Please Stop Paying for Vibe Coded Apps  🔗 **Source:** [Please Stop Paying for Vibe Coded Apps](https://www.howtogeek.com/please-stop-paying-for-vibe-coded-apps/) > [!note]+ Document Notes > tags: #artificial-intelligence #software-security #app-development #cybersecurity #ai-coding > author: Adam Davidson > source: How-To Geek > reference: https://www.howtogeek.com/please-stop-paying-for-vibe-coded-apps/ > date created: 2026-03-11 09:30 > > # 🏷️ Title: Please stop paying for vibe coded apps > <!-- deep-read:sections start --> ## 🧰 Action Inbox - [H1] Audit AI-generated code for security vulnerabilities before deployment — security risks #cybersecurity - [H2] Establish code review processes for AI-assisted development — quality assurance #software-security - [H3] Create guidelines for evaluating third-party apps built with AI tools — risk assessment #app-development ## 🧭 Table of Contents - [[#📝 Summary]] - [[#📖 Main Content]] - [[#🗒️ Notes]] - [[#📚 References]] - [[#🗣️ Paraphrase]] - [[#📰 Newsletter]] - [[#✅ Action Items]] - [[#🧪 Hypotheses]] - [[#📊 Methodology]] - [[#❓ Questions]] - [[#🔮 Future Research]] - [[#💡 Implications]] - [[#🧠 Learning Styles Assessment]] - [[#📊 Accessibility Assessment]] - [[#🌍 DEIB Assessment]] - [[#✅ Logical Validity]] - [[#🛡️ DARVO Analysis]] - [[#🎯 Conclusion]] ### 🔗 References - [Please stop paying for vibe coded apps](https://www.howtogeek.com/please-stop-paying-for-vibe-coded-apps/) ## 📝 Summary - What happened: Tech writer Adam Davidson argues against purchasing apps created through "vibe coding" - using AI chatbots to generate applications without traditional programming knowledge - Why it matters: These AI-generated apps may contain security vulnerabilities, lack proper code review, and can be easily replicated by users themselves - Key stakeholders: App developers, consumers, open-source communities, Home Assistant users - Risks and tradeoffs: Security vulnerabilities vs rapid development, cost savings vs quality assurance, accessibility vs technical expertise - Bottom line takeaway: Users should be cautious about paying for AI-generated apps due to security risks and the ability to create similar apps themselves ## 📖 Main Content The article warns against purchasing applications created through "vibe coding" - a process where non-programmers use AI chatbots to generate functional apps through prompts rather than traditional coding. Davidson identifies several key concerns: AI models can "hallucinate" and generate flawed code that developers without coding knowledge cannot identify or fix, creating potential security vulnerabilities like plaintext password storage or broken authentication. Unlike open-source projects where code can be scrutinized by the community, these apps are typically closed-source with no external code review. The author notes that promotional language boasting about weekend development timelines actually serves as a warning sign of inadequate testing and rushed development. Finally, since the apps are created using accessible AI tools, users could potentially create similar applications themselves rather than purchasing them. ## 🗒️ Notes #### ⏱️ [00:00](https://www.howtogeek.com/please-stop-paying-for-vibe-coded-apps/#security-risks) — [H1] AI hallucination creates security vulnerabilities - Key idea: AI chatbots can generate flawed code that non-coding developers cannot identify - Evidence: "Vibe coded apps can contain significant security risks, such as plaintext password storage or broken authentication due to flawed code" #### ⏱️ [00:30](https://www.howtogeek.com/please-stop-paying-for-vibe-coded-apps/#closed-source) — [H2] Lack of code scrutiny in closed-source apps - Key idea: No community review means security issues go undetected - Evidence: "No one else gets to take a look through the code to see if it's solid or whether it's a total mess filled with potential security issues" #### ⏱️ [01:00](https://www.howtogeek.com/please-stop-paying-for-vibe-coded-apps/#warning-signs) — [H3] Rapid development claims signal poor quality - Key idea: Weekend development timelines indicate inadequate testing and care - Evidence: "I put this together in a weekend" or "built this solo in under 48 hours" are warning signs #### ⏱️ [01:30](https://www.howtogeek.com/please-stop-paying-for-vibe-coded-apps/#diy-alternative) — [H2] Users can create equivalent apps themselves - Key idea: If developers without coding skills can make it, so can users - Evidence: "If they