Gitdocs AI generates high-fidelity READMEs by analyzing your codebase architecture. Pick a structural base and let our models do the heavy lifting.
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Minimalist
Scripts, Config, Small LibsPreparing markdown...
A comprehensive description of the library that clearly explains what problem it solves. For example: "A type-safe HTTP client for TypeScript that provides automatic request/response validation, intelligent caching, and seamless error handling. Built for modern web applications that need reliability and developer experience."
Explain the key value propositions:
# npm
npm install my-cool-package
# yarn
yarn add my-cool-package
# pnpm
pnpm add my-cool-package
Here's a 30-second introduction to get you up and running:
import { createClient } from 'my-cool-package';
// Create a client instance
const client = createClient({
baseURL: 'https://api.example.com',
timeout: 5000
});
// Make a request
const data = await client.get('/users/123');
console.log(data);
// GET request
const user = await client.get('/users/123');
// POST request
const newUser = await client.post('/users', {
body: { name: 'John Doe', email: 'john@example.com' }
});
// PUT request
const updated = await client.put('/users/123', {
body: { name: 'Jane Doe' }
});
// DELETE request
await client.delete('/users/123');
const client = createClient({
baseURL: 'https://api.example.com',
timeout: 10000,
headers: {
'Authorization': 'Bearer token',
'Custom-Header': 'value'
},
retry: {
attempts: 3,
delay: 1000
},
cache: {
enabled: true,
ttl: 60000 // 1 minute
}
});
import { z } from 'zod';
// Define your schema
const UserSchema = z.object({
id: z.number(),
name: z.string(),
email: z.string().email()
});
// Type-safe request
const user = await client.get('/users/123', {
schema: UserSchema
});
// user is now typed as { id: number; name: string; email: string }
try {
const data = await client.get('/users/123');
} catch (error) {
if (error.isNetworkError) {
console.error('Network failed:', error.message);
} else if (error.isValidationError) {
console.error('Response validation failed:', error.details);
} else {
console.error('Request failed:', error.status, error.message);
}
}
createClient(config: ClientConfig): ClientCreates a new HTTP client instance.
Parameters:
config.baseURL (string): Base URL for all requestsconfig.timeout (number, optional): Request timeout in milliseconds (default: 30000)config.headers (object, optional): Default headers for all requestsconfig.retry (object, optional): Retry configurationconfig.cache (object, optional): Cache configurationReturns: Client instance
client.get(path: string, options?: RequestOptions)Performs a GET request.
Parameters:
path (string): Request path (relative to baseURL)options.schema (Schema, optional): Validation schemaoptions.headers (object, optional): Request-specific headersoptions.params (object, optional): Query parametersReturns: Promise resolving to the response data
client.post(path: string, options?: RequestOptions)Performs a POST request with the same options as GET, plus:
options.body (any): Request body (automatically serialized)import { createClient } from 'my-cool-package';
import { z } from 'zod';
// Define schemas
const UserSchema = z.object({
id: z.number(),
name: z.string(),
email: z.string().email(),
role: z.enum(['admin', 'user'])
});
const UsersListSchema = z.array(UserSchema);
// Create client
const api = createClient({
baseURL: 'https://api.myapp.com',
headers: {
'Authorization': `Bearer ${process.env.API_TOKEN}`
}
});
// Fetch all users
async function getUsers() {
return await api.get('/users', {
schema: UsersListSchema,
params: { limit: 100, sort: 'name' }
});
}
// Create a new user
async function createUser(name: string, email: string) {
return await api.post('/users', {
schema: UserSchema,
body: { name, email, role: 'user' }
});
}
// Update user
async function updateUser(id: number, updates: Partial<User>) {
return await api.put(`/users/${id}`, {
schema: UserSchema,
body: updates
});
}
| Feature | my-cool-package | axios | fetch | ky |
|---|---|---|---|---|
| TypeScript Support | ✅ Built-in | ⚠️ Via types | ⚠️ Via types | ✅ Built-in |
| Runtime Validation | ✅ Yes | ❌ No | ❌ No | ❌ No |
| Auto Retry | ✅ Yes | ⚠️ Via plugin | ❌ No | ✅ Yes |
| Bundle Size | 4KB | 14KB | 0KB (native) | 3KB |
| Browser Support | ✅ Modern | ✅ All | ✅ Modern | ✅ Modern |
Benchmarks on M1 Mac, Node.js 20:
// Before (axios)
const response = await axios.get('https://api.example.com/users');
const data = response.data;
// After (my-cool-package)
const client = createClient({ baseURL: 'https://api.example.com' });
const data = await client.get('/users');
// Before (fetch)
const response = await fetch('https://api.example.com/users');
const data = await response.json();
// After (my-cool-package)
const client = createClient({ baseURL: 'https://api.example.com' });
const data = await client.get('/users');
We welcome contributions! Here's how to get started:
Fork and clone the repository
git clone https://github.com/yourusername/my-cool-package.git
cd my-cool-package
Install dependencies
npm install
Run tests
npm test
Start development mode
npm run dev
git checkout -b feature/amazing-feature)npm test)git commit -m 'Add amazing feature')git push origin feature/amazing-feature)We use ESLint and Prettier. Run npm run lint to check your code.
