Vibe coding: All you need to know

AI-driven software development is evolving at an unprecedented pace, ushering in the era of vibe coding, where developers shift from writing syntax to orchestrating AI-driven workflows. This paradigm is democratizing development, enabling both seasoned engineers and non-technical users to build applications with minimal manual coding.

But with this rapid transformation comes new risks. Security vulnerabilities, technical debt, and compliance blind spots are emerging as critical challenges. As AI-generated code becomes the norm, ensuring robust security practices, scalable architectures, and maintainable codebases is more important than ever.

This blog delves into the power and pitfalls of vibe coding, breaking down key concerns like security risks, API exposure, and sustainable AI-driven development—and how platforms like GoCodeo are tackling these challenges head-on.

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What is Vibe Coding?

Vibe coding is one of the most recent trends emerging from the wave of GenAI, LLMs, and AI-driven development. While there have been multiple interpretations of what it entails, the term is primarily attributed to Andrej Karpathy, who famously quipped, “The hottest new programming language is English.”

At its core, vibe coding refers to the practice of describing desired software behavior in natural language, with AI-powered models handling most—if not all—of the code generation. Instead of manually writing functions, debugging syntax errors, and structuring architecture, developers (or AI-assisted creators, if we want to call them that) now communicate intent to AI models, which generate working code.

This shift is further corroborated by Y Combinator’s (YC) latest cohort, where 25% of startups reportedly have codebases that are almost entirely AI-generated. The implications are profound: AI and LLMs are no longer just augmenting development; they are actively replacing the need for traditional programming in many scenarios.‍

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How Does Vibe Coding Work?

Vibe coding fundamentally alters the software development lifecycle by integrating AI-driven code generation at every step. Here's how:

1. Prompt-Driven Development – Instead of manually coding, developers describe what they want in structured or loosely defined prompts. The AI interprets the request and generates code accordingly, making vibe coding an intuitive way to build applications.
2. Iterative Refinement – Developers refine outputs by tweaking prompts, leading to multiple iterations until the desired logic, structure, and performance criteria are met.
3. AI-Augmented Debugging & Testing – Rather than manually debugging, AI-powered tools identify issues and generate potential fixes or test cases, reducing the need for extensive manual intervention.
4. Deployment with Minimal Manual Intervention – AI-native deployment tools integrate directly with platforms like Vercel, Supabase, and GitHub Actions, enabling rapid deployment without traditional infrastructure complexities.
   
   

Key Characteristics of Vibe Coding

1. Higher-Level Abstraction – Developers shift from writing detailed code to defining the overall system logic. AI handles syntax, patterns, and best practices.
2. Fast Prototyping & MVP Development – What previously took weeks can now be done in hours or days, accelerating product development timelines.
3. Broad Democratization of Development – With LLMs handling coding, non-technical founders and entrepreneurs can create software without formal programming expertise.
4. AI-Native Tooling – AI-integrated IDEs like Cursor IDE, GitHub Copilot X, and GoCodeo are leading the way in enabling fully AI-driven development environments.‍

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Traditional Coding vs. Vibe Coding

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The Shift from “Code as Instructions” to “Code as Dialogue”

Vibe coding represents a fundamental shift in how we perceive software development. Traditional coding is imperative: developers explicitly instruct the machine on what to do, line by line. Vibe coding, on the other hand, is declarative and conversational, resembling a back-and-forth dialogue with an AI agent.

Instead of writing:

A developer using vibe coding might input:
"Generate a Python function to compute the factorial of a number recursively."

The AI then generates and refines the function accordingly.

Vibe Coding: The New Software Development Paradigm?

Vibe coding challenges traditional software development, shifting the focus from writing syntax to designing system behavior and optimizing AI interactions. While AI handles most coding, developers must now excel in:

• Prompt Engineering – Crafting precise inputs for optimal AI-generated code.
  
  
• System Architecture & Optimization – Ensuring efficiency, scalability, and security.
  
  
• AI Orchestration & Fine-Tuning – Using multiple AI tools to generate, test, and refine code.
  
  

As AI advances, the line between developer and AI collaborator continues to blur. This shift has begun to democratize software development, enabling non-technical users to generate applications without traditional coding expertise. However, this accessibility comes with risks—security vulnerabilities, inefficient architectures, and compliance challenges—especially for users unfamiliar with software development best practices.

