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A web of trust made from nostr follows and reports
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README.md

noswot

A web of trust made from nostr follows and reports

Welcome to noswot. The vision here is to see what becomes possible when we can trust strangers on the internet.

Hypothesis: A web of trust algorithm that is comprehensive, and can run entirely client-side, will enable a range and volume of activity that the world has never seen.

This trial tests the algorithm in a database web application that gathers follows and reports, then calculates trust scorings from every known perspective every 24-48 hours.

You can participate in the trial by using the tool at noswot.org

If you have any questions or don't see what you're looking for please make an issue or mention David Sterry on nostr.

Requirements

  • Python 3.9+
  • MariaDB or MySQL
  • RabbitMQ: sudo apt-get install rabbitmq-server

Installation

  • pip3 install -r requirements.txt
  • Copy config/default_settings.py to config/settings.py and fill in parameters
  • Create a database in mariadb then mariadb -u dbuser -p {dbname} < schema.sql

Cron Scripts

  • hoover.py - downloads and saves the day's worth of notes, contact lists, and reports
  • process_profiles.py - parses out profile fields into profile table
  • update_contacts.py - saves follows to contact table, creating a follow log along the way
  • score.py - calculate trust scores
  • npub_name_mapping.py - generates nn.js for front-end to replace known npubs with names
  • preferred_relay.py - populates profile with each user's most commonly used relay and many more...

Celery Scripts

  • runner.py - main celery process
  • fill_in_profiles.py - sends profile grabber tasks to celery queue

Development

  • Run python3 hoover.py to pull in recent nostr data Then process profiles and contacts with:
  • time python process_profiles.py | sort | uniq -c to get a count of errors
  • python update_contacts.py
  • To collect missing profiles referred to in contact lists run celery: celery -A runner worker --loglevel=DEBUG
  • Then fire off profile grabbing tasks: python fill_in_profiles.py

After running hoover, profiles, contacts, and score you can run python main.py to start the Flask server.

Docker Compose

To run the project with docker-compose, you can use the following commands:

docker compose up -d

Open a shell in the web container to run the python scripts for example:

docker compose exec web ash
python hoover.py

Contributing

This project uses C4 (Collective Code Construction Contract) process for contributions.