The Limits and Future of Vibe Coding

Vibe coding can shorten the distance between an idea and a working application.

A person can describe a feature in natural language, ask an AI coding assistant to inspect a project, generate code, explain an error, write tests, or prepare deployment files. Tasks that once required hours of manual typing may begin with a short conversation.

That speed is real, but it can be misleading.

Generating code quickly is not the same as defining the right product, building a maintainable system, protecting user data, or proving that the software works. As AI coding tools improve, the human role does not disappear. It moves toward requirements, judgment, review, testing, architecture, and accountability.

This final article looks beyond the Personal Reading List Tracker and examines what vibe coding can do well, where it remains limited, and which skills will matter as AI-assisted development continues to evolve.

• Previous article: How to Test and Deploy Your First Vibe-Coded App

Version note: AI coding tools, model capabilities, pricing, privacy controls, and supported workflows change frequently. Verify tool-specific details against current official documentation before choosing a product or publishing technical instructions.

What You Will Learn

By the end of this guide, you should be able to:

• explain the practical value of vibe coding;
• recognize the difference between code generation and software engineering;
• identify common technical and product limitations;
• understand why review work can become the main bottleneck;
• recognize security, privacy, and maintenance risks;
• identify projects where vibe coding works well;
• identify situations that require greater caution;
• understand which human skills become more valuable;
• evaluate likely directions for future coding agents;
• create a responsible learning path after your first application.

What Vibe Coding Has Already Changed

Traditional programming often begins with a person translating an idea directly into code.

An AI-assisted workflow may look different:

Describe the goal
→ Ask the AI to inspect the project
→ Review a proposed plan
→ Approve a limited change
→ Test the result
→ Revise or commit

The developer may type fewer individual lines while spending more time deciding:

• what the application should do;
• how large the task should be;
• which files may change;
• whether the result matches the requirement;
• how the change should be tested;
• whether the implementation is safe to keep.

This can make software creation more accessible.

A beginner can experiment with:

• a small personal website;
• a reading list;
• a text-processing tool;
• a local dashboard;
• a simple browser extension;
• a data-conversion utility.

An experienced developer can use the same tools for:

• repetitive implementation;
• test generation;
• code explanation;
• documentation;
• repository exploration;
• migration planning;
• debugging support.

The value is not limited to one skill level.

The risks are not limited to beginners either.

Code Generation Is Not the Whole Development Process

A functioning application requires more than source code.

A complete software process may include:

Problem definition
→ Requirements
→ Design
→ Implementation
→ Review
→ Testing
→ Security analysis
→ Deployment
→ Monitoring
→ Maintenance

An AI coding assistant can contribute to every stage.

It cannot independently guarantee that every stage was completed correctly.

For example, an AI may generate an authentication feature. That does not prove:

• user permissions are correct;
• credentials are protected;
• sessions expire safely;
• error messages avoid leaking information;
• account recovery works;
• audit requirements are satisfied.

The code may be syntactically valid and still be unsuitable for real users.

The central limitation of vibe coding is therefore not simply that AI sometimes writes incorrect syntax.

The deeper limitation is that software quality depends on context, judgment, evidence, and responsibility.

Limitation 1: A Vague Idea Still Produces Vague Software

AI tools are designed to fill gaps.

When your request is incomplete, the tool may choose:

• the framework;
• the data model;
• the folder structure;
• the user flow;
• the validation rules;
• the visual style;
• the storage method;
• the deployment target.

Those choices may be reasonable.

They may also conflict with your actual needs.

Consider this prompt:

Build an app that helps people manage books.

The assistant must guess whether the app needs:

• one user or many users;
• local or cloud storage;
• private lists or public reviews;
• ratings;
• recommendations;
• social features;
• payments;
• book-cover images;
• an external API.

A more powerful model can make more sophisticated guesses.

It cannot transform missing requirements into verified facts.

This is why the project brief in this series mattered.

The brief defined:

• one target user;
• one primary problem;
• a small feature set;
• excluded features;
• technical constraints;
• acceptance criteria;
• a definition of done.

Better requirements improve both human-written and AI-generated software.

Limitation 2: AI Context Is Always Incomplete

A coding agent may inspect many files, but it rarely has complete knowledge of the project’s real environment.