Q: Can I use this in a browser? A: Yes! It works in all modern browsers.
Q: Does it support authentication? A: Yes, through headers and interceptors.
Q: Is it production-ready? A: Yes, used by 500+ companies in production.
See CHANGELOG.md for version history.
Distributed under the MIT License. See LICENSE for more information.
Standard Open Source
NPM Packages, Libraries, ToolsPreparing markdown...
Backend API Service
REST, GraphQL, DockerPreparing markdown...
A comprehensive data science project that analyzes customer behavior patterns and predicts churn probability using ensemble methods (Random Forest, XGBoost, LightGBM). This project includes full exploratory data analysis, feature engineering, model comparison, and deployment-ready prediction pipeline.
Project Status: ✅ Completed | 📊 Accuracy: 86.3% | 🎯 AUC-ROC: 0.91
Customer churn (the rate at which customers stop doing business with a company) is a critical metric for subscription-based businesses. This project builds a machine learning solution to:
TelcoConnect (fictional company) is experiencing a 27% annual churn rate, which is costing the company $12M annually in lost revenue. The business needs to:
Dataset: Telco Customer Churn
License: CC0: Public Domain
Size: 7,043 rows × 21 columns
The dataset contains customer information for a telecommunications company:
gender: Customer gender (Male, Female)SeniorCitizen: Whether customer is 65+ (0, 1)Partner: Whether customer has a partner (Yes, No)Dependents: Whether customer has dependents (Yes, No)tenure: Number of months as customer (0-72)PhoneService: Phone service subscription (Yes, No)MultipleLines: Multiple phone lines (Yes, No, No phone service)InternetService: Type of internet (DSL, Fiber optic, No)OnlineSecurity: Online security add-on (Yes, No, No internet service)OnlineBackup: Online backup add-on (Yes, No, No internet service)DeviceProtection: Device protection add-on (Yes, No, No internet service)TechSupport: Tech support add-on (Yes, No, No internet service)StreamingTV: TV streaming service (Yes, No, No internet service)StreamingMovies: Movie streaming service (Yes, No, No internet service)Contract: Contract type (Month-to-month, One year, Two year)PaperlessBilling: Paperless billing (Yes, No)PaymentMethod: Payment method (Electronic check, Mailed check, Bank transfer, Credit card)MonthlyCharges: Current monthly charge ($18.25 - $118.75)TotalCharges: Total amount charged to dateChurn: Whether customer churned (Yes, No)| Issue | Count | Resolution |
|---|---|---|
| Missing Values | 11 in TotalCharges | Imputed with median |
| Duplicates | 0 | None found |
| Outliers | 3% in MonthlyCharges | Retained (legitimate high-spend customers) |
| Class Imbalance | 73.5% / 26.5% | SMOTE applied |
customer-churn-prediction/
│
├── data/
│ ├── raw/ # Original dataset
│ │ └── WA_Fn-UseC_-Telco-Customer-Churn.csv
│ ├── processed/ # Cleaned and engineered data
│ │ ├── train.csv
│ │ ├── test.csv
│ │ └── validation.csv
│ └── external/ # External reference data
│
├── notebooks/
│ ├── 01_EDA.ipynb # Exploratory Data Analysis
│ ├── 02_Data_Cleaning.ipynb # Data cleaning and preprocessing
│ ├── 03_Feature_Engineering.ipynb # Feature creation
│ ├── 04_Baseline_Models.ipynb # Simple model benchmarks
│ ├── 05_Model_Training.ipynb # Full model training
│ ├── 06_Model_Evaluation.ipynb # Model comparison & selection
│ └── 07_Model_Interpretation.ipynb # SHAP analysis
│
├── src/
│ ├── __init__.py
│ ├── data/
│ │ ├── load_data.py # Data loading utilities
│ │ ├── preprocess.py # Preprocessing functions
│ │ └── feature_engineering.py
│ ├── models/
│ │ ├── train.py # Training scripts
│ │ ├── predict.py # Prediction scripts
│ │ └── evaluate.py # Evaluation metrics
│ ├── visualization/
│ │ └── plots.py # Plotting functions
│ └── utils/
│ ├── config.py # Configuration
│ └── helpers.py # Helper functions
│
├── models/
│ ├── random_forest_v1.pkl # Saved model files
│ ├── xgboost_v1.pkl
│ ├── lightgbm_v1.pkl
│ └── final_model.pkl # Best performing model
│
├── api/
│ ├── main.py # FastAPI application
│ ├── schemas.py # Request/response models
│ └── requirements.txt
│
├── tests/
│ ├── test_preprocessing.py
│ ├── test_models.py
│ └── test_api.py
│
├── reports/
│ ├── figures/ # Generated plots
│ ├── results.md # Model results summary
│ └── business_insights.pdf # Executive summary
│
├── deployment/
│ ├── Dockerfile
│ ├── docker-compose.yml
│ └── kubernetes/
│
├── .gitignore
├── environment.yml # Conda environment
├── requirements.txt # Python dependencies
├── README.md
└── LICENSE
# Clone the repository
git clone https://github.com/username/customer-churn-prediction.git
cd customer-churn-prediction
# Create conda environment
conda env create -f environment.yml
# Activate environment
conda activate churn-analysis
# Verify installation
python -c "import sklearn, xgboost, lightgbm; print('All packages installed!')"