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A Longer Game of Chinese Whispers

The need for clear and precise requirements is more critical than ever. In traditional development, non-technical stakeholders could provide functional requirements or a rough PRD, and developers would bridge the gaps, translating vague ideas into a working codebase. Even when certain details were missing, experienced engineers could infer intent and fill in the blanks.

With vibe coding, AI has replaced this human interpretation. Non-technical users are now communicating directly with language models rather than human developers, making it essential to articulate requirements with absolute clarity. Unlike human engineers, AI lacks contextual reasoning and cannot infer unstated assumptions, which means ambiguous prompts can lead to incorrect or suboptimal implementations.

While vibe coding streamlines development, it also highlights the importance of structured, well-defined specifications—transforming software development into an exercise in precision rather than intuition.

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The Debugging Dilemma

As Ben South aptly puts it, 

The reality is that generating code with high-level prompts is one thing, understanding and debugging it is another. Developers traditionally rely on deep knowledge of system internals, data structures, and algorithms to diagnose issues. But with AI-generated code, debugging becomes a black-box problem where:

• Error Propagation: AI models can introduce subtle logical errors that cascade across a codebase. Without clear traceability, identifying the root cause can be a nightmare.
  
  
• Opaque Code Structures: Unlike human-written code, AI-generated functions might lack intuitive structure, making it difficult for engineers to interpret and modify.
  
  
• Prompt Sensitivity: Minor variations in prompts can lead to vastly different outputs, making it hard to standardize debugging practices.

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Security Risks & Technical Debt

A major concern with vibe coding is security—or the lack thereof. AI-generated code often lacks rigorous validation for vulnerabilities (e.g., SQL injections, buffer overflows, privilege escalation risks), leading to:

• Critical vulnerabilities embedded in applications, requiring extensive audits and patches later.
  
  
• Security knowledge gaps, as developers increasingly rely on AI without deeply understanding security implications.
  
  
• Compliance failures, especially in regulated industries where AI-generated code may not meet strict security and data protection requirements.
  
  

Beyond security, technical debt is an inevitable consequence of AI-generated code. LLMs prioritize new code generation over reuse or refactoring, leading to:

• Redundant, fragmented functions that perform similar operations under different names, bloating the codebase.
  
  
• Diminished maintainability, where human developers struggle to decipher AI-generated logic over time.
  
  
• AI-driven code sprawl, as models generate solutions without considering long-term efficiency, skipping refactoring in favor of rewriting.
  
  

Without proactive oversight, organizations risk accumulating silent technical debt, making future optimizations costly and complex.

Mika Kuusisto's take on AI-generated code highlights another looming issue: technical debt. 

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 If AI-written code dominates development without human oversight, the result may be an influx of inefficient, redundant, or fragile systems. This will inevitably require senior engineers to intervene and untangle the mess, leading to skyrocketing maintenance costs.

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The Growing Gap in Developer Expertise

A rising concern in the developer community is that many junior developers entering the industry today lack fundamental coding knowledge—not because they aren't talented, but because AI SaaS tools like GitHub Copilot are doing the heavy lifting. While these tools accelerate development, they also introduce a critical problem: understanding versus execution.

• AI reliance without comprehension – Many junior developers can ship functional code using vibe coding, but without deeply understanding data structures, algorithms, or system design, debugging and optimizing become insurmountable challenges.
  

• Surface-level problem-solving – Instead of breaking down problems and architecting solutions, some newer developers simply rely on AI to generate snippets, often without questioning efficiency, scalability, or security.
  
  
• Diminished debugging skills – When AI-written code inevitably fails, developers need deep problem-solving expertise. However, if someone has never truly written the code they’re debugging, where do they even begin?

This trend raises difficult questions for the industry. What is vibe coding, and how does it impact long-term software development expertise? If junior developers learn to code by consuming AI-generated solutions, will they ever develop the critical thinking skills necessary to innovate rather than just assemble?

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Security Risks in AI-Generated Code

Beyond the skills gap, another overlooked issue is the source of training data for large AI models. Most AI coding assistants are trained on vast collections of open-source software, which brings its own set of challenges:

• Unpatched vulnerabilities – Many open-source repositories contain known security flaws with no available fixes, meaning AI models may unknowingly propagate vulnerable patterns into production code.
  
  
• Code rot and outdated dependencies – AI may generate code that relies on outdated libraries, unmaintained projects, or insecure dependencies. Given that software ages like milk rather than wine, this technical debt accumulates quickly, especially when using vibe coding techniques to accelerate development.
• No proprietary security enforcement – AI models aren’t trained on curated, “secure” enterprise codebases (even if such a thing existed). This means they don’t inherently prioritize security best practices unless explicitly prompted.

