March 2019
March 28, 2019
- Wilfred Hughes: The siren song of little languages. “Sometimes a usable language struggles simply because it’s too much fun to write your own. Developers end up building their own implementation rather than actually using the language.” #
March 25, 2019
- CIO: Why employees don’t use collaboration tools. “G2 Crowd found that only four platforms could regularly boast adoption rates above 75 percent, and no product achieved 100 percent adoption.” #
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We can teach women to code, but that just creates another problem.
The computing historian Marie Hicks can’t stand it when people tout coding camps as a solution to technology’s gender problem. “I think these initiatives are well-meaning, but they totally misunderstand the problem. The pipeline is not the problem; the meritocracy is the problem. The idea that we’ll just stuff people into the pipeline assumes a meritocracy that does not exist.”
# - MIT Technology Review: A quantum experiment suggests there’s no such thing as objective reality. #
March 14, 2019
- The Atlantic: A lack of confidence isn’t what’s holding back working women. #
- Jacson Rawlings: The 17 cognitive biases that explain Brexit. #
- My favorite explanation of Jobs To Be Done involves a 7-year-old and swimming flippers. #
March 13, 2019
- Real Story Group: Your CMS vendor still wants to sell you services. Why you shouldn’t use a platform vendor as your integrator — more nuance in the “buy” side of the “build vs buy” argument. #
March 7, 2019
- Logic Magazine: An interview with an anonymous algorithmic trader. Some interesting points on how difficult it is to prove your algorithm works, and that it doesn’t necessarily have to: in order to sell your fund, you need a narrative, and simply saying you have AI can be enough. Also, did you know that: “JP Morgan Chase employs 50,000 technologists, two-thirds of which are software engineers. That’s more engineers than many big tech firms; Facebook, for example, employs about 30,000 people total.” #
March 6, 2019
- Mozilla releases Common Voices, the largest to-date public domain transcribed voice dataset. #
