Imagine if you could delegate the most monotonous, time-consuming, or outright unpleasant aspects of your daily work life to an automated system—a silent digital assistant capable of flawlessly executing your least favorite task without complaint or fatigue. This concept, once purely hypothetical, is increasingly becoming a tangible reality in today’s technology-driven world. Industry leaders such as Alphabet’s CEO Sundar Pichai, along with other prominent figures in the tech ecosystem, are actively encouraging even those with no traditional programming background to engage in a new, intuitive approach called ‘vibe coding.’ This practice relies on the use of artificial intelligence to craft code through plain, conversational language, refining outputs via an iterative dialogue between human intention and machine generation.
Driven by both curiosity and a genuine desire to simplify my workflow, I decided to explore this idea firsthand. Despite my absolute lack of coding knowledge, I was determined to create something personally beneficial—an automated tool that could eliminate one of the more tedious parts of my professional routine. What began as an innocent experiment gradually evolved into a half-day odyssey filled with both frustration and fascination. By the end, though, I had gained profound insights into the underlying mechanics of large language models (LLMs) and had developed a realistic appreciation for the inevitable learning curve that accompanies meaningful interaction with AI tools.
My motivation was rooted in a specific professional irritation. As a journalist, I am expected to stay up to date with the thoughts, announcements, and idiosyncratic musings of major technology executives on social media—an environment that often feels more draining than enlightening. Constantly jumping from one platform to another, juggling tabs to view around a dozen separate feeds, quickly proved to be a remarkably inefficient and joyless process. My goal, therefore, was deceptively simple: to construct a single webpage capable of consolidating multiple feeds. I envisioned three neat columns, each dedicated respectively to President Donald Trump, Tesla’s CEO Elon Musk, and OpenAI’s CEO Sam Altman. Ideally, these columns would refresh automatically whenever new posts appeared, sparing me the burden of manual updates or logins.
I embarked on the project at around two o’clock in the afternoon, beginning with full disclosure to ChatGPT that I possessed no programming expertise whatsoever. After carefully explaining my concept, I received from the AI a neatly structured plan, which included downloading the necessary development tools, creating specific files, and initiating a local web server on my MacBook via Terminal—a command-line interface that had always seemed intimidatingly technical. ChatGPT then produced a lengthy string of code and instructed me to place it into a file created with TextEdit, the simple text editor functioning here as my makeshift coding environment. Up to this point, the process unfolded smoothly. However, my optimism evaporated once I tried to run the webpage locally. Instead of neat feeds, I was met with error messages populating every column.
Following ChatGPT’s troubleshooting advice, I experimented with adjusting the refresh rate of the feeds and replaced certain web addresses with alternative links it provided. Yet, the problem persisted. I asked the AI to rewrite the problematic sections of code, only to witness a cascading series of new and cryptic errors with every attempt—each more perplexing than the last. Eventually, the chatbot began supplying instructions that clearly exceeded my basic comprehension. Soon after, my persistent queries were met with a chilling “gateway timeout” message, effectively locking me out of the AI interface for two whole hours. My grand experiment had, for the moment, imploded.
By seven in the evening, humbled but not defeated, I reached out to my partner for assistance. He, a Ph.D. student with backgrounds in both computer science and physics, quickly identified the flaw in my approach. My mistake, he explained, was in attempting to tackle an overly ambitious, multi-element project without mastering the fundamentals. His advice was simple yet transformative: delete everything and start small. Focus, he said, on fetching the feed of a single person as a proof of concept. Crucially, he also redefined how I should communicate with ChatGPT—treat it not as an omniscient search engine, but rather as an enormously knowledgeable toddler. The AI needs clear direction and patience, he emphasized, and I should ask it to explain its reasoning so that I could genuinely learn from the process, instead of expecting instant perfection.
With renewed perspective, I initiated a second attempt—this time scaling the project down to emulate only Donald Trump’s Truth Social posts. Before diving into code, I asked ChatGPT for guidance on how best to approach the problem and phrase my prompts. The AI seemed almost enthusiastic at my more methodical mindset. It suggested beginning with a simulated test—a mock feed composed of imaginary posts—to ensure that the structural logic of the code functioned properly before connecting to any real data sources. Following its step-by-step instructions, I created and named several TextEdit files, carefully pasted the corresponding code snippets, and soon watched as a simple mock webpage materialized, displaying several placeholder messages representing sample posts.
Encouraged by this small success, I continued collaborating with the AI as it meticulously pointed out which specific lines to alter in order to transition from dummy entries to authentic content. Predictably, another challenge emerged: a stubborn “cannot fetch feed” error. This time, however, the solution proved surprisingly mundane. Rather than rewriting code, ChatGPT recommended switching my web browser from Chrome to Safari or Firefox. I complied, reloaded the page, and after a brief thirty-second delay, my minimalist web app opened successfully—automatically displaying Trump’s most recent posts without ever requiring me to log into Truth Social. It was my first tangible victory in the world of vibe coding.
Reflecting on the experience, I came to appreciate a fundamental truth emphasized by computer scientists: even the most intelligent form of AI assistance cannot disguise inexperience. If you lack foundational understanding, your missteps will be evident in every missed semicolon and unresolved error message. Still, my small triumph carried valuable lessons. When applied patiently, AI-powered tools can become powerful extensions of human curiosity—collaborators that amplify problem-solving, provided we invest effort into understanding how they think and respond. The key lies in balance: embracing automation as a partner in learning, not as a shortcut to avoid it.
Ultimately, my half-day adventure illuminated how ‘vibe coding’ is not about conjuring instant solutions but about cultivating an iterative relationship with technology. Success emerges from active participation—experimenting, analyzing outputs, testing mock versions before integrating real data, and continually refining one’s approach. This mindset, calm yet inquisitive, transforms coding with AI from a frustrating guessing game into a dynamic process of discovery. Perhaps, if my little webpage continues to spare me from the monotony of scrolling through social media feeds, those hours spent wrestling with code will indeed reclaim far more of my time than they consumed in the first place.
Sourse: https://www.businessinsider.com/what-my-trial-and-error-vibe-coding-taught-me-chatgpt-2025-10
