It is a well known fact that decision making is tiring. One of the more difficult decisions our team face every day is where to go for lunch. To avoid post lunch decision fatigue, we started automating the process.
Here is the first version of the script using the venerable
rl -c 1 << HERE | say Papa Pane Japanese Soup Canteen Honigmond HERE
In this case we had to generate the candidates manually. In a lot of decision situations that is acceptable. However there are also situations where the machine can help generating candidates. For our lunch problem we devised the following solution:
curl -s \ --data-urlencode "cat=eat-drink" \ --data-urlencode "in=52.5304,13.3864;r=640" \ --data-urlencode "size=15" \ --data-urlencode "pretty" \ --data-urlencode "app_code=NYKC67ShPhQwqaydGIW4yg" \ --data-urlencode "app_id=demo_qCG24t50dHOwrLQ" \ --get 'http://demo.places.nlp.nokia.com/places/v1/discover/explore' \ | jsed --raw 'function(r) r.results.items.map(function(i) i.title + " (distance " + i.distance.toString() + "m)" ).join("\n")' \ | rl -c 1
This would yield something like this:
Weinbar Rutz (distance 252m)
It uses the Nokia RESTful Places API to find places within 640m around our office. Conveniently its playground environment already creates a curl statement for us. Then we pipe the result through jsed to extract the important information from the JSON response, before we task rl with taking the actual decision for us.