1. ⁘  ⁘  ⁘
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  3. Abo, Akinori 9
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  10. Ammer, Ralph 6
  11. Anderson, Gretchen 7
  12. anxiety 9
  13. Appleton, Maggie 5
  14. Aptekar-Cassels, Wesley 5
  15. Arango, Jorge 4
  16. architecture 110
  17. art 86
  18. Asimov, Isaac 5
  19. attention 17
  20. Auping, Michael 6
  21. Aurelius, Marcus 14
  22. Bachelard, Gaston 12
  23. Baker, Nicholson 10
  24. beauty 58
  25. Behrensmeyer, Anna K. 7
  26. Bell, Larry 3
  27. Bjarnason, Baldur 8
  28. Blake, William 5
  29. blogging 22
  30. body 11
  31. Boeing, Geoff 7
  32. boredom 9
  33. Botton, Alain de 38
  34. Brand, Stewart 4
  35. Bringhurst, Robert 16
  36. Brooks, Frederick P. 22
  37. Broskoski, Charles 6
  38. brutalism 7
  39. building 16
  40. bureaucracy 12
  41. Burnham, Bo 9
  42. business 15
  43. Byron, Lord 14
  44. Cagan, Marty 8
  45. Calvino, Italo 21
  46. Camus, Albert 13
  47. care 6
  48. Carruth, Shane 15
  49. Cegłowski, Maciej 6
  50. Cervantes, Miguel de 7
  51. chance 11
  52. change 16
  53. Chiang, Ted 4
  54. childhood 6
  55. Chimero, Frank 17
  56. choice 8
  57. cities 51
  58. Clark, Robin 3
  59. Cleary, Thomas 8
  60. Cleary, J.C. 8
  61. code 20
  62. collaboration 18
  63. collections 31
  64. Collison, Simon 3
  65. color 23
  66. commonplace 11
  67. communication 31
  68. community 7
  69. complexity 11
  70. connection 24
  71. constraints 25
  72. construction 9
  73. content 9
  74. Corbusier, Le 13
  75. Coyier, Chris 4
  76. craft 66
  77. creativity 59
  78. crime 9
  79. Critchlow, Tom 5
  80. critique 10
  81. Cross, Nigel 12
  82. Cross, Anita Clayburn 10
  83. css 11
  84. culture 13
  85. curiosity 11
  86. cycles 7
  87. Danielewski, Mark Z. 4
  88. darkness 28
  89. Darwin, Will 10
  90. data 8
  91. death 38
  92. Debord, Guy 6
  93. decisions 10
  94. design 131
  95. details 31
  96. Dickinson, Emily 9
  97. Dieste, Eladio 4
  98. discovery 9
  99. doors 7
  100. Dorn, Brandon 11
  101. drawing 23
  102. Drucker, Peter F. 15
  103. Duany, Andres 18
  104. Eatock, Daniel 4
  105. economics 13
  106. efficiency 7
  107. Eisenman, Peter 8
  108. Eliot, T.S. 14
  109. emotion 8
  110. ending 14
  111. engineering 11
  112. Eno, Brian 4
  113. ethics 14
  114. euphony 38
  115. Evans, Benedict 4
  116. evolution 9
  117. experience 14
  118. farming 8
  119. fashion 11
  120. features 25
  121. feedback 6
  122. flaws 10
  123. Flexner, Abraham 8
  124. food 16
  125. form 19
  126. Fowler, Martin 4
  127. Franklin, Ursula M. 30
  128. friendship 6
  129. fun 7
  130. function 31
  131. games 13
  132. gardens 26
  133. Garfield, Emily 4
  134. Garfunkel, Art 6
  135. geography 8
  136. geometry 18
  137. goals 9
  138. Gombrich, E. H. 4
  139. goodness 12
  140. Graham, Paul 37
  141. graphics 13
  142. Greene, Erick 6
  143. Hamming, Richard 45
  144. happiness 17
  145. Harford, Tim 4
  146. Harper, Thomas J. 15
  147. Hayes, Brian 28
  148. heat 7
  149. Heinrich, Bernd 7
  150. Herbert, Frank 4
  151. Heschong, Lisa 27
  152. Hesse, Herman 6
  153. history 13
  154. Hoffman, Yoel 10
  155. Hofstadter, Douglas 6
  156. home 15
  157. Hoy, Amy 4