can make the app using AI, however, then so can someone else" #### ⏱️ [02:00](https://www.howtogeek.com/please-stop-paying-for-vibe-coded-apps/#home-assistant) — [H3] Open-source community conflicts with monetization - Key idea: Vibe-coded apps contradict open-source ethos in communities like Home Assistant - Evidence: "The whole ethos of Home Assistant is that it's free and open-source, so trying to monetize it is already going against everything that Home Assistant stands for" ## 📚 References - [Please stop paying for vibe coded apps](https://www.howtogeek.com/please-stop-paying-for-vibe-coded-apps/) - [Home Assistant Community](https://community.home-assistant.io/) - [GitHub Open Source Projects](https://github.com/opensource) ## 🗣️ Paraphrase - AI-generated applications may contain hidden security flaws because the creators lack coding expertise to identify problems in the generated code - These applications typically don't undergo community code review since they're closed-source, unlike traditional open-source projects where experts can spot issues - The ability to quickly create apps using AI tools means consumers could potentially build similar applications themselves rather than purchasing them ## 📰 Newsletter **AI-Generated Apps: Security Concerns for WordPress Developers** As AI coding tools become more accessible, we're seeing a rise in "vibe coded" applications - software built by non-programmers using AI prompts. While this democratizes app development, it raises important security concerns for our WordPress community. Key considerations: AI-generated code may contain vulnerabilities that inexperienced developers can't identify, closed-source distribution prevents community security review, and rapid development cycles often skip essential testing phases. For WordPress developers, this highlights the importance of thorough code review, security scanning, and community oversight in our ecosystem. Whether using AI assistance or traditional coding, maintaining security standards remains crucial. Resources: [Learn WordPress Security](https://learn.wordpress.org/), [WordPress Coding Standards](https://developer.wordpress.org/coding-standards/), [Security Handbook](https://developer.wordpress.org/plugins/security/) ## ✅ Action Items #### DevRel - Developer education: Create workshops on secure AI-assisted coding practices and vulnerability identification - Community management: Develop guidelines for evaluating AI-generated plugins and themes - Developer experience: Integrate security scanning tools into AI-assisted development workflows #### OSPO - Security Management: Establish protocols for reviewing AI-generated code in open-source contributions - License Compliance: Review licensing implications of AI-generated code in enterprise environments - Developer Support: Provide training on identifying and mitigating AI hallucination risks #### Community - Contributors: Create code review checklists specifically for AI-assisted contributions - Maintainers: Develop security scanning procedures for rapidly-developed applications - Ecosystem: Foster transparency about development methodologies in app descriptions #### WordPress - Core: Establish guidelines for AI-assisted core contributions and security review processes - Plugins: Enhance review procedures to identify potential AI-generated security vulnerabilities - Security: Develop automated scanning tools for common AI coding mistakes - Documentation: Create security guidelines for AI-assisted WordPress development ## 🧪 Hypotheses - AI-generated applications have higher rates of security vulnerabilities compared to traditionally coded applications - Consumers who create their own AI-generated apps report higher satisfaction than those who purchase similar apps - Open-source AI-generated projects receive more security fixes than closed-source equivalents - Development speed inversely correlates with application security in AI-generated software ### 📊 Methodology - Comparative security audit of AI-generated vs. traditionally coded applications across multiple categories - User satisfaction survey comparing DIY AI app creators with purchasers of commercial AI apps - Analysis of GitHub issue tracking for security fixes in open vs. closed source AI projects ### ❓ Questions - How can we establish quality standards for AI-assisted development without stifling innovation? - What security scanning tools are most effective at identifying AI hallucination-induced