Project Overview

To start off I invented a fictional product to which our lectures topic, Print to Mobile(Web) and the use of QR Codes can be applied to. I worked to gain added value for the product, as the title suggest my approach was by extending the physical product with a digital audio book. The project aims to provide an immersive experience through the scanning process and accommodate readers who may prefer to listen to books over reading or just want to listen while being occupied by something else.

A moment after the QR code is scanned the user is met with a book that opens its cover (the same as the physical book cover) and reviles an audio player where the user can listen to the audio book. Test it your self HERE.

Implementation

I decided to create a static web application as the title suggests. This was in order to simplify the project by having no server side. For the creation of the app I used a famous front end framework called Nuxt.js that is based on Vue.js for template files. Following the chronological order of how the user would encounter interact with the app. The opening of the book cover is created using Tailwindcss and some additional custom css code where the built in tools of tailwind were not enough. When the book is opened the user encounters the audio player which is created using the Web Audio API that is available in the browser by default. Finally I used the extremely user friendly deployment service of Netlify to publish my application to the web within minutes.

Summary

The product of this project is a prototype that shows how there can be added value to an existing products by incorporating the Print2Mobile concept. Future iterations of the project could also make use of augmented reality to further enhance the book by showing the plane models “pop-out” of the book through the users cameras into their surroundings and allow them to have a closer look.

I personally had fun seeing what’s the most creative way I can integrate the Print2Mobile concept in my project.

Find the application at: https://curious-mousse-8bd688.netlify.app/?auto=1

Disclaimer: This is a personal project created for educational and illustrative purposes. It is not affiliated with any existing organizations or products.

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This blog will show: What it is, why it’s popular, what can go wrong, and a checklist for safely.

What Is “Vibe Coding

“Vibe coding” is programming using natural language. AI generates the code and the developer iterates based on the results. In this approach. “the coder does not need to understand how or why the code works” (Merriam-Webster, 2025) The term became mainstream in 2025 and is now widely used to describe building software by prompting AI and iterating quickly, often with minimal manual code review.

Instead of understanding code, developers when vibe coding focus on a simple question
“Does it work?”

Traditional coding focuses on understanding, structure, and design, while vibe coding focuses on speed, iteration, and end-result.

The Vibe Coding Loop

Vibe coding typically follows a simple loop:

1. Describe feature in natural language
2. AI generates code
3. Run it and test it
4. paste errors back
5. Fix and repeat

This loop further emphasizes the speed of iteration vibe coding allows its users. Results are produced faster and the developer can focus more on refinement of the results or extending features.

Why People Love It: Speed

The biggest reason developers vibe code is speed.
As they say time equals money. AI assistants can greatly reduce task completion time. Its been measured that developers using GitHub Copilot finished a coding task ~55.8% faster
(Peng et al., 2023). Naturally this number can change depending on the scenario but it clearly shows that Ai assistance accelerates implementation.

Reality Check: “Looks Right” ≠ “Is Right”

Results can be misleading, just because code looks correct does not mean it actually is correct. The more “long-term maintenance” and “high stakes,” the more we must shift from vibes to verification. A well known phenomenon hallucination can occur in AI-generated code. While the code can be syntactically correct it can contain factual inaccuracy. ” (Alansari & Luqman, 2025)

Bugs may also be subtle:

• Edge cases may not be handled
• Race conditions may exist
• Wrong assumptions may be built into the logic
• Maintenance becomes risky if nobody understands the code later

This leads to systems that work initially but become fragile and difficult to maintain.

Real-World Security Risks

Often AI-generated code contain serious vulnerabilities for example: Data exfiltration (exportation), unsafe actions by AI agents injecting instructions.

