# Case Study: Auto-Enhance
# (your most recent, freshest memory)
## The Problem
Users manually adjust brightness,
contrast, and saturation on every
image. 73% of edits follow the same
pattern. They wanted one-click
improvement.
## My Approach
1. Researched how Google Photos,
Apple Photos, and Lightroom solve
this problem
2. Wrote a design doc: scope,
UX flow, API, data model
3. Chose client-side histogram
analysis over server-side ML
(faster, no server cost, MVP
ships in 3 sessions vs 3 months)
4. Presented design, incorporated
feedback, then implemented
## Technical Implementation
- Image analysis engine scans every
pixel: brightness histogram,
contrast range, saturation level
- Suggestion functions calculate
optimal corrections from statistics
- Web Worker prevents UI blocking
on 4K images (33MB of pixel data)
- Before/after slider with CSS
clip-path (GPU-accelerated)
- Analytics track user adjustments
to improve the algorithm
## Key Decisions
- Client-side vs server-side:
chose client for MVP speed.
Server ML is the next iteration.
- Return parameters, not pixels:
the API returns suggestions, the
client applies them. Reversible,
adjustable, fast.
- Graceful degradation: if analysis
fails, the editor still works.
## Results
- Feature shipped in 4 sessions
(design + 3 implementation)
- Fully accessible (WCAG AA)
- 100% test coverage on the engine
- Analytics pipeline ready for
data-driven algorithm improvement
## What I Learned
- The polish phase (edge cases, a11y,
tests) took as long as building
the core feature
- Design docs saved time: the team
caught two issues before I wrote
a single line of code
# Case Study: From 8s to 100ms
## The Problem
Gallery page loaded in 8 seconds.
Users bounced. Lighthouse score: 62.
The image-heavy page was rendering
500 unoptimized images on first load.
## Investigation
- Chrome DevTools Performance tab:
8.2s LCP (Largest Contentful Paint)
- 500 images loaded eagerly (25MB)
- No code splitting: 1.2MB JS bundle
- No image optimization: full-size
PNGs served at thumbnail size
## Solutions Applied
1. Lazy loading: loading="lazy" on
images below the fold → cut
initial load by 80%
2. Image optimization: sharp library
generates WebP thumbnails at
300px width (2KB vs 2MB each)
3. Code splitting: React.lazy() for
routes → main bundle 1.2MB → 180KB
4. Virtualization: react-window for
gallery → only render visible rows
5. CDN: serve static assets from
Cloudflare edge nodes
## Results
- LCP: 8.2s → 1.1s
- Lighthouse: 62 → 94
- Bundle: 1.2MB → 180KB
- Gallery renders 10,000 images
without performance degradation
## What I Learned
- Measure first, optimize second.
Without DevTools profiling, I would
have guessed wrong about the
bottleneck.
- The biggest wins were the simplest:
lazy loading and proper image sizes
accounted for 70% of the improvement.
# Case Study: Real-Time Collaboration
# (or choose another major feature)
## The Problem
Users wanted to edit images together.
Sharing meant exporting a file and
emailing it. No live collaboration.
## Architecture
- WebSocket server (Socket.IO) for
real-time communication
- Room-based sessions: each project
is a room, users join via link
- Event broadcasting: when user A
applies a filter, user B sees it
instantly
- Cursor presence: see where other
users are working on the canvas
## Conflict Resolution
Chose "last write wins" for MVP:
- Simple, predictable
- Acceptable for image editing (unlike
text editing where OT/CRDT needed)
- Undo history per-user prevents
accidental overwrites
## Scaling Considerations
- Socket.IO with Redis adapter for
multi-server sticky sessions
- Each room stored in memory during
session, persisted to DB on save
- Heartbeat for presence detection
(30s timeout = user disconnected)
## What I Learned
- Real-time adds exponential
complexity to every feature
- "Last write wins" is fine for 90%
of collaborative use cases.
Don't over-engineer conflict
resolution until data says you must.
