I'm Safdar Ali. I build React and Next.js for a living — client work on the side, and this portfolio you're reading on safdarali.in.

For the last year, my editor has been Cursor with Claude as the model behind Agent mode.

Not because AI writes perfect code (it doesn't), but because when I treat it like a fast junior who reads the whole repo first, I ship features in a third of the time it used to take me to context-switch, grep, and boilerplate alone.

This isn't a "10 prompts that will change your life" thread.

It's the exact Cursor AI React workflow I run every day — how I open a task, what I refuse to let the agent touch, and how I review output so it matches how teams at serious product companies ship.

If you're searching for a real cursor ai react setup instead of another Copilot vs Cursor comparison, this is it.

Why Cursor + Claude (and not just autocomplete)

Autocomplete saves keystrokes.

Agent mode saves hours — when you use it correctly.

Cursor indexes your workspace, runs terminal commands, edits multiple files, and follows instructions across a feature branch.

Claude (Sonnet / Opus depending on the task) is what I use for reasoning-heavy work:

• refactors
• boundary decisions
• “find every place this breaks”
• long-form implementation plans

My split:

• Tab completion — inline JSX, Tailwind classes, repetitive TypeScript
• Chat (Cmd+L) — “Explain this hook,” “Why is this hydrating wrong?”
• Agent (Cmd+I) — multi-file features, audits, refactors

This is where the 3× speed lives.

I don't bounce between five tools.

One repo.
One editor.
One model family I've learned to review critically.

That consistency matters more than whichever model scored 2% higher on a benchmark last Tuesday.

My Daily Loop — From Ticket to PR

Every feature follows the same rhythm.

Boring on purpose — boring scales.

1. Scope in human words first

Before I touch Agent, I write a one-paragraph goal:

• user outcome
• affected files
• “done” criteria
• constraints (SEO, dark mode, mobile)

Vague prompts produce vague diffs.

Instead of:

“Make the projects page faster”

I write:

“Convert app/projects/page.jsx to a server component; move data to data/; keep interactive cards client-only; run build after.”

2. Agent reads before it writes

My first agent message is almost never:

“Implement X”

It's:

Analyze this codebase for [feature/bug].
Return:
- affected files
- existing patterns to follow
- risks (SEO, hydration, bundle)

Do not edit yet.

On this portfolio, that's how a full audit found:

• a 404 typo (wf-ull)
• duplicate tsparticles IDs
• a 400-line client-only projects page

Before a single line changed.

Reading first prevents the classic AI failure mode:

A clean-looking patch that ignores your conventions.

3. One bounded implementation pass

Second message:

Implement with constraints.

I always include:

• Match existing naming and folder structure
• Minimal diff only
• Preserve metadata and structured data
• Run build after implementation

The agent handles:

• boilerplate
• data extraction
• repetitive refactors
• wiring boundaries

I keep my attention for:

• UX decisions
• architecture
• API design

4. My review gate (non-negotiable)

I read every changed file.

Not skim.
Read.

Checklist:

1. Correctness — does it compile?
2. Boundaries — are client/server components clean?
3. Security — no secrets or unsafe rendering?
4. SEO — canonical tags, metadata, crawlability
5. Taste — would I merge this from a junior dev?

If something fails:

I don't blindly patch forward.

I tell the agent exactly what failed.

Example:

“You used index keys in a sorted list. Use stable IDs instead.”

Small corrections improve future output dramatically.

React Rules That Keep AI Output Production-Grade

Server first, client leaves

New pages default to server components.

Only isolate client components when state/effects/events require them.

This keeps:

• bundle sizes smaller
• hydration cleaner
• SEO stronger

Point at patterns, not abstractions

Instead of saying:

“Follow best practices”

I say:

Follow the same structure as app/blog/example/page.tsx:
- metadata export
- structured data
- prose layout
- existing animation wrappers

Do not invent a new layout system.

Reference files beat vague instructions every time.

Performance prompts I reuse

I constantly reuse prompts like:

• “Defer anything hurting LCP”
• “Use next/image”
• “Avoid unnecessary client components”
• “Compare build output after refactor”

AI implements the checklist.

I verify Lighthouse and real-device feel.

Real Example — Full Portfolio Audit in One Session

One Agent session found and fixed:

Manual estimate:

Half a day.

With Agent:

~45 minutes including review.

That's the real 3× gain.

Not “AI built my app.”

But:

“AI removed friction.”

When AI Slows You Down

I stop the agent when:

• it loops repeatedly
• it over-engineers
• it hallucinates APIs
• the task needs product taste

AI is incredible for:

• structure
• speed
• repetition

But judgment still matters.

A lot.

Cursor Setup I Actually Use

• Small .cursor/rules
• One logical commit per task
• Sonnet for daily work
• Opus for deep debugging/refactors
• Let Agent run build/lint in terminal

I don't maintain 50 prompts.

I maintain:

• constraints
• reference files
• conventions

That scales much better.

Where the “3× Faster” Comes From

The gains are biggest on:

• audits
• migrations
• repetitive architecture work

Not on deep product thinking.

TL;DR — Copy My Workflow

1. Write clear scope + constraints
2. First pass = analysis only
3. Second pass = implementation
4. Review every changed file
5. Reference existing repo patterns
6. Narrow scope when Agent loops

Final Thoughts

Cursor AI React development isn't about replacing engineers.

It's about removing the tax on work that was never the hard part:

• renaming files
• fixing repetitive bugs
• extracting modules
• boilerplate implementation

I still own:

• architecture
• review
• production decisions

The workflow above is simply how I ship faster without shipping garbage.

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