Schreiber & Tippe (2025) — Analysed 7,703 AI-generated files and found thousands of CWE-mapped vulnerabilities.
Sabra, Schmitt & Tyler (2025) — SonarQube analysis showed hard-coded credentials and other severe issues even when unit tests passed. SonarQube is a tool for Static Code Analysis for all languages.
Tihanyi et al. (2024) — Large-scale model study found ~62% of generated code was insecure by formal verification. This formal verification is done through Efficient SMT-based Context-Bounded Model Checker (ESBMC).
Fu et al. (2025) — Copilot and similar assistants produced code with ~24–30% serious Common Weakness Enumeration (CWE) security flaws across languages.

Even if your own code is fine, your tooling and agent workflow can be the attack surface especially when the AI has “powers” (terminal access, file writes, network).

Security Risks in Vibe Coding

Prompt Injection

LLM app guard rails can be manipulated by prompt injection. OWASP lists prompt injection as a top LLM app risk category (OWASP, 2024). UK NCSC warns prompt injection is not like SQLi and may be harder to fully “fix” with a single mitigation (UK National Cyber Security Centre (NCSC), 2025).

Insecure Code Generation

LLMs may generate code with known vulnerabilities Prone to insecure code, but can improve with feedback/hints (Yan et al., 2025)

Guardrails: How to Vibe Code Responsibly

Vibe coding can be powerful but only with proper guardrails.
Responsible use should include:

• Generate tests first
• Force model to justify by explanations
• Use linters + type checking
• Prompt “avoid CWE top issues; use safe defaults”
• Never let an agent run with broad permissions by default
• Code review: auth, input validation, file/network access

Using AI-assisted development should be done with taking the necessary precautions. Keep a fast loop but add verification gates. Effort from  typing code to validating it.

Takeaways

Vibe coding is a new way to code that is becoming more and more standardised. It is fast, iterative, and AI-driven. It helps in rapid development and experimentation, but it also introduces risks in quality, security, and long-term maintainability.

Used responsibly, vibe coding can be a powerful tool in modern development. Without guardrails, it can quickly become hidden tech-debt and security issues.

References

Alansari, A., & Luqman, H. (2025). Large Language Models Hallucination: A Comprehensive Survey (No. arXiv:2510.06265). arXiv. https://doi.org/10.48550/arXiv.2510.06265

Collins Dictionary. (2025). Collins—The Collins Word of the Year 2025 is… https://www.collinsdictionary.com/woty

Fu, Y., Liang, P., Tahir, A., Li, Z., Shahin, M., Yu, J., & Chen, J. (2025). Security Weaknesses of Copilot-Generated Code in GitHub Projects: An Empirical Study (No. arXiv:2310.02059). arXiv. https://doi.org/10.48550/arXiv.2310.02059

Merriam-Webster. (2025). Vibe coding. https://www.merriam-webster.com/slang/vibe-coding

OWASP. (2024, 2025). LLMRisks Archive. OWASP Gen AI Security Project. https://genai.owasp.org/llm-top-10/

Peng, S., Kalliamvakou, E., Cihon, P., & Demirer, M. (2023). The Impact of AI on Developer Productivity: Evidence from GitHub Copilot (No. arXiv:2302.06590). arXiv. https://doi.org/10.48550/arXiv.2302.06590

Sabra, A., Schmitt, O., & Tyler, J. (2025). Assessing the Quality and Security of AI-Generated Code: A Quantitative Analysis (No. arXiv:2508.14727). arXiv. https://doi.org/10.48550/arXiv.2508.14727

Schreiber, M., & Tippe, P. (2026). Security Vulnerabilities in AI-Generated Code: A Large-Scale Analysis of Public GitHub Repositories (Vol. 16219, pp. 153–172). https://doi.org/10.1007/978-981-95-3537-8_9

Tihanyi, N., Bisztray, T., Ferrag, M. A., Jain, R., & Cordeiro, L. (2024). How secure is AI-generated code: A large-scale comparison of large language models. Empirical Software Engineering, 30. https://doi.org/10.1007/s10664-024-10590-1

UK National Cyber Security Centre (NCSC). (2025). Prompt injection is not SQL injection (it may be worse). https://www.ncsc.gov.uk/blog-post/prompt-injection-is-not-sql-injection

Yan, H., Vaidya, S. S., Zhang, X., & Yao, Z. (2025). Guiding AI to Fix Its Own Flaws: An Empirical Study on LLM-Driven Secure Code Generation (No. arXiv:2506.23034). arXiv. https://doi.org/10.48550/arXiv.2506.23034

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The Idea

Buying a used car can be overwhelming. Traditional marketplaces often present users with long, filter-heavy pages filled with static listings. My goal was to improve that experience. The swipe mechanic makes browsing fun.

