How vbs-0/quantumlink-pqc works
QuantumLink-PQC is a local Flask peer-to-peer messaging research prototype. A user registers in a browser, adds a peer, requests and accepts a connection, then exchanges messages; each peer process handles plaintext and cryptographic operations locally. The implementation also includes neural seed generation, dataset creation/training, persistent account/peer/connection data, and visible security and ML status. Edges reflect the README and shown source references/calls; unsampled route wiring is not inferred.
Main entry points: app.py Flask peer · ml_seed_generator.py Seed generation · ml_model_trainer.py Model training and prediction · app.js Browser interactions
Components 13
Browser client
- Chat dashboard
templates/dashboard.html - Browser interactions
static/js/app.js
Peer application
- Flask peer
app.py - Peer HTTP client
network_manager.py - Account and connection data
database.py - Chat database
database.py
Cryptography
- PQC message engine
crypto_engine.py
Seed modeling
- Seed generation
ml_seed_generator.py - Model training and prediction
ml_model_trainer.py - Synthetic dataset
dataset_generator.py
Other components
- Chat user
- Remote Flask peer
- TensorFlow runtime
Connections 15
- Chat user → Chat dashboard: uses
README.md - Chat dashboard → Browser interactions: loads interactions
README.md - Browser interactions → Flask peer: HTTP and Socket.IO
README.md - Flask peer → Account and connection data: reads and updates
app.py - Account and connection data → Chat database: persists records
database.py - Flask peer → PQC message engine: processes handshakes
app.py - Flask peer → Peer HTTP client: uses peer client
app.py - Peer HTTP client → Remote Flask peer: sends peer requests
network_manager.py - Remote Flask peer → Flask peer: handles requests
README.md - PQC message engine → Seed generation: creates peer generators
crypto_engine.py - Seed generation → Model training and prediction: predicts and trains
ml_seed_generator.py - Seed generation → Synthetic dataset: creates training data
ml_seed_generator.py - Model training and prediction → Synthetic dataset: loads dataset
ml_model_trainer.py - Model training and prediction → TensorFlow runtime: trains and predicts
- Flask peer → Browser interactions: reports status and events
README.md