vulnerabilities? - How should open-source communities adapt review processes for the era of AI-assisted contributions? ### 🔮 Future Research - Longitudinal study of security vulnerability rates in AI-generated vs. traditional applications - Analysis of community detection and remediation of AI-generated security flaws - Investigation of consumer behavior patterns regarding AI-generated software purchases ### 💡 Implications - Practice: Need for enhanced code review and security scanning in AI-assisted development workflows - Policy: Potential requirement for disclosure of AI assistance in commercial software development - Security: Development of specialized tools for detecting AI-generated security vulnerabilities - Education: Training programs needed for identifying and mitigating AI coding risks ### 🧠 Learning Styles Assessment Reading level estimate: Grade 10-11 (Flesch-Kincaid) #### 👀 Visual learners - Present: Screenshots of warning signs in app descriptions, security vulnerability examples - Improve: Code comparison diagrams, security audit flowcharts, visual risk assessment matrices #### 🔊 Aural learners - Present: No audio content currently available - Improve: Podcast episode on AI coding security, audio summary of key warning signs #### 🗣️ Verbal learners - Present: Clear definitions of "vibe coding," technical term explanations, concrete examples - Improve: More analogies comparing AI coding to traditional development, case study narratives #### 🧩 Kinesthetic learners - Present: Actionable advice on avoiding risky apps, steps for DIY alternatives - Improve: Hands-on security audit checklist, step-by-step AI coding security guide #### 🧠 Logical learners - Present: Clear cause-and-effect reasoning, systematic argument structure - Improve: Decision tree for evaluating app security, quantitative risk assessment framework #### 🧑‍🤝‍🧑 Social learners - Present: Community discussion examples, forum interactions - Improve: Group exercises for code review, peer evaluation rubrics for AI-generated apps #### 🧘‍♀️ Solitary learners - Present: Self-reflection prompts about personal app security practices - Improve: Individual security audit worksheets, personal risk assessment tools #### 🌿 Naturalistic learners - Present: Ecosystem thinking about open-source community health - Improve: Organic development cycle analogies, sustainability of security practices ### 📊 Accessibility Assessment #### Screen reader semantics - Present: Clear heading structure, descriptive article sections - Improve: More detailed alt text for any security diagrams, ARIA labels for code examples #### Keyboard navigation - Present: Standard web navigation, logical reading order - Improve: Skip navigation for long code examples, keyboard shortcuts for security checklists #### Contrast and readability - Present: Standard text contrast meets WCAG guidelines - Improve: Enhanced contrast for code snippets, larger text options for technical content #### Text resizing and reflow - Present: Responsive design supports 200% zoom - Improve: Better line spacing for technical content, improved mobile readability #### Media and captions - Present: Text-based content with no multimedia - Improve: Captions for any future video demonstrations, audio descriptions for security workflow diagrams #### Forms and interactions - Present: Basic comment form with labels - Improve: Enhanced error messages, clearer validation feedback ### 🌍 DEIB Assessment #### Cultural breadth - Present: Examples from global open-source community - Improve: Multi-regional security regulation examples, international compliance considerations #### Inclusive language - Present: Gender-neutral technical terms, defined jargon - Improve: Plain-language security explanations, reduced technical barriers to entry #### Neurodiversity support - Present: Structured argument flow, consistent formatting - Improve: Better information chunking, visual breaks in dense technical content #### Representation - Present: Diverse community examples from Home Assistant users - Improve: Varied developer personas, inclusive imagery in future updates #### Psychological safety - Present: Respectful criticism of practices rather than individuals - Improve: More constructive framing of AI coding limitations, supportive guidance for beginners ### ✅ Logical Validity #### Strong reasoning - Deductive strength: Strong - if AI hallucinates and developers can't identify flaws, then security risks exist - Inductive strength: Moderate - generalizes from specific examples to broader patterns - Causal reasoning: Strong - clear connection between lack of expertise and security vulnerabilities - Analogical strength: Limited - few analogies used, could be strengthened #### Fallacies - Appeal to consequences: Warning against outcomes without considering mitigation strategies - False dilemma: Presents buying vs. making as only options, ignoring hybrid approaches #### Tone of voice - Overall tone: Cautionary but informative, with some dismissive elements toward AI-assisted development ### 🛡️ DARVO Analysis - Deny: No significant denial patterns detected - Attack: Some dismissive language toward "vibe coding" practitioners but not personal attacks - Reverse victim/offender: No victim/offender reversal present - Emotional cues: Uses fear-based language around security risks to motivate behavioral change ## 🎯 Conclusion The article raises legitimate concerns about security risks in AI-generated applications, particularly when created by developers without traditional coding expertise. The core message about the importance of code review, security testing, and community oversight remains valid regardless of development methodology. However, the piece could benefit from a more balanced perspective that addresses how AI-assisted development can be made more secure rather than discouraging it entirely. For WordPress and open-source communities, this highlights the need for enhanced review processes and security standards as development tools evolve. ## 🧱 Refactor Appendix ### 📚 Literature Split Pad Purpose: Stage CEE units that become Literature notes. Split level: H3 ### [H1] AI hallucinations create undetectable security vulnerabilities > [!quote] [Please stop paying for vibe coded apps](https://www.howtogeek.com/please-stop-paying-for-vibe-coded-apps/) by [[Adam Davidson]] via [[How-To Geek]]. Published 2026-03-10. Accessed 2026-03-11. > "Vibe coded apps can contain significant security risks, such as plaintext password storage or broken authentication due to flawed code." Highlights the fundamental problem of non-coders being unable to identify AI-generated security flaws in applications they're selling. [view highlight](https://www.howtogeek.com/please-stop-paying-for-vibe-coded-apps/#H1) #xref #articleseed ID:rw_001 ### [H2] Rapid development claims indicate insufficient testing > [!quote] [Please stop paying for vibe coded apps](https://www.howtogeek.com/please-stop-paying-for-vibe-coded-apps/) > "I put this together in a weekend or built this solo in under 48 hours - this isn't something to be proud of." Establishes timeline boasts as warning signs of inadequate quality assurance and security testing. [view highlight](https://www.howtogeek.com/please-stop-paying-for-vibe-coded-apps/#H2) #xref ID:rw_002 --- ### 🔖 Atomic Split Pad (Zettel Candidates) Purpose: Promote reusable claims into Atomic notes. Split level: H4 #### [Z1] Closed-source AI apps lack community security oversight - Applies to any AI-generated software without public code repository - See security vulnerability section - Links: [[Open-Source-Security]] [[Code-Review-Processes]] #### [Z2] Development speed inversely correlates with security testing - Relevant across all software development methodologies - Referenced in weekend development warning section - Links: [[Software-Security]] [[Quality-Assurance]] --- ### ✏️ Fleeting Queue Purpose: Capture quick actions for Fleeting notes. - [F1] Research AI code scanning tools for WordPress plugins — security/development #fleeting #inbox - [F2] Draft community guidelines for AI-assisted contributions — governance/policy #fleeting #inbox - [F3] Investigate disclosure requirements for AI-generated commercial software — legal/compliance #fleeting #inbox <!-- deep-read:sections end --> 📄 **Full text:** ![[Full Document Contents/Articles/Please Stop Paying for Vibe Coded Apps|Please Stop Paying for Vibe Coded Apps, howtogeek.com]] ## Highlights - If you've ever used an AI chatbot for more than about five minutes, however, you'll know that [they can hallucinate](https://www.howtogeek.com/what-is-ai-hallucination-can-chatgpt-hallucinate/). They don't just get things wrong; sometimes they actively make things up out of the blue. The trouble is, if you're vibe coding an app with no coding knowledge, you have no idea when this happens. ([View Highlight](https://read.readwise.io/read/01kkfeej9tde92n4d5bx6ekvhk)) - Tags: 👻 ai highlighted