Project PAI is an open-source, blockchain-based platform designed to allow everyone to create, manage, and use their own Personal Artificial Intelligence (PAI). The PAI Blockchain Protocol (PAI blockchain) enables a decentralized AI economy where application developers can create products and services that will be beneficial to the PAI ecosystem and users can contribute their PAI data to improve and enhance the platform's AI algorithms. In addition, companies and developers can easily create their own token on top of the PAI blockchain to facilitate interaction and transaction in their own unique experiences. The focal point of all interactions on the PAI blockchain are PAIs - intelligent 3D avatars that look, talk and behave just like their human counterparts, made from the digital profiles of the user's online behavior.
PAI Coin is a digital currency that enables instant payments to anyone, anywhere in the world. PAI Coin uses peer-to-peer technology to operate with no central authority: managing transactions and issuing money are carried out collectively by the network. PAI Coin Core is the name of the open-source software which enables the use of this currency.
Read the whitepapers here.
PAI Coin Core is released under the terms of the MIT license. See COPYING for more information or see https://opensource.org/licenses/MIT.
The master branch is regularly built and tested, but is not guaranteed to be
completely stable. Tags are created
regularly to indicate new official, stable release versions of PAI Coin Core.
The contribution workflow is described in CONTRIBUTING.md.
The developer forum should be used to discuss complicated or controversial changes before working on a patch set.
Testing and code review is the bottleneck for development; we get more pull requests than we can review and test on short notice. Please be patient and help out by testing other people's pull requests, and remember this is a security-critical project where any mistake might cost people lots of money.
Developers are strongly encouraged to write unit tests for new code, and to
submit new unit tests for old code. Unit tests can be compiled and run
(assuming they weren't disabled in configure) with: make check. Further details on running
and extending unit tests can be found in /src/test/README.md.
There are also regression and integration tests, written
in Python, that are run automatically on the build server.
These tests can be run (if the test dependencies are installed) with: test/functional/test_runner.py
Changes should be tested by somebody other than the developer who wrote the code. This is especially important for large or high-risk changes. It is useful to add a test plan to the pull request description if testing the changes is not straightforward.