Weekly Recap 053008: ‘No Comment’

The week that was…


Good discussion this week about comments…first, there was the latest installment of this issue: comment dispersion away from the originating blog…Fred Wilson at A VC weighed in: Jackson instigated the conversation with that post. His reward is the comments it generates…interestingly, bloggers with big established audiences agreed with him…Chris Brogan wrote this on Fred’s blog: One part of the currency I crave from doing a blog is that conversation, especially on my blog, where I spend lots of effort building the posts to be conversation starters, not just fully formed ideas…Mathew Ingram wrote a concurring blog post Bloggers get “paid” with comments

Which made me wonder, do you think there’s a divide between larger established bloggers and smaller, newer bloggers on this issue of distributed conversations?


Next up on the comment discussions…who actually owns the comments?…there was a controversy early in the week where Rob La Gesse was irritated at the comments that were occurring on FriendFeed about his blog post…so he pulled his blog RSS from FriendFeed, which eradicated that post and all its comments from the FriendFeed UI…this raised the question of who owns the comments, and whether FriendFeed should do a better job of keeping records…Mathew Ingram reached out to FriendFeed co-founder Paul Buchheit, who noted the bias is toward blogger control of their feeds and that they will look at ways to solve to better retain comments…

Later, Daniel Ha of Disqus wrote a post called A Commenter’s Rights…kind of a Bill of Rights for those who leave comments on blogs…one Right that I liked: ‘The ability to edit and remove their comments’…too many blogs don’t allow that, including wordpress.com…

We’ll close this out with a quote from my favorite cranky blogger Steven Hodson: This whole discussion about comments is becoming borderline stupid


FriendFeed is growing, and not surprisingly, it’s getting its share of…um…interesting personalities…click this link which takes you to a search for “tweets totally f%(#ed twitter”…you’ll understand what I mean…


Hats off to a couple of developers this week…I wrote a post titled FriendFeed ‘Likes’ Compatibility Index…I manually pulled together some stats to see which other FriendFeeders had the same Likes as me…well Yuvi wrote a script that he could run from his computer for any FriendFeed handle he entered…a bunch of us wanted our stats manually calculated, and he obliged…he blogged about it, and hit Techmeme…very nice Yuvi…

Then another developer, felix, created a UI where anyone could enter their FriendFeed handle to see the people who shared Likes the most…and then felix thought, “I’m going to turn this one up to 11″…he made pie charts out of the results, which have become a big hit on FriendFeed…FriendFeed co-founder Bret Taylor gave his thumbs up on felix’s blog, “Very cool!“…very nice work felix…

BTW, we’re all one playing for second place to Shey in the Likes department…


Jeremiah Owyang apparently has an interesting post on FriendFeed that he’s writing for Saturday 5/31/08…Robert Scoble talked with Jeremiah, and gave this update:

I just talked with Jeremiah. He says FriendFeed will turn on a new functionality that Jeremiah is calling “MiniMeme.” He wouldn’t give me more details, but I am intrigued.

So check your RSS reader for Jeremiah’s post, and maybe we’ll be talking about that here next week….


See this item on FriendFeed: http://friendfeed.com/search?q=%22Weekly+Recap+053008%3A+%E2%80%98No+Comment%E2%80%99%22&public=1


FriendFeed ‘Likes’ Index: Case Study in Value of Distributed Conversations

By keeping comments distributed, or decentralized, more than one discussion is able to take place. New ideas are likely to be heard since readers often start with a blank slate and are more likely to participate.

Shey Smith, introspective snapshots, The Case For Distributed Conversations

Today, a great example of the value of distributed conversations took place. What started as a blog post here ended up with three different developers coming up with innovative new scripts that FriendFeeders were digging. And it all happened because of distributed conversations, not despite them.

The very smart and in-tune Fred Wilson wrote a piece yesterday decrying the distribution of conversations all over the Web, including on FriendFeed. Mathew Ingram followed up with a concurring blog post. I understand where they’re coming from, but I think they overlook the value of distributed conversations.

What I’d like to do is briefly describe the action today, and then point out how distributed conversations made innovation possible today.

FriendFeed ‘Likes’ Index Calculators

Wednesday night, I posted a piece titled FriendFeed ‘Likes’ Compatibility Index. The post reported some number crunching I did to figure out who most often Liked the same things that I do. The idea was to see what other FriendFeeders shared the same interests. At the end of the post, I made a request for someone to automate the analysis.

From this post, two separate conversations emerged. The RSS feed for the blog post hit FriendFeed (Original Post). And Louis Gray shared it on Google Reader (Shared Post), which started a second conversation.

What happened? There were three different places where conversations were happening: on this blog and on two different items in FriendFeed. And it resulted in three separate developers coming up with solutions.


Yuvi, a 17-year old wunderkind who does amazing stats analysis, was interested in automating this analysis. He posted the same comment on all three locations: “I could automate this…if friendfeed fixed this bug.” Yuvi was concerned about a bug in FriendFeed that won’t allow you to go more than 11 pages back in your history.

His comment generated responses in FriendFeed on both the Original Post and on the Shared Post.

Original Post:

  • Phil Glockner: “Yuvi, does that bug exist when doing queries against the API?”
  • Yuvi: “Yes, it exists in the API too.”

Shared Post:

  • Shey: Yuvi, could you automate it up to page 11?
  • Hutch: Does the limit of going back beyond page 11 risk the script failing? Or does it limit the data collected?
  • Yuvi: @Hutch: Limits data collected.
  • Yuvi:@Shey: Well, I could… But, it’ll be of limited use, no?
  • Bwana: I say do it now so when they do fix it, it’ll be ready, plus there seems to be an interest
  • Shey: @Yuvi Limited yes, but I think 11 pages of data is of some use for analysis of recent data
  • Cyndy: Yuvi, I’m not sure it’s a bug. I think it’s a limit that they have set. Since the variable is passed in the URL, if you try to go past that number of posts manually, it still won’t go. Could be that they are only pulling from cache?
  • Yuvi: @Cyndy: Well, they’ve been mum on this – so I don’t really know. But, if even *I* can’t access my old stuff, isn’t that wrong on at least “some” level?
  • Benjamin Golub: I don’t think it’s a bug either. I feel that there DB sharding might be setup such that it is very very quick to pull recent data.
  • Bwana: Well if there is a limit imposed, pages after 11 shouldn’t even be shown. It’s a bug of some kind either way.
  • Yuvi: @Benjamin: Yep, agree on that, but there should be ‘some’ way to get the older data out, no?
  • Yuvi: Just repeating – the API has the same limit in place. Script ready anyway – First Target – LouisGray 😉

So in that sequence, you see that fragmented conversation, away from the blog post itself, resulted in Yuvi creating a script to determine who shares your Likes.

