key Insights:
- API routes connect UI input to codegen logic.
- Real-time feedback and message display are important for UX.

Actionable Advice:
- Modularize code for easier integration.
- Use long-term memory for recurring process instructions (e.g., dev server management).

Examples:
- Successfully generates a Connect 4 game, but notes file organization and scaling challenges.

Connection:
Completes the first end-to-end loop, highlighting integration challenges and UX
considerations.

Scaling & Issues with Local Code Writes (26:01)

Core Concepts:
Discusses the risks and limitations of letting AI write code directly to the
project’s local file system.

Key Insights:
- Security and scalability are concerns: generated code could overwrite or corrupt the main codebase.
- Positive reinforcement helps guide Claude Code’s behavior.

Actionable Advice:
- Consider real-time logging and friendlier message rendering.
- Plan to isolate generated code (containers, sandboxes).

Connection:
Transitions from basic prototyping to thinking about production-grade
architecture.

Creating Isolated Bubbles with Daytona (30:04)

Core Concepts:
Introduces the concept of "bubbles"—isolated execution environments for safe
code generation, leveraging external services like Daytona and E2B.

Key Insights:
- Isolated sandboxes prevent main codebase corruption.
- Providers like Daytona offer ready-made solutions for “bubbles.”

Actionable Advice:
- Use third-party isolated environments for user-generated code.
- Research and select tools based on documentation and integration capabilities.

Connection:
Marks a shift toward advanced infrastructure, essential for scaling and
security.

Debugging & Testing Bubbles (34:24)

Core Concepts:
Attempts to set up and debug isolated environments using Daytona, focusing on
clear task definition and prompt engineering.

Key Insights:
- Effective prompt engineering requires specifying both immediate and long-term goals.
- Human oversight is necessary: AI can get stuck or misinterpret documentation.

Actionable Advice:
- Scope tasks carefully for the AI.
- Use pseudo/real code plans to validate AI understanding.

Connection:
Demonstrates the interplay of AI autonomy and human-in-the-loop debugging.

Test Execution in Daytona Sandbox (42:48)

Core Concepts:
Executes and validates the sandboxed code generation process, iteratively
debugging failures.

Key Insights:
- Testing in isolated environments surfaces new challenges (e.g., permissions, networking, previewing).
- AI may require manual intervention and additional documentation.

Actionable Advice:
- Validate every step (e.g., can you preview the generated site?).
- Use logs, SSH, and manual checks to supplement AI outputs.

Connection:
Continues the theme of iterative, multi-layered debugging and validation.

All Components Working Together (52:32)

Core Concepts:
Successfully integrates all components: a front end, backend codegen logic, and
isolated execution environments.

Key Insights:
- Achieving full integration requires both AI and human-driven problem solving.
- Context management (updating AI memory) is crucial for complex projects.

Actionable Advice:
- Continually update project memory and documentation for AI context.
- Run and test the integrated pipeline before deploying.

Connection:
Represents the culmination of the build: a functional, safe, and modular Lovable
clone.

The Big Reveal & Testing (54:17)

Core Concepts:
Final demonstration and testing of the Lovable clone; side-by-side comparison
with the original Lovable.

Key Insights:
- The AI-built clone is impressively close to the original in both function and appearance.
- Most issues stemmed from context or documentation gaps, not AI capability.
- Human skills in guiding LLMs and navigating docs remain critical.

Actionable Advice:
- Test various use cases (blogs, image tools, link trees) to validate robustness.
- Plan to open source the code for community testing.

Connection:
Delivers on the initial promise: an AI-powered, end-to-end developed clone,
built in two hours with minimal manual coding.

Cross-Chapter Synthesis

Recurring Themes:
- Iterative Development: Each chapter builds on the previous, moving from backend logic to UI, integration, scaling, and advanced infrastructure.
- Prompt Engineering & Context Management: Effective use of Claude Code requires clear, scoped instructions and ongoing context updates (Ch. 4, 5, 12, 13).
- Human in the Loop: Despite AI’s power, human oversight, debugging, and documentation remain essential (Ch. 7, 13, 14).
- Isolation for Safety: Transition from local file writes to isolated sandboxes is crucial for production-ready AI codegen (Ch. 11, 12).
- Testing & Validation: Continuous testing at every stage prevents compounding errors and ensures functional integration.

