A Kindle Fire Review (from a Media Fan)

I'm a Kindle fan, and an Amazon fan. I really like their media content: I buy Amazon music, Amazon Kindle books, TV shows, Android apps. So when my Kindle Fire came, it was pretty much pre-loaded, and that was really nice. All my stuff is sitting there with a little "download to device" arrow, which rocks.

I got this thing because of upcoming travel over the holidays (I don't own an iPad, I think they're too big). I was never intending to take the Fire instead of my reading Kindle, and after 5 days, I still wouldn't. Partly that's battery-life-related; I adore my reading Kindle for the everlasting, never-needing-to-charge-it, one-handed reading wonder that it is. The Kindle Fire battery supposedly lasts about 8 hours, and that may not be true with video watching and wifi on (I haven't tested that part yet).

So, this is not a Kindle-killer, anymore than it's an iPad killer, 'nuff said there.

More specifically, I got the Fire for video watching, web browsing/email/twitter, PDF reading, and light app use (Solitaire, Angry Birds, etc), in about that order of priority. So let's hit those, with some UI observations along the way, because that's where the chance for the Fire's improvements really lies. Then I'll finish up with a few comments on major navigational issues, e.g., scrolling, selecting, typing, which permeate the product.

Video Watching and Disk Space

The Fire seems to want you to mostly stream, which doesn't surprise me. The 8GB drive, and the free Amazon Prime (streaming only) support this. Netflix and Hulu Plus work on it (install their free apps from the store). If you have ever paid for a TV show ep (I sure have!) from Amazon, THOSE can be downloaded to your device.  (Browse to a show you have bought episodes for, and they tell you they're still yours, and you can download to your device now!)

Why does this matter? If you're wanting to use it on an airplane, or in iffy hotels off the grid, which I do, you need to download to your device.  And if you want to load video you already have, I did the research: It only recognizes MP4, so you need to convert stuff. (I'm using AVS Video Converter; my version does only one file at a time, which is proving to be a giant slow babysitting process.)

You can load videos (or PDFs, or mobi files, or anything else) when you attach your device by USB cable. Drag them into the Videos folder.

But don't expect them to show up in the Videos section of the UI, reachable by the top tabs! They will be found in the rather hidden pre-installed "Gallery" app, which is where your photos and videos live. And then you may be surprised by how poor the UI is for the videos (I am praying they fix this, it's un-manageable!) They appear as a tiny thumbnail with no text; you must select, and then choose "Properties," in order to figure out which one is which. This will get old fast, not just because selection is so funky on this device (more on that later). Here's the videos display with 2 videos:

A short season of one show could run just over 3GB. The actual disk space available to you is not 8GB, because of the OS etc; it's really 6GBish. To find out what you're using, you need to hunt a bit. There is no disk meter in the top accessory bar where Wifi, battery, and other settings live. Tap that bar, and you'll see options like volume. (Yes, it says "Lynn's 5th Kindle," I don't want to talk about it.)

You need to hit "More" and then click into "Device" to see the disk usage. That's really annoying for a device with such a small drive. I wouldn't be hoarding content on it, but for non-wifi situations, having downloaded content seems pretty important to me. I'm really befuddled by this one.

This said, my MP4 videos do look and sound nice. I'll be spending the evening getting ready for that trip.

Web Browsing, Email, Twitter, Etc.

I am guardedly pleased with this so far. I had some issues getting the built-in email app to recognize a Verizon Yahoo address, but the Yahoo mail app worked fine. Tweetcaster works nicely, and I even get a tiny cute beep in the notifications bar when someone @ mentions me, which is nice (same cute beep for email I receive).

The web browser does support tabs, which is great; but the favorites/bookmarks have one major issue: There seems to be no way to delete one. Huh? So it came built in with ESPN.com and a few others I never use, and I can't remove them. If this was a UI design mistake, it's shocking; if it was policy for some payment by partners -- unlike Amazon in so many ways, who are usually all about the customer.

Please fix this, Amazon.

Web pages also allow you to remember passwords, which - thank goodness. Typing is such a damn pain (see below).

Since web pages look good, and play video (including flash), this is a real plus on the device. Selection of links is funky, and I sometimes don't know if the selection problems I am having are due to the OS, touchscreen, or some web loading/processing issue.

PDF Files

PDFs on the e-ink "reading" Kindles are terrible - when they took away the text reflowing option for PDF docs, it becomes impossible to really read them, requiring too much zooming, scrolling, etc, and any images take forever to load and are, of course, B&W.