And Yuvi blogged about it, linking to my blog post and even mentioning me by name. Everything a blogger could want.

Do you see what I mean Fred and Mathew?

Ole Begemann

On Louis’s Shared Post, a second developer Ole Begemann weighed in:

  • Ole Begemann: I’ve written a Python script that does this, too (for practice). Interestingly, Phil is no. 12 on my list of Louis Gray’s most compatible likers. If there’s interest, I’ll try to wrap it up on a web page (it’s command line at the moment) and publish it.
  • Hutch: @Ole – Yeah, I’d like to have a page where you could see these results.
  • Ole Begemann: I’ll get around to it Hutch. It might take me a few days. It’s my first try as a Python programmer. 😉

A second developer came up with a script for this. Again, via conversations that happened entirely away from the originating blog.


Finally, back on my Original Post in FriendFeed, a developer named felix added this comment:

“I just created a little javascript to go and grab the last 30 likes of anyone and do a basic calculation. Have a couple more features I want to add, but no more time today – what do y’all think? http://is.gd/nLc

That link goes to a blog post, where Ole links back to my original blog post. Again, as a blogger who wrote something I thought might be interesting, this is all really good stuff.

None of it occurred on my blog. And it doesn’t bother me in the least! in fact, check out felix’s blog post. You’ll see that he, Yuvi and Ole are having a conversation about FriendFeed API limits.

Why the Distribution of the Conversation Made a Difference

Three points to make here.

1. Go where the conversations are

If I’d been hung up on forcing everyone back to my blog for comments, this likely would not have been as successful as it turned out. FriendFeed offers a dead simple commenting function that makes it incredibly easy to comment. People find it easy to interact around content, rather than everyone having to travel from blog to blog to hold conversations.

Some blogger removed his RSS from FriendFeed recently, because he didn’t like all the FriendFeed comments along with it. Really? I remember the story, but can’t find the link to his blog. Seriously.

2. Connect to people outside your blog subscriber base

Digg, StumbleUpon, FriendFeed…all of these give exposure to your blog outside of those who subscribe to it or bookmark it. And when conversations about your blog occur on these venues, you’re getting vital exposure.

Make no mistake about this. A Like or a Digg or a Stumble is great. But if you really want to attract people to your blog post, comments are king. They tell people that the post is interesting, and that they better go read to get in on the discussion.

Louis Gray has a bigger, and different, community than I do. So his share of the post on Google Reader, and the subsequent conversation, attracted people who might never have bothered with my post.

felix, who developed the really cool app where you can see who shares your Likes, does not subscribe to me in FriendFeed, nor does he subscribe to my blog. I looked at his subscriptions, and we do have a number of FriendFeeders in common including Louis. I presume that’s how he found his way to the conversation about the blog post. Would he have been attracted to the blog without the conversation going on inside FriendFeed? Unlikely.

Embrace distributed conversations. They are free advertising for your blog.

3. Use the everyone search feature

Have people figured out this one yet? On FriendFeed, you can run a search for your blog post title in the ‘everyone’ tab. It can be a little hectic, but also fascinating. Click here for the everyone search for the FriendFeed ‘Likes’ Compatibility Index post.

Note that not only will you see all the different instances of my original blog post. You’ll see Yuvi’s post as well as Thomas Hawk’s post on the subject. I like seeing comments on those related posts as well.

As a blogger, I get a lot of value out of seeing who liked the blog post, and all the conversations among the different tribes. They help me improve.

Final Thoughts

Would that blog post have resulted in three separate scripts being developed if conversations only happened on the blog? No. At least not for me. If you’ve got a huge subscriber base like Fred Wilson or Mathew Ingram, it might.

But if you’re small fry, the distribution of conversations provides enormous value. Now let me go see who shares my Likes on FriendFeed…


See this item on FriendFeed: http://friendfeed.com/search?q=%22FriendFeed+%E2%80%98Likes%E2%80%99+Index%3A+Case+Study+in+Value+of+Distributed+Conversations%22&public=1

FriendFeed ‘Likes’ Compatibility Index

A favorite feature of FriendFeed is the Like. You get to indicate your interest in an item with a simple click of the Like button.

The act of applying a Like does two things:

  • Provides feedback to the content creator
  • Reveals what your interests are

It’s that second point that is interesting. Amazon.com matches you to other shoppers based on what you buy in order to provide recommendations. Toluu matches you with others based on common RSS feeds. Diigo matches you based on common bookmarks and tags.

How about matching people based on common FriendFeed Likes? Call it the FriendFeed Likes Compatibility Index.

Curious about this, I went to my Likes tab on FriendFeed. I went back to my 50 most recent Likes, and tallied the number of Likes by others. By doing this, I figured I’d see with whom I had the most in common.

The top 29 people are shown below – I put the cutoff at having 4 Likes in common. Some of these folks I know, others I really haven’t interacted with yet.

Here are my top matches in FriendFeed:

  1. Atul Arora (13 likes in common)
  2. Louis Gray (13)
  3. Mitchell Tsai (11)
  4. Shey (11)
  5. Robert Scoble (10)
  6. Thomas Hawk (9)
  7. Julian Baldwin ( 8 )
  8. Jason Kaneshiro ( 8 )
  9. Mark Trapp (7)
  10. Charlie Anzman (6)
  11. Mark Dykeman (6)
  12. Bearded Dave (5)
  13. Bwana McCall (5)
  14. Mack D. Male (5)
  15. Mike Fruchter (5)
  16. Phil Glockner (5)
  17. Alejandro S. (4)
  18. Andrew Badera (4)
  19. Anthony Farrior (4)
  20. Dobromir Hadzhiev (4)
  21. edythe (4)
  22. Kenichi Matsumoto (4)
  23. Marco (4)
  24. Nikpay (4)
  25. Rob Diana (4)
  26. Ruth Ferguson (4)
  27. Shawn L Morrissey (4)
  28. Susan Beebe (4)
  29. Timothy Neilen (4)

One small observation – I’m not in sync with a lot of women, am I? What’s up there? FriendFeed Is from Mars, Twitter Is from Venus?

