i've spent the last year deep in this world. built apps, shipped products, helped others learn. i've seen what works and what wastes time.
here's the complete roadmap to master vibe coding in 30 days.
every tool. every technique. every prompt. no fluff.
let's go.
what is vibe coding (really)?
let's kill the confusion first.
vibe coding is programming through conversation instead of syntax.
you describe what you want in plain english. the AI writes the code. you review, refine, and guide.
old way:
learn syntax
memorize functions
write every line manually
debug character by character
google stack overflow endlessly
vibe coding way:
describe the outcome you want
AI generates the code
you review and redirect
AI fixes issues
you ship faster
you're not writing code. you're directing code.
think of it like this:
traditional coding = you're the construction worker vibe coding = you're the architect with a robot construction crew
you still need to understand what good looks like. you still make decisions. you still own the outcome.
but you're not laying every brick yourself.
that's the shift.
the mindset change that matters most
before tools and prompts, you need a mental upgrade.
most people fail at vibe coding because they approach it wrong.
old mindset: "i need to know exactly how to code this" new mindset: "i need to know exactly what i want built"
old mindset: "let me figure out the syntax" new mindset: "let me describe the behavior"
old mindset: "i'll write it perfectly the first time" new mindset: "i'll iterate until it's right"
old mindset: "AI is a shortcut for lazy people" new mindset: "AI is leverage for ambitious people"
vibe coding rewards clarity of thought over knowledge of syntax.
if you can describe something precisely, you can build it.
that's the unlock.
the tools you need (ranked)
here's every major vibe coding tool, ranked by use case.
tier 1: daily drivers
cursorwhat: AI-native IDE built on vscode best for: everyday coding, visual feedback, team projects cost: free tier, $20/month pro learning curve: 1-2 days
cursor is where most people should start.
it looks like vscode. feels like vscode. but AI is baked into everything.
features that matter:
tab autocomplete (predicts your next lines)
cmd+k edit (highlight code, describe changes)
composer (multi-file edits from one prompt)
agent mode (autonomous task completion)
if you know vscode, you'll feel at home in hours.
claude codewhat: terminal-based autonomous coding agent best for: complex projects, large codebases, backend work cost: $20/month (claude pro) learning curve: 1 week
claude code is cursor's opposite.
no GUI. no visual editor. just terminal.
but it's the most capable agent for deep work.
features that matter:
200k token context (reads entire codebases)
autonomous execution (runs for hours without supervision)
MCP integration (connects to external tools)
CLAUDE.md config (custom rules per project)
developers who try claude code become obsessed. it hit $1B revenue in six months.
use cursor for everyday work. use claude code for heavy lifting.
tier 2: strong alternatives
windsurfwhat: AI IDE with autonomous agent (cascade) best for: developers who want more automation than cursor cost: free tier, $15/month pro learning curve: 2-3 days
windsurf is cursor's main competitor.
the cascade agent is more proactive. it pulls context automatically and executes multi-step tasks without micromanagement.
some developers prefer this "AI-first" approach over cursor's "human-first" philosophy.
try both. pick based on preference.
github copilotwhat: AI autocomplete for any IDE best for: existing workflows, github integration cost: $10/month individual, $19/month business learning curve: 1 hour
copilot is the gateway drug.
it plugs into your existing editor and predicts code as you type.
less powerful than cursor or claude code for complex tasks. but lowest friction to start.
if you're nervous about changing tools, start here.
openai codexwhat: cloud-based coding agent best for: parallel tasks, sandboxed execution cost: chatgpt plus ($20/month) learning curve: 2-3 days
codex runs in the cloud. you give it a task, it works in a sandbox, returns results.
good for tasks you want isolated from your local machine.
less common than cursor/claude code but growing fast.
tier 3: specialized tools
v0 by vercelwhat: AI frontend generator best for: react components, landing pages, UI cost: free tier, $20/month pro learning curve: 30 minutes
v0 is magic for frontend.
describe a component. get production-ready react code with tailwind styling.
"create a pricing table with three tiers, toggle for monthly/annual, dark mode support"
boom. done.
use for UI components. don't use for backend logic.
bolt.newwhat: full-stack app generator best for: MVPs, prototypes, simple apps cost: free tier, usage-based pricing learning curve: 1 hour
bolt generates entire applications from prompts.