Most of this is awesome on the Kindle Fire! Definitely a reasonable PDF reader. The documents look great, and my only issue is the weird scroll-down, then to the right, for navigating a large document. It would be nice to have an option for "just scroll down" to get through a PDF document, instead of trying to use the book/paper metaphor of flipping pages. Here is how pretty PDFs look (yes, this is fanfiction, deal with it; of course I tested academic articles too).

Here's a page in portrait, with arrows suggesting how I need to scroll (down to get to bottom of the page, then flick to the right to "flip" to next page).

Thumbs up on PDF reading. They appear in your Documents folder as you would expect, and you don't need to send them to your device for conversion, they just "work." There isn't a Kindle Fire Instapaper app, but since you can save the site in your bookmarks and read text only, or download as Mobi files, you are all set there.

App Use: Angry Birds

Angry Birds is great. So is Solitaire. I haven't tried to install any apps that aren't in the Amazon app store, although you can (instructions abound on this). Note: I installed these on my Android phone and can't use them on it, screen is too small to really do it right. This form-factor is just fine for games that need a wider field of vision, or for people who are getting older and blinder.

I also installed a drawing app, but I don't much want to draw with my finger, so.

I like, and have always liked, the Amazon Android Apps store experience. In some ways, it's better than Google's app store. I'd expect that from Amazon UI, but it's nice to see on Amazon's first dedicated Android device.

Typing, Scrolling, Selecting, Turning the Page

The use of the touch screen is my biggest peeve. It's just buggy! If it's software, I expect a fix update -- Amazon is always good about updating Kindle software. If it's hardware, it's just a damn shame, and I'm kind of shocked it shipped this way.

Typing: The on-screen keyboard behaves very badly in portrait mode. My space bar and the letter "c" seem to be hyperactive for any key I pick on the right side of the keyboard. It's so bad, I will just switch to landscape for anything I ever need to type. The typing issues make the device less fun for email/twitter than I hoped. I am very sad about this.

Scrolling and Selecting: I have had so much trouble trying to scroll vs. selecting what's under my finger that I even looked it up in the help, and watched some demo videos online to see if they were doing it differently. It's not obvious. I have similar problems on my Android phone, which either means the OS itself is to blame, or making good touch screens is really really hard and Amazon's hardware providers failed. I spend a lot of time hitting the "back" button to undo a selection I didn't mean to make while I tried to scroll, especially in Tweetcaster or email.

If I were creating an Android app, I might consider making a dedicated scroll bar, just because it would offer some (admittedly old-school) way around this crappiness in the UI.

Incidentally, scrolling is very important in the apps that Amazon built for showing your bookshelf, your music, your videos... so this problem is quite profound.

Turn the Page: For books, without the e-ink hardware buttons, you need to flick or tap to turn the page. It's a slightly delicate maneuver, since it's easy to hit too hard and bring up the menu bars etc. Also, I am not so convinced this is a read-with-one-hand device. I'm not convinced by the reviews of the Amazon Touch either; if you're holding it in your left hand, tapping on the left side goes to the previous page. This is another surprising gaff on the UI side, for me. I'm not left handed, but I read left-handed about half the time.

Summary

I am very pleased by the PDF experience, and mostly like the apps and Web experience. I didn't buy this to replace my reading Kindle, so no real comment on that side.

I am shocked by these things, and expect software updates to fix some, if not all:

• Lack of a disk usage meter on the top info bar. Related to having very little storage on the device -- I admit, I wondered how hard it would be to crack it open and install a larger hard drive. We all did that with our first TiVos for years...
  
• Touch screen badness - for typing, selecting, scrolling... If this is hardware, we're rather screwed, I bet.
  
• Inability to delete web bookmarks (sheesh, seriously, Amazon?)
  
• Better UI for seeing your installed videos on the device. Option to see what the darn video is, without having to select it and go into "properties" first. Which is hard because of the touch screen issues.
  
• Possible option to just use a down-scroll on PDF docs, rather than flick-right to turn the page.

My quibbles aside, I do like it, especially for PDFs and apps. I'm looking forward to the Fire evolution and expect to see software updates (or at least good apps) addressing some of these problems very soon.

A Personal Take on Infovis 2011

I haven't had time to go thru the papers I liked and didn't like yet, but I have been musing on some other aspects of Infovis that I thought I'd recap. To situate this, I usually go every other year to Infovis, and have been doing so since mid-2000's, I guess.

