Basket has been my favourite notetaking software for a long time, until I had switched to mindmaps. Quite recently I’ve discovered another use for it – a writing aid. Basket in one-column mode allows to rearrange your notes just by dragging them up or down (there’re keyboard shortcuts for that as well). When I’m writing a longer piece, I don’t need to hold a structure of the article in my head. I just collect all the pieces (quotes, blog posts fragments, my own notes, links, tweets etc.) and then rearrange it as much as it’s needed. When the flow of the thoughts is optimal, I start to connect these pieces by writing some text in between :).
I don’t have DevonThink (I don’t have Mac) but for finding similar things in my archive I use SCAN. SCAN can aggregate content from a number of sources (it has plugins to read PDFs, OpenOffice and MSOffice files or even RSS feeds), analyze it, automatically assign tags, extract metadata etc. It has Lucene engine built in and does quite a good job of finding related pieces in the archive. It’s quite buggy, doesn’t read all PDFs (such as encrypted), metadata extraction doesn’t work as expected but overall the tool has a potential (and there’s no similar program available on Linux platform anyway). Its development was recently restarted so there’s hope it’s going to be improved in a near future. Additionally, it has a nice eye-candy – a visual overview of relations between tags.
This strategy is similar to the workflow described by Steven Johnson, but without DevonThink. So far I haven’t found anything better under Linux, but probably I need to check online apps – things do change every month.
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Freelancing science 
Timestamped FriendFeed activity – really public “profile”
Accidentaly, I have found a simple way for obtaining a time stamp for each entry and comment any person with publicly available lifestream makes on FriendFeed (except “Likes”, which do not seem to be timestamped at all). Activity of semi-randomly choosen person during the day (summarized over couple of weeks (!)) is shown below:
FriendFeed usage during 24 hours, summarized over couple of days.
While relation between AM and PM periods is correct, time-zone is manually shifted, so it’s more difficult to guess who’s this activity is (but it’s not Robert Scoble if you want to ask). What does it tell? Basically, this person does not close FriendFeed window for the most of the day. Additionally, there’s a period of the day in which “catching-up” has place. Nothing interesting so far? Original data has much more details. It is possible for example to collect information when during the day particular person usually watches videos on YouTube. Guess – is that during working hours? 🙂
Ability to get that data for couple of weeks back without any trouble (I didn’t need to track this person’s activity for such period) was kind of disturbing. I knew it’s very simple to start tracking my habits, but I wasn’t aware of the fact that it’s also easy to see what I was doing over the last three weeks. Do you think it makes a difference?
Posted by Pawel Szczesny on January 29, 2009 in Comments, Visualization
Tags: activity tracking, Blog, FriendFeed, RSS