Progressive Learning Path

  1. Tool Introduction: Understand Claude Code’s value proposition and setup (Chs. 1–2).
  2. Foundational Skills: Learn context management and SDK basics (Chs. 3–5).
  3. Backend Prototyping: Build and test simple codegen functions (Chs. 6–7).
  4. Proof of Concept: Validate functionality with simple projects
    (tic-tac-toe) (Ch. 8).
  5. UI Integration: Transition to building and refining the front end (Ch.
    9).
  6. Full-Stack Integration: Connect frontend and backend, test user flows
    (Ch. 10).
  7. Scaling Considerations: Address safety, scalability, and code
    organization (Ch. 11).
  8. Advanced Infrastructure: Implement isolated execution with third-party
    tools (Chs. 12–14).
  9. Comprehensive Testing: Debug, validate, and synthesize all components
    (Chs. 15–16).
  10. Final Reflection: Evaluate successes, limitations, and next steps.

Key Takeaways & Insights

  • AI coding tools are powerful but require human guidance. (Chs. 7, 13)
  • Context management is critical: Use project/user memory and continually update AI context. (Ch. 4, 15)
  • Start small and iterate: Simple, scoped tasks yield better results. (Ch. 6)
  • Isolate codegen for safety: Use sandboxes/containers to prevent codebase corruption. (Chs. 11–12)
  • Prompt engineering is an evolving skill: Be explicit about both current and end goals. (Ch. 13)
  • AI’s knowledge of new tools may lag: Supplement with documentation and manual research. (Ch. 7, 14)
  • Testing at every stage is essential: Validates assumptions and uncovers hidden issues. (Chs. 8, 14, 16)
  • Positive reinforcement steers AI behavior. (Ch. 11)
  • Open sourcing and community feedback can drive further improvements. (Ch. 16)

Actionable Strategies by Chapter

  • Ch. 2: Develop wrappers using the Claude Code SDK to extend its utility.
  • Ch. 4: Use project memory for task-specific context; user memory for global preferences.
  • Ch. 5: Feed relevant documentation and use plan mode for scoped, research-driven tasks.
  • Ch. 6: Request minimal, standalone code for clarity and testability.
  • Ch. 8, 10: Validate each module (backend, frontend, integration) before proceeding.
  • Ch. 11: Start considering containerization early for security and scalability.
  • Ch. 12: Research and leverage third-party sandbox providers (Daytona, E2B).
  • Ch. 13: Scope prompts tightly and clarify both immediate and long-term objectives.
  • Ch. 14: Combine AI-generated plans with manual debugging and SSH access.
  • Ch. 15: Update project memory and documentation to maintain context.
  • Ch. 16: Test a variety of outputs and compare against reference implementations.

Warnings & Common Mistakes

  • Letting AI write directly to the main codebase is risky. (Ch. 11)
  • Insufficient permissions/configuration can block functionality. (Ch. 8)
  • AI may not be current on new SDK/tooling—always supplement with docs/manual research. (Ch. 7, 14)
  • Assuming AI knows the end goal without explicit context leads to errors. (Ch. 13)
  • Overly broad prompts can result in confusion or suboptimal code. (Ch. 13)
  • Not testing each component in isolation compounds debugging challenges. (Chs. 8, 14)

Resources & Next Steps

  • Ch. 2, 5: Claude Code SDK documentation (searchable via Google).
  • Ch. 12, 13: Daytona (https://www.daytona.io/) and E2B (https://e2b.dev/) docs for isolated environments.
  • Ch. 15: Use markdown files and persistent memory in Claude Code for ongoing project context.
  • Ch. 16: GitHub repo (to be shared in video description) for the finished Lovable clone.
  • Next Steps:
  • Explore advanced prompt engineering to further streamline AI-assisted coding.
  • Experiment with other isolated environment providers.
  • Contribute to or fork the open-sourced Lovable clone.
  • Continue refining integration and UX for production readiness.

This comprehensive summary distills each chapter’s key contributions, highlights
cross-cutting themes, and provides a clear, actionable blueprint for viewers
inspired to emulate or extend the project.