Important context may exist in:

• conversations;
• undocumented business rules;
• production data;
• customer expectations;
• deployment settings;
• monitoring systems;
• legal agreements;
• organizational policies;
• historical decisions;
• external services.

A function may look inefficient without the history explaining why it was written that way.

A field may look unused while another system depends on it.

A database query may work with sample data but become expensive on a production table.

A configuration value may appear unnecessary while protecting backward compatibility.

The AI works from the context it can access.

The project may depend on context that was never written down.

Larger Context Does Not Eliminate the Problem

Future tools will likely inspect larger portions of a repository and retain more project information.

That will help.

It will not automatically reveal:

• undocumented user expectations;
• hidden operational constraints;
• inaccurate documentation;
• data that should not be shared;
• decisions made outside the repository.

Better context reduces some errors.

It does not remove the need for human confirmation.

Limitation 3: Generated Code Can Be Plausible but Wrong

One of the most difficult AI errors is not obviously broken code.

It is code that looks correct.

For example:

function getVisibleBooks() {
  return books.filter((book) => book.status = activeFilter);
}

The condition uses assignment rather than comparison.

The code may run, but it changes each book’s status while filtering.

A beginner may not notice the difference between:

book.status = activeFilter

and:

book.status === activeFilter

The generated function appears simple and professional.

Its behavior is wrong.

Other plausible errors include:

• using the wrong API field;
• accepting an invalid status value;
• failing to save after removal;
• displaying unsafe user content through innerHTML;
• checking the full list instead of the filtered list;
• treating a browser environment variable as private;
• writing a test that never exercises the real feature.

The most dangerous output is often not obviously nonsensical.

It is output that passes a quick visual review.

Limitation 4: Generation Can Be Faster Than Review

An AI assistant can generate hundreds of lines quickly.

A human may need much longer to understand those lines.

This creates a review imbalance:

Generation speed
> Human understanding speed

When the generated change is small, the imbalance is manageable.

When the change touches:

• many files;
• several dependencies;
• configuration;
• authentication;
• storage;
• deployment;

the review burden grows rapidly.

This is why large one-shot prompts are risky.

The tool may produce a complete-looking application before the user has verified:

• the architecture;
• the data model;
• the security boundary;
• the dependency choices;
• the failure behavior.

The bottleneck moves from typing to judgment.

Future AI tools may also assist with review, but one AI system agreeing with another is not independent evidence.

Tests, observed behavior, documentation, and human reasoning still matter.

Limitation 5: AI Can Hide Complexity Behind a Simple Prompt

A prompt can be short while the requested system is complex.

For example:

Add login.

Those two words may imply decisions about:

• password storage;
• account registration;
• email verification;
• session management;
• access control;
• rate limiting;
• password recovery;
• data deletion;
• audit logging;
• privacy;
• abuse prevention.

A beginner may see a generated form and conclude that authentication is finished.

The visible interface is only one part of the system.

Vibe coding can make complex features feel deceptively small because natural language hides the number of technical and policy decisions involved.

A useful habit is to ask:

What systems and responsibilities are hidden inside this request?

Limitation 6: Security Does Not Appear Automatically

AI tools can generate secure patterns.

They can also generate:

• hard-coded credentials;
• exposed browser keys;
• unsafe HTML insertion;
• weak validation;
• overly broad permissions;
• insecure defaults;
• sensitive logs;
• unnecessary network access.

Security depends on the complete system, not one code snippet.

For example, moving an API key from source code into a frontend environment variable may look like an improvement.

If the value is included in the browser bundle, it remains visible.

Similarly, adding a server-side proxy may hide the key but create an unprotected public endpoint that anyone can call.

Each solution changes the security boundary.

AI-generated code should be reviewed by asking:

• What data enters the system?
• Where is it stored?
• Who can access it?
• What leaves the system?
• Which operations require permission?
• What happens when input is malicious?
• What happens when an external service fails?

Security is not one prompt added at the end.

It is part of the design.

Limitation 7: Privacy Can Be Lost Through the Tooling Process

Using an AI coding assistant may involve sharing project context with a service.

That context could include:

• source code;
• file names;
• comments;
• logs;
• configuration;
• test data;
• error messages;
• business rules.

Before using any tool with private or organizational code, review its current:

• privacy controls;
• data-retention settings;
• enterprise policies;
• training-data options;
• repository permissions;
• administrative controls.