The danger here isn’t just in the code being generated—it’s in how quickly it’s being shipped. Developers using AI-assisted coding may inadvertently introduce these security flaws without ever stopping to assess the risks.

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Slowing Down to Build Resilient Software

The key takeaway? Engineering rigor cannot be optional. While AI can enhance productivity, developers must still:

• Understand what they’re building – Learning core programming concepts is still crucial, regardless of AI assistance.
  
  
• Prioritize security reviews – Code should be analyzed for vulnerabilities, even if it’s AI-generated.
  
  
• Stay updated on dependencies – Avoid shipping AI-suggested code that relies on outdated or unmaintained components.
  
  

AI is transforming software development, but blind reliance on it may lead to a future where debugging, security, and deep problem-solving become lost arts.

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Security Challenges of Vibe Coding and AI-Generated Code

The rise of vibe coding, or prompt-driven development, has significantly accelerated software creation, but it also introduces security concerns that must be addressed at scale. One of the primary risks is the over-reliance on AI-generated code without a deep understanding of its security implications.

Client-Side API Exposure: Why It Matters

When developers write traditional backend-driven applications, API keys are securely stored on the server, and clients communicate with a backend service that handles authentication and data access. However, in modern AI-assisted development—where speed and automation take priority—many non-technical users and AI-generated codebases unknowingly make direct API calls from the frontend.

This approach creates a serious security risk because API keys, when embedded in frontend code, become visible to anyone inspecting the browser’s Network tab. Attackers can easily extract these keys and make unauthorized requests, potentially accessing or modifying sensitive data.

Even platforms with built-in security models, like Supabase, cannot prevent API key exposure if applications are not designed securely. This is why understanding and implementing best practices is critical.

Mitigating API Exposure Risks in Supabase

While Supabase and similar backend services provide security features, developers must take proactive steps to prevent unauthorized access:

• Use an anon key, not a service role key – Supabase provides an anon key designed for unauthenticated access. The service role key, which grants full database privileges, should never be exposed in the frontend.
  
  
• Enforce Row Level Security (RLS) – RLS ensures that even if an anon key is exposed, unauthorized queries are blocked at the database level. Without RLS, an attacker could potentially query the entire database.
  
  
• Use backend APIs as an intermediary – Instead of exposing API keys in the frontend, queries should be routed through a secure backend API or Supabase Edge Functions, acting as a controlled access layer.

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AI Coding Agents: GoCodeo’s Approach to Secure Token Management

AI coding agents like GoCodeo streamline application development by generating backend integrations automatically. However, security remains a top priority. In contrast to naive implementations that hardcode API credentials, GoCodeo follows a secure approach to token management:

• Secure OAuth processes: GoCodeo generates a Supabase access token based on the user's OAuth flow so that the user can select which Supabase project to connect with GoCodeo.
• Scoped access control: GoCodeo enforces least privilege access, ensuring that generated tokens only have the permissions necessary for specific operations.
• Encouraging Environment Variable Use – GoCodeo prioritizes secure key management through environment variables, guiding AI-generated code toward best practices. However, implementation may vary based on the LLM’s response, allowing flexibility while maintaining a security-first approach.
  
  

The Broader Risk: AI-Generated Code and Security Gaps

A major security concern with AI-generated code is its dependency on large-scale open-source datasets, which often contain vulnerabilities, outdated dependencies, and insecure configurations. These risks include:

• Dependency Drift and Code Rot – Many AI-generated projects rely on outdated open-source libraries, some of which have known vulnerabilities that may not be actively patched.
  
  
• Lack of Security-Aware Code Generation – Most large language models (LLMs) generating code are not trained on security-validated datasets, making them prone to producing insecure default implementations.
  
  
• Lack of Contextual Security Constraints – AI-generated code lacks the contextual awareness needed to enforce security policies dynamically. For instance, hardcoded secrets, missing access control checks, and unsafe API calls often appear in AI-generated outputs.
  
  

Mitigating Risks in AI-Generated Code

Security in AI-driven development is not an afterthought—it must be built into the workflow. Developers should enforce:

1. Automated security scanning: Use tools like Semgrep, Snyk, or GitHub Dependabot to detect vulnerabilities in AI-generated code before deployment.
   
   
2. Security linting and static analysis: Implement static code analysis tools to detect hardcoded credentials, unsafe API usage, and missing authorization checks.
   