# Clone the repository
git clone https://github.com/username/customer-churn-prediction.git
cd customer-churn-prediction
# Create virtual environment
python -m venv venv
# Activate virtual environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate
# Install dependencies
pip install -r requirements.txt
Key packages (full list in requirements.txt):
pandas==2.0.3
numpy==1.24.3
scikit-learn==1.3.0
xgboost==2.0.0
lightgbm==4.0.0
catboost==1.2
matplotlib==3.7.2
seaborn==0.12.2
plotly==5.15.0
jupyter==1.0.0
shap==0.42.1
imbalanced-learn==0.11.0
mlflow==2.5.0
fastapi==0.100.0
uvicorn==0.23.0
# Create data directory
mkdir -p data/raw
# Download from Kaggle (requires kaggle API setup)
kaggle datasets download -d blastchar/telco-customer-churn -p data/raw
# Unzip
unzip data/raw/telco-customer-churn.zip -d data/raw
# Start Jupyter Lab
jupyter lab
# Navigate to notebooks/ folder and run in sequence:
# 01_EDA.ipynb → 02_Data_Cleaning.ipynb → ... → 07_Model_Interpretation.ipynb
# Preprocess data
python src/data/preprocess.py --input data/raw/WA_Fn-UseC_-Telco-Customer-Churn.csv --output data/processed/
# Train models
python src/models/train.py --model random_forest --data data/processed/train.csv
# Train all models with hyperparameter tuning
python src/models/train.py --model all --tune --cv 5
# Predict on new data
python src/models/predict.py --model models/final_model.pkl --input data/new_customers.csv --output predictions.csv
cd api
uvicorn main:app --reload --host 0.0.0.0 --port 8000
# Test the API
curl -X POST "http://localhost:8000/predict" \
-H "Content-Type: application/json" \
-d '{
"gender": "Female",
"SeniorCitizen": 0,
"Partner": "Yes",
"Dependents": "No",
"tenure": 12,
"PhoneService": "Yes",
"InternetService": "Fiber optic",
"Contract": "Month-to-month",
"MonthlyCharges": 70.5
}'
Key Findings:
Visualizations Created:
Steps Taken:
Missing Value Treatment:
Feature Encoding:
Feature Scaling:
Class Imbalance:
New Features Created:
Feature Importance (Top 10):
| Rank | Feature | Importance | Description |
|---|---|---|---|
| 1 | tenure | 0.185 | Months as customer |
| 2 | MonthlyCharges | 0.162 | Current monthly bill |
| 3 | Contract_Month-to-month | 0.143 | Short-term contract |
| 4 | TotalCharges | 0.109 | Lifetime spending |
| 5 | InternetService_Fiber | 0.087 | Fiber internet |
| 6 | PaymentMethod_Electronic | 0.076 | Auto-pay method |
| 7 | TechSupport_No | 0.068 | No tech support |
| 8 | OnlineSecurity_No | 0.054 | No online security |
| 9 | service_count | 0.049 | Total services |
| 10 | PaperlessBilling_Yes | 0.041 | Paperless billing |
Models Evaluated:
| Model | Description | Use Case |
|---|---|---|
| Logistic Regression | Baseline linear model | Interpretable baseline |
| Decision Tree | Simple tree-based model | Feature importance |
| Random Forest | Ensemble of trees | Robust predictions |
| XGBoost | Gradient boosting | High performance |
| LightGBM | Fast gradient boosting | Large datasets |
| CatBoost | Handles categorical data | Categorical-heavy data |
Hyperparameter Tuning:
Cross-Validation Strategy:
Evaluation Metrics:
| Model | Accuracy | Precision | Recall | F1-Score | ROC-AUC | PR-AUC | Training Time |
|---|---|---|---|---|---|---|---|
| Logistic Regression | 80.1% | 0.68 | 0.62 | 0.65 | 0.82 | 0.71 | 2s |
| Decision Tree | 78.5% | 0.63 | 0.71 | 0.67 | 0.79 | 0.68 | 5s |
| Random Forest | 85.3% | 0.82 | 0.71 | 0.76 | 0.91 | 0.84 | 45s |
| XGBoost | 86.3% | 0.84 | 0.73 | 0.78 | 0.90 | 0.83 | 38s |
| LightGBM | 84.7% | 0.80 | 0.75 | 0.77 | 0.90 | 0.82 | 12s |
| CatBoost | 85.1% | 0.81 | 0.72 | 0.76 | 0.90 | 0.83 | 95s |
🏆 Best Model: XGBoost
Classification Report:
precision recall f1-score support
Not Churned 0.88 0.95 0.91 1038
Churned 0.84 0.73 0.78 371
accuracy 0.86 1409
macro avg 0.86 0.84 0.85 1409
weighted avg 0.87 0.86 0.86 1409
ROC-AUC Score: 0.9037
PR-AUC Score: 0.8291
