Written in Indonesian for readers interested in entering software engineering, this post sets out to separate fact from fiction about the field so that newcomers form realistic expectations, particularly as new trends emerge. It opens with a quotation from Martin Fowler — “Any fool can write code that a computer can understand. Good programmers write code that humans can understand.” — which it reads as stressing code quality and readability over mere functionality, and returns to that remark throughout.
Most of the post addresses general myths about the profession. Its last substantive section turns to Vibe Coding, which it introduces as one of the new terms and approaches that the rapid development of AI has brought into software engineering, and treats with the same myth-versus-reality framing.
Key Points
- The post rejects the myth that software engineering requires a mathematical genius with a high IQ: strong logic and problem-solving matter, it says, but analytical thinking, problem decomposition and a strong willingness to learn are often far more crucial.
- It rejects the image of the solitary engineer: modern projects involve teams, and writing code “humans can understand” also requires effective communication.
- It says writing code is only one part of the job, alongside requirements analysis, design, testing, documentation, maintenance and meetings, and that senior engineers must present their code and its impact on business processes or revenue.
- It argues that there is no single best development method: Agile’s flexibility suits dynamic projects, while Waterfall can fit small-to-medium, short projects with a clear scope.
- It rejects the myth that AI will fully replace programmers through vibe coding: generative AI such as LLMs helps produce code quickly, but real creativity, deep understanding of business goals, out-of-the-box problem solving and accountability still require humans, and ensuring that code is secure, efficient, fits the larger context and is maintainable remains the engineer’s responsibility.
- It says vibe coding may speed up prototyping or repetitive tasks, but human intervention and oversight remain crucial.
- It argues that vibe coding and other AI tools change the developer’s focus rather than eliminating the role — toward architecture design, product thinking and making sure the AI produces fitting solutions — with more monotonous tasks delegated to AI, while warning that using AI-generated code without full understanding can be risky.
Context
The post is published by an online programming-education platform and is interleaved with calls to register for its courses, so its intended audience is prospective learners. Its treatment of vibe coding is brief and framed around careers: it closes by saying that success in the field takes hard work, dedication and continuous learning, including adapting to new things such as vibe coding and sharpening non-technical skills.