Papers We Love Guide

papers-we-love/
├── distributed_systems/
│   ├── README.md          # Curated list with descriptions
│   ├── lamport-clocks.pdf
│   └── raft.pdf
├── machine_learning/
├── programming_languages/
├── security/
├── databases/
├── networking/
├── information_retrieval/
├── artificial_intelligence/
├── concurrency/
├── operating_systems/
└── ... (40+ categories)

# Clone the repository
git clone https://github.com/papers-we-love/papers-we-love.git

# Browse categories
ls papers-we-love/

# Each directory has a README with curated descriptions
cat papers-we-love/distributed_systems/README.md

import os
import glob

PWL_PATH = "./papers-we-love"

# List all categories
categories = [d for d in os.listdir(PWL_PATH)
              if os.path.isdir(os.path.join(PWL_PATH, d))
              and not d.startswith('.')]
print(f"Categories: {len(categories)}")

# Find papers in a category
ml_papers = glob.glob(f"{PWL_PATH}/machine_learning/*.pdf")
for p in ml_papers:
    print(f"  {os.path.basename(p)}")

# Search across all READMEs for a topic
import re
for readme in glob.glob(f"{PWL_PATH}/*/README.md"):
    with open(readme) as f:
        content = f.read()
    if re.search(r"consensus|paxos|raft", content, re.I):
        category = os.path.basename(os.path.dirname(readme))
        print(f"Found in: {category}")

# PWL chapters host monthly meetups worldwide
# Find local chapters at paperswelove.org

chapters = {
    "New York": "meetup.com/papers-we-love",
    "San Francisco": "meetup.com/papers-we-love-too",
    "London": "meetup.com/papers-we-love-london",
    "Berlin": "meetup.com/papers-we-love-berlin",
    # 40+ chapters globally
}

# Video talks on YouTube
# youtube.com/@PapersWeLove — recorded presentations
# Each talk: 30-60 min paper walkthrough by practitioner

# Curate a personal reading list from PWL
essential_distributed = [
    "Time, Clocks, and the Ordering of Events (Lamport, 1978)",
    "The Byzantine Generals Problem (Lamport et al., 1982)",
    "Impossibility of Distributed Consensus (FLP, 1985)",
    "Paxos Made Simple (Lamport, 2001)",
    "In Search of an Understandable Consensus Algorithm (Raft, 2014)",
    "Dynamo: Amazon's Key-Value Store (DeCandia et al., 2007)",
    "MapReduce: Simplified Data Processing (Dean & Ghemawat, 2004)",
]

essential_ml = [
    "A Few Useful Things to Know About ML (Domingos, 2012)",
    "Dropout: A Simple Way to Prevent Overfitting (Srivastava, 2014)",
    "Batch Normalization (Ioffe & Szegedy, 2015)",
    "Attention Is All You Need (Vaswani et al., 2017)",
    "BERT: Pre-training of Deep Bidirectional Transformers (2018)",
]

## How to Contribute

1. Fork the repository
2. Add paper PDF to appropriate category directory
3. Update the category README.md with:
   - Paper title and authors
   - Year of publication
   - Brief description (2-3 sentences)
   - Why it matters
4. Submit a pull request

### README Entry Format
- :scroll: [Paper Title](link) — Brief description.
  Authors (Year). *Venue*.