Exact features and policies change, so current official documentation matters.

Regardless of tool settings, avoid sharing:

• passwords;
• private keys;
• access tokens;
• customer data;
• medical or financial records;
• confidential company information;
• production database exports.

The safest secret in an AI prompt is the one never pasted there.

Limitation 8: Maintenance Begins After the First Demo

A generated application may work on the day it is created.

Later, you may need to:

• fix bugs;
• update dependencies;
• respond to API changes;
• improve accessibility;
• support another browser;
• recover lost data;
• explain the code to another person;
• remove a feature;
• migrate hosting.

Software that cannot be understood is difficult to maintain.

A beginner may successfully generate an application but hesitate to change it because:

• functions are too large;
• naming is inconsistent;
• dependencies are unfamiliar;
• the project structure is excessive;
• there are no useful tests;
• the AI session that created it is gone.

This creates a maintenance gap.

The application exists, but the owner does not feel in control of it.

A strong vibe coding workflow reduces that gap through:

• small tasks;
• clear commits;
• project documentation;
• acceptance criteria;
• test coverage;
• simple architecture;
• consistent naming.

Limitation 9: Dependencies Can Accumulate Quietly

AI assistants often know popular libraries and frameworks.

That can lead them to add a package for problems that the existing platform can already solve.

Each dependency may introduce:

• additional code;
• version requirements;
• security updates;
• licensing considerations;
• build configuration;
• compatibility risk;
• future migration work.

A package can still be the right decision.

It should be added deliberately.

Before accepting one, ask:

• What problem does it solve?
• Can the current platform solve the problem?
• How much code does it add?
• Is it actively maintained?
• Does the project already use an equivalent?
• Will the application depend on it at runtime?
• What happens when it changes?

The Personal Reading List Tracker used browser features for storage, rendering, and interaction because the project did not require a large framework.

That decision kept the code easier to inspect.

Limitation 10: Tool Dependence Can Become Project Dependence

A project may gradually depend on one AI tool’s:

• instruction format;
• repository memory;
• editor integration;
• proprietary configuration;
• automated workflows;
• account access.

This may be convenient.

It can also make the project harder to continue when:

• pricing changes;
• features change;
• access is lost;
• the team adopts another tool;
• the model behaves differently;
• a plugin is discontinued.

The source code should remain understandable outside the AI tool.

Important project knowledge should live in durable files such as:

README.md
PROJECT_BRIEF.md
CONTRIBUTING.md
TESTING.md
architecture notes
issue tracker
Git history

An AI conversation should not be the only place where critical decisions exist.

Where Vibe Coding Works Best

Vibe coding is especially useful when a task is:

• clearly defined;
• small enough to review;
• easy to test;
• low in sensitivity;
• based on familiar technical patterns;
• reversible through Git.

Examples include:

• creating a small static page;
• adding a form;
• transforming text data;
• generating focused tests;
• explaining an unfamiliar function;
• adding a low-risk validation rule;
• drafting documentation;
• building a personal local tool;
• prototyping a user flow.

These tasks provide visible feedback.

They also allow the user to reject the result without causing serious harm.

Where Greater Caution Is Needed

More caution is appropriate when the software handles:

• authentication;
• payments;
• medical information;
• financial decisions;
• legal records;
• children’s data;
• workplace secrets;
• critical infrastructure;
• safety-sensitive operations;
• large-scale production data.

These systems may require:

• specialized expertise;
• formal security review;
• regulatory knowledge;
• threat modeling;
• performance testing;
• audit trails;
• incident-response planning.

AI can assist qualified people working in these areas.

It should not create false confidence for someone who cannot evaluate the consequences.

A Practical Risk Matrix

You can evaluate a task using two questions:

1. How difficult is the change to review?
2. How serious would failure be?

A simple matrix looks like this:

Review Difficulty	Failure Impact	Suggested Approach
Low	Low	Use normal small-feature workflow
High	Low	Reduce scope and add more tests
Low	High	Require specialist review and strong verification
High	High	Do not rely on unsupervised vibe coding

For example:

Change:
Update button spacing

Review difficulty:
Low

Failure impact:
Low

This is a suitable AI-assisted task.

Now compare:

Change:
Modify authorization rules for customer accounts

Review difficulty:
High

Failure impact:
High

That task requires much more than a convincing generated diff.