Predicted
Not Churn Churn
Actual Not Churn 987 51
Churn 100 271
Interpretation:
Top Drivers of Churn:
Based on model predictions on 1,000 customers:
| Metric | Value | Financial Impact |
|---|---|---|
| At-risk customers identified | 268 | - |
| True churners caught | 195 (73% recall) | $234,000 saved |
| False alarms | 73 | $14,600 wasted effort |
| Net benefit | - | $219,400 |
Assumption: Retention campaign costs $200 per customer, saves $1,200 if successful
A FastAPI service for real-time churn predictions:
# Start the API server
cd api
uvicorn main:app --reload --port 8000
# Server runs at: http://localhost:8000
# Docs available at: http://localhost:8000/docs
API Endpoints:
Example Request:
import requests
customer_data = {
"gender": "Female",
"SeniorCitizen": 0,
"Partner": "Yes",
"Dependents": "No",
"tenure": 12,
"PhoneService": "Yes",
"MultipleLines": "No",
"InternetService": "Fiber optic",
"OnlineSecurity": "No",
"OnlineBackup": "No",
"DeviceProtection": "No",
"TechSupport": "No",
"StreamingTV": "No",
"StreamingMovies": "No",
"Contract": "Month-to-month",
"PaperlessBilling": "Yes",
"PaymentMethod": "Electronic check",
"MonthlyCharges": 70.35,
"TotalCharges": 844.2
}
response = requests.post(
"http://localhost:8000/predict",
json=customer_data
)
print(response.json())
# Output: {"churn_probability": 0.78, "prediction": "Churn", "risk_level": "High"}
# Build image
docker build -t churn-prediction-api:v1 .
# Run container
docker run -d -p 8000:8000 churn-prediction-api:v1
# Test
curl http://localhost:8000/health
For production deployment:
Duration: ~30 minutes
Output: 15 visualizations, statistical summaries
Key Sections:
Key Insights:
Duration: ~20 minutes
Output: Cleaned dataset saved to data/processed/
Cleaning Steps:
Duration: ~25 minutes
Output: 6 new engineered features
Features Created:
tenure_group: Binned tenure categoriesavg_monthly_charges: TotalCharges / tenureservice_count: Number of subscribed serviceshas_premium: Flag for premium servicescontract_payment: Interaction featurecharge_ratio: MonthlyCharges / median ratioDuration: ~15 minutes
Output: Baseline performance benchmarks
Establishes performance baselines:
Duration: ~2 hours (with hyperparameter tuning)
Output: 6 trained models with optimized hyperparameters
Trains and tunes:
Duration: ~30 minutes
Output: Comprehensive evaluation metrics and plots
Evaluation Includes:
Duration: ~40 minutes
Output: SHAP plots, feature importance, partial dependence plots
Analysis Includes:
Finding: Month-to-month customers churn at 42% vs 11% for contract customers
Recommendations:
Expected Impact: 25% reduction in month-to-month churn
Finding: Customers without tech support churn 63% more often
Recommendations:
Expected Impact: 18% reduction in overall churn
Finding: Fiber customers churn 2.1x more despite premium service
Recommendations:
Expected Impact: 15% reduction in fiber optic churn
Target Segment: Top 10% churn risk (based on model scores)
Campaign Elements:
Budget: $200 per customer in campaign Expected ROI: 3.5x (save $1,200 per successful retention)
Issue: "TotalCharges cannot be converted to float"
# Solution: Convert with error handling
df['TotalCharges'] = pd.to_numeric(df['TotalCharges'], errors='coerce')
df['TotalCharges'].fillna(df['TotalCharges'].median(), inplace=True)
Issue: ImportError with XGBoost
# Solution: Install with conda instead of pip
conda install -c conda-forge xgboost
Issue: Kernel dying during model training
# Solution: Reduce hyperparameter search space
param_grid = {
'n_estimators': [50, 100], # Reduced from [50, 100, 200]
'max_depth': [3, 5], # Reduced from [3, 5, 7, 10]
}
Issue: API returns 422 validation error
# Solution: Check that all required fields are included
# See api/schemas.py for complete field list
Contributions welcome! Areas for improvement:
See CONTRIBUTING.md for guidelines.