Who Went, Who Didn't; Design vs. "Science"

Partly due to irritable blog exchanges in the past couple years, and partly due to perceived relevance of papers and audience, many of the artistic practitioners of infovis did not come. Or, if they did, I didn't know they were there. By this I mean academic artistic sorts like Golan Levin and Casey Reas and Dan Shiffman, and the practitioners like Stamen, Moritz Stefaner, JanWillen Tulp, Jer Thorp, Wes Grubbs, Ben Fry and Fathom, David McCandless, etc. (Kim Rees from Periscopic did attend. I wish I'd gotten a chance to talk with her.)

Martin Wattenberg and Fernanda Viegas, who are successful straddlers of artistic, industrial, and academic infovis, didn't make it either. They weren't boycotting, it was due to work and personal reasons. (Google+ Ripples, a project of theirs, launched while we were sitting in paper sessions.) I mention them because a handful of years ago they tried to bridge the communities (with Golan) in starting an art track. I don't think the momentum has been entirely conserved. Certainly the papers didn't reflect great focus on emotional, artistic, or design processes. The one most focused on design as process was a very dry and obvious overview how to do "user-centered design for beginners" that caused an industrial colleague of mine to observe "the bar for acceptance seems very low here." (It's not, but that one did make me raise my eyebrows.)

Again, this said, Amanda Cox's brilliant capstone talk, which was largely about design process and decisions at the NYT, was a huge success. As was Jessica Hullman's talk on visual engagement methods (or "chart junk, the sequel," as someone noted--Jerome Cukier, possibly).

I know some members of the program committee are trying to figure out how to get more industrial attendance. CHI has been through this for years, and added various case study tracks, panels dedicated to industrial talks, alt.chi for less mainstream academic works, among other strategies. Infovis could use some of this, but attracting people who have successful careers already, and convincing them there is value in attending given the pricetag, needs some more thinking through. I see value for them in the algorithm side of many of the papers -- but that might not be worth the cost of attendance for them.

Maybe the drinking would? I know some of us talked about the artistic non-attendees over drinks, since they weren't there to participate. More on this below...

One more contingent: there were a lot of folks from the intelligence communities, DoD, the government in general. My perception is that this has increased. And I think they asked smarter questions this year; they certainly weren't shy about going to the mic.

Paper Experience Sure Differs, Depending on Your Perspective

During a bunch of papers, the demo or video had some astoundingly beautiful angle or process moment that just wasn't published "point" -- it was almost incidental. I'm thinking especially of the beautiful organic edge bundling videos from "Skeleton-Based Edge Bundling for Graph Visualization" by Ozan Ersoy et al. (see this page for some recap.). My comment to Jen Lowe was that Jer Thorp and the Processing crowd would have loved this, and with the algorithm detail in the paper, would be able to implement and tweak quite easily. I can't find their videos anywhere, though! (Note: Even the first questioner afterwards said "I could watch your videos forever," but it was kind of in an undertone, not her point either. Let's have more talks where creating beautiful effects is a part of the point, perhaps?)

Mike Bostock's D3.js talk was fascinating to those of us who had read his slides from SVG beforehand, but hadn't heard his commentary on them; and if you knew the DataMarket protovis-vs-d3 history online. It was also nerve-wracking worrying about who would ask what afterwards given some of that historical controversy. Apparently not so for other attendees, I heard later! I find Mike's arguments convincing, although I have not tried to build anything sizable in D3 yet.

Jo Wood's et al.'s BallotMaps talk about name-order biases in voting districts was a wonderful "process" talk on using their HIVE system to visually test hypotheses. (For general info, see their org page.) I feel that the talk with demo of stages of visual exploration was important in making the story work, and the paper isn't as easy or fun to grok. Aidan Slingsby et al's talk on showing uncertainty in cluster results was similar (and surprisingly, the paper seems to differ quite a bit in the system design shown).

Program Committee: I'd like to see more videos in the proceedings!

Student Distractions: To Finish or Not?

As an ex-research type myself, I'm always interested in what grad students are going through now, what topics they and their advisors find valuable to study, and what my friends are facing as advisors. Stanford and Berkeley students seem to have a lot more distractions from start-ups given the "big data" and "data science" world we're in now. At the Stanford-sponsored party, I actually found myself recapping all the reasons to finish a Ph.D. to some poor guy who had no intention of quitting his. (Sorry, S, too many drink tickets.)