Now what I need to do is to subscribe to those on this list that I haven’t yet. Also of note – there were 241 different people with whom I shared a Like in this analysis. Really great how FriendFeed lets you come into contact with a wide range of people.

Would be cool if a script could automate the FriendFeed Likes Compatibility Index…


See this item on FriendFeed: http://friendfeed.com/search?q=%22FriendFeed+%27Likes%27+Compatibility+Index%22&public=1

Tag Recommendations for Content: Ready to Filter Noise?

In a recent post, I suggested that the semantic web might hold a solution for managing noise in social media. The semantic web can auto-generate tags for content, and these tags can be used to filter out subjects you don’t want to see.

As a follow-up, I wanted to see how four different services perform in terms of recommending tags for different content.

I’ve looked at the four services, each of which provide tag recommendations. Here they are, along with some information about how they approach their tag recommendations:

  • del.icio.us: Popular tags are what other people have tagged this page as, and recommended tags are a combination of tags you have already used and tags that other people have used.
  • Twine: Applies natural language processing and semantic indexing to just that data (via TechCrunch)
  • Diigo: We’ll automatically analyze the page content and recommend suitable tags for you
  • Faviki: Allows you to tag webpages you want to remember with Wikipedia terms.

Twine and Diigo take the initiaitve, and apply tags based on analyzing the content. del.icio.us and Faviki follow a crowdsourced approach, leveraging the previous tag work of members to provide recommendations.

Note that Faviki just opened its public beta. So it suffers from a lack of activity around content thus far. That will be noticed in the following analysis.

I ran the six articles through the four tagging services:

  1. The Guessing Game Has Begun on the Next iPhone – New York Times
  2. TiVo: The Gossip Girl of DVRs – Robert Seidman’s ‘TV by the Numbers’ blog
  3. Twitter! – TechCrunch
  4. Injury ‘bombshell’ hits Radcliffe – BBC Sport
  5. Why FriendFeed Is Disruptive: There’s Only 24 Hours in a Day – this blog
  6. Antioxidant Users Don’t Live Longer, Analysis Of Studies Concludes – Science Daily

The tag recommendations are below. Headline on the results? Recommendations appear to be a work in progress.

First, the New York Times iPhone article. Twine wins. Handily. At Diigo gave it a shot, but the nytimes tags really miss the mark. del.icio.us and Faviki weren’t even in the game.

Next, Robert Seidman’s post about Tivo. Twine comes up with several good tags. Diigo has something relevant. And again, del.icio.us and Faviki weren’t even in the game.

Now we get to the trick article, Michael Arrington’s no text blog entry Twitter! The table turn here. Twine comes up empty for the post. Based on the post’s presence on Techmeme and the 400+ comments on the blog post, a lot of people apparently bookmarked this post. This gives del.icio.us and Faviki something to work with, as seen below. And Diigo offers the single tag of…twitter!

Switching gears, this is a running-related article covering one of the top athletes in the world, Paula Radcliffe. Twine comes up the best here. Diigo manages “bombshell”…nice. del.icio.us and Faviki come up empty, presumably because no users bookmarked this article. And none of them could come up with tags of “running” or “marathon”.

I figured I’d run one of my own blog posts through this test. The post has been saved to del.icio.us a few times, so I figured there’d be something to work with there. Strangely, Twine comes up empty. Faviki…nuthin’.

Finally, I threw some science at the services. This article says that antioxidants don’t actually deliver what is promised. Twine comes up with a lot of tags, but misses the word “antioxidants”. Diigo only gets antioxidant. And someone must have bookmarked the article on del.icio.us, because it has a tag. Faviki…nada.


Twine clearly has the most advanced tag recommendation engine. It generates a bevy of tags. One thing I noticed between Twine and Diigo:

  • Twine most often draws tags from the content
  • Diigo more often draws tags from the title

Obviously my sample size isn’t statistically relevant, but I see that pattern in the above results.

The other thing to note is that these services do a really great job with auto-generating tags. For instance, the antioxidant article has 685 words. Both Twine and Diigo were able to come up with only what’s relevant out of all those words.

With del.icio.us and Faviki, if someone else hasn’t previously tagged the content, they don’t generate tags. Crowdsourced tagging – free form on del.icio.us, structured per Wikipedia on Faviki – still has a lot of value though. Nothing like human eyes assessing what an article is about. Faviki will get better with time and activity.

Note that both Twine and Diigo allow manually entered tags as well, getting the best of both auto-generated and human-generated.

When it comes to using tags as a way to filter noise in social media, both system- and human-generated tags will be needed.

  • System-generated tags ensures some level of tagging for most new content. This is important in an app like FriendFeed, where new content is constantly streaming in.
  • Human-generated tags pick up where the system leaves off. In the Paula Radcliffe example above, I’d expect people to use common sense tags like “running” and “marathon”.

The results of this simple test show the promise of tagging, and where the work lies ahead to create a robust semantic tagging system that could be used for noise control.


See this item on FriendFeed: http://friendfeed.com/search?q=%22Tag+Recommendations+for+Content%3A+Ready+to+Filter+Noise%3F%22&public=1

Why FriendFeed is Disruptive: There’s Only 24 Hours in a Day

Forget fractured conversations. How about fractured attention?

MG Siegler has a post up at ParisLemon titled FriendFeed Should Kill Those Who Accuse It of Murder. In the post, he writes that the current meme about FriendFeed killing Twitter and Google Reader is overblown and that all the services will exist in relative harmony for the foreseeable future.

To which I ask: did someone just extend the day to 25 hours?

Because there really is a zero sum game aspect to social media. We only have 24 hours in a day, and we have to decide where to spend those hours.

That daily time limit is what makes FriendFeed so disruptive.

Allocation of the 24 Hour Day

The chart below is a hypothetical day of a relatively advanced social media user (no laughs about Facebook please):

The chart shows our social media user at three different points. I’ve taken the liberty of assuming that certain core life stuff is maintained consistently: sleep, eat, work, family. All else is flex time.

So with the core life stuff constant at 19.5 hours, and more time spent on FriendFeed, something’s got to give? But what?

Not websites and blogs. In fact, their page views go up because of FriendFeed. Their content is the currency of FriendFeed conversations.

