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  1. proslijedio/la je Tweet
    11. sij

    Talks at this conference would be way more valuable than the average conference talk. Also, there should be a "turns out we were wrong" track; speakers explain how aspects of the tool/practice they'd aggressively boosted in past talks ended up being a terrible idea.

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  2. proslijedio/la je Tweet
    2. velj

    Performance artist generates virtual traffic jams in Google Maps by pulling a wagon full of smartphones

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  3. proslijedio/la je Tweet
    29. sij

    This is the only map that matters.

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  4. proslijedio/la je Tweet

    Best product management metrics wrong answers only

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  5. proslijedio/la je Tweet

    Everyone who worked on this should be in jail. From management, to strategy, to design, to engineering. Not only did they betray the people they’re supposed to look out for, they did it while those people were seeking care. They knew what it did and they built it. Shame on them.

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  6. proslijedio/la je Tweet

    honesty is the best policy ;) LOL

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  7. proslijedio/la je Tweet
    19. sij

    Why do smart companies & orgs make stupid mistakes? A thread:

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  8. proslijedio/la je Tweet
    16. sij
    Odgovor korisniku/ci

    That you don’t have to start college at 18. Take a year or two to intern somewhere(s) and find a mentor. You’ll grasp what matters in school and in business so much more effectively than you would going straight to 13th grade.

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  9. proslijedio/la je Tweet
    17. sij

    I wanted to share an amazingly useful idea I got from and her amazing book Manage It! (2007) Initiatives have: * Drivers * Constraints * Floats Drivers ... what is "driving" the effort. Constraints constrain. And Floats are levers that you are free to move (1/5)

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  10. proslijedio/la je Tweet
    17. sij

    Along "make the hard change easy then make the easy to change" lines, I found this from 20 years ago.

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  11. proslijedio/la je Tweet

    I am obsessed with Gifaanisqatsi, a random Koyaanisqatsi generator that pulls GIFs from Giphy to recreate the 1983 wordless documentary primarily made up of slow motion and time-lapse footage 😂

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  12. proslijedio/la je Tweet

    There are 8 types of waste in manufacturing, all of which translate SURPRISINGLY well and accurately to development: 1. Overproduction: working on features which are not useful to customers 2. Waiting time: One team waiting for another to deploy a dependency

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  13. proslijedio/la je Tweet
    16. sij

    This thread does an amazing job of making you feel like you were there for the duration of the flight. That is not necessarily a good thing. Also, Chad is not paid enough.

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  14. proslijedio/la je Tweet

    Of course, for cognitively complex creatures like humans, mirrors have nothing at all to do with signaling.

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  15. proslijedio/la je Tweet

    OKAY *cracks knuckles* i’ve got a glass of austrian rosè that’s making me sweat like i’m literally dying & my husband went to sleep at 9:30 with a migraine so LET’S DO THIS 1 like, 1 design opinion

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  16. proslijedio/la je Tweet
    11. pro 2019.
    Odgovor korisnicima

    I believe a lot can be accomplished with minimal use of content semantics just by mapping players and interactions. See, for example, this excellent analysis of the Boeing 737MAX disaster by : cc:

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  17. proslijedio/la je Tweet
    6. pro 2019.

    It's important to be positive about the future. Not as wishful thinking. Not as blind optimism. But as actual constructive bets and contributions to the good that exists, and it certainly exists.

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  18. proslijedio/la je Tweet

    💥NEW My OpEd in the today's NYTimes: “Our Brains Are No Match for Our Technology.” 🧠 PUNCH LINE: In order to tame Big Tech we must realign it with the values that make us human. Highly recommend reading & watching videos below in this thread…🧵

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  19. proslijedio/la je Tweet
    10. stu 2019.

    Imagine if all the straight white men, incensed by ’s story of how algorithmic bias affected him and his wife personally, decided to read the work of the (primarily women and POC )scholars who’ve been warning us for years. It would include:

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  20. proslijedio/la je Tweet
    14. stu 2019.

    Machine learning is a bit like cocaine in the 1880s: - been used in a weaker form for centuries - some surprisingly successful early applications led to it now being over-prescribed - beginning to understand that performance degrades after repeated use, negative feedback loops

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