Also, check out my e-book on how to mastering vibe-coding with ease, and not fall into the technical debt trap. I'm a software engineer made data scientist and AI engineer, and I'll help you code with AI like a pro:
stan.store/codingmerma...
Vibe coding can launch your ideas faster than ever — but only if you know how to guide it.
Learn the rules, avoid the traps, and you’ll build apps that actually last. 🚀
If you're serious about building with AI, follow me for more vibe coding lessons and real-world tips!
Everyone ignores the technical debt
Fast, dirty building feels great in the beginning.
But every messy shortcut you let slip today becomes tomorrow’s mountain of tech debt.
Most are not aware about the security vulnerabilities that come with vibe coding
When AI writes your app, it often skips crucial things like input validation, authentication checks, and rate limiting.
This leaves giant holes hackers can exploit.
AI Doesn't Understand Real-World Business Logic
AI only follows instructions; it doesn’t anticipate messy real-world problems.
Business logic has to come from you.
Troubleshooting produces even more problems than when you started
Sometimes, you ask AI to fix one bug — and it causes three more.
Without clear guidance, AI tries to "help," but often patches things in weird, unstable ways.
You can’t understand the code AI produced
One of the biggest traps: letting AI spit out big chunks of code you don’t actually understand.
It feels productive... until something crashes and you have no clue where to even start.
Can’t actually code (not even a little)
You don't need to be a senior engineer to vibe code — but total coding ignorance will limit you.
Even basic skills like reading Python, fixing simple bugs, or understanding API calls make a huge difference.
Can’t expand the app beyond MVP
Many people manage to build a beautiful MVP in record time — but when it's time to scale, things break down. Without a real foundation underneath, you realize you can't easily add new features, handle more users, or even update the backend easily.
Prompts that are quick to lose context
When you start vibe coding, it feels magical. But if your prompts aren't detailed enough, AI quickly loses track of what you're building. One small misunderstanding early on? It snowballs into confusing, broken apps.
Last week, I helped teach vibe coding to 70 employees!
Here are mistakes every vibe coder makes at the beginning
Can't wait to share what I've worked on. 🤍
Follow for more content on entrepreneurship, data science, AI
But it's not something you can afford to have unless you're very successful and you can broaden it
How I applied the course I bought?
On data science because I wanted to provide my followers and students with a better and more approachable learning experience.
In today's economy, most people can't afford to fail more than once, but the path to successful entrepreneurship is rocky and barefoot.
And another tip, if creators tell you you don't need a niche, know that you're actually the niche.
Most creators who sell these courses are relevant to do so because they worked with different brands, academies and have actually failed multiple times before launching.
Because I have over 9 years of experience in digital marketing through copywriting, content strategy, marketing strategy, marketing analyst.
I've helped entrepreneurs grow and build wealth before personal brand became the buzzword.
I see so many people with usernames like
- MoneywithX
- WealthwithY
- AbundantGirlZ
I immediately know they're fresh graduates from a digital marketing course. Why am I relevant to say this?
Instead apply your branding course knowledge on something you already know how to do.
Make a course on crocheting, coding, copywriting - something in your niche. And if you want to really be good in personal branding offer it as a service and not product.
You shouldn't use what you learned to sell another course on branding or digital marketing as a whole, because that shows you didn't learn anything and you don't have the necessary experience that makes you credible to sell another course.
In 2023,I learned I oversaturated my niche and it was time to learn something new. I purchased a course on personal branding.
There were things I already knew and there were new skills I learned, not one moment I regretted buying it and you shouldn't either.
But there is one thing you're doing wrong from the start. You're copying the niche and sales strategy of your course creators and it's hindering your growth.
Why?
Because there's so many of you projecting the strategy so many personal brands are utilizing.
Is this you?
Recently laid off, recent graduate, student SAH Mom, dad, you name it. You purchased a course that costs $500, did everything right, yet nothing works?
No one tells you that personal brand journey is full of ups and downs, trial and error and failures.
Let me deinfluence you on why your personal brand is failing, as person who's had a personal brand that's 9 years old before the social media leverage it has today. 👇🏻
So OpenAI says it plans to charge up to $20,000/month for specialized AI agents.
Time to cancel my ChatGPT subscription and just fine tune DeepSeek R1 to become my PhD AI agent from Temu.
Follow @codingmermaid.ai and check out my AI engineering roadmap that will help you become not only AI-literate but give you the AI building mindset that will enable you to build in AI.
From Python to Agentic AI
stan.store/codingmerma...
The future: Agentic AI
• Understand Autonomous Agents
• Master Goal-Driven Behavior
• Explore Multi-Agent Systems
• Learn Reinforcement Learning Basics
• Hands-On Practice with premade models
Thank you for checking my post out. I hope you found it insightful.
Instead of relying only on pre-learned knowledge, it searches a database for relevant information and then generates accurate, updated responses. —great for chatbots, research tools, and knowledge management systems.
RAGs (Retrieval Augmented Generation)
Retrieval-Augmented Generation (RAG) combines language models with external data retrieval.
Vector databases store and search these embeddings quickly. They're used to find similar items fast, like recommending products, finding related documents, or powering AI search tools.
Vector embeddings and databases
Vector embeddings turn text, images, or data into numerical lists (vectors) that capture meaning and similarity. They help AI understand relationships—like knowing "king" and "queen" are related.