I think the two services that get hit the hardest as FriendFeed grows will be:

  • Twitter
  • Crowdsourced aggregators: Digg, Stumbleupon, LinkRiver, Reddit


I left this comment on Corvida’s post The #1 Reason FriendFeed Will Not “Dethrone” Twitter at SheGeeks.net:

My two cents. FriendFeed direct posts feel like Twitters, only you can see the whole conversation, not just part of it. FriendFeed lacks the @reply and DM, so if those are important use cases, yeah it’s not replacing Twitter. But for putting something out there and having your subscribers weigh in…well, it feels like Twitter.

I’m not the only one. Two heavyweights in the blogging world have expressed their feelings about using FriendFeed in lieu of Twitter:

  • Steve Rubel :”Who’s spending less time on Twitter and more time here? I am.”
  • Duncan Riley: “@geechee_girl true, and if I can switch to FF with everyone on Twitter, I’d start considering swapping most if not all of the time”

The key to Twitter’s success is not it’s haiku format, it’s the community, as Duncan Riley mentions. Twitter is growing fantastically, as more people adopt it (and unfortunately stress its current platform). That community is what makes it vibrant special. FriendFeed appears to be rapidly growing its own community. I’ll be curious what the Compete.com May numbers look like for FriendFeed.

Note in the allocation of the day, I don’t eliminate Twitter. People have built up their networks there, and tweeting has become a habit. Also, the @reply function is quite popular, as is the DM. One might ask if those functions aren’t essentially covered by instant messaging and email, but Twitter fans love ’em.

But I see the direct post + comments as taking interaction away from Twitter.

Crowdsourced Aggregators

The basic function of these applications is to surface the content receiving the most votes. Digg, StumbleUpon, Reddit and LinkRiver are great for discovering content that others have found valuable. Digg includes robust, active commenting.

Well, doesn’t that sound like FriendFeed? The system of ‘Likes’ and comments ensures that community-ranked content appears at the top of your FriendFeed page.

Again, FriendFeed doesn’t kill these services. StumbleUpon, for example, has a persistence to it that FriendFeed lacks. Content gets its moment in the sun on FriendFeed, then gets buried in pages further back. I’ve noticed the StumbleUpon activity around content can last for days, weeks.

But over time, as users discover ranked content on FriendFeed, I’d expect them to cut back their time on the other crowdsourced aggregators. Not stop using these other services, but check in on them less frequently.

Final Thoughts

Perhaps as MG Siegler said, there really is room for all of these social media apps. Folks will just expand the amount of time they devote to them. But I question that assumption. Your employer still pays you for your hours. Your kids still want your time. The human body needs its sleep. And you still need to eat.

FriendFeed is disruptive because it challenges a number of other applications. If you find something that offers an outstanding experience and provides a good percentage of what you like in other social media apps, wouldn’t you spend more time there?

I mean, there’s only but so many hours in a day.


See this item on FriendFeed: http://friendfeed.com/search?q=who%3Aeveryone++%22Why+FriendFeed+is+Disruptive%3A+There%E2%80%99s+Only+24+Hours+in+a+Day%22

Weekly Recap 052308: If You Love Your Blog, Set It Free

The week that was…


Things kicked off with a pair of posts about the next stage of blogging. Yes, fractured comments and all…Duncan Riley wrote Blogging 2.0: It’s All About The User. He writes: If blogging 1.0 was about enabling the conversation on each blog, blogging 2.0 is about enabling the conversation across many blogs and supporting sites and services…Louis Gray followed up with Blogging 2.0 Causing Friction With 1.0 Bloggers…Louis nicely defines the old blogging paradigm: Blogging 1.0 centered around who could: (i)Amass the most page views; (ii) Display the most ads; (iii) Get the most comments; and (iv) Attract the most RSS subscribers

As a relatively novice blogger, I pretty easily fall into the Blogging 2.0 camp…why on earth would I want to keep the conversations limited to my little blog?…that’d be a recipe for having a stale blog…

But Blogging 1.0 is still a strong instinct out there…one example: see Allen Stern’s post on CenterNetworks, Let’s Get Serious About FriendFeed; the 1995 Message Board, the Smart Consolidator and the Stolen Conversation…read not just the post, but check out some of the comments…Blogging 1.0 will die hard…


Help! I’ve fallen, and I can’t get up!…bad week for Twitter, everyone’s favorite social chat room: outages, outages, outages…this seems to be getting progressively worse, as Twitter’s success is killing it…

To show disapproval for Twitter’s handling of these outages, several folks staged a Twit-Out on Wednesday May 21…a number of regular Twitterers went the whole day without going over to Twitter…they also hid tweets from their FriendFeed streams…even the biggest Twitterer of all, Robert Scoble, joined in…

It wasn’t met with universal love, but they made their point…oh, and Twitter did go down that day…

But one bright spot: Twitter apparently scored a new $15 million round of VC funding…


One outcome of the twitter issues this week…some bigger names in the social media world started to embrace it much more…Jeremiah Owyang, who previously marked the date when new Twitter subscribers could not be considered as early adopters, got into it again with FriendFeed…first he posted on FriendFeed that he now had a new place (FriendFeed) to look for conversations, which elicited a bunch of hearty “welcome aboard” type of messages…

Well that got Jeremiah fired up, and went into throw-down mode: Dudes, I’ve been on FriendFeed for a while, not a late adopter…he challenged Robert Scoble to list his date of FriendFeed registration…geek cred…

Of course, if you looked at his activity stats at that time, he had no comments, no likes…but he’s much more engaged now, which is cool…he even wrote a post about FriendFeed…


One thing I’ve noticed in some favorited Flickr photos…models wearing little to nothing…not that I’m complaining, I love art…Thomas Hawk has some strong opinions about making this even easier here


FriendFeed now has Rooms!…Rooms are separate spaces on FriendFeed where people can direct post items, and re-share items into a Room…they accomplish two things: (i) allow a focus around specific topics to follow; (ii) remove some of the items that were considered noise by many users…

Bwana McCall (second reference in this post, nice!) has a good initial set of use cases for rooms here…my favorite is the use of Rooms for live blogging like from one of those Apple events…

One bit of hilarity was the land grab that occurred for Room topics…Michael Nielsen asked Any plans to prevent squatting? I can see people snapping up thousands of “rooms” on the off chance that one day they’ll be worth something…um, well, uh…I managed to score Web 2.0, Enterprise 2.0, Running, Obama 2008 and Coca Cola among others…no idea what I’ll do with them, but anyone’s free to join…I wonder if the Obama campaign will want their Room?