The Human Skills That Become More Valuable

As code generation improves, several human skills become more important rather than less important.

Problem Definition

The ability to describe the real user problem helps prevent unnecessary software.

Useful questions include:

• Who is the user?
• What are they trying to accomplish?
• What is the smallest useful outcome?
• Which features can wait?
• What does success look like?

A clear problem statement improves every later AI prompt.

Task Decomposition

Large features become manageable when divided into small, testable units.

For the reading list project:

Build the app

became:

Create page structure
→ Add form behavior
→ Validate titles
→ Render books
→ Add removal
→ Add filtering
→ Add persistence
→ Test
→ Deploy

This skill is central to reliable vibe coding.

Code Review

Review becomes more important when more code is generated.

A reviewer asks:

• Does the change match the requirement?
• Which files changed?
• Is the implementation more complex than necessary?
• Were dependencies added?
• Does invalid input behave safely?
• Are tests meaningful?
• Can the change be reversed?

The goal is not to find fault in every line.

It is to identify risk before the code becomes trusted history.

Debugging

AI can propose fixes, but effective debugging still requires:

Reproduce
→ Observe
→ Hypothesize
→ Test
→ Verify

A person who can collect evidence will get better results from an AI assistant than someone who repeatedly asks it to guess.

Testing

A successful demonstration is not a complete test.

Strong testing includes:

• normal behavior;
• invalid input;
• empty data;
• edge cases;
• feature interactions;
• failure states;
• regression checks;
• deployed behavior.

AI can generate test cases.

Humans must decide whether those cases represent real risk.

Systems Thinking

A local code change can affect:

• storage;
• APIs;
• deployment;
• permissions;
• performance;
• monitoring;
• maintenance.

Systems thinking asks how the pieces interact.

This skill becomes more valuable as agents gain the ability to change several parts of a project at once.

Communication

Software development includes explaining decisions to:

• teammates;
• reviewers;
• users;
• future maintainers;
• clients;
• yourself months later.

Clear project briefs, commit messages, documentation, and issue descriptions remain valuable regardless of who generated the implementation.

What Future Coding Agents May Improve

The future of vibe coding is likely to involve more than better code completion.

Several areas are likely to become increasingly important.

These are directions rather than guarantees.

Better Repository Understanding

Future agents may become better at:

• mapping dependencies;
• identifying related files;
• reading project documentation;
• remembering accepted conventions;
• comparing code with requirements;
• recognizing recent Git changes.

This could reduce incorrect assumptions.

The project will still benefit from accurate documentation.

An agent cannot rely on information that was never recorded.

More Test-Driven Agent Loops

A stronger agent workflow may look like:

Read requirement
→ Propose test
→ Confirm test fails
→ Implement change
→ Run test
→ Review diff
→ Report evidence

This is more reliable than:

Generate code
→ State that it should work

Tests will not prove every quality, but they provide stronger evidence than a natural-language summary.

Specialized Review Agents

Different AI systems may focus on:

• security;
• performance;
• accessibility;
• API compatibility;
• test quality;
• dependency risk;
• documentation.

Specialization could improve review depth.

It could also create a false sense of certainty if users treat multiple AI opinions as independent proof.

Human ownership and real testing will still matter.

Stronger Permission Boundaries

Future tools may offer more precise controls over:

• readable files;
• writable files;
• allowed commands;
• network access;
• secret access;
• deployment permissions;
• dependency installation.

A safer agent does not only understand the task.

It operates within a limited environment where mistakes have smaller consequences.

Better Change Provenance

Useful future workflows may record:

• which request produced a change;
• which model or tool proposed it;
• which files were modified;
• which tests were run;
• which human approved it;
• which deployment contains it.

This could improve auditability and debugging.

Git already provides part of this history.

Future tools may connect prompts, diffs, tests, and approvals more directly.

More Local and Private Workflows

Some teams may prefer models or tools that run within controlled environments.

Possible motivations include:

• code privacy;
• regulatory requirements;
• reduced external data sharing;
• predictable infrastructure;
• custom internal knowledge.

Local or private deployment does not automatically make a model accurate or secure.

It changes where the data and computation are controlled.

More Natural Prototyping

Vibe coding may continue lowering the cost of testing an idea.

A designer, analyst, researcher, or small business owner may be able to build an interactive prototype without first mastering a large toolchain.