I don't necessarily use my own Ph.D. (except maybe socially at conference parties), but I have certainly concluded that spending years in a university surrounded by other smart people is not a bad thing. After all, the business world is usually not as smart, face it. And you will have many years to work a 9-7 job after school, so why rush out? The chance to sit in on other departments' classes, even when it's not a requirement, is a chance you don't usually get after graduation. Infovis, like HCI, is (or should be) interdisciplinary; being able to be in stats courses, graphic design courses, programming courses, psychology courses... well, if I were a student now, I'd want take advantage of those wonderful distractions. (I did when I was finishing up, but did NOT take enough stats. Luckily this is fixable with online courses, to some degree.)

Overall, More Drinking Than Usual

I definitely had more fun drinking with people who knew a lot about drinks than I have in previous years. They knew about whisky, cocktails, wine, vodka infusions. Beer too. I was humbled by their depth of alcohol knowledge. Doesn't this convince you to come next year? Stanford threw a good party too, to try to improve the conference party scene.

Maybe you'll come next year.

Combing Through the Infovis Twitter Network Hairball

A month or two ago, Moritz Stefaner posted this image of "infovis" folks on twitter, with nodes sized by number of followers ("in-degree"):

I dropped him a note wondering if he'd tried any social network analysis methods to simplify it, or otherwise break it down -- so he sent me the data and said "have a go!" If I had crawled twitter links myself, I might not have used his criteria or seed set, but I was curious if I could make any more sense of his data set as is. (So I've neither re-crawled nor added any info such as frequency of tweet or content of tweets to the data set).

I compared some of the measures calculated by the python library NetworkX (NetworkX) with measures calculated by Gephi. Similar metrics actually come out a little differently in the 2 tools, although I haven't investigated why this is. I've made my spreadsheet of the calculated stats available for you to browse on Google Docs. (Variables with "NX" are saved from NetworkX, "Gi" from Gephi.)

First, some overall stats on the network in Moritz's dataset:

• 1644 twitter id's are represented, and there are 145,382 edges or links between id's.
  
• Gephi reports the average path length is 2.5.
  
• Gephi and NetworkX say it is a connected graph; Gephi reports 1 weakly and 5 strongly connected components.
  
• The average degree is 89.9, but the median is 51. There is a long tail here, meaning that some nodes have very high degree (see below) but most do not.

A very excellent tutorial for NetworkX by Derek Green suggests doing community detection using a python library for the Louvain method. At superficial review, it's similar to the Gephi modularity class detection algorithm, but I got slightly different results from the two methods. NetworkX finds 5 communities, and Gephi alternates between finding 4 or 5. (Makes me want a Bayesian method to handle this!) One confusion matrix, showing the differences in the two, squares sized according to number of nodes in each group:

Interpreting this: In one run, Gephi split up the folks who are in NetworkX's community 0 into Gephi's communities A, B, C, D. Gephi's community C mostly overlaps NetworkX's community 1.

For the rest of this post, I'll illustrate from NetworkX's community divisions, which I spent more time investigating and plotting. When I looked at the force-directed layouts and stats for the community members, I decided on these approximate group names:

• Group 0: The Authorities
  
• Group 1: The Researchers
  
• Group 2: The Processing Crowd
  
• Group 3: The Small NYT Group
  
• Group 4: MSLima's Crowd

These are a bit arbitrary as names - based on who I myself recognized among the high degree members. (I myself live in group 1, way down the list-- check out where you live, in the spreadsheet!)

To make sensible (less hairy) plots, I filtered for the top 5% of the degree calculation. "Degree" corresponds to sum of in-degree and out-degree edges; in other words, how many people a node (twitter id) is linked from and to. High "in-degree" count usually implies someone is a type of perceived authority. High "out-degree" suggests a social media corporate type. Well, not necessarily - it means they follow a lot of folks, and could be a useful information source if they also have high centrality and share their information. (Like I said, I didn't look at who said what or how often they tweeted.)