Something that Rooms will foster: an increase in FriendFeed direct posts…regular feeds from your social media sites won’t stream automatically into Rooms…


See this item on FriendFeed: http://friendfeed.com/search?q=%22weekly+recap+052308%22&public=1

Analyzing My FriendFeed Stats: I Should Be Direct Posting More

I’m curious about the level of interaction that occurs around the different content that streams through FriendFeed. Distributed conversations are fine by me, and I wonder what sparks them most often for content. So I did a little analysis of the ‘likes’ and comments that have happened for me.

Below are some pie charts. The first set analyze the ‘likes’. To the left is the percentage of my FriendFeed stream that comes from different content sources. To the right, I counted the number of ‘likes’ for the various content sources. For the ‘likes’ I only counted for the month of May, but I think it’s a decent approximation of my overall activity.

A couple observations:

  • Blog posts and FriendFeed Direct Posts are the biggest sources of ‘likes’
  • Google Reader shares and Twitter are a big part of my stream, but don’t generate a comparable percent of ‘likes’

Now let’s see how the comments look:

Would you look at that? FriendFeed direct posts dominate the comments. My blog posts are #2.

What’s It Mean?

I imagine everyone’s experience will vary. For me, I draw four conclusions.

My FriendFeed use is similar to people who Twitter: With FriendFeed direct posts, I’ll sometimes just make an observation. Other times, I direct post a website, generally with a graphic. This strikes me as similar to Twitter in that I’m posting something that can be consumed by anyone who subscribes to me. Also, these posts mean someone can stay within FriendFeed. Seems to make a difference in interaction when people can stay on the site. Like Twitter.

‘Likes’ dominate my blog posts: The Likes:Comments ratio for my blog posts is running at 4:1. For all the concern about fractured comments, I’d say people are overlooking basic recommendations of your content via ‘likes’. It’s not about the comments, it’s about the ‘likes’!

Comments on my posts frequently occur on someone else’s stream: There are several of my blog posts that have generated good comments. They just haven’t occurred on the RSS feed from my blog. These bigger comment fests have been when someone with much larger following and FriendFeed ‘presence’ (and I’m not going to write his name, because I use it too often…). But you know what? I’ll take those comments! They obviously weren’t happening just off my own post. In the long run that kind of exposure is vital for us smaller bloggers.

Google Reader shares suffer from repetition: Good blog posts will often be shared by several FriendFeed members, including those with larger followings. So when I share, I may be following others. So the repetition diminishes the interaction. I still share – there is some interaction. And Google Reader shares end up in several other places, like RSSmeme and ReadBurner. These services will show the most popular shares, so I want to vote for these blog posts.

Final Thoughts

Colin Walker has some interesting thoughts about using FriendFeed as a blogging platform. Looking at how FriendFeed Direct Posts and my blog generate the biggest activity, maybe he’s on to something.


See this item on FriendFeed: http://friendfeed.com/search?q=%22analyzing+my+friendfeed+stats%22&public=1

FriendFeed Noise Control, Semantic Web and Dave Winer

On a FriendFeed discussion about the noise on the Web in general, Lindsay Donaghe posted this comment:

Actually I think it’s the same problem we have in general with the firehose of information we’re exposed (or expose ourselves) to on a daily basis. The struggle of where to apply our attention will only be resolved once someone develops intelligent agents to filter the bad stuff and alert us to the good stuff. Wish someone would hurry up and make those. That will be the ultimate killer app.

Louis Gray wrote this recently in his post Content Filters Proving Evasive for RSS, Social Media Sites:

So far, despite many users calling for content-based filters, solutions to block keywords or topics are missing from the vast majority of information spigots.

The recent meme about FriendFeed noise points to the frustration of some people with an inability to manage what content hits their screens. The two comments above underscore this feeling.

Here’s me own example. Dave Winer has two passions: technology and politics. For me personally, technology = signal. Politics = noise. I went through his FriendFeed stream for the month of May, and here are the 38 different political terms that show up:

So what to do? I’d like to suggest that the semantic web might be a solution for down the road.

What Is the Semantic Web?

Semantic web is still a confusing term. Two quotes from Wikipedia help describe it. This quote tells you generally what it’s about and importantly notes that there’s much development for the future:

The Semantic Web is an evolving extension of the World Wide Web in which the semantics of information and services on the web is defined, making it possible for the web to understand and satisfy the requests of people and machines to use the web content. At its core, the semantic web comprises a set of design principles, collaborative working groups, and a variety of enabling technologies. Some elements of the semantic web are expressed as prospective future possibilities that are yet to be implemented or realized.

This quote describes the problem that the semantic web will solve:

With HTML and a tool to render it (perhaps Web browser software, perhaps another user agent), one can create and present a page that lists items for sale. The HTML of this catalog page can make simple, document-level assertions such as “this document’s title is ‘Widget Superstore'”. But there is no capability within the HTML itself to assert unambiguously that, for example, item number X586172 is an Acme Gizmo with a retail price of €199, or that it is a consumer product.

It’s that last sentence there that addresses the noise issue. How does a server know that part X586172 can be categorized as a “consumer product”? That’s where the semantic web comes into play.

And how the noise can be controlled on FriendFeed.

Noise Control: Simplify Users’ Lives

One way to think of the semantic web is as tagging on steroids. In the example above, part X586172 is tagged as “consumer product”. And the tagging occurs without human intervention.

This is what’s needed on FriendFeed. The ability to take a wide range of terms that humans can understand are related. The relationship among the terms is tag.

Here’s what such an algorithm would do for Dave Winer’s political terms:

Now, imagine this in FriendFeed. Semantically-derived tags are appended to every item that flows through. Meanwhile, users have a new ‘Hide’ feature. Hide by topic. They could elect to hide streams with terms on a one-by-one basis. For instance, I’ll hide “robert reich”. I’ll hide “republicans”. I’ll hide “congress”. I’ll hide “obama”. I’ll hide “mitt romney”. I’ll hide…well, you get the picture.

In addition, users could just hide all items with the tag “politics”, and be done with it. Simple.

This could apply for all manner of topics: football, banking, Iraq, etc.

Just How Would These Semantic Tags Be Generated?

I’m not sure anything quite with this purpose exists yet. Reuters has been a leading player in the semantic web with its Open Calais initiative. However, Open Calais focuses of its tagging on people, places, and companies. So if Open Calais was applied to Dave Winder’s FriendFeed stream would have a lot of tags related to those topics. But not metadata tags.

