| // Available variables: | |
| // - Machine | |
| // - interpret | |
| // - assign | |
| // - send | |
| // - sendParent | |
| // - spawn | |
| // - raise | |
| // - actions |
| ## For educational purposes only! Not a substitute for individualized advice from a qualified legal practitioner. ## | |
| SOFTWARE CONSULTING AGREEMENT | |
| THIS AGREEMENT is entered into on <contract date> by and between Lava X Technologies Sdn Bhd, (hereinafter "Developer") and <client company>, (hereinafter "Company"). | |
| RECITALS | |
| WHEREAS, Company wishes to retain Developer to develop certain <software type> software (hereinafter defined and referred to as “Software”); and |
brew install docker-machine-driver-xhyve
sudo chown root:wheel $(brew --prefix)/opt/docker-machine-driver-xhyve/bin/docker-machine-driver-xhyve
sudo chmod u+s $(brew --prefix)/opt/docker-machine-driver-xhyve/bin/docker-machine-driver-xhyve
I’ve been working on Police UK Open Data dataset for quite some time. But it requires server side scripting to do the scraping and importing the data into database for further process. It was a pain. Fortunately Neo4j has now come with a data import functionality. Therefore I’m now taking this challenge to re-do a small part of my existing application using Neo4j. :)
The domain model is fairly simple and straight forward. Each :Phone node is connected to at least one :Manufacturer, :Type, :OS and :Category node. Different 'weight' were given to different type of relation between these nodes. These 'weight' are the factors that will affect the ranking of the recommendation. It rates what people are more concerned with when considering for a new phone.