vain

Helium freezes iPhones as discussed here - https://news.ycombinator.com/item?id=18340693

I know Apple is big on security, but could this possibly be a vulnerability? Could an iPhone be frozen with helium, and then disassembled to read off the SSD?

There are just days to go to the referendum that decides whether the U.K. stays in the European Union.

I am trying to cast an informed ballot, and get more confused with every article I read, every debate I watch.

I can not find an objective way of deciding this. How would you decide? Please suggest ways in which I might perhaps quantify this?

I've been tweaking a book recommendation engine for twitter. The algorithm is based on tf-idf of words in a user's tweets. Relevant words are extracted then fed into the amazon api. Would appreciate feedback.

http://foxblot.com/

sample recommendations: http://foxblot.com/pgraham http://foxblot.com/barakobama http://foxblot.com/fredwilson

the recommendations would get much better if I used phrases instead of words. All feedback appreciated.

Helper functions for the mongodb shell

1. db.grep() -Search through all fields in all collections in a database for a value. By default it searches only indexed fields. To include non-indexed fields, specify the second parameter as true.

2. db.collection.fields() -list all keys in a collection -by default, it only checks 10100 documents to get an exhaustive list, if you document varies across the collection, do: db.collection.fields(true)

To use, copy and paste the contents of mongohelper.js into your mongo shell. or: load("/path/to/mongohelper.js");

available here: https://github.com/blowhacker/mongohelper/blob/master/mongohelper.js

http://foxblot.com I built this site over a weekend two weeks ago.

I've spent this weekend matching tweets to books. If you mouseover a book, it shows the tweets relevant to that book.

Internally, keywords are rated inversely with their frequency of occurrence. The one problem is that my word frequency database does not correspond very well with twitter's vocabulary. To base my frequency database out of tweets is for another weekend.

Please let me know what you think. Specifically: 1. here can I go with this? 2. is it useful? 3. is slowness an issue? (it's running of a 256M rackspace cloud instance)

I made a site this weekend, foxblot.com that recommends amazon books based on tweets. The obvious extension is to make it a facebook app too. But what are ways I can make this profitable? I am looking for suggestions that do not involve a lot of work, as I can only dedicate time on weekends.

related post with functional details: http://news.ycombinator.com/item?id=4212994

it's here: http://foxblot.com Spent some hours doing this today. it has started to look interesting, but i am biased!

How it works: javascript:

   1. fetches latest 100 tweets by json api
   2. removes urls and @mentions
   3. removes stop words
   4. count frequency of all words
   5. post list to backend

backend:

   6. sort words entropy looking up in a frequency database
   7. search highest entropy words at amazon.co.uk
   8. send back data as json

This has mostly been for fun, and I think I will add facebook soon.

What do you guys think? Ideas?

(Apologies for stealing the color theme and css from here!)