A company called GroupSwim described their semantic tagging approach:

We use natural language processing to analyze the data our customers put into their sites. Our datasets tend to be much smaller but are high quality since someone doesn’t add something to GroupSwim unless they want to share it. Then, we compare the language used in the content to other semantic sources including WordNet, Wikipedia, etc. to do our automatic tagging and analysis.

Interesting, not sure what the tags they produce are. But it does give insight into a requirement: a core foundation of data against which all other data can be compared to derive tags. Something that would correctly map Obama and Clinton to a politics tag.

I’m sure there are other interesting approaches. It’d be great if someone was working on something in this area.

If anyone reading this knows of any semantic approaches that can apply metadata type of tags, feel free to leave a comment.


See this item on FriendFeed: http://friendfeed.com/search?q=who%3Aeveryone+%22friendfeed+noise+control+semantic+web+dave+winer%22

Yes, FriendFeed Will Be Mainstream (by 2018) and Here’s Why

We recently went through a Twitter meme about whether it was mainstream yet. There is no debate as to whether FriendFeed is mainstream today – it’s not. The question really is, will FriendFeed ever see mainstream adoption? Robert Scoble played both sides of the coin (here, here).

FriendFeed will go mainstream. My definition of mainstream: 33% of Internet users are on it. It’s just going to take time, and it’ll look different from the way it does now.

Four points to cover in this mainstreaming question:

  1. What will FriendFeed replace?
  2. What is a reasonable timeline?
  3. What content will drive the activity on FriendFeed?
  4. What topics will drive engagement?

What Will FriendFeed Replace?

Harvard professor John Gourville has a great framework for analyzing whether a new technology will succeed. His “9x problem” says a new technology has to be nine times better than what it replaces. This is because of two reasons:

  • We overvalue what we already have by three times
  • We undervalue the benefits of a new technology by three times

What does this mean in everyday terms? There’s comfort in the status quo, and fear of the unknown.

There’s the argument that FriendFeed is a complement, not a replacement to existing services. There’s some truth there, but the bottom line is that we only have 24 hours in day. Where will end up spending our time?

Here’s what FriendFeed will replace:

  • Time spent on the individual social media that stream into FriendFeed (blogs, Flickr, etc.)
  • Visits to static, top-down media properties (e.g. CNN, ESPN, Drudge Report, etc.)
  • Visits to other user-driven aggregator sites (Digg, StumbleUpon, Yahoo! Buzz)
  • Usage of Google search (search human-filtered content on FriendFeed)

In terms of the “9x problem”, the nice thing is that people do not have to replace what they already do. Visit CNN? You can keep doing that. Like to see what’s on Digg? You can keep doing that.

Searching on FriendFeed will advance. You can do a search on a keyword or a semantically-derived tag, and specify the number of shares, likes or comments.

FriendFeed doesn’t require you to leave your favorite service. It’s the FriendFeed experience that will slowly steal more of your time. That mitigates the issue of people overvaluing what they already have. They won’t lose it, they’ll just spend less time on it. Thomas Hawk continues to be an active participant on Flickr, but more of his time is migrating to FriendFeed. As he says:

One of the best things about FriendFeed is that it gives you much of what you get from your favorite sites on the internet but in better ways.

I think FriendFeed will have the 9x problem beat, but it will take time.

What Is a Reasonable Timeline for FriendFeed to Go Mainstream?

The chart below, courtesy of Visualizing Economics, shows how long several popular technologies took to be adopted in the U.S.

Using my mainstream definition of 33% household penetration, here’s roughly when several technologies went mainstream:

  • Color TV = 11 years
  • Computer = 15 years
  • Internet = 8 years

In addition, here are some rough estimates of current levels of adoption for other technologies. Estimates are based on the number of U.S. Internet users, the recent Universal McCann survey of social media usage (warning, PDF opens with this link) and search engine rankings.

  • Google search = 68% of searches after 10 years
  • RSS = 19% of active Internet users after 4.5 years of RSS readers
  • Facebook = 9% of Internet users after 4.5 years (20mm U.S. members / 211mm U.S. Internet users)
  • Twitter = 0.6% of Internet users after 2.2 years (1.3mm members / 211mm U.S. Internet users)

Yes, the date of FriendFeed mainstream adoption is pure speculation. But looking at the adoption rates of several other technologies, ten years from now is within reason (i.e. 2018). The RSS adoption is a decent benchmark.

What Content Will Drive FriendFeed Activity?

Alexander van Elsas had a recent post where he listed the percentage for different content sources inside FriendFeed. The results were compiled by Benjamin Golub.

Not surprisingly, Twitter dominates the content sources. Original blog posts are a distant #2 content source, and Google Reader shares are #3. That speaks volumes into the world of early technology adopters.

When FriendFeed becomes mainstream, the sources of content will change pretty dramatically as shown in this table:

The biggest change is in the FriendFeed Direct Post. Relative to blogging or Twittering, putting someone else’s content into the FriendFeed stream is the easiest thing for people to do. FriendFeed Direct Posts are similar to Diggs or Stumbles. Since all the content we create, submit, like or comment is part of our personal TV broadcast on FriendFeed, Direct Posts can be just as much fun for users as newly created content by someone you know.

Direct Posts will draw from both traditional media sites as well as from other people’s blogs. Expect media sites and blogs to have a “Post to FriendFeed” link on every article.

Twitter drops as a percentage of content here. Why? FriendFeed’s commenting system replaces a lot of what people like about Twitter. Blogs drop a bit as well. More people will blog in 2018, but many of those will be sporadic bloggers. Still, 10% of the content consisting of original author submissions is pretty good.

Google Reader shares hold as a percentage as more people recognize the value of RSS versus regular-old bookmarks inside their browsers. ‘Other’ goes up, because who knows what cool other stuff will be introduced over the next ten years.

What Topics Will Drive Engagement?

Human nature won’t change. The same stuff that animates people today will continue to do so in the future. Politics, sex, technology and sports will be leaders in terms of what the content will be. There will be plenty of other topics as well. I can see the Iowa Chicks Knitting Club sharing and commenting on new patterns via FriendFeed.

One issue that will arise is that people will have multiple interests. They’ll essentially have various types of programming on their FriendFeed “TV channels”. For a good example of that today, see Dave Winer’s FriendFeed stream. Dave has two passions: technology and politics. I like the technology stuff, but I tend to ignore the political streams.