  158. Hoyt, Ben 5
  159. html 11
  160. Hudlow, Gandalf 4
  161. humanity 16
  162. humor 6
  163. Huxley, Aldous 7
  164. hypermedia 22
  165. i 18
  166. ideas 21
  167. identity 33
  168. images 10
  169. industry 9
  170. information 42
  171. infrastructure 17
  172. innovation 15
  173. interaction 10
  174. interest 10
  175. interfaces 37
  176. intuition 8
  177. invention 10
  178. Irwin, Robert 65
  179. Isaacson, Walter 28
  180. Ishikawa, Sara 33
  181. iteration 13
  182. Ive, Jonathan 6
  183. Jackson, Steven J. 14
  184. Jacobs, Jane 54
  185. Jacobs, Alan 5
  186. Jobs, Steve 20
  187. Jones, Nick 5
  188. Kahn, Louis 4
  189. Kakuzō, Okakura 23
  190. Kaufman, Kenn 4
  191. Keith, Jeremy 6
  192. Keller, Jenny 10
  193. Kelly, Kevin 3
  194. Keqin, Yuanwu 8
  195. Ketheswaran, Pirijan 6
  196. Kingdon, Jonathan 5
  197. Kitching, Roger 7
  198. Klein, Laura 4
  199. Kleon, Austin 13
  200. Klinkenborg, Verlyn 24
  201. Klyn, Dan 20
  202. knowledge 29
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  204. Kramer, Karen L. 10
  205. Krishna, Golden 10
  206. Kuma, Kengo 18
  207. language 20
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  210. light 31
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  215. Luu, Dan 8
  216. Lynch, Kevin 12
  217. MacIver, David R. 8
  218. MacWright, Tom 5
  219. Magnus, Margaret 12
  220. making 77
  221. management 14
  222. Manaugh, Geoff 27
  223. Markson, David 16
  224. Mars, Roman 13
  225. material 39
  226. math 16
  227. McCarter, Robert 21
  228. meaning 33
  229. media 16
  230. melancholy 51
  231. memory 28
  232. metaphor 10
  233. metrics 19
  234. microsites 49
  235. Miller, J. Abbott 10
  236. Mills, C. Wright 9
  237. minimalism 10
  238. Miyazaki, Hayao 30
  239. Mod, Craig 15
  240. modularity 6
  241. Mollison, Bill 31
  242. morality 8
  243. Murakami, Haruki 21
  244. music 16
  245. Müller, Boris 7
  246. Naka, Toshiharu 8
  247. names 11
  248. Naskrecki, Piotr 5
  249. nature 51
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  320. silence 9
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  322. Simms, Matthew 19
  323. Simon, Paul 6
  324. simplicity 14
  325. Singer, Ryan 12
  326. skill 17
  327. Sloan, Robin 5
  328. Smith, Cyril Stanley 29
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  330. Smith, Rach 4
  331. socializing 7
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  333. software 68
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  336. Sorkin, Michael 56
  337. sound 14
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  343. Strunk, William 15
  344. Ström, Matthew 13
  345. style 30
  346. Sun, Chuánqí 15
  347. symbols 12
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  349. Sōetsu, Yanagi 34
  350. Sōseki, Natsume 8
  351. Tanaka, Tomoyuki 9
  352. Tanizaki, Jun'ichirō 15
  353. taste 10
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  356. teamwork 17
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  360. Thoreau, Henry David 8
  361. time 54
  362. Tolkien, J.R.R. 6
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  367. truth 15
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Our Quantified Life