Here's a plot of Gephi's authority calculation vs. degree, strongly correlated (you may see why I named Community 0 "The Authorities"):




Sorting by degree, the top players are these (pulled from the spreadsheet):

Label	NetworkX Community	Gephi Class	Degree	Closeness Centrality	Betweenness Centrality
flowingdata	0	D	1394	0.446930423	0.043313537
datavis	0	A	1376	0.482190168	0.072294856
infosthetics	0	A	1362	0.435290991	0.034115345
infobeautiful	0	D	1074	0.391210891	0.007498017
blprnt	2	B	932	0.410115173	0.02337346
ben_fry	2	B	882	0.365625	0.006936445
moritz_stefaner	0	B	870	0.452361226	0.028942168
eagereyes	1	C	861	0.455126424	0.031837862
mslima	4	A	828	0.433448002	0.014404322
VizWorld	4	A	828	0.524495677	0.08984938

Showing edges in hairball graphs makes things really complicated. For the following network graphs, I've limited the displayed nodes and edges to the top 5% by degree measure. Here's an animation of the difference between all edges visible vs. just community-internal edges (I know it's subtle, sorry; the id names are sized by relative degree):

Non-animated, larger versions: With All Edges, Only Intra-Community Edges

The largest names are purple, community 0, which I called "The Authorities" (a proxy for degree in this case).

Since I chose "degree," for relative sizing, it's worth seeing that in- and out-degree are not always correlated. Here you can see that some "true" authorities have much higher in-degree than out-degrees.  In particular, VizWorld has very high out-degree, but rather smaller in-degree.  And by NetworkX's community assignment, he does not end up grouped with the purple community 0.  (Click for larger view.)

However, when we look at betweenness-centrality, VizWorld scores quite high. Betweenness-centrality (or centrality) roughly measures connectedness to components of the larger graph.

If you'd like to inspect the internal linkage structure corresponding to each community subgroup, click on the small images below to view. I've filtered out all but the top 5% by degree, to highlight the authorities in each sub-group.  (Note that this was insufficient for community 3 -- so I expanded it a bit more.)  The curved edges indicate "mutual" follow relations, while the straight edges indicate uni-directional (but I did not add arrowheads to indicate directionality).

community 0, The Authorities

community 1, the Researchers

community 2, the Processing Crowd

community 3, the small NYT group

community 4, MSLima's crowd

Notice that community 0, the purple one, has a surprising number of unidirectional links - as does community 3. The others seem to be dominated by curved lines, a high degree of mutuality. (Hopefully I can explore this later!)

Depending on what you know about the players in these graphs, you will probably see things I don't see.  I myself have very little familiarity with the names in communities 3 and 4, while I admit to being surprised or entertained by the links and organization in the other 3 graphs.  For example, in community 0, the placement of Visually, and its straight line uni-directional links, is especially interesting to me.  (Remember this graph represents the top 5% by degree-- so Visually at this time scored high on degree, and was classified as a member of the Authority group by the community algorithms, but was not itself closely followed by the others in this elite group.)   Green community 2 is also interesting; certainly the artistic folks are there, including the founders, authors, and teachers of Processing courses (ben_fry, REAS, shiffman, blprnt, toxi, mariuswatz, ...); but this group also includes Brainpicker and well-known design firms like Stamen and PitchInteractiv.

Wrapping up, tools I used for the analysis, charts, and graphs: Excel, Tableau (scatterplots), Python, R (correlation plots not shown), Gephi, Google Docs, Illustrator and Photoshop. It took more time than I expected, in part because of Gephi's alpha status, and having to adjust a lot of the plots in other graphics tools! My goal was to see if in a given data set (Moritz's crawl), I could use existing tools that are primarily free to break up the hairball and find structure within it. While I remain slightly uneasy about the differences between Gephi and NetworkX, the fact that both are open-source means nailing down their differences shouldn't be hard, and you can then decide which method you prefer to sort thru the tangles.

Postscript: While I was working on this, MS Lima's new book, Visual Complexity, shipped from Amazon.  It's a beautiful collection of network visualizations.

PyCon 2011 - Data, Men, and Me

In the past couple years, I've switched from sending myself to research conferences (like CHI) to more down-and-dirty developery conferences. I'm looking for skills development and tools I can use day-to-day. This spring I went to PyCon in Atlanta, since I've been using Python more and more for data analysis problems. (Complete talk videos are here on blip.tv.)

The initial draw was the tutorials. I aimed for cloud data and machine learning. Olivier Grisel's tutorial Applied Machine Learning in Python with scikit-learn was a definite high point of the conference for me. His talk on text analysis was very good as well -slides here, and video here. His French accent was very nice, but I kept mishearing "scikit-learn" as "psychic learn." :-) I also really enjoyed the talk on Genetic Algorithms by Eric Floehr, a fellow who seems to do weather prediction consulting. His slides and a bunch of other interesting supporting material (including code) are up on his site.

