The rise of "vibe coding" has stirred considerable debate among developers. Coined by OpenAI’s founding engineer, Andrej Karpathy, vibe coding envisions a future where developers guide software development using natural language prompts, allowing AI to handle much of the coding. This contrasts with traditional AI-assisted programming, where AI tools support developers in lower-level tasks like code completion, bug detection, and generation. Despite initial enthusiasm, real-world experiences with vibe coding have often fallen short of expectations. Developers frequently report frustration and impracticality, with some suggesting it is better suited for non-technical users. Karpathy himself acknowledged the challenges in implementing the concept shortly after its introduction. Experimenting with LLM Code Generation To better understand vibe coding, I tested various large language models (LLMs) directly, without middleware or IDE plugins. The models included ChatGPT, Gemini 2.5 Pro, Claude, DeepSeek, Qwen 3, and Mercury. The task was to build a basic doctor online booking web app in Python. Model Performance ChatGPT: Provided a full basic Flask app with feature descriptions, technical design, and project structure. The implementation was straightforward but lacked advanced configurations. Gemini 2.5 Pro: Offered a detailed comparison of Django, Flask, and FastAPI, settling on a more sophisticated Flask app. It included practical considerations like database indexing and caching strategies. Claude: Returned a basic project structure similar to ChatGPT, with all HTML content embedded in a single file, requiring manual splitting for practical use. DeepSeek: Produced a well-rounded and professional implementation, complete with best practices, database setup, and cleaner project organization. Mercury: Focused on fundamentals, generating a minimal Flask app with step-by-step development instructions, ideal for educational purposes. Qwen 3: Stood out for its reasoning capabilities, explaining its thought process, considering requirements, and outlining key development phases and considerations. Insights from the Experiment Code Quality and Consistency The generated code was generally clean, consistent, and logically structured. LLMs demonstrated the ability to catch and correct bugs quickly, suggesting they can perform at or above the level of an average developer for straightforward tasks. Lack of Real-World Configuration However, the LLMs treated the app as a simple, self-contained local project, neglecting real-world constraints like deployment targets, infrastructure choices, secret management, and scalability considerations. This gap between idealized and practical scenarios is a significant drawback. Poor User Interaction Another major issue was the lack of two-way communication. Models either provided a skeletal framework or a complete solution without pausing to confirm user requirements. This communication gap often leads to misaligned outputs and is a core reason why vibe coding can fail in practice. Absence of Phased Development LLMs often skipped planning phases, diving directly into implementation. Even reasoning-capable models like Qwen didn3't reflect their internal planning in their output, leaving users without a clear roadmap or checkpoints for review and refinement. Distinct Personalities Each LLM has its own "personality." Some focus on deep technical detail, while others guide users through a development flow. This lack of consistency can be frustrating in collaborative settings. Testing Vibe Coding IDEs: Cursor, WindSurf, and Trae I also tested three vibe coding IDEs: Cursor, WindSurf, and Trae. These tools aim to improve the developer experience by integrating LLMs with more user-friendly interfaces. Despite their unique features, they still suffer from many of the same issues as standalone LLMs: Premature Solutions: Often rush into solutions without clarification, leading to misaligned outcomes. Subpar Solution Quality: Generated solutions typically lack enterprise-grade standards, scalability, and thorough validation. No Checkpoints for Human Feedback: Absence of clear checkpoints for review and refinement. Opaque Memory Handling: Unclear and unreliable memory retention, especially during extended sessions. Weak Project Understanding: Limited ability to grasp broader architectural design and business goals. Model Cutoff Limitations: Depend on LLMs with static knowledge cutoffs, potentially providing outdated information. Embracing Vibe Coding: A Shift in Mindset To effectively use vibe coding, developers need to adopt a new mindset: 1. Be a Product Manager: Guide the LLM by outlining requirements, constraints, and expectations. Think of yourself as the team lead or project manager. 2. Plan First: Start by having the LLM help you plan the project, outlining development phases and considerations. 3. Documentation is Your Ally: Keep detailed documentation to aid the LLM in understanding the project's direction and purpose. 4. Define Your Rules: Set custom global and project-specific rules to align the LLM with your preferred coding practices. 5. Test Everything: Validate the LLM’s output thoroughly, as logical errors and unintended consequences are still common. 6. Version Control: Use Git rigorously to manage changes and ensure a well-organized development process. Industry Evaluation Industry insiders recognize that vibe coding holds significant promise but is still in its early stages. The technology is advancing rapidly, and the potential benefits in productivity and efficiency are substantial. However, for it to truly become a viable development approach, it must address the limitations identified, particularly in user interaction, real-world configuration, and phased development. Company Profiles Scale AI: A leading data-labeling company, recently valued at $29 billion after a "significant" investment from Meta. Known for producing and labeling data for AI models, Scale AI has been a crucial partner for top AI labs like OpenAI. Meta: A tech giant investing heavily in AI, including generative models and superintelligence efforts. The partnership with Scale AI aims to enhance Meta’s AI capabilities in a competitive landscape dominated by Google, OpenAI, and Anthropic. Shopify: Recently announced a new policy requiring employees to prove that AI can't handle a job before requesting additional headcount, reflecting the increasing adoption of AI in development processes. By understanding these insights and adopting the right mindset, developers can navigate the evolving landscape of AI-assisted programming and make the most of emerging tools like vibe coding.