Close
  • -2000 Lines Of Code

    An Article by Andy Hertzfeld
    www.folklore.org

    Bill Atkinson...who was by far the most important Lisa implementor, thought that lines of code was a silly measure of software productivity. He thought his goal was to write as small and fast a program as possible, and that the lines of code metric only encouraged writing sloppy, bloated, broken code.

    ...He was just putting the finishing touches on the optimization when it was time to fill out the management form for the first time. When he got to the lines of code part, he thought about it for a second, and then wrote in the number: -2,000.

    I'm not sure how the managers reacted to that, but I do know that after a couple more weeks, they stopped asking Bill to fill out the form, and he gladly complied.

    1. ​​The amount of work not done​​
    • metrics
    • code
    • management
    • productivity
  • The primary measure of progress

    Working software is the primary measure of progress.

    Manifesto for Agile Software Development
    • metrics
  • SAFe is oriented around volume, not value

    In all this focus on volume metrics, estimation, and churning work through the pipeline, the concept of what’s actually valuable or successful is easily lost. It’s often assumed that more work shipped out the door must be “value”, even if the experience of the product is actually suffering and users are not benefiting from the additional features.

    Sean Dexter, Beware SAFe, an Unholy Incarnation of Darkness
    • metrics
    • quality
  • Outcomes decide

    High levels of U.S. patient satisfaction are mainly associated with hospitality (greeters at the door, empathetic staff, comfortable rooms) – but also with more treatments, high costs, and substantially higher mortality even after adjusting for baseline health and comorbidities. Several plausible stories explain these big n and replicated observational findings. Whatever the case, post-treatment patient satisfaction/gratitude does not measure whether a treatment works or not. Patient outcomes decide.

    Edward Tufte, Seeing With Fresh Eyes
    • health
    • metrics
  • Design as an engineering problem

    The Silicon Valley giants, testifying with their runaway success, claimed to have “solved” design as an engineering problem. The solution substituted the human essence of design — intuition, ingenuity, and taste— with the tangibles, measurables, and deliverables.

    Companies say they are “design-driven”, but designers are actually driven by dashboards filled with metrics like CSAT, NPS, CES, DAU, MAU. We rigorously run tests, studies, experiments as if innovative ideas are hidden in spreadsheets, waiting to be extracted by data scientists.

    Chuánqí Sun, The vanishing designer
    • intuition
    • taste
    • metrics
  • Obsessed with absolute numbers

    Modernist planning was obsessed with absolute numbers, including the minimum dimensions of rooms, open space per capita, and the one-size-fits-all head counts of neighborhood units. This was often pegged at five to seven thousand and was used as a formula for determining the distribution of schools, shops, sports fields, and other facilities. The failure of such planning is not in its effort to be comprehensive or to equalize access to necessary facilities. It is, rather, the attempt to rationalize choice on the basis of a homogeneous set of subjects, a fixed grammar of opportunities, a remorseless segregation of uses, and a scientistic faith in technical analysis and organization that simply excludes diversity, eccentricity, nonconforming beauty, and choice. The utopian nightmare.

    Michael Sorkin, 20 Minutes in Manhattan
    1. ​​Desired qualities of light​​
    2. ​​Predicted Mean Vote​​
    • planning
    • metrics
    • diversity
  • Lost purposes

    There’s chocolate at the supermarket, and you can get to the supermarket by driving, and driving requires that you be in the car, which means opening your car door, which needs keys. If you find there’s no chocolate at the supermarket, you won’t stand around opening and slamming your car door because the car door still needs opening. I rarely notice people losing track of plans they devised themselves.

    It’s another matter when incentives must flow through large organizations—or worse, many different organizations and interest groups, some of them governmental. Then you see behaviors that would mark literal insanity, if they were born from a single mind. Someone gets paid every time they open a car door, because that’s what’s measurable; and this person doesn’t care whether the driver ever gets paid for arriving at the supermarket, let alone whether the buyer purchases the chocolate, or whether the eater is happy or starving.

    Eliezer Yudkowsky, Rationality: From AI to Zombies
    1. ​​So many tactics, so well entrenched​​
    • metrics
    • goals
  • The case against heatmaps

    An Article by Oliver Palmer
    www.oliverpalmer.com
    Image from www.oliverpalmer.com on 2021-11-17 at 1.53.57 PM.png

    Visualised aggregations of click activity are a low effort, low signal waste of time and best avoided in favour of actual research.