If you use this project in your research or work, please cite:
@misc{churn_prediction_2024,
author = {Your Name},
title = {Customer Churn Prediction using Machine Learning},
year = {2024},
publisher = {GitHub},
url = {https://github.com/username/customer-churn-prediction}
}
Author: Your Name
Email: your.email@example.com
LinkedIn: Your Profile
GitHub: @yourusername
This project is licensed under the MIT License - see LICENSE file for details.
Last Updated: December 2024
Project Status: ✅ Complete and Deployed
Maintained: Yes, active development
Data Science / ML
Python, Jupyter, ResearchPreparing markdown...
Documentation Heavy
Frameworks, Complex Apps, SaaSPreparing markdown...
A high-performance monorepo containing all ACME Corp's web properties, shared libraries, and internal tools. Built with Turborepo, pnpm workspaces, and modern tooling for maximum developer productivity.
🚀 Apps: 3 production apps | 📦 Packages: 8 shared libraries | 👥 Team: 45 developers
This repository is the single source of truth for ACME Corp's web ecosystem. It contains:
✅ Single Source of Truth: One repo, one version, one deploy
✅ Code Sharing: Share components, utils, and configs effortlessly
✅ Atomic Changes: Update shared code and all consumers in one PR
✅ Unified Tooling: One linting config, one testing setup
✅ Better Collaboration: See the whole picture, review across apps
✅ Faster CI/CD: Smart caching with Turborepo (up to 85% faster builds)
acme-monorepo/
│
├── apps/ # Production applications
│ ├── web/ # Next.js marketing website
│ │ ├── src/
│ │ ├── public/
│ │ ├── package.json
│ │ └── next.config.js
│ │
│ ├── dashboard/ # React SaaS dashboard
│ │ ├── src/
│ │ ├── package.json
│ │ └── vite.config.ts
│ │
│ ├── docs/ # Documentation site (Docusaurus)
│ │ ├── docs/
│ │ ├── blog/
│ │ └── docusaurus.config.js
│ │
│ ├── mobile-api/ # Node.js API for mobile apps
│ │ ├── src/
│ │ ├── package.json
│ │ └── tsconfig.json
│ │
│ └── admin/ # Internal admin panel
│ ├── src/
│ └── package.json
│
├── packages/ # Shared packages
│ ├── ui/ # Shared React component library
│ │ ├── src/
│ │ │ ├── Button.tsx
│ │ │ ├── Card.tsx
│ │ │ └── index.ts
│ │ ├── package.json
│ │ ├── tsconfig.json
│ │ └── README.md
│ │
│ ├── utils/ # Shared utility functions
│ │ ├── src/
│ │ │ ├── date.ts
│ │ │ ├── string.ts
│ │ │ └── validation.ts
│ │ └── package.json
│ │
│ ├── api-client/ # Shared API client
│ │ ├── src/
│ │ └── package.json
│ │
│ ├── eslint-config/ # Shared ESLint configuration
│ │ ├── index.js
│ │ ├── react.js
│ │ └── package.json
│ │
│ ├── tsconfig/ # Shared TypeScript configs
│ │ ├── base.json
│ │ ├── react.json
│ │ ├── node.json
│ │ └── package.json
│ │
│ ├── tailwind-config/ # Shared Tailwind config
│ │ ├── index.js
│ │ └── package.json
│ │
│ ├── database/ # Prisma database client
│ │ ├── prisma/
│ │ │ └── schema.prisma
│ │ ├── src/
│ │ └── package.json
│ │
│ └── auth/ # Authentication library
│ ├── src/
│ └── package.json
│
├── tooling/ # Development tools
│ ├── scripts/ # Automation scripts
│ │ ├── setup.sh
│ │ └── deploy.sh
│ └── github/ # GitHub actions
│ └── workflows/
│
├── .github/ # GitHub configuration
│ ├── workflows/
│ │ ├── ci.yml
│ │ ├── deploy.yml
│ │ └── release.yml
│ └── CODEOWNERS
│
├── turbo.json # Turborepo configuration
├── package.json # Root package.json
├── pnpm-workspace.yaml # pnpm workspaces config
├── .npmrc # npm configuration
├── tsconfig.json # Base TypeScript config
├── .eslintrc.js # Root ESLint config
├── .prettierrc # Prettier config
└── README.md
Ensure you have the following installed:
npm install -g pnpm)# 1. Clone the repository
git clone https://github.com/acme/monorepo.git
cd monorepo
# 2. Install all dependencies (for all apps and packages)
pnpm install
# 3. Build all packages
pnpm build
# 4. Run development servers for all apps
pnpm dev
That's it! All apps should now be running:
Copy environment files for each app:
# Marketing site
cp apps/web/.env.example apps/web/.env.local
# Dashboard
cp apps/dashboard/.env.example apps/dashboard/.env.local
# API
cp apps/mobile-api/.env.example apps/mobile-api/.env
Fill in your environment variables (see individual app READMEs for details).