This can improve communication.

A working prototype often reveals questions that a static document does not.

The risk is that a prototype may be mistaken for a production-ready system.

Clear labeling and review remain important.

The Likely Future Is Human-Directed, Not Human-Absent

A fully autonomous agent may be able to complete increasingly large tasks.

That does not mean human direction becomes unnecessary.

Software projects involve choices about:

• values;
• priorities;
• acceptable risk;
• user experience;
• privacy;
• cost;
• responsibility.

These are not purely coding questions.

A practical future workflow may resemble:

Human defines intent and boundaries
→ AI proposes and implements
→ Automated tools test
→ Human reviews evidence and risk
→ Controlled system deploys
→ Humans monitor real outcomes

The exact balance will vary by project.

Low-risk personal tools may allow more automation.

High-impact systems should require stronger oversight.

Will Vibe Coding Replace Developers?

The answer depends on what is meant by “developer.”

If development means typing every line manually, AI already changes that activity.

If development means:

• understanding users;
• designing systems;
• making trade-offs;
• reviewing risk;
• debugging failures;
• operating software;
• maintaining it over time;

then the role is broader than code generation.

AI may reduce demand for some repetitive tasks.

It may also increase the number of people who can attempt software projects and the amount of software that needs review, maintenance, and integration.

The safer conclusion is not that developers disappear or remain unchanged.

The work changes.

People who can direct, evaluate, and maintain AI-assisted systems are likely to be more effective than people who either reject the tools entirely or trust them without review.

Can a Non-Developer Build Real Software?

Yes, within the limits of the project and the person’s ability to verify it.

A non-developer can build a useful:

• personal tool;
• prototype;
• static website;
• small local application;
• workflow helper.

As the project grows, additional knowledge becomes necessary.

For example, moving from a local reading list to a multi-user cloud service introduces:

• authentication;
• database design;
• access control;
• privacy;
• backups;
• deployment;
• monitoring;
• maintenance.

The first application can be a starting point.

It should not create the belief that every future system has the same risk level.

What Beginners Should Learn Next

After completing the Personal Reading List Tracker, continue learning through increasingly difficult but controlled projects.

Strengthen JavaScript Fundamentals

Learn:

• variables;
• functions;
• arrays and objects;
• conditions;
• loops;
• events;
• asynchronous code;
• modules;
• error handling.

Ask the AI to explain generated code, then test the explanation with small experiments.

Learn the Browser Platform

Study:

• the DOM;
• forms;
• storage;
• network requests;
• accessibility;
• browser developer tools;
• security basics.

These concepts make browser-based AI-generated code easier to evaluate.

Improve Git Skills

Practice:

• branches;
• merges;
• conflict resolution;
• reverts;
• tags;
• pull requests;
• selective staging.

Git is not only a storage system.

It is your recovery and review framework.

Add Meaningful Automated Tests

Begin with small logic functions.

For example:

function normalizeBookTitle(value) {
  if (typeof value !== "string") {
    return "";
  }

  return value.trim();
}

Useful tests include:

normal title
surrounding spaces
empty string
spaces only
null
number

Then move toward testing complete user behavior.

Learn Basic Security Principles

Study:

• input validation;
• output encoding;
• authentication versus authorization;
• secret management;
• browser security;
• dependency risk;
• least privilege;
• safe logging.

Security knowledge becomes more important as applications handle more valuable data.

Learn Deployment and Operations

A published application needs more than a URL.

Learn about:

• environments;
• logs;
• monitoring;
• backups;
• rollback;
• domains;
• HTTPS;
• configuration;
• service limits.

A system that works locally but cannot be operated reliably is incomplete.

Build a Slightly More Complex Second Project

A useful next project should add one new category of complexity.

Examples include:

• a reading tracker with editing and export;
• a small app using a public API;
• a multi-page static site;
• a browser extension;
• a local file-processing tool;
• a small application with automated tests.

Avoid adding every new technology at once.

Choose one learning goal.

A Responsible Vibe Coding Maturity Model

You can think of progress in stages.

Stage 1: Prompt and Observe

The user asks for code and checks whether the interface appears.

Risk:

• limited understanding;
• weak testing;
• broad trust.

Stage 2: Prompt and Review

The user checks:

• files;
• diffs;
• behavior;
• errors.

This is a major improvement.