Well, this will become a bigger issue as FriendFeed expands. I personally like the noise of the people I follow, but my subscriptions seem to generally stick with recurring topics. But as more mainstream users come on board, the divergence of topics for any single person will likely increase.

FriendFeed will employ semantic web technologies to identify the topic of submitted items. These semantically-derived tags will be used to categorize content. Users can then subscribe only to content matching specific categories. How might this work?

A Dave Winer post with “Obama” in it is categorized as Politics. I could choose to hide all Dave Winer updates that are categorized in Politics.

Final Thoughts

The constant flow of new content, the rich comments and easy ‘Likes’, and the social aspect of FriendFeed will drive its mainstream adoption. It’s a terrific platform for self-expression and for engaging others who share your interests. It’s also got real potential to be a dominant platform for research. In the future, look for stories in magazines and newspapers asking, “Are we losing productivity because of FriendFeed?”

So what do you think? Will FriendFeed ever be mainstream? In ten years?


See this item on FriendFeed : http://friendfeed.com/search?q=who%3Aeveryone+%22yes.+friendfeed+will+be+mainstream+%28by+2018%29%22

Hey Yahoo! Forget MSFT, GOOG. Change the Search Rules.

These I wish I knew the moment I was turned off on Yahoo and what the root cause may be, but I no longer use anything Yahoo (except my Flickr account if you want to count that).

Vince DeGeorge, on FriendFeed

I was doing the same thing until I started using delicious as a search tool. Finally realized how powerful it was, and have been using it since.

Shaun McLane, on FriendFeed

I have previously written that Delicious search is one of the best ways of searching for things when a standard search doesn’t pull up what you are looking for. After Google, it is my favorite “search engine.”

Michael Arrington, TechCrunch, Delicious Integrated Into Yahoo Search Results

The latest news is that Microsoft is reaching out to Yahoo again. In fact, a couple reports (here, here) say that Microsoft wants to buy Yahoo’s search business.

Before any such transaction occurs, it seems worthwhile to think about what Yahoo could do with its existing assets. The three comments above are insightful. Yahoo is slowly losing share of mind, although it’s existing base of users will be around for a while. At the same time, there are nuggets in the Yahoo empire.

Search via del.icio.us ranks as one of those nuggets. Another nugget? Yahoo! Buzz. According to ReadWriteWeb, Yahoo! Buzz has surpassed Digg in terms of traffic, and its demographics better reflect web users.

Yet, Yahoo struggles against Google in the highly lucrative search market. Google increased to 67.9% of searches in April 2008, compared to Yahoo’s decline to 20.3% of searches.

What should Yahoo do? Stop playing Google’s game. Rewrite the search rules by embracing the social web fully, leveraging the social media assets it has.

And in doing so, demonstrate an aggressive path to make Yahoo a social media titan.

A Proposal for “Socializing” Yahoo Search

In January 2008, TechCrunch ran a post with a preview of del.icio.us integrated with regular Yahoo search results. Included in the search result links would be stats that tell a user:

  • Number of del.icio.us users who bookmarked the page
  • The top tags they used on the page

Both of those stats appear to be clickable. By clicking on the number of users stat, I assume a user would be taken to the del.icio.us page showing the users who bookmarked the page. If one clicked a tag, you’d land on the del.icio.us page for all web pages with that tag.

That’s a good start. But Yahoo can do better. Below is a diagram that shows how Yahoo can use its existing assets, combined with a good dose of the new social media experience, to radically change search:

Here’s a breakdown of what’s going on with the proposal.

Search Rankings

From what I’ve read, Yahoo has pretty much caught up to Google in terms of search performance. That means the use of links and clicks to rank websites is pretty common across the two search engines. However, Google does have the advantage of three times the traffic, which makes its insight into what’s relevant better than Yahoo.

But Yahoo has its own in-house advantages: del.icio.us and Yahoo! Buzz. Both address shortcomings in the links and clicks rankings for search engines:

  • Links require a media site or blogger to take the time to link. These links are insightful, but lack the broader reach of what Web users find relevant.
  • Clicks occur before a searcher knows whether the landing site is valuable. They don’t describe its usefulness after someone has clicked onto the site.

With del.icio.us and Yahoo! Buzz, Yahoo can tap into users sentiments about websites in a way that Google cannot. These insights can be used to influence the ranking of search results.

Search Results – Your Friends or Everyone

Here’s where it can really interesting. Notice I keep the general search results outside the influence of what your friends think. I think that’s important. A person should see results outside their own social circle. Otherwise, it will be hard to find new content.

But there is real power in seeing what your friends find valuable (e.g. see FriendFeed). So Yahoo should let you easily subscribe to other people for content discovery. Yahoo already has a head start on letting you set up your subscriptions:

  • Yahoo Mail
  • Yahoo Instant Messenger

In addition to that, you should be able to easily subscribe to anyone who publicly shares content they find interesting. Both del.icio.us and Yahoo! Buzz have public-facing lists for every user of what they bookmark or ‘buzz’. After viewing those lists, I should be able to easily subscribe to these users.

Once your network is developed, it becomes a powerful basis for improving information discovery.

Search Results – Associated Tags

Whenever tags are available from del.icio.us, they should be visible for each web site shown in the search results. This is what TechCrunch previewed. What do tags tell a user?

  • A way to discover other sites that might be relevant
  • Context for the web site
  • That someone thought enough of the web page to actually tag it

Tags should come in two flavors: everyone and your network. Clicking on a tag should display the top 10 associated sites right on the search results page. For more sites associated to the tag, the user is taken to del.icio.us.

Keeping the top sites on the search results page is important to make people use the functionality. Leaving the search results page just to see the sites associated to a tag will cause adoption to drop signficantly.

Search Results – Associated People

Each web page in the search results will show the number of people who have (i) bookmarked the site; or (ii) Yahoo! Buzzed the site. These numbers give a direct indication of how many people, not websites, found the web page valuable.

Clicking these numbers displays a list of the people, along with their most recent activity. This gives users a sense of whether they want to subscribe to a given user or not.

Search Agent

Once users perform a search, they will be able to subscribe to new content matching their search results. These subscriptions can be based on different criteria:

  • Any new content matching the search term (Google does this via Google Alerts) or a tag
  • Any new content matching the search term/tag and bookmarked by someone to whom the user subscribes
  • Any new content matching the search term/tag and Yahoo! Buzzed by someone to whom the user subscribes
  • Any new bookmarks or Yahoo! Buzzes by someone to whom the user subscribes

New content notifications occur via email or RSS. RSS can be anywhere, including on the user’s My Yahoo page. Again, FriendFeed has shown the power of these content streams.