apps/web)Tech Stack: Next.js 14, React 18, Tailwind CSS
URL: https://acme.com
Purpose: Public-facing marketing and SEO-optimized content
Key Features:
Local Development:
cd apps/web
pnpm dev
apps/dashboard)Tech Stack: React 18, Vite, TanStack Query, Zustand
URL: https://app.acme.com
Purpose: Customer-facing SaaS dashboard
Key Features:
Local Development:
cd apps/dashboard
pnpm dev
apps/docs)Tech Stack: Docusaurus 3, React, Algolia
URL: https://docs.acme.com
Purpose: Developer documentation and API references
Key Features:
Local Development:
cd apps/docs
pnpm dev
apps/mobile-api)Tech Stack: Node.js, Express, PostgreSQL, Redis
URL: https://api.acme.com
Purpose: REST API for mobile applications
Key Features:
Local Development:
cd apps/mobile-api
pnpm dev
apps/admin)Tech Stack: React, Material-UI, React Admin
URL: https://admin.acme.com (internal)
Purpose: Internal admin tools for customer support and ops
Key Features:
Local Development:
cd apps/admin
pnpm dev
packages/ui)Shared React components used across all applications.
Components (50+):
Usage:
import { Button, Card, Modal } from '@acme/ui';
export default function MyComponent() {
return (
<Card>
<Button variant="primary" size="lg">
Click Me
</Button>
</Card>
);
}
Storybook: http://localhost:6006 (pnpm storybook)
packages/utils)Shared utility functions and helpers.
Categories:
formatDate, parseDate, timeAgotruncate, slugify, capitalizeisEmail, isURL, isPhoneNumbergroupBy, unique, sortBydeepMerge, pick, omitformatCurrency, formatNumber, clampUsage:
import { formatDate, truncate, isEmail } from '@acme/utils';
const date = formatDate(new Date(), 'MMM DD, YYYY');
const excerpt = truncate(longText, 100);
const valid = isEmail('user@example.com');
packages/api-client)Type-safe API client for communicating with backend services.
Features:
Usage:
import { createClient } from '@acme/api-client';
const client = createClient({
baseURL: process.env.API_URL,
token: session.accessToken
});
// Fully typed requests
const user = await client.users.get('123');
const posts = await client.posts.list({ page: 1, limit: 10 });
packages/database)Prisma database client shared across backend services.
Schema: PostgreSQL with Prisma ORM
Models: Users, Organizations, Posts, Comments, etc.
Usage:
import { prisma } from '@acme/database';
const users = await prisma.user.findMany({
where: { active: true },
include: { posts: true }
});
Migrations:
cd packages/database
pnpm prisma migrate dev
pnpm prisma generate
packages/eslint-config){
"extends": ["@acme/eslint-config/react"]
}
packages/tsconfig){
"extends": "@acme/tsconfig/react.json"
}
packages/tailwind-config)module.exports = {
presets: [require('@acme/tailwind-config')],
}
# Run only the dashboard
pnpm --filter dashboard dev
# Run dashboard with dependencies
pnpm --filter dashboard... dev
# Build only the marketing site
pnpm --filter web build
# Run specific apps
pnpm --filter "{web,dashboard}" dev
# Run all apps except docs
pnpm --filter "!docs" dev
# Watch mode for UI package
cd packages/ui
pnpm dev # Rebuilds on file changes
# Apps importing @acme/ui will auto-reload
# Add to a specific app
pnpm --filter web add lodash
# Add to a package
pnpm --filter @acme/utils add date-fns
# Add to root (dev dependencies)
pnpm add -D -w prettier
# Use the generator
pnpm generate:app
# Or manually
mkdir apps/new-app
cd apps/new-app
pnpm init
# Add to pnpm-workspace.yaml
# Use the generator
pnpm generate:package
# Or manually
mkdir packages/new-package
cd packages/new-package
pnpm init
Turborepo intelligently caches build outputs and only rebuilds what changed.