"build a todo app with user authentication and dark mode"
it creates the frontend, backend, database, and deployment config.
great for prototypes. less control for complex apps.
replit agentwhat: browser-based AI coding environment best for: beginners, quick experiments, no local setup cost: free tier, $25/month pro learning curve: 30 minutes
replit runs in your browser. no installation. no configuration.
the AI agent can build and deploy apps without you touching code.
good for learning. less powerful for serious projects.
lovablewhat: AI app builder for non-technical founders best for: MVPs without coding knowledge cost: free tier, $20/month pro learning curve: 1 hour
lovable is the most "no-code" option.
describe your app. it builds everything. you customize through conversation.
good for validating ideas. you'll outgrow it fast.
the 30-day roadmap
here's exactly what to do each week.
week 1: foundations (days 1-7)
goal: get comfortable with one tool and basic prompting
day 1: setup
pick your primary tool.
my recommendation:
if you code already → cursor
if you're technical but not a developer → claude code
if you're completely new → replit or bolt
download it. set it up. open a blank project.
day 2: your first vibe-coded feature
build something trivial.
prompt: "create a simple counter app. a number that starts at 0. two buttons: one adds 1, one subtracts 1. style it nicely."
watch the AI work. see how it structures code. notice what it assumes.
don't worry about understanding every line. focus on the process.
day 3: the edit loop
take yesterday's counter. modify it through prompts.
"add a reset button that sets the count back to zero" "change the color scheme to dark mode" "add animation when the number changes" "make it mobile responsive"
this is the core skill: iterative refinement through conversation.
day 4: your first real project
pick something you actually want.
ideas:
personal portfolio site
simple tool for your workflow
landing page for an idea
calculator for something specific to your work
nothing complex. something you can finish in a day.
start with: "i want to build [thing]. it should [features]. help me plan the structure first."
day 5: debugging with AI
intentionally break your project.
delete some code. introduce bugs. mess things up.
then fix it through prompts:
"this code is throwing an error. here's the error message: [paste]. find and fix the bug."
"this feature isn't working as expected. it should [behavior] but instead it [wrong behavior]. diagnose the issue."
debugging is where most people struggle. practice it deliberately.
day 6: reading and understanding
you've been generating code. now understand it.
take a file from your project. prompt:
"explain this code to me line by line. assume i'm a beginner. tell me what each part does and why it's structured this way."
vibe coding doesn't mean ignoring the code. it means understanding it at the right level of abstraction.
day 7: rest and review
look back at what you built.
what worked? what was frustrating? what do you want to learn next?
write it down. this reflection compounds over time.
week 2: core skills (days 8-14)
goal: develop the prompting patterns that actually work
day 8: prompt structures that win
bad prompt: "make a login page"
good prompt: "create a login page with:
email and password fields
input validation (email format, password min 8 chars)
show/hide password toggle
'forgot password' link
'create account' link
error messages for invalid inputs
loading state on submit button
responsive design for mobile
clean minimal styling"
the difference is specificity.
AI fills gaps with assumptions. more detail = fewer wrong assumptions.
day 9: the context sandwich
structure prompts in three layers:
context (what exists)
task (what to do)
constraints (how to do it)
example:
"CONTEXT: i have a react app with a user dashboard. users can see their profile and settings. we use tailwind for styling and zustand for state management.
TASK: add a notification bell to the header that shows unread notifications. clicking it opens a dropdown with the notification list.
CONSTRAINTS:
match existing styling patterns
notifications should mark as read when clicked
show badge with count when unread > 0
max height on dropdown with scroll
empty state when no notifications"
this structure works for any task.
day 10: multi-file projects
single files are easy. real projects have many files.
learn to give AI context about project structure:
"my project structure: /src /components Header.jsx Footer.jsx /pages Home.jsx Dashboard.jsx /utils api.js /styles globals.css
i need to add a new feature that [description]. which files need changes and what changes should each file have?"
cursor's composer and claude code handle multi-file edits natively. learn to use them.
day 11: the planning prompt
before building anything complex, ask for a plan:
"i want to build [feature]. before writing code:
break this into subtasks
identify which files/components are needed
flag any potential complications
suggest the order to build things
don't write code yet. just plan."
planning prevents rabbit holes.
day 12: version control habits
vibe coding generates lots of code fast.
that's dangerous without version control.
setup:
initialize git in every project
commit after each working feature
use descriptive commit messages
branch for experiments
prompt for commit messages: "look at the changes i just made. write a clear commit message that describes what changed and why."
if something breaks, you can always go back.
day 13: testing with AI
untested code is broken code waiting to happen.
prompt: "write tests for [component/function]. cover:
happy path (normal usage)
edge cases (empty inputs, max values)
error handling (what should fail gracefully) use [jest/vitest/pytest] format."