Stage 3: Specify, Review, and Test

The user defines:

• requirements;
• constraints;
• acceptance criteria;
• test cases.

The AI works within a clearer system.

Stage 4: Design for Recovery

The user uses:

• branches;
• checkpoints;
• focused commits;
• rollback;
• documented decisions.

The project becomes safer to change.

Stage 5: Operate and Maintain

The user considers:

• production behavior;
• monitoring;
• security;
• upgrades;
• user feedback;
• long-term ownership.

The goal is not to rush through these stages.

Each stage adds a layer of control.

The Complete Workflow from This Series

This series followed a structured path.

Understand vibe coding
→ Choose a small project
→ Write a project brief
→ Create focused prompts
→ Build one feature at a time
→ Review generated code
→ Debug with evidence
→ Recover drifting sessions
→ Use Git checkpoints
→ Handle APIs and secrets safely
→ Test and deploy
→ Evaluate limits and future growth

The process matters more than the specific reading list application.

You can reuse it for another project.

A Final Vibe Coding Checklist

Before beginning a project:

• Can I describe the user and problem?
• Is the first version small?
• Can it work without sensitive data?
• Can I test it myself?
• Are excluded features documented?

Before an AI task:

• Is the Git state clean?
• Does the task have one main outcome?
• Are allowed files defined?
• Are constraints explicit?
• Is verification planned?

After an AI task:

• Which files changed?
• Does the diff match the prompt?
• Were dependencies added?
• Can I trace the main behavior?
• Did I test invalid input?
• Did existing behavior remain intact?

Before committing:

• Is the change understood?
• Is the scope focused?
• Are secrets excluded?
• Is the staged diff correct?
• Does the commit message describe the behavior?

Before deployment:

• Has the release scope stopped changing?
• Did the complete test plan pass?
• Were repository artifacts reviewed?
• Was the live version tested?
• Is a rollback path available?

Before trusting the result:

• What evidence supports it?
• What assumptions remain?
• What could fail?
• Who is responsible when it does?

Practical Advice from Long-Term Software Work

The fastest way to generate code is rarely the fastest way to finish reliable software.

A large generated change may save typing while creating hours of review, debugging, and recovery.

A smaller sequence of verified changes often moves faster overall because each step remains understandable.

The most valuable AI-assisted development habit is not finding a magical prompt.

It is preserving control.

Control comes from:

• clear intent;
• small scope;
• visible evidence;
• reversible changes;
• honest limits.

When those qualities are present, AI tools can be highly useful.

When they are missing, faster generation can produce faster confusion.

Conclusion

Vibe coding lowers the cost of turning ideas into working software.

It can help beginners learn, help experienced developers move faster, and make prototypes easier to create. It can assist with planning, code generation, explanation, testing, debugging, and documentation.

Its limitations are equally important.

AI tools work with incomplete context. They can produce plausible errors, unnecessary complexity, insecure patterns, weak tests, and projects that become difficult to maintain. The speed of generation can exceed the speed of human review.

The future of vibe coding will likely include better project understanding, more capable agents, stronger testing loops, improved permission boundaries, and richer development automation.

Human responsibility will remain central.

The durable workflow is:

Define clearly
→ Generate narrowly
→ Review carefully
→ Test with evidence
→ Commit deliberately
→ Deploy responsibly
→ Maintain continuously

The Personal Reading List Tracker began as a simple idea and became a planned, implemented, reviewed, tested, and deployable application. More importantly, it demonstrated a process that can be applied to future projects.

The goal of vibe coding should not be to avoid understanding software.

It should be to use AI while steadily increasing your ability to understand, evaluate, and improve what you build.

Frequently Asked Questions

Is vibe coding only useful for prototypes?

No.

It can support real software development, but production systems require stronger review, testing, security, monitoring, and maintenance than a prototype.

The acceptable level of AI autonomy should depend on the project’s risk.

Will better AI models remove the need to learn programming?

They may reduce the amount of syntax a person needs to memorize.

Understanding logic, data, APIs, testing, security, debugging, and system behavior will still make it easier to recognize incorrect or unsafe results.

What is the most important skill for future AI-assisted developers?

No single skill is sufficient.

The strongest combination is clear problem definition, task decomposition, code review, testing, debugging, and the judgment to stop when evidence is insufficient.

Next article: What Comes After Your First Vibe-Coded App?