Irene B. RizzoSenior DeveloperYou describe in plain language what you want to build and an AI generates the code, runs it, finds the errors and fixes them on its own. This is not science fiction: it is already happening in product teams all over the world, and it has a name: vibe coding. The central idea is to shift the effort from syntax to intent, from "how to write it" to "what I want to get".
At Scallent, we feel this shift in concrete terms. More and more founders and product managers who come to the platform ask whether the developer they are going to hire has mastered these methodologies, and what that means in practice. The question is a fair one, but it hides pitfalls worth knowing about before making any decision.
This article explains what vibe coding is, how it works, in which contexts it is genuinely advantageous, which tools are available and, above all, what changes for anyone hiring technical talent in this new landscape.
What vibe coding is and why it is everywhere
From writing syntax to defining intent
The term was popularised by Andrej Karpathy, former director of AI at Tesla and a leading researcher in the field. The central idea is simple: instead of writing code line by line, the person describes the desired outcome and the AI handles the rest. Different platforms converge on the same reading. Salesforce frames it as AI-driven development in which anyone can design, build, test and publish software using natural language instructions. Replit adds the iterative dimension: the agent writes, receives criticism and adjusts until it arrives at the intended product. IBM broadens the scope even further, defining it simply as asking AI tools to generate code instead of writing it manually.
The point of convergence between these readings is clear: the focus moves from writing syntax to defining intent. Those who use these methodologies guide, assess and refine; the AI produces, runs and corrects. The typical flow is: intent, plan, initial code, execution, feedback, refinement.
vibe coding vs. AI-assisted development: what is the difference
There is an important distinction that many technical sources underline. In the strict sense of the term, prompt-based programming implies little human review and understanding of the code produced: the person accepts what the AI generates without necessarily grasping how it works. AI-assisted development is different: the programmer reviews, tests, understands and maintains the code, using AI as an accelerator and not as a substitute for technical judgement.
For any project in production, the recommendation from the main platforms and from the technical community is unequivocal: always keep human review, testing and accountability for what is delivered. This distinction has direct consequences when it comes to assessing a freelance developer. It is not enough to know whether they use AI; what matters is knowing how they use it.
How development is changing in startups and SMEs
Prototypes in days, not weeks
The most visible impact for startups and SMEs is the drastic reduction in the time needed to validate an idea. A team that once needed weeks to have a working prototype can today have one in days, without committing the development budget before confirming that the direction makes sense. Iteration cycles become much faster: changing a flow, testing a product variant or adding a new feature no longer requires a full sprint.
Imagine a Portuguese SME that wants to validate a booking platform for local service providers. With intent-driven development tools, it is possible to have a navigable prototype in a few days, test it with real users, gather feedback and decide whether it is worth investing in a robust version. This rapid validation cycle is probably the greatest practical benefit of these methodologies for those who do not have the resources of a large company.
When it makes sense and when it does not
Prompt-based programming has contexts where it is genuinely advantageous: prototypes, MVPs and proofs of concept where speed is worth more than immediate robustness. Outside these scenarios, the approach has serious limits.
In systems with sensitive data, mission-critical applications or products already in production with real users, generating code without rigorous technical review is a risk that no amount of speed justifies. Speed has a real cost when there is no technical competence supervising what is produced. This is the rule of thumb: the more critical the system, the more indispensable the engineer behind the prompts.
The main platforms and what each one offers
For building from scratch: the most accessible platforms
For anyone who wants to turn an idea into an application quickly, the market offers several options. It is worth distinguishing them from low-code or no-code tools (low-code and no-code): while the latter rest on visual interfaces and predefined flows, vibe coding platforms generate real code from natural language, which brings greater flexibility but also greater technical responsibility.
Replit Agent is particularly suited to prototypes and MVPs: it generates and modifies complete applications from natural language instructions, creates interfaces and databases, debugs and allows publishing in a single environment in the browser. Vercel's v0 is especially well suited to React/Next.js interfaces and prototypes, with the possibility of importing Figma files directly. Bolt.new allows JavaScript web applications to be created in the browser at speed. Lovable stands out for MVPs and internal products that require conversational refinement over time.
The choice between these platforms depends on the context: Replit is better suited to complete applications, v0 to interfaces with precise design, Bolt to quick JavaScript prototypes and Lovable when you want to iterate conversationally on the product.
For existing projects: agent tools for developers
When there is already a codebase, the relevant set of tools is different. Cursor and Windsurf are development environments with contextual agents that work across multiple files in an existing repository, making precise changes and maintaining coherence with the architecture already established. Claude Code is a terminal agent geared towards extensive refactoring, testing and working directly with the file system. GitHub Copilot remains the benchmark for code completion, test generation and contextual assistance in any development environment.