    • ux
    • metrics
  • It's All Over

    An Essay by Justin E. H. Smith
    justinehsmith.substack.com

    It has come to seem to me recently that this present moment must be to language something like what the Industrial Revolution was to textiles. A writer who works on the old system of production can spend days crafting a sentence, putting what feels like a worthy idea into language, only to find, once finished, that the internet has already produced countless sentences that are more or less just like it, even if these lack the same artisanal origin story that we imagine gives writing its soul. There is, it seems to me, no more place for writers and thinkers in our future than, since the nineteenth century, there has been for weavers.

    1. ​​Into the system of flight​​
    2. ​​The gutting of our human subjecthood​​
    3. ​​A performative contradiction​​
    4. ​​The one reveals a subject and the other reveals an algorithm​​
    • metrics
  • Time-based analytics

    An Article by Ryan Singer
    feltpresence.com
    Image from feltpresence.com on 2021-09-05 at 2.07.11 PM.jpeg

    Analytics apps don't tell you much about usage behavior. You might be able to see how many users performed an event, or how many times they did it. But none of the analytics packages out there are good at showing you how often people do things. Are they using to-dos once a week? Every day? Only signing into the app once a month but happily paying for years?

    Time matters. You can't understand usage without time.

    • analytics
    • metrics
    • features
    • visualization
  • Fast Path to a Great UX – Increased Exposure Hours

    An Article by Jared Spool
    articles.uie.com

    As we’ve been researching what design teams need to do to create great user experiences, we’ve stumbled across an interesting finding. It’s the closest thing we’ve found to a silver bullet when it comes to reliably improving the designs teams produce.

    The solution? Exposure hours. The number of hours each team member is exposed directly to real users interacting with the team’s designs or the team’s competitor’s designs. There is a direct correlation between this exposure and the improvements we see in the designs that team produces.

    • ux
    • research
    • metrics

    An important caveat:

    Each team member has to be exposed directly to the users themselves. Teams that have dedicated user research professionals, who watch the users, then in turn, report the results through documents or videos, don’t deliver the same benefits. It’s from the direct exposure to the users that we see the improvements in the design.

  • Metrics have a strange hold on the imagination

    A Fragment by Shawn Wang
    www.swyx.io

    Once in place, metrics have a strange hold on the imagination: I've seriously had a CTO carelessly reject my genuine idea out of hand because "it doesn't help OKRs", the same OKRs we previously agreed should not describe all that we do.

    I agree with Amir Shevat that we should "do the right things over the easy to measure things."

    • metrics
    • ux
  • How would you feel if you could no longer use the product?

    An Article
    review.firstround.com

    The product/market fit definitions I had found were vivid and compelling, but they were lagging indicators — by the time investment bankers are staking out your house, you already have product/market fit. Instead, Ellis had found a leading indicator: just ask users “how would you feel if you could no longer use the product?” and measure the percent who answer “very disappointed.”

    • ux
    • metrics
  • Site performance is potentially the most important metric

    A Fragment by Kealan Parr
    css-tricks.com

    Site performance is potentially the most important metric. The better the performance, the better chance that users stay on a page, read content, make purchases, or just about whatever they need to do. A 2017 study by Akamai says as much when it found that even a 100ms delay in page load can decrease conversions by 7% and lose 1% of their sales for every 100ms it takes for their site to load which, at the time of the study, was equivalent to $1.6 billion if the site slowed down by just one second.

    • performance
    • ux
    • metrics
  • The McNamara fallacy

    A Definition
    en.wikipedia.org

    The McNamara fallacy, named for Robert McNamara, the US Secretary of Defense from 1961 to 1968, involves making a decision based solely on quantitative observations (or metrics) and ignoring all others. The reason given is often that these other observations cannot be proven.