There were a lot of talks on data, big data, cloud data, and scaling Python (to handle big data and data problems). Other examples: A talk on Pypes by Eric Gaumer included a good reminder that big data problems existed in the search engine space long before other kinds of big data became "hot" to work on. Pypes is a quasi-visual toolkit for doing data processing inspired by Yahoo pipes. (The gist being that since a lot of data handling involves discrete steps to clean and transform, you can put these steps into little modules that allow you to view the big picture of what's going on with your data munging.)

Hilary Mason's excellent keynote made a lot of us data geeks happy; she called for programming language evolution to get closer to the data problems, and to be less cryptic when it comes to support for multi-threading and map-reduce strategies needed these days. (I loved her "WTF?" comment on her multithreading code example.) Yelp's "mrjob" library for the cloud might answer some of her issues, but I missed that talk for some reason!

Another talk on big data that was well-tweeted was C. Titus Brown's "Handling Ridiculous Amounts of Data with Probabilistic Data Structures." Slides here - probably requires the video to fully interpret this, at least it does for me (yes, I missed this one too).

Not all talks were excellent, of course. My linguistics degrees got grouchy during one on the linguistics of twitter -- or maybe it was my geeky side asking "what can I do with this?" Some talks were nice surprises, too, kind of the point of going to conferences! Based on lunch table happenstance, I ended up going to a Blender API talk by Chris Allan Webber, a subject about which I knew zilch. Blender is apparently beefing up its API for external calls and automation; as a visualization person, I'm interested in tools that I can "drive" with data as input. I have big hopes for the evolution of processing.py and Nodebox2, two pythonic visualization options, but I am not sure they're there yet for me as a data vis person.

My sad female nerd note: I was one of 3 women in the Machine Learning tutorial. Out of perhaps 40? I later heard via Twitter a guess that there were only 8% women at the conference as a whole, based on t-shirt orders. I loved Hilary's talk, but was a bit bummed out by the Dropbox keynote that featured the social network of "friends of Arash" who started that company -- yeah, all men.

A final comment for any UX folks reading this: This would've been a great audience for a talk on UI design in open source, or UI design for Python UI's. There were a lot of companies presenting: Dropbox did their "we use Python" talk; Evite apparently has rewritten their entire java backend in Python; Threadless, a sponsor, is all Python... One of the reasons for its growth at these companies is the ease of writing things fast in Python; the "prototype and iterate" philosophy showed up over and over in various presentations as a real strength of Python. As a light coder myself, I can't agree more. I was there as a data-oriented geek, but I saw UX opportunity everywhere, for the right kinds of UX folks.

Fan Video Editing Community and Copyright

In April, I gave a talk at UIUC's HCI department on fan video remix artists, or "vidders," as they are known within the fan media community. To build the talk, I drew on several years of LiveJournal network data, and a large 2-part survey I did in the spring of 2010 to document current attitudes, trends, and self-reported demographics of the community. Afterwards, I made my slides available in an annotated deck for the vidders themselves, as I had promised I would -- there were some interesting comments, including disagreement with certain aspects of the technical commentary (whether meta-data is really useful and available for management of clip collections) and whether the quote I pulled about "political correctness" as a dampener on some fans' "fun" was fair and balanced as a critique of the recent years' vidding discussions on issues of race and gender in vids. I haven't updated the annotations or the deck -- I'm posting it as I posted it to them; if someone is interested in hearing more about the community discussion, I'm happy to reply in comments or email.

I'm posting about this now because of two great things happening for this group of video editing fans -- this weekend is the annual meeting of Vividcon, a fan-run con all about vidding and vids as art and fun. I'm following the tweets with great jealousy -- I never made it off the entry waitlist this year.

The second great thing of recent days is the passage of the new DMCA exemptions from copyright-infringement laws for vidders (and other video artists) using copyright materials for artistic purposes. Since Internet sharing began, fans have regularly had their videos removed from many media sharing sites by copyright police. Some still post on password-protected private servers, rather than making them public and findable by "The Powers That Be."

Francesca Coppa posted on the blog of the Organization of Transformative Works that the case for the copyright-law exemption had been made in part based on the artists' need for high quality original source material for their remix works.