Final Thoughts

My little post here isn’t the only idea someone could float. But it does at least address taking Yahoo much more deeply into the social media world, where users drive the value.

Yahoo revealed details of a proposed del.icio.us integration back in mid-January. And then nothing. Yahoo previewed Yahoo Mash, a new social network in September 2007. And then…nothing. The last post on the Yahoo Mash blog was January 11, 2008.

Yahoo has so many amazing assets. Search, email, portal home page. Several beloved social media apps (Flickr, del.icio.us, Upcoming). Yet they have not put them together into a cohesive strategy and experience.

And now, talk of selling the search business? C’mon Yahoo. You’ve got too much going on to give up yet. Stop playing by others’ rules. Make your own rules with the amazing assets you have.


See this item on FriendFeed: http://friendfeed.com/e/1b07226a-b51b-f386-fbb8-bdaece83e9fe

The Noise About FriendFeed Noise

I’m actually enjoying the “noise” of FriendFeed. Anyone else?

Corvida, one of my favorite bloggers, has a post up on ReadWriteWeb titled Don’t Be So Naive: Friendfeed Adds to the Noise. In the post, she argues that FriendFeed is contributing to the noise with a lot of stream that hold no interest to her. Her examples include Flickr and Seesmic streams, as well as Twitters without a comment.

Now there is some truth to the noise issue, but I don’t think it rises to a “we’ve GOT to correct this ASAP” level.

In fact, I find the whole thing somewhat confusing. I love seeing the variety of topics and services that cross my FriendFeed page. Heck, I even added the Greasemonkey script to expand the list of items per page to 100 from the current 30. I hated missing stuff by relying only on the 30 items that appear on the first page.

So what am I doing differently from Corvida? Not sure really. Here’s what I know.

Number subscribers. I checked her subscriptions, and I’m subscribed to 55 more people than she is. So seemingly my risk of noise is higher. But it doesn’t bother me.

Blogger bias. I choose my subscriptions carefully. When I’m deciding whether to subscribe to someone, I tend to prefer someone who blogs. That requirement right there is a good one for managing noise. Bloggers seem to have a good level of signal in their FriendFeed streams. If someone only Twitters or shares items on Google Reader, I tend to hold off on subscribing. These rules aren’t ironclad, but they guide me.

Hiding. As I said, I’m not hiding much. I subscribe to one person, whose friends tend to blog in Chinese. I can’t read those, so I’ve been hiding these friends-of-friend on a one-by-one basis. I may need to hide all of his friends. I’m also close to hiding Jason Calacanis tweets as well. His tweets have a low signal-to-noise ratio for me. But it’s only a fraction of what I’m seeing.

See Louis Gray’s post about the various Hide features FriendFeed has – they’ll help clean up any noise issues you have.

Let’s Keep It Simple

Over-engineering a solution to noise is exactly the wrong thing to do. Beware the unintended consequences. The FriendFeed guys have put a lot of power in users’ hands to manage what is seen.

I have suggested a couple possibilities for cleaning up the duplicate links that can show up in FriendFeed. My guess is the FriendFeed guys are working on something related to that. That would be a help.

But really, let the streams flow. Your noise is my signal. I’m enjoying the content and conversations a lot. I even like the multiple times the same link shows up, because I’m piecing together an implicit social network based on that.

Bring the noise!


See this item on FriendFeed: http://friendfeed.com/search?q=%22the+noise+about+friendfeed+noise%22&public=1

Weekly Recap 051608: Duncan Riley ‘Likes’ FriendFeed

The week that was…

The awards for some of my blogger comrades just keep coming…Eric Berlin, of the Online Media Cultist, was named by The Industry Standard to its Top 25 B-to-Z List Blogs…the list covers blogs that have not yet achieved A-List status, but that readers should be following…Colin Walker was honored with placement in Guy Kawasaki’s Alltop list for social media…and whoa, check out his placement there, it’s pretty high..congrats to both Eric and Colin…


Blogger disclosure got a good airing this week…in this case, “disclosure” means bloggers should reveal any conflicts of interest they have in the companies they cover, or even avoid covering those companies altogether…in discussing Michael Arrington’s conflicts of interest, Wired managed to use the tag “ButtMunch” for its blog post…I mean, they could have been a little more clever…”ButtCrunch“….

Michael Arrington, Fred Wilson, plenty of others….I’ve got no beef with them blogging about their investments…we’re talking blogs for goodness sakes…and obviously something was notable about the companies to make them invest, so it’s no surprise they write favorably about their companies…Wired does make a valid point about crossing the line from blogging to journalism, but not to the point of stopping the Washington Post from running TechCrunch stories on its website…

One conflict of interest that I’ve never been able to resolve for newspapers is the political endorsements…you come out in favor of a given candidate, what should readers expect when they read your campaign coverage?


Duncan Riley, recently of TechCrunch and now writing The Inquisitr, seems to have turned around when it comes to FriendFeed…back in March on TechCrunch, he wrote FriendFeed Is This Years Twitter, But Why?, where he wondered what all the fuss was about…on his personal blog he later wrote FriendFeed = More Hyped Yawn

Well, this week he ran a couple pieces that reflected a change of heart…on Monday, there was a post on The Inquisitr in which he asked Louis Gray to explain what’s great about FriendFeed…later, in The Inquisitr Update: One Week In, he noted that he’s added inline support for FriendFeed commenting to his blog…heck, he even added a comment to something I shared on FriendFeed…yes, interacting with us little folks, which is a great part of FriendFeed…

Louis Gray has a nice blog post up describing in more detail the dust-up he had with Duncan Riley over FriendFeed…check that out to get a full measure of how far Duncan Riley has come in terms of FriendFeed…


Have you read Robin Cannon’s blog, Fog of Eternity?…lots of good posts there….my favorite line from a post today, Can’t Get Off the Blog Merry-Go-Round:

Downtime is vital though. Even if we enjoy blogging it’s still important that we get time off. Otherwise we burn out, the quality of our posts decreases, and we offer less value to our visitors.

Also, some really good, helpful posts analyzing traffic to his blog, like this one…check Fog of Eternity out sometime…


See this item on FriendFeed: http://friendfeed.com/e/31dcb3bb-df74-8dd1-7081-183a366d10a3