   
3. Manual security reviews: AI-generated code should undergo peer reviews with a focus on security implications, especially for authentication flows and sensitive data handling.
   
   
4. Runtime security monitoring: Leverage tools like OpenTelemetry for real-time API request monitoring, anomaly detection, and logging access patterns to detect potential breaches.

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Opportunities in AI-Driven Development and Vibe Coding

While vibe coding introduces security challenges, it also presents transformative opportunities that could redefine software engineering practices. The evolution of Large Language Models (LLMs) and agentic AI workflows opens doors to enhanced automation, improved security postures, and more efficient software delivery cycles.

1. AI as a Security Co-Pilot: Closing the Developer-to-Security Gap

One of the most pressing challenges in security is the severe imbalance between developers and security engineers, often reported to be around 100:1 in many organizations. This disparity makes it difficult for security teams to review every line of code or assess every deployment in real time.

LLMs can help bridge this gap by acting as AppSec copilots, performing:

• Automated static code analysis – Identifying vulnerabilities such as SQL injection, XSS, and insecure API calls in real time as developers write code.
• Context-aware security suggestions – Instead of generic security recommendations, LLMs can provide context-specific fixes based on project architecture, dependencies, and known threat models.
• Compliance enforcement – Ensuring that code adheres to frameworks such as OWASP ASVS, CIS Benchmarks, and NIST guidelines without requiring constant manual review.

This shift enables a Secure-by-Design paradigm, where AI not only assists in development but also enforces security controls from the ground up. In the context of AI SaaS, these security-focused AI agents can be embedded into AI app builders and AI website builders, ensuring that applications are secure from the outset.

2. AI-Driven Auto-Remediation: The Next Step in Secure Software Development

While many organizations may hesitate to fully trust AI-driven auto-remediations, the potential for AI to identify, assess, and patch vulnerabilities autonomously is too significant to ignore. A structured human-in-the-loop approach could pave the way for safe and controlled adoption of AI-based remediation, where:

• LLMs scan and detect security issues, prioritizing them based on exploitability and impact.
• AI agents propose secure fixes, surfacing them to developers for approval.
• Over time, confidence scores and versioned learning mechanisms could allow AI to autonomously remediate low-risk issues, freeing up developers to focus on higher-priority work.

This is particularly beneficial for teams leveraging vibe coding, where AI-generated code needs automated security validation before deployment. With AI SaaS integrations, auto-remediation can seamlessly fit into AI app builders and AI website builders, improving the overall security posture of AI-generated applications.

3. Agentic Workflows: Collaborative AI Systems for End-to-End Security

Beyond individual LLM-based copilots, multi-agent AI workflows present an even greater opportunity. Rather than a single AI model handling all tasks, organizations can deploy specialized AI agents that:

• Develop code with security-aware coding practices.
• Review generated code, flagging potential security issues.
• Test security constraints, ensuring proper authentication, authorization, and data handling.
• Remediate findings, either autonomously or with developer oversight.

These AI agents can interact continuously throughout the development lifecycle, functioning as a collective security force that scales far beyond what human teams can achieve alone. AI SaaS platforms are already exploring agentic workflows, integrating them into AI app builders to facilitate security-aware software development.

4. AI-Augmented Threat Modeling and Attack Simulations

Current threat modeling practices require manual effort, often limiting their frequency and effectiveness. With AI-driven development, there’s an opportunity to:

• Automate threat modeling processes, dynamically assessing application architectures and predicting potential attack vectors.
• Simulate attacks against AI-generated code, allowing LLMs to test security assumptions and identify weaknesses proactively.
• Enhance incident response readiness, using AI to analyze security logs, correlate attack patterns, and recommend defensive actions.

By integrating AI SaaS capabilities into proactive security assessments, organizations can shift security left, identifying vulnerabilities before they become production risks. With vibe coding becoming a dominant paradigm, these AI-powered security tools will be essential in ensuring that AI-generated applications remain resilient against emerging threats.

Vibe coding is transforming software development, making AI-driven code generation more accessible to both developers and non-technical users. However, this shift introduces critical challenges—security vulnerabilities, technical debt, and the need for precise requirements. Without proper safeguards, AI-generated applications risk becoming inefficient, insecure, and difficult to maintain.

To navigate these challenges, developers must adopt structured validation, enforce security best practices, and optimize AI workflows. GoCodeo ensures that AI-powered development remains secure and scalable by integrating scoped access controls, secure token management, and responsible automation. As vibe coding evolves, the focus must shift from just speed to building AI-assisted software that is both robust and maintainable.