That said (and it's true), it's ironic to me that my own history goes back to the pre-Internet-sharing days, when we borrowed n-th generation tapes and made fuzzy vids with stone knives and bear skins. Check out my slide deck (pdf) for more on this. My talk includes some network analysis, one slide of which shows the "age" effect for when a vidder started vidding, and whose work they admire -- the VCR-era folks (including myself) are now off to the edges on the right and top. Fortuitously, right after my talk, Mimi Ito's article on anime fan editors came out in First Monday. I had already exchanged mail with her about her anime research, and it influenced my second round of survey questions to the vidders. Anime editors differ enormously from the vidder community; one major difference is that fan vidders are mostly women, while anime is more mixed, tending towards more male, and anime editors seem to be younger or to have started earlier, from what Mimi found. In my network graphs and quotes from the community, I show some points of overlap between anime and fan vidders, points and nodes which have increased in the past few years as the two groups learn about each other online and at cons like Vividcon.

Anyway, here are my slides: "Vidding Evolution: Community Change Among Fan Video Editors" (2010).

My Take on Big Company Suckage

Scott Berkun wrote a good post on Why Big Companies Suck, at popular request on his site. It made me think about my own experiences at small, medium, and large companies over the past 20 years. I'm assuming Scott and his readers were mostly talking about "why it often sucks to WORK at a big company," rather than why big companies suck from the outside looking in.

The individual is lost in the machine. The opportunity to be noticed, to get feedback for doing a job well, or for improving someone's life in or outside the company, is that much less. Which may limit your chance of a promotion, or just make you feel your job is pointless. One company I worked at had a famous management mantra, "If you're not making the product or selling it, what are you doing here?" Which is absolute bullshit, and dismisses the administrative roles of accounting, the benefits department, travel and admin staff, tech support, and other critical team members at a large software business. Functional companies need a lot of people doing different things, otherwise the people writing the code can't do THAT job.

Communication from the top is often poor, intentionally or not. The message that trickles down from management, or that's delivered from main stage at company meetings, tends to be diluted for the common denominator, which means it's not really addressed to anyone in particular. Sometimes it contains no information at all, as a result. Scott may have covered this under "they believe their own bullshit," but I think there's another slant on it: They don't know what's going on. The more levels and divisions there are, the more distorted the signal is, and the more likely you are to hit people who aren't sure they're supposed to be telling you something that they know.

Secretiveness. I don't get it - but bigger companies tend to keep more secrets from their own employees. I have nothing to say about this right now, because I'm just baffled by it.

Managers are rarely evaluated well or fairly at any sized company. Even in times of turnover or economic distress, management are the last to go (unless they're a very public, board-threatened figure, like a CEO, in a very bad political environment). Scott mentioned the Peter Principle, but I think it's more profound -- with power working as sum of the people below you, the weakest point in the tree is the bottom. Even when the bottom is arguably the most valuable part of the workforce. Companies with a lot of management structure will have fewer people doing quantifiably good work, occupying the org chart and protecting themselves at the expense of the workers under them.

Evaluation of what's good or valuable often happens on idiotic scales. When I worked at AT&T Labs 15 years ago, the company didn't think about anything but a sure business that would pull in billions -- never mind betting on smaller startup ideas to see if they could create new markets or businesses. Other companies like 3M and Google have since made this a visibly stupid way to do business, but it's definitely an easy way that managers can avoid risky bets on new verticals or product lines.

The small company made crap, and now the big company has to support it. Staff in big companies are sometimes stuck in trying to repair what was made by the small company, or what was acquired from the small company. This really sucks for the people in the big company. Bad design that was produced in a "proof of concept prototype" as VC's pounded on the door and the cash ran out -- well, those guys saying the good old days were great got rich off that crap, and now it's everyone else's job to "fix it."

Because the small company made so many mistakes, and the bigger company learned from them, there is more process and review of decisions. Face it - a lot of the processes and checks and balances in bigger companies exist because of bad things that happened when the company was smaller. The big company "learned." It decided it was too risky to do that stuff again. Stuff like having no usability review of the most important feature of the release!

Smaller companies sometimes feel more homogeneous -- the individuals know each other better, and there's usually less role differentiation and processes involving a lot of people you don't understand. This can make it more pleasant, give the impression that you're "getting things done," but it can also mean less original or high quality work is produced in the end. See above, about producing crap and making mistakes.

