=======
Support
=======
.. image:: https://camo.githubusercontent.com/11b2f47d7b4af17ef3a803f57c37de3ac82ac039/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f70617970616c2d646f6e6174652d79656c6c6f772e737667
:target: https://www.paypal.me/jeff1evesque
.. image:: https://camo.githubusercontent.com/c705adb6695b3d8f60b9a005674cb58b3f1ef1cc/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f6e6174652d626974636f696e2d677265656e2e737667
:target: http://coinbase.com/jeff1evesque
Donations are very appreciated. Smaller donations, could fund a latté, during
a late night meddling code. While larger donations, could fund further
research, by assisting the cost for the following:
- server(s): this could be made open to the public, and implementing machine-learning.
- peripheral device(s): these device(s) could connect to the machine-learning server(s):
- `raspberry pi `_: these devices could communicate to the machine-learning server(s), or *peripheral device(s)*.
- `xbee chip `_: these chips could implement the `zigbee `_ wireless protocol, allowing peripheral device(s) to transmit data between one another, and finally to the machine-learning server(s).
- `sensor `_: multiple types of sensors could be connected via the `zigbee `_ wireless protocol to other sensor(s), raspberry pi(s), or directly to the machine-learning server(s).