Benefits:
// turbo.json
{
"pipeline": {
"build": {
"dependsOn": ["^build"],
"outputs": ["dist/**", ".next/**"]
},
"dev": {
"cache": false,
"persistent": true
},
"lint": {
"outputs": []
},
"test": {
"outputs": ["coverage/**"]
}
}
}
# Build everything
pnpm build
# Build with remote caching
pnpm build --remote-cache
# Force rebuild (ignore cache)
pnpm build --force
# Dry run (see what would run)
pnpm build --dry-run
# See dependency graph
pnpm turbo run build --graph
# Clear cache
pnpm turbo prune
# Generate visual graph
pnpm turbo run build --graph=graph.html
Opens an interactive graph showing:
| Type | Tool | Coverage |
|---|---|---|
| Unit Tests | Vitest | 87% |
| Component Tests | React Testing Library | 82% |
| E2E Tests | Playwright | Critical paths |
| Visual Tests | Chromatic | UI components |
| API Tests | Supertest | 91% |
# Run all tests
pnpm test
# Run tests for specific app
pnpm --filter dashboard test
# Run tests in watch mode
pnpm test:watch
# Run E2E tests
pnpm test:e2e
# Generate coverage report
pnpm test:coverage
// packages/ui/src/Button.test.tsx
import { render, screen } from '@testing-library/react';
import { Button } from './Button';
describe('Button', () => {
it('renders with correct text', () => {
render(<Button>Click me</Button>);
expect(screen.getByText('Click me')).toBeInTheDocument();
});
it('handles click events', () => {
const handleClick = vi.fn();
render(<Button onClick={handleClick}>Click</Button>);
screen.getByText('Click').click();
expect(handleClick).toHaveBeenCalledOnce();
});
});
GitHub Actions workflow runs on every push:
| App | Platform | URL |
|---|---|---|
| Marketing | Vercel | https://acme.com |
| Dashboard | Vercel | https://app.acme.com |
| Docs | Vercel | https://docs.acme.com |
| API | Railway | https://api.acme.com |
| Admin | Vercel | https://admin.acme.com |
# Deploy all apps
pnpm deploy
# Deploy specific app
pnpm --filter web deploy
# Deploy to staging
pnpm deploy:staging
# Deploy to production (requires approval)
pnpm deploy:production
Each app has its own environment variables:
# Marketing site
apps/web/.env.local
# Dashboard
apps/dashboard/.env.local
# API
apps/mobile-api/.env
Never commit .env files! Use Vercel/Railway secret management.
# Check all services
pnpm health:check
# Output:
# ✅ web: https://acme.com (200 OK)
# ✅ dashboard: https://app.acme.com (200 OK)
# ✅ api: https://api.acme.com/health (200 OK)
Problem: "Cannot find module '@acme/ui'"
# Solution: Build packages first
pnpm build
# Or run in watch mode
cd packages/ui && pnpm dev
Problem: "Port 3000 already in use"
# Solution: Kill process or use different port
pnpm --filter web dev -- --port 3001
Problem: "Out of memory during build"
# Solution: Increase Node memory
export NODE_OPTIONS="--max-old-space-size=4096"
pnpm build
Problem: "Changes in package not reflecting in app"
# Solution: Clear Turborepo cache
pnpm turbo prune
pnpm build
[HELP] prefixgit checkout -b feature/amazing-featurepnpm lint && pnpm test && pnpm buildgit push origin feature/amazing-feature# Format: type(scope): message
feat(dashboard): add user analytics chart
fix(ui): resolve button hover state bug
docs(readme): update installation steps
chore(deps): upgrade react to v18.3
test(utils): add date formatting tests
# Check for outdated packages
pnpm outdated
# Update all dependencies
pnpm update -r
# Check for vulnerabilities
pnpm audit
| App | Initial JS | Total Size | Lighthouse |
|---|---|---|---|
| Marketing | 85 KB | 320 KB | 99/100 |
| Dashboard | 210 KB | 890 KB | 94/100 |
| Docs | 120 KB | 450 KB | 98/100 |
This monorepo is proprietary and confidential.
© 2024 ACME Corporation. All rights reserved.
Unauthorized copying, distribution, or use is strictly prohibited.
Marketing (@marketing-team)
web, docsProduct (@product-team)
dashboardAPI (@api-team)
mobile-apiInternal Tools (@tools-team)
adminQuestions? Ask in #eng-help or email platform@acme.com
Last Updated: December 2024
Maintainers: @platform-team
Monorepo / Workspace
Turborepo, Yarn WorkspacesPreparing markdown...
