About a month ago I was thinking about how I want to read more long, hard books — specifically The Brothers Karamazov. Back in college I plowed through Don Quixote in a week for my Comp Lit class (and I still think that’s slightly insane), and I read lots of other long books as well, all because I had a deadline.
What Bookkeeper is
It’s a reading goal tracker. You give it a book, the number of pages in the book, a start date, and a deadline, along with which days you’d like to read (since sometimes you’ll want to take weekends off or what have you), and Bookkeeper will tell you how many pages per day you have to read to hit your goal. If you miss a day, or if you read ahead, it’ll adjust that number (the red line on the chart).
Where to get it
I’m not planning to host a public instance of it, at least not right now, but if anyone wants to do that, let me know so I can forward people to it. And of course anyone is welcome to fork the code and do whatever they want with it.
How to use it
You add books, then whenever you read, you update the page number (in the upper right). And that’s about it. The list of tabs on the left shows you your current books; the All Books tab will let you also see books you’ve finished and books you’ve hidden (for when you temporarily put a book on the back burner).
On the Account page (the link’s at the lower right) you can export your book/entry data as JSON. We figured it’d be nice to have some easy way to get your data out. (And the link doesn’t change, so you could set up a cronjob to curl the JSON weekly or something if you really want regular backups.)
As we make Bookkeeper more social going forward, we’re thinking it’d be cool to build book recommendations based at least partly on reading curves. Looking at my Well of Ascension reading curve up there, you can see that it starts to go up quickly and it’s ahead of the curve, which pretty much means I really got into the book and must have liked it. That’s not a surefire method for determining which books are good and which aren’t, but we’re interested in seeing what kinds of recommendations we can get from reading data like this.