tests catch bugs before users do.
day 14: refactoring practice
take messy code and clean it:
"this code works but it's messy. refactor it to be:
more readable
better organized
following [language] best practices
with clear comments on complex parts
explain each change you make."
refactoring is how good codebases stay good.
week 3: intermediate techniques (days 15-21)
goal: handle real complexity
day 15: working with existing codebases
most work isn't greenfield. it's modifying existing code.
prompt for unfamiliar codebases: "analyze this codebase. tell me:
what does this project do
what's the architecture
what are the main components
where would i make changes to [feature]
any patterns or conventions i should follow"
understanding before editing prevents disasters.
day 16: API integration
most apps need external data.
prompt: "integrate [API name] into my project.
show me how to structure the API calls
handle loading states
handle errors gracefully
cache responses if appropriate
here's the API documentation: [paste or link]"
if documentation is long, paste the relevant sections. AI needs context.
day 17: database work
data persistence is essential.
prompt: "i need a database for [use case].
recommend the right database type
design the schema
show me how to connect it
create CRUD operations
handle migrations"
start with sqlite or supabase. simplest setup.
day 18: authentication
most apps need user accounts.
prompt: "add authentication to my project using [clerk/auth0/supabase auth/next-auth].
signup with email/password
login flow
protected routes
session management
logout functionality"
don't build auth from scratch. use established providers.
day 19: deployment
code that runs locally is worthless. ship it.
prompt: "prepare this project for deployment to [vercel/railway/fly.io].
what environment variables do i need
any build configuration required
how to set up the production database
walk me through the deployment steps"
deploy early. deploy often. broken deployments are learning opportunities.
day 20: debugging complex issues
when simple "fix this bug" doesn't work:
"i have a complex bug. let me describe it:
EXPECTED BEHAVIOR: [what should happen]
ACTUAL BEHAVIOR: [what actually happens]
STEPS TO REPRODUCE:
[step]
[step]
[step]
WHAT I'VE TRIED:
[attempt 1]
[attempt 2]
RELEVANT CODE: [paste code]
ERROR MESSAGES: [paste errors]
help me diagnose this systematically."
systematic debugging prompts get systematic solutions.
day 21: performance optimization
fast apps win.
prompt: "analyze this code for performance issues.
identify bottlenecks
suggest optimizations
estimate impact of each change
prioritize by effort vs improvement
focus on [loading speed / runtime performance / bundle size]."
don't optimize prematurely. but do optimize eventually.
week 4: advanced mastery (days 22-30)
goal: build production-quality projects independently
day 22: CLAUDE.md and cursor rules
configure AI to work your way.
create CLAUDE.md (for claude code) or .cursorrules (for cursor):
# Project Guidelines
## Tech Stack
- typescript (strict mode)
- react with hooks
- tailwind css
- zustand for state
- tanstack query for data fetching
## Code Style
- functional components only
- custom hooks for reusable logic
- descriptive variable names
- comments for complex logic only
## Patterns
- keep components under 150 lines
- extract logic to utils
- one component per file
- colocate tests with components
## Off Limits
- no class components
- no inline styles
- no any types
- don't delete tests
## When Uncertain
- ask before major refactors
- suggest options for architecture decisions
- flag potential security issues
the AI reads this file and follows your rules. customize for each project.
day 23: multi-agent workflows
complex tasks benefit from multiple AI passes:
planning agent: breaks down requirements
coding agent: implements features
review agent: checks for issues
documentation agent: writes docs
prompt: "act as a code reviewer. examine this code for:
bugs
security vulnerabilities
performance issues
readability problems
missing edge cases
be critical. pretend you're a senior engineer reviewing a junior's PR."
then fix what it finds with your coding tool.
day 24: building full features end-to-end
practice complete feature development:
"i need to add [feature] to my app.
walk me through the complete process:
requirements clarification
technical planning
implementation
testing
documentation
let's do each step one at a time. start with requirements."
this simulates real development workflow.
day 25: handling AI limitations
AI gets things wrong. learn to catch it.
common failures:
hallucinating APIs that don't exist
using outdated syntax
overcomplicating simple things
missing edge cases
ignoring existing code patterns
when something seems wrong: "are you sure about [specific claim]? show me the documentation or explain your reasoning."
trust but verify.
day 26: building your prompt library
collect prompts that work.
create a document with:
planning prompts
debugging prompts
refactoring prompts
testing prompts
documentation prompts
review prompts
reuse them across projects. refine over time.
