Below are the titles to 10 blog posts that never saw the light of day (as noted in the Drafts section of Wordpress).

1. Small Kindnesses
2. Why follow?
3. Rivalry
4. Out of Sight. Out of Mind.
5. Simile and Metaphor in Hip Hop
6. A Million Penguins
7. Disclaimer
8. A Broken Record Store
9. Time was. Time is. Time shall Be.
10. They Found the White Gene

Seth Godin wonders:

In a world where twitter and facebook and blogs can spread an idea around the world in a few seconds, how do you have a conversation with someone in confidence?

I hadn’t really really thought about it until a few weeks ago when the contents of one of my emails (one I thought was private) found its way into a blog post.

Seth proposes that we include “NFYB” (Not For Your Blog) in emails that we want to remain private. While I like the idea, unless it finds widespread usage, the acronym is a bit clumsy.

I saw the following footer the other day on a listserv that I am on that I thought hit the mark.

This e-mail is [ ] bloggable [x] ask first [ ] private

I like the third category here that Seth NFYB doesn’t address, namely, that I wouldn’t mind certain people blogging the contents of a private email if I trust them and/or have talked to them about what they intend to blog.

Either way, I think it is a fascinating point that will only become more important in the future. Until shared cultural norms are in place, being explicit about the intended audience of your emails cannot hurt.

I didn’t see the game, but it sounds like it was an ominous start to the Browns season. If I wasn’t such a fan, the antics below probably would have been hilarious.

The Browns imploded shortly after kickoff.

On their first fourth down, newly signed Cleveland punter Paul Ernster let the snap slip through his hands before scrambling to get off a 15-yarder.

Cleveland was whistled for four infractions — two holds, an illegal formation and having a man downfield — on the play and the Steelers scored moments later when Roethlisberger hit Hines Ward on a 5-yarder.

The romp was on.

“I’d like to think we got the bad one out of our system,” said Browns wide receiver Joe Jurevicius.

(via Yahoo Sports)

This is a graph of the connections between members of my Facebook network produced by the Friend Wheel application (please note names have been removed to protect the innocent).

A few take away notes:

1. As I mention in the title of the post, I like this visualization because it spells out very clearly that my network exists whether I am a part of it or not. Note that, while this is ostensibly my network, I am not on the graph.

2. My law school friends have adopted Facebook in far larger numbers than my other friend groups. This is partially due to the fact that the average age of my law school classmates is ~5 years younger than my college friends. I imagine that this is also partly due to the fact the size of one’ school network diminishes over time.

3. My brother was a year behind me in school and we attended the same schools from pre-k through college. It’s cool to see lines connecting my brother to friends of mine (and his) from nearly every stage of my life.

4. I wonder what this graph would look like if I was liberal with my friend requests and acceptances? There are no real surprises here mostly because I know nearly everyone on this graph well. What interesting connections would I find if I opened up the network a bit?

5. The grouping by network has its advantages and disadvantages. I like the dense parts of the graph. It shows you the relative size of each of your networks when all those belonging to a particular network are grouped next to each other on the circle. It quickly becomes clear that not only is my law school network larger, but that it is also more interconnected than my other networks. But, such grouping makes it difficult to tell who the nodes are. I would like have access to a view that showed me who in my network are are super connected and, alternatively, who have fewer ties. It’s a blurry haze for the most part now.

6. Finally, I can’t figure out how this app determines its groupings. It’s clear that those who actually belong to the same network are grouped together. But this app is smarter than that (sort of). It manages to group my five cousins together despite the fact that I have only confirmed “friend details” (cuz details?) with one of them. More interestingly, it manages to connect five of my friends who all work together despite the fact that there is no network on Facebook for their employer. It makes sense, they are connected to me and to each other, and to know one else. But then why can’t the program figure out that my “random law school friend” should be grouped with the law school kids? While she opted for her work network over her school network, she is clearly tied to at least five people in the school network, and isn’t tied to anyone else.

   Anyway, I have over thought this, but it’s interesting to me. I like the visual interpretation of how my network is connected. While there are no surprises, I can imagine a time where my network becomes large and varied enough on Facebook that there will be.