    The fallacy refers to McNamara's belief as to what led the United States to defeat in the Vietnam War—specifically, his quantification of success in the war (e.g., in terms of enemy body count), ignoring other variables.

    1. ​​Artifice, blindness, and suicide​​
    • war
    • logic
    • metrics
    • quality
  • Artifice, blindness, and suicide

    A Quote
    en.wikipedia.org

    The first step is to measure whatever can be easily measured. This is OK as far as it goes. The second step is to disregard that which can't be easily measured or to give it an arbitrary quantitative value. This is artificial and misleading. The third step is to presume that what can't be measured easily really isn't important. This is blindness. The fourth step is to say that what can't be easily measured really doesn't exist. This is suicide.

    1. ​​The McNamara fallacy​​
    • metrics
    • quality
    • goodness

    Daniel Yankelovich, "Corporate Priorities: A continuing study of the new demands on business" (1972).

  • Weighing up UX

    An Article by Jeremy Keith
    adactio.com

    Metrics come up when we’re talking about A/B testing, growth design, and all of the practices that help designers get their seat at the table (to use the well-worn cliché). But while metrics are very useful for measuring design’s benefit to the business, they’re not really cut out for measuring user experience.

    1. ​​Two levels of veto​​
    2. ​​Our obedience to the king​​
    • metrics
    • ux
    • business
    • research
    • ethics
  • Predicted Mean Vote

    A Definition
    www.designingbuildings.co.uk

    The predicted mean vote (PMV) was developed by Povl Ole Fanger at Kansas State University and the Technical University of Denmark as an empirical fit to the human sensation of thermal comfort. It was later adopted as an ISO standard. It predicts the average vote of a large group of people on the a seven-point thermal sensation scale where:

    • +3 = hot
    • +2 = warm
    • +1 = slightly warm
    • 0 = neutral
    • -1 = slightly cool
    • -2 = cool
    • -3 = cold
    1. ​​Thermal Delight in Architecture​​
    2. ​​Obsessed with absolute numbers​​
    • heat
    • metrics
  • What Do Metrics Want? How Quantification Prescribes Social Interaction on Facebook

    A Research Paper by Benjamin Grosser
    computationalculture.net
    Image from computationalculture.net on 2020-07-29 at 9.07.10 AM.png

    What are the effects of this enumeration, of these metrics that count our social interactions? In other words, how are the designs of Facebook leading us to act, and to interact in certain ways and not in others? For example, would we add as many friends if we weren’t constantly confronted with how many we have? Would we “like” as many ads if we weren’t told how many others liked them before us? Would we comment on others’ statuses as often if we weren’t told how many friends responded to each comment?

    In this paper, I question the effects of metrics from three angles. First I examine how our need for personal worth, within the confines of capitalism, transforms into an insatiable “desire for more.” Second, with this desire in mind, I analyze the metric components of Facebook’s interface using a software studies methodology, exploring how these numbers function and how they act upon the site’s users. Finally, I discuss my software, born from my research-based artistic practice, called Facebook Demetricator (2012-present). Facebook Demetricator removes all metrics from the Facebook interface, inviting the site’s users to try the system without the numbers and to see how that removal changes their experience. With this free web browser extension, I aim to disrupt the prescribed sociality produced through metrics, enabling a social media culture less dependent on quantification.

    • metrics
    • technology

See also:
  1. ux
  2. quality
  3. research
  4. technology
  5. goals
  6. planning
  7. diversity
  8. heat
  9. performance
  10. business
  11. ethics
  12. war
  13. logic
  14. goodness
  15. intuition
  16. taste
  17. analytics
  18. features
  19. visualization
  20. health
  21. code
  22. management
  23. productivity
  1. Benjamin Grosser
  2. Eliezer Yudkowsky
  3. Michael Sorkin
  4. Kealan Parr
  5. Jeremy Keith
  6. Chuánqí Sun
  7. Shawn Wang
  8. Jared Spool
  9. Ryan Singer
  10. Edward Tufte
  11. Justin E. H. Smith
  12. Andy Hertzfeld
  13. Oliver Palmer
  14. Sean Dexter