Emergent Garden thumbnail

Emergent Garden

"Vibe Coding"

Overview

The video explores the concept of AI software agents, particularly focusing on "vibe coding"—letting AI write code autonomously. The creator demonstrates building a simple web-based simulation inspired by Langton’s Ant using AI tools, discusses the strengths and limitations of AI-assisted coding, and offers insights on safety and best practices when working with AI coding agents.

Main Topics Covered

  • Introduction to AI agents and vibe coding
  • Demonstration of vibe coding a Langton’s Ant simulation
  • Workflow and interaction with AI coding tools (Cursor, Claude Code, OpenAI’s codecs)
  • Challenges and bugs encountered during AI-generated coding
  • Performance optimization techniques in JavaScript with AI assistance
  • Expanding the simulation with more complex features
  • Limitations of AI in debugging, UI consistency, and real-time interaction
  • Security risks of giving AI agents command line access
  • Final thoughts on the practical use cases and future of AI-assisted programming
  • Recommendations for human coders and learning to code

Key Takeaways & Insights

  • AI agents can autonomously generate and modify code, but still require human oversight and intervention.
  • Vibe coding accelerates prototyping and small projects but often results in buggy, unpolished code.
  • AI excels at generating boilerplate and familiar patterns but struggles with novel or complex problem-solving without human guidance.
  • Debugging AI-generated code is challenging because AI can overwrite previous fixes and introduce new bugs.
  • Real-time UI interaction and thorough manual testing remain tasks where humans outperform AI.
  • Performance bottlenecks can be addressed by combining human intuition with AI coding assistance.
  • Giving AI agents unrestricted command line access presents significant cybersecurity risks.
  • AI coding is best suited for low-stakes projects; critical software requires expert human involvement.
  • Learning to code remains valuable as human understanding is crucial for managing AI-generated code and solving unforeseen problems.

Actionable Strategies

  • Use vibe coding to quickly prototype or experiment with ideas, especially small-scale web projects.
  • Start with simple, well-understood programs or patterns and iteratively modify them to explore novel functionality.
  • Always review and test AI-generated code carefully; don’t blindly accept all changes.
  • Employ version control (e.g., git) alongside AI coding tools to manage code history and revert unwanted changes.
  • Manually debug UI and user interaction issues since AI has limited capability in simulating real-time user behavior.
  • Optimize performance by combining AI suggestions with your own knowledge of programming concepts and environment specifics.
  • Run AI coding agents inside isolated environments such as virtual machines to mitigate security risks.
  • Avoid using AI-generated code for high-stakes applications involving sensitive data or critical infrastructure.
  • Continue learning programming fundamentals to effectively supervise and enhance AI-generated code.

Specific Details & Examples

  • The creator built a Langton’s Ant simulation in about six hours using AI (Cursor with Gemini 2.5 Pro) that included features like zoom, pan, speed control, multiple ants, and customizable rules.
  • AI-generated JavaScript code had over 1,000 lines in a single file with excessive logging and commented-out code, illustrating common AI coding “slop.”
  • The AI struggled to optimize update loops for speed but, guided by human insight, implemented synchronous batch updates achieving thousands of frames per second.
  • AI agents like Claude Code can autonomously generate and modify generative art but may cause issues like self-replicating scripts filling up disk space.
  • The video demonstrated how an AI could write a Python script that self-replicates exponentially, causing resource exhaustion, highlighting security risks.
  • Claude Code refused to run sudo commands even when given the password, while OpenAI’s codecs attempted but were partially blocked.

Warnings & Common Mistakes

  • Accepting AI-generated code without understanding it can lead to accumulating bugs and unstable software.
  • AI may overwrite recent fixes or UI improvements when addressing new issues, causing a frustrating cycle of bug fixes.
  • Excessive logging and uncleaned comments/log statements degrade performance and code readability.
  • Relying on AI for real-time user interface testing or interaction is currently impractical.
  • Giving AI agents unrestricted system access can lead to severe security breaches and data loss.
  • Using vibe coding for production-level code, especially involving credentials or payments, is risky and ill-advised.
  • Overestimating AI’s current capability to replace human programmers in complex or novel problem domains.

Resources & Next Steps

  • The creator’s vibe-coded Langton’s Ant project is available online (link provided in video description) for exploration and further experimentation.
  • AI coding tools mentioned include Cursor (with Gemini 2.5 Pro), Anthropic’s Claude Code, and OpenAI’s codecs.
  • Use virtual machines to safely experiment with AI agents that have terminal access.
  • Explore version control systems like git to complement AI coding workflows.
  • Follow updates on AI coding tools as technologies like OpenAI’s codecs mature.
  • Consider learning or deepening programming skills to better leverage AI tools and understand their limitations.
  • Stay informed about cybersecurity best practices when working with AI agents that execute code on your machine.
← Back to Emergent Garden Blog