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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesAI made coding feel approachable to Chetan Vashistth when he used a hand-drawn webpage sketch as the starting point for a real page. But his account is less a story of instant mastery than of learning, friction and revision: he moved from early ChatGPT experiments to coding tools and terminal workflows, made mistakes, and eventually returned to software design fundamentals. The useful lesson is not that AI can do the learning for you. It is that a project can give you a reason to learn—and you still have to understand the code you use.
How the journey began: turn an idea into something visible
In his first-person account, Vashistth describes being struck by what ChatGPT could do, then trying a more personal experiment: translating a hand-drawn webpage sketch into HTML and CSS. The sketch gave him a concrete target. Instead of beginning with abstract exercises, he could compare the page he imagined with the page the code produced.
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That is a useful starting point for a beginner because a visible result creates questions worth pursuing: Which element controls the layout? Why does the page look different on a phone? What changes when a color, margin or heading is edited? AI can help propose answers and code, but the experience is one person’s account—not evidence that AI always teaches better than a course or that a generated page demonstrates programming skill.
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Vashistth’s account moves from ChatGPT to Cursor and Claude Code, but the change in tools did not make every part of coding effortless. He describes struggling with copying and pasting code, then spending days refining a terminal workflow. Those details matter: using an AI coding tool still involves understanding where project files are, how changes fit together, and how to inspect what happened when a command or edit does not work.
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Tool fluency is its own skill. A beginner may need to learn how to give a coding assistant access to the relevant project, interpret its proposed changes, and work comfortably in a terminal. Each of those tasks can be confusing before it becomes routine. Progress can therefore feel uneven: a tool may produce a useful answer quickly while the surrounding workflow remains unfamiliar.
When a working result is not the same as understanding
AI-generated code can run and still leave its user unsure why it works. That distinction appears in both Vashistth’s account and research on programming with AI, though the studies examine different people and tasks.
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| Evidence | What was studied | What it found—and what it does not show |
|---|---|---|
| IEEE conference paper, 2024 | Introductory programming activities that integrated ChatGPT and GitHub Copilot with critical-thinking practices. | The paper’s abstract reports increased student awareness of AI’s possibilities and limits, alongside increased reported critical-thinking practices after the assignment. This is evidence from a course assignment, not proof that every learner or tool produces the same result. |
| ACM ICER study, 2025 | A controlled experiment with 10 undergraduate computing students doing brownfield tasks in a legacy web app, with and without Copilot. | Participants completed tasks 34.9% faster with Copilot, and the abstract reports 50% more solution progress. Students also raised concerns about understanding why suggestions worked. The small sample and specific task setting do not establish a general productivity or learning effect. |
| Anthropic study, January 2026 | A randomized trial with 52 mostly junior software engineers who used Python regularly but were learning the unfamiliar Trio library. | The AI-assisted group averaged 50% on an immediate quiz, compared with 67% for the hand-coding group. This measured immediate comprehension of a new library, not long-term skill or the experience of absolute beginners learning to code from scratch. |
These results are not contradictory measures of one universal effect. The 2025 study asked how students performed on unfamiliar legacy-code tasks; Anthropic’s 2026 trial examined comprehension after learning a new Python library. They involved different participants, tasks and outcomes. Neither establishes that AI always makes coding faster or that using it inevitably weakens learning.
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How to use AI without handing over the learning
A practical way to keep yourself responsible for understanding is to treat generated code as a proposal to inspect, not an answer to accept blindly.
- Start with a small, meaningful goal. Use a page, feature or bug that gives you a clear way to tell whether the result behaves as intended.
- Ask for the idea as well as the code. Request an explanation of the relevant concepts, how a proposed change fits the existing project, and what alternatives might work.
- Read the changes before relying on them. Trace what the edited code does and identify unfamiliar functions, settings or files. Ask follow-up questions about the parts you cannot explain.
- Run and test the result. Check the behavior that matters to your goal, rather than treating code generation as confirmation that the change is correct.
- Practice debugging. When something fails, inspect the error and reason through likely causes before asking for a fix. Compare the explanation with what the application actually does.
- Return to fundamentals when needed. If you can use a solution but cannot explain its structure, revisit the relevant programming or design concept instead of adding another layer of generated code.
Anthropic’s analysis associated stronger mastery with participants who used AI for explanations and conceptual questions rather than simply delegating code. The report explicitly says those qualitative patterns do not establish causation, so they are a useful learning approach to try—not a proven formula.
A mistake can change how you work
Vashistth also recounts a database incident that prompted him to strengthen configuration and guardrails. The account does not establish that a particular safeguard would prevent every database mistake, but it illustrates a broader point: when generated changes can affect persistent data, the consequences deserve more attention than whether the code merely compiles.
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For a learner, that means understanding what a command or change will touch before running it, and being especially deliberate around databases and other consequential parts of a project. An assistant can suggest a fix; responsibility for deciding whether to apply it remains with the person working on the system.
Experimentation is useful, but fundamentals still matter
Later in the account, Vashistth experiments with MCP and Blender, then turns back toward software design fundamentals. That return is not a rejection of AI tools. It reflects a useful distinction: exploring what tools can do is different from building the concepts that help you decide what should be built, how components fit together, and whether a proposed solution makes sense.
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The research supports keeping that distinction in view. The IEEE paper connects AI use in an introductory programming assignment with critical-thinking practices, while the Anthropic trial found that AI assistance did not guarantee stronger immediate comprehension. An assistant can help produce output; the learner still benefits from reading, questioning, testing and debugging it.
What this journey can—and cannot—tell a beginner
Vashistth’s story is a personal account, not a representative measure of how long it takes to learn programming, how much tools cost, or what every beginner will experience. Its value is in the shape of the process: a personally meaningful first project, periods of tool friction, a consequential mistake, broader experimentation and renewed attention to fundamentals.
His closing words capture that unfinished quality: “That is where I am today. Still figuring it out — just faster than before.” The point is not to reach a stage where every tool or concept feels effortless. It is to use assistance in ways that leave you more able to understand the next change than you were before.
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