# PixelCraft ✨
A production-grade web-based image
editor built from scratch. Filters,
layers, auto-enhance, real-time
collaboration, and full keyboard
accessibility.
## ✨ Features
- 🎨 Canvas-based image editing
- ✨ AI auto-enhance (one-click)
- ↔️ Before/after comparison
- 🔌 Plugin system for extensibility
- ⌨️ Full keyboard shortcuts (press ?)
- ♿ WCAG 2.1 AA accessible
- 🌍 i18n (English, Spanish, Japanese)
- 📱 Responsive (mobile + desktop)
- 🔄 Undo/redo with branching history
## 🏗️ Architecture
```
[React + TypeScript Frontend]
↕ REST API / WebSocket
[Express + Node.js Backend]
↕
[MongoDB] [Redis] [S3]
```
## 🛠️ Tech Stack
**Frontend:** React 18, TypeScript,
Zustand, Vite, Tailwind
**Backend:** Express, Node.js,
MongoDB, Redis, BullMQ
**DevOps:** Docker, GitHub Actions,
Vercel, Railway, Sentry
## 🚀 Quick Start
```bash
git clone https://github.com/
you/pixelcraft.git
cd pixelcraft
docker-compose up
# → http://localhost:5173
```
## 📐 Key Technical Decisions
| Decision | Why |
|----------|-----|
| Zustand over Redux | Simpler API,
selective re-renders |
| MongoDB over Postgres | Flexible
schema for image metadata |
| Client-side enhance | No server
cost, instant results |
| Web Workers | Offload 33MB pixel
analysis from main thread |
## 📝 Case Studies
- [Auto-Enhance](docs/case-auto.md)
- [Performance](docs/case-perf.md)
- [Collaboration](docs/case-collab.md)
## 📄 License
MIT
# Demo Video Script (3 minutes)
## 0:00 - 0:15 — Hook
"This is PixelCraft — a full-stack
image editor I built from scratch in
123 sessions. Let me show you."
## 0:15 - 0:45 — Core Editing
- Upload an image (drag & drop)
- Apply brightness, contrast filters
- Show real-time slider adjustments
- Undo/redo with keyboard shortcuts
## 0:45 - 1:15 — Auto-Enhance
- Click ✨ Auto-Enhance
- Show the loading animation
- Drag the before/after slider
- Adjust the suggestions
- "One click. Instant improvement."
## 1:15 - 1:45 — Pro Features
- Keyboard shortcuts (press ?)
- Selection + transform tools
- Color picker with eyedropper
- History panel with branching
## 1:45 - 2:15 — Under the Hood
- Open DevTools: show Web Worker
- "33 million pixels analyzed
off the main thread"
- Show Lighthouse score: 94
- Show accessibility audit: 0 errors
## 2:15 - 2:45 — Architecture
- Quick architecture diagram
- "React + TypeScript, Express,
MongoDB, Redis, Docker"
- "CI/CD: every push runs 200+ tests"
## 2:45 - 3:00 — Close
"Built from <h1>Hello World</h1>
to production-grade image editor.
123 sessions. Every line mine."
# Clean up commit messages:
# BAD commits:
# "fix"
# "wip"
# "asdf"
# "update stuff"
# GOOD commits:
# "Add auto-enhance with histogram
# analysis (#87)"
# "Fix memory leak in undo stack
# when history exceeds 50 entries"
# "Improve gallery LCP from 8.2s
# to 1.1s via lazy loading + WebP"
# Interactive rebase to clean up:
git rebase -i HEAD~20
# → squash WIP commits
# → reword unclear messages
# → reorder for narrative flow
# Ensure every PR description:
# - States the problem
# - Describes the solution
# - Links the ticket number
# - Shows before/after (if visual)
# Tag the final release:
git tag -a v1.0.0 -m \
"PixelCraft v1.0: complete"
git push origin v1.0.0
Your portfolio answers one question: "Can this person think?"
Resumes list facts. Portfolios demonstrate judgment, decision-making, and the ability to learn. Hiring managers spend 30 seconds per project. Make those seconds count.