People who have their own money at stake, or make a lot of money from something they did, tend to be very engaged and happier about their contribution. This is a guess, but I think this study about hourly workers supports it. The study says people feel a stronger correlation between happiness and rate when they are paid hourly, rather than by salary. There's a direct reward connection between money and time. People who got a lot of money from a startup --either from selling one, or being there and getting the stock profits -- no doubt feel they were rewarded by the world for something of value that they did. It's less easy to feel rewarded either monetarily or by subjective feeling in a big company. Because the individual contribution is much harder to make or to recognize.

Finally, a few ways in which small companies can suck, too: There's never enough money, or for long enough; there isn't enough staff to do things that need doing (travel booking, accounting, etc?); the hours can really suck, related to the money issue, no doubt; there are STILL cowboy coders and often secret politics about decisions and design directions and what we're hiring for next.

Tips and Tidbits from the UI14 Conference

Back to blogging after a long hiatus of work and travel... UI14 in Boston had some good stuff for designers and managers! I heard a lot about techniques for creativity and design generation before winnowing; good reminders to generate multiple approaches before "settling."

Dan Rubin had his workshop group generate 20 different thumbnail sketches in 5 minutes. (Maybe it was less - it seemed like less.) Then combine the best aspects of their favorites into one bigger one. Hard, and a good way to make you get crazy early, if not go crazy, or just to think very broadly.

Leah Buley's charming "How to Be a UX Team of One" presentation is worth watching online. Her link includes her templates for wireframes with notes - useful for her excercise of 6 designs in 5 minutes (or less? again, I forget). After doing those sketches, she took a room vote on which idea in the 1-6 range the audience preferred out of their generated sketches. Most of the votes indicated it was not the first idea drawn.

There were some other interesting creativity exercises in Scott Berkun's excellent "Myths of Innovation" workshop (based on his excellent book of the same name). For a stuck team that's gone dry on good ideas, try brainstorming the worst product features possible for a while -- this will open up the wild and funny ideas. Then invert them, for the germ of some good ideas to pursue. Another alternative was to go from completely unconstrained brainstorming (such as "ideal features of the perfect cell phone") to slightly more constrained ("ideal features of a $10 cell phone").

I found Scott's most entertaining activity to be one in which the group listed 30-40 (again, early onset memory loss) random words - nouns describing things/activities/states and adjectives. Each small group had to pick 3 of them, brainstorm a new product or service around them, and create a pitch for it. All in ten minutes.

Strangely, from our workshop list with "TiVo" (I did not propose it!) and "guitar" and various sports... many of the groups picked the word "tomato." No idea why. But their weird product pitches were all very clever and funny. Scott pointed out afterward that this illustrates how a bunch of people who had never met before could self-organize, be creative, and even have a good time doing something that initially seemed impossible. On the "self-organization" topic, he noted that the person who takes notes in the group (self-nominated, of course) is usually the person who ends up delivering the pitch for the group, which also seemed to be true for several of these groups. As a conclusion to that one, he said that the reasons for this activity being difficult for many people (despite their success!) were these:

• Creativity creates confusion
• Unclear roles [group self-organization takes a few minutes or lots more]
• Responses to uncertainty differ
• Responses to subjective criteria differ
• Group dynamics influence decisions
• Time pressure [creates more stress]
• Lack of trust / relationships [although I noticed that one team had a bunch of people from the same company, a team I wish I'd observed during the activity]

Scott gave out copies of his newest book, Confessions of a Public Speaker, at his second UI14 talk. That talk was great fun as well.

Dan Rubin's short talk on Visual Design tips was excellent as well. A couple of his tricks will definitely go in my toolbox, especially the use of an image that a client chooses for setting a color scheme using kuler. (My take on it: Choose a bunch of images that might convey the mood of a site or product that a customer wants, and be sure the palettes are sufficiently different. Ask her to choose the "mood" she likes, and generate the colors from sampling that image.) Another of his tricks - using a 1 pixel sample of a photo to generate a gradient lighting effect with layer blending - was deeply cool!

UI14 is a great conference, and this year it was in an even better venue than it has been before (a plush hotel in more accessible South Boston instead of cramped Cambridge). Some things that make it annually so good: power strips under every table, and working wifi; a great drinks party; long workshop sessions as well as sampler short talks; accessible speakers who hang out and attend each other's sessions (or else, just check the bar). For a tech-industry conference, it does an excellent job of being gender-balanced for speakers. UI14 also has the odd honor of being the funniest conferences I've been to in a while -- possibly because Berkun, Gerry McGovern, and Jared Spool are all very entertaining! Check it out next year.