🏆 Winner - Best Use of AI, Global Hackathon 2025
⏱️ Built in 24 hours by a team of 4 sleep-deprived developers
🐱 Currently connecting 127 cats across 3 continents
The result? Unhappy cats, stressed owners, and a lot of knocked-over water glasses.
SuperNova is a mobile-first social platform that matches cats based on:
🎯 Personality Compatibility
🍽️ Dietary Preferences
🎮 Activity Levels
💬 AI-Powered Translation
See what boxes are popular in your area. This cardboard apartment complex got 847 paws up!
Swipe right for cats that share your love of 3 AM parkour. Swipe left on dogs (obvious).
"Meow meow meow" → "I found an excellent sunbeam. Come immediately."
Track your cat's daily zoomies, box time, and nap quality. Export reports for your vet.
🌐 Try it now! (Demo credentials: cat@example.com / password123)
📹 Watch the video demo (2 min)
🎤 Pitch deck (if you prefer slides)
We went FULL STACK in 24 hours:
Expect:
data2 and finalFinal# 1. Clone this beautiful mess
git clone https://github.com/yourteam/supernova.git
cd supernova
# 2. Install dependencies (grab a coffee)
npm install
# 3. Set up environment variables
cp .env.example .env
# NOW EDIT .env WITH YOUR KEYS! (don't skip this)
Create a .env file:
# Supabase (get from https://supabase.com/dashboard)
VITE_SUPABASE_URL=https://your-project.supabase.co
VITE_SUPABASE_ANON_KEY=your-anon-key-here
# OpenAI (get from https://platform.openai.com/api-keys)
VITE_OPENAI_API_KEY=sk-your-key-here
# Optional but recommended
VITE_SENTRY_DSN=your-sentry-dsn # Catch those 3 AM bugs
# Development server
npm run dev
# Should open at http://localhost:5173
# If not, we probably hardcoded localhost:3000 somewhere 🤦♂️
npm run build
npm run preview
# Deploy to Vercel
npm i -g vercel
vercel --prod
What we ACTUALLY built in 24 hours:
Supabase over custom backend
React over [insert other framework]
OpenAI API over training our own model
Tailwind over custom CSS
┌─────────────────┐
│ React App │ ← You are here
└────────┬────────┘
│
┌────┴────────────────┐
│ │
┌───▼──────┐ ┌──────▼───────┐
│ Supabase │ │ OpenAI API │
│ │ │ │
│ • Auth │ │ • GPT-4 │
│ • DB │ │ • Whisper │
│ • Real │ │ • DALL-E │
│ -time │ └──────────────┘
└──────────┘
// users table
{
id: uuid,
email: string,
name: string,
created_at: timestamp
}
// cats table
{
id: uuid,
owner_id: uuid, // FK to users
name: string,
photo_url: string,
personality: json, // {playful: 8, social: 6, ...}
favorite_treats: string[],
zoomie_time: time
}
// matches table
{
cat_a: uuid, // FK to cats
cat_b: uuid, // FK to cats
compatibility_score: number,
matched_at: timestamp
}
// messages table
{
from_cat: uuid,
to_cat: uuid,
message: string,
translated: boolean,
created_at: timestamp
}
🧙♂️ Alex "Frontend Wizard" Chen (@alexchen)
⚙️ Jamie "Backend Boss" Rodriguez (@jrodriguez)
🤖 Sam "AI Overlord" Kim (@samkim)
🎨 Morgan "Design Guru" Taylor (@mtaylor)
Combined Stats:
MIT License - Use it, abuse it, ship it to prod (but please don't)
We built this in 24 hours. It's a beautiful disaster. Use at your own risk.
Sure! Here's what needs work:
Just open a PR! We'll merge almost anything at this point.
Found a bug? Of course you did. There are probably hundreds.
Want to chat?
Press inquiries:
We built this in 24 hours fueled by caffeine, pizza, and the collective stress of 4 developers who should have been sleeping.
Is it perfect? No.
Does it work? Mostly.
Are we proud? Absolutely.
If you made it this far in the README, you're either:
Thanks for checking out SuperNova! May your cats find their purrfect matches. 🐱💕
⭐ If you like this project, give us a star on GitHub!
🐛 If you find bugs, please tell us gently, our egos are fragile
🚀 If you want to help, PRs welcome!
Built with 💙, 😴, and an unhealthy amount of ☕ by Team SuperNova
"In space, no one can hear you meow." - Team SuperNova, 2025
Project Status: 🔥 On fire (in a good way... we think)
Code Quality: 🎢 It's a roller coaster
Cat Approval Rating: 🐱🐱🐱🐱 (4/5 cats)
Would Build Again: Maybe with more sleep
Hackathon / MVP
Prototype, Demo, Event