Objectives and Learning Goals

Three main goals: technical, business, and personal learning.

Technical Objectives

To develop a mobile-web application that demonstrates:

• authentication with protected routes
• creation and management of car listings
• swipe-based browsing
• favorites management
• filtering by make, model, and price
• frontend-backend integration in a structured monorepo
• database-backed persistence for users, listings, images, and swipe decisions

Although the initial plan mentioned Ionic and Angular, the project evolved into using:

• Nuxt 4 for the frontend
• NestJS for the backend API
• PostgreSQL + Prisma ORM for persistence
• Cookie-based JWT authentication for session handling
• Docker for local database infrastructure

This is due to my more dependable previous experience with this stack to build a full-stack monorepo.

Project Scope

MVP

• Mobile-first UI
• User registration and login
• Car listing creation and management
• Swipe-based browsing of cars
• Filtering by make, model, and price
• Favorites page
• Backend-connected database

What the Current MVP Includes

• user registration and login
• cookie-based authenticated sessions
• protected pages for logged-in users
• a swipe feed for browsing available cars
• like and pass actions
• a favorites page for liked cars
• a “My Cars” area for sellers
• car listing creation
• car listing editing
• car listing removal through soft delete
• car detail pages
• optional image upload for listings
• simple client-side filters for make, model, and maximum price

Explicitly Excluded

• Payment processing between users
• Full production-grade infrastructure
• Chat functionality
• Location-based matching
• Analytics systems
• App Store / Play Store deployment

Key Deliverables

The final outcome of RevMatchMarket includes:

1. A working MVP
   A swipe-based used car marketplace where:
   • Sellers can create, Edit, Delete listings
   • Listings have images
   • Buyers can browse cars
   • Users can like or pass listings
   • Users can apply filters to listings
   • Favorites are stored and retrievable
   • Full authentication
2. Documentation
   • Detailed project and milestone planning
   • This blog post summarizing the process and learnings
3. Final Presentation
   • Live demo of the application
   • Reflection on challenges
   • Discussion of future improvements

Challenges and Lessons Learned

Creating a UI for mobile devices required careful consideration. Authentication and protecting routes was required a few iterations. Managing JWT tokens via httpOnly cookies and handling session bootstrapping on the frontend. Features need to be limited. It is tempting to add chat, real-time updates, and other features but keeping the MVP focused was essential.

Diagrams – Database & Flow Chart

Final Thoughts

RevMatchMarket redefines a simple interaction pattern. This project, I strengthened my skills in full-stack development, mobile-first UI design, backend integration, and structured project planning.

This project started as an assignment but it has the can become much more.

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Abstract

Real-time 2D human pose estimation using smartphone cameras has
become a highly relevant technology for interactive applications
such as physical education, gesture-based interaction, and aug-
mented reality. While recent advances have significantly improved
pose estimation models, using such systems on mobile devices
remains challenging due to computational, energy, and latency con-
straints. This paper presents a state-of-the-art review of real-time
2D pose estimation approaches targeting smartphones, following
a PRISMA inspired systematic literature review. Two recent and
relevant papers, LMFormer and LiPE, are analyzed and compared
based on design choices, efficiency metrics, evaluation scope, and
suitability for mobile and human–computer interaction (HCI) prac-
tical use cases. The review highlights the balancing of accuracy and
computational efficiency, identifies limitations in current evaluation
practices, and outlines open research gaps related to end-to-end
mobile performance and interaction-oriented evaluation criteria.

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