If you’re growing in the tech field, make sure you have public and relevant projects on your GitHub.
And when I say “relevant,” I’m not talking about complex enterprise systems —
complete study projects are perfectly fine, such as:
- Well-structured APIs
- Functional frontend applications
- Small full-stack apps
- Tools that solve real problems
- Project templates and boilerplates
In other words: something that shows your technical maturity, not just random bootcamp exercises or generic CRUDs thrown together.
These projects help recruiters and tech leads understand:
- How you structure an application
- Your architectural thinking
- Whether you apply industry best practices
- Your awareness of security
- Your ability to document your work
- Code clarity and consistency
- How you make technical decisions
When I used to review candidates, I checked all of this.
And professionals who ignored these details lost points in the evaluation, even if they were technically strong.
“But AI can generate all the code now.”
Yes, AI helps a lot.
But here are two important truths:
1) AI will not do everything for you — especially when it comes to deep understanding, technical reasoning, and decision-making.
Not to mention the situations where AI generates way more code than necessary.
2) It’s easy to spot AI-generated code that wasn’t reviewed, or when a developer can’t explain what they built.
And let’s be honest: even before AI, many people were copying code or entire projects without understanding them.
The problem is not using AI.
The problem is using it without reviewing, without understanding, and without developing real critical thinking.
If you want to stand out:
👉 publish complete, meaningful projects
👉 review your code (AI-generated or not)
👉 demonstrate technical maturity in practice
👉 build a portfolio that truly represents your skills
Trust me: it makes a huge difference.
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