One important note: these tools presuppose the technical competence to supervise what the agent produces. The developer remains responsible for the quality, security and maintainability of the code. Changing development environment does not remove that responsibility.
The real risks you cannot ignore
Security, quality and code that "works but is insecure"
Studi
es on AI-generated code consistently point to a gap between functioning and security: a significant proportion of the code produced runs correctly, but has not been through any form of security review. This pattern sums up the central problem: working code is not the same as secure code.
The most common vulnerabilities in applications built with AI-generated code include endpoints without access control, authorisation flaws that expose other users' data, XSS due to a lack of input validation, SQL injection and API keys or tokens left directly in the code or in public repositories. Add to that outdated or vulnerable dependencies that widen the attack surface. In at least one documented case, data belonging to thousands of users was exposed through flaws in automatically generated authentication logic.
The problem of putting unreviewed code into production
Quality problems are not limited to security. Several rounds of instructions can generate an incoherent architecture: coupled components, scattered business rules, error handling that only covers the expected path and tests that do not validate limits, abuse or permissions. Maintainability deteriorates when nobody fully understands the generated code.
The most serious risk is not automatic generation in itself, but publishing that code in production without review by someone capable of assessing architecture, security and non-functional requirements. Code that nobody can audit is code that nobody can fix when something goes wrong. This is why technical competence remains indispensable, regardless of the tools used.
What this changes for anyone wanting to hire a freelance developer
The developer who masters AI and traditional engineering
There is a mistaken idea that vibe coding will reduce the need for competent developers. Exactly the opposite is happening. A developer who uses AI agents without a solid engineering base produces code quickly, but with no guarantee of quality, security or maintainability. The most valuable profile in 2026 is the developer who knows how to use these tools and has the fundamentals to review, test and stand behind what they deliver.
What sets this profile apart is concrete: they know how to break problems down into small, verifiable increments; they critically assess the generated code instead of accepting it without question; they write tests that go beyond the expected path; they manage secrets and dependencies responsibly; and they can explain the architectural decisions they have made. Generation speed only has value if review quality matches it.
What to ask before hiring
When assessing a freelance developer in this context, there are concrete questions that make the difference. Ask how they integrate AI tools into their workflow and what they do with the generated code before delivering it. Ask how they ensure review and testing, and how they handle security and dependencies in projects with sensitive data. It is also worth understanding their experience with projects of a similar size and complexity to yours.
The warning signs are equally clear: very fast delivery with no mention of tests, refusal to explain technical decisions, an inconsistent stack across components with no justification and the absence of any conversation about security or architecture. A developer who uses AI responsibly talks naturally about the review process; one who does not have that habit rarely mentions it.
How Scallent ensures talent prepared for this new paradigm
Curation that assesses methodology, not just stack
At Scallent, curation goes beyond the portfolio and the technologies listed on a profile. When selecting developers for each project, the team assesses whether the professional has the engineering fundamentals needed to work with or without AI assistance, and whether they are able to ensure quality and security regardless of the methodology they use. In a market where anyone can generate code, the difference lies in the ability to review it, test it and deliver it responsibly.
This approach reflects a simple reality: generation speed has increased for everyone, but the ability to stand behind what is delivered remains scarce. That is precisely the ability Scallent looks for and validates in every talent joining the platform.
Delivery quality, whatever the method
Scallent's model protects clients on both sides of the risk. Active curation ensures you do not hire the wrong profile. Guaranteed replacement in the event of non-compliance protects the project. Milestone payments are only released when the agreed stages are met, which means delivery quality is not an abstract promise: it is a condition for payment to go ahead.
If your next project requires a developer who masters both vibe coding and good engineering practice, Scallent makes that selection for you, in days, not months. Talk to our team and tell us what you need to build.
Frequently asked questions about vibe coding
What is vibe coding, in simple terms?
It is an approach to software development in which the person describes in plain language what they want to build and an AI generates, runs and corrects the code. The focus shifts from writing syntax to defining intent.
When should I use vibe coding?
Prompt-based programming is especially useful in prototypes, MVPs and proofs of concept where speed is the priority. For systems in production, with sensitive data or of a mission-critical nature, it is essential to maintain rigorous technical review of the generated code.
Does vibe coding replace developers?
No. On the contrary, it raises the value of developers with solid engineering fundamentals. Generating code quickly is within anyone's reach; ensuring that code is secure, maintainable and correct still requires technical competence.
What is the difference between vibe coding and low-code or no-code tools?
Low-code and no-code tools are based on visual interfaces and predefined flows. vibe coding generates real code from natural language, which offers more flexibility but also implies greater responsibility in reviewing what is produced.
How can I find a developer with vibe coding experience?
At Scallent, we select developers who have mastered these methodologies and have the engineering fundamentals to stand behind what they deliver. If you are looking for that profile, talk to our team.