this is your personal AI operating system.
day 27: teaching AI your style
AI can learn your preferences.
prompt: "here are three examples of code i've written that i like: [paste examples]
analyze my style:
patterns i use
naming conventions
structure preferences
comment style
from now on, write code that matches this style."
personalized AI produces personalized results.
day 28: shipping a real project
time to prove your skills.
build something real. something you'll actually use or share.
ideas:
tool that solves a problem you have
website for a project or business
app that automates part of your workflow
something you can show others
go from idea to deployed product. no tutorials. just you and AI.
day 29: code review and improvement
take your project from day 28.
prompt: "do a complete review of this project.
code quality
architecture decisions
potential improvements
security considerations
performance opportunities
user experience issues
prioritize by impact. be direct about problems."
implement the top suggestions.
day 30: reflection and next steps
you made it.
look back at day 1. notice how much changed.
write down:
what you can build now that you couldn't before
which tools work best for you
which prompting patterns you rely on
what's still challenging
what you want to learn next
mastery isn't a destination. it's a direction.
the prompts that changed everything for me:
the architect prompt:"before writing any code, act as a software architect. analyze my requirements, identify components needed, suggest the tech stack, flag potential issues, and create a high-level implementation plan. don't write code until i approve the plan."
the rubber duck prompt:"i'm stuck on [problem]. i'm going to explain my thinking out loud. listen, then help me see what i'm missing. here's what i know: [explain]"
the code translator prompt:"i know how to do this in [language/framework]. translate this approach to [new language/framework], adjusting for idiomatic patterns."
the senior engineer prompt:"review this code as if you're a senior engineer who's seen too many production incidents. be paranoid about edge cases, security, and failure modes. hurt my feelings if needed."
the simplifier prompt:"this code works but feels complicated. simplify it. reduce lines. remove unnecessary abstraction. make it readable for someone new to the project."
the documentation prompt:"write documentation for this project:
readme with setup instructions
architecture overview
api documentation
component documentation include examples for common use cases."
the learning prompt:"i don't understand [concept]. explain it:
simple analogy first
then technical details
then how it applies to my code
then common mistakes to avoid"
save these. adapt them. build on them.
the mistakes that slow people down:
mistake 1: not being specific enough
"make it better" tells AI nothing.
"improve error handling by adding try-catch blocks, user-friendly error messages, and logging for debugging" tells AI exactly what to do.
always be specific.
mistake 2: accepting first output
AI's first answer is rarely the best answer.
iterate. push back. ask for alternatives.
"this works but it's verbose. show me a cleaner approach."
mistake 3: ignoring the code
vibe coding doesn't mean blind coding.
read what AI generates. understand the structure. catch obvious errors.
you're the pilot. AI is the copilot.
mistake 4: no version control
vibe coding generates code fast. mistakes happen.
without git, one bad prompt can destroy hours of work.
commit constantly.
mistake 5: skipping planning
jumping straight to "build me X" creates chaos.
start with planning prompts. get the architecture right. then build.
mistake 6: fighting the AI
if AI keeps misunderstanding, the prompt is wrong.
don't repeat the same prompt louder. rephrase. add context. give examples.
mistake 7: not learning fundamentals
vibe coding doesn't replace understanding.
learn basic programming concepts. understand how databases work. know what APIs do.
the better your foundation, the better you can direct AI.
what separates good from great:
good vibe coders generate code.
great vibe coders:
plan before building
specify requirements precisely
iterate rapidly on feedback
understand what the code does
catch AI mistakes before they ship
maintain clean, organized codebases
document as they go
test thoroughly
ship consistently
the tools are available to everyone.
the skills make the difference.
resources to continue learning:
youtube channels:
fireship (fast, dense tutorials)
theo - t3.gg (react/nextjs focus)
web dev simplified (fundamentals)
communities:
cursor discord
claude code discord
r/ChatGPTPro
r/LocalLLaMA
practice platforms:
build projects from frontendmentor.io
recreate sites you like
automate your own workflows
newsletters:
tldr AI
the rundown AI
ben's bites
the future of coding:
vibe coding isn't going away.
it's going to become more powerful. more accessible. more essential.
the people who master it now will have an unfair advantage for years.
they'll build faster. ship more. create things that seemed impossible.
the barrier to building software is collapsing.
the only question is: are you on the right side of that collapse?
30 days. the tools are ready. the prompts are here.
all that's missing is you starting.
your day 1 begins now
________
Hey, I'm Muhammad Ayan
Follow me @socialwithaayan for more insights like this on building with AI
