Week ending 2021-07-04

week-in-review

Cooked

  1. Sweetcorn curry. ★★★
    A save of the busted tomaticán recipe from the Chilean Kitchen.
  2. Nik Sharma’s meatloaf. ★★★★★
    Always a hit. Although maybe I will learn to do the prep at 4p like I should.
  3. Egg curry. ★★★★
    Maybe a lighter hand with the tamarind.

Seen

  1. Haikyu!!. ★★★
    Need to find another good competition manga after this is done.

Listened

  1. Conversation with Tim O’Reilly and Shoshana Z. Notes below
  2. “Why Do We Work So Damn Much?”, Ezra Klein Show with James Suzman

Upcoming

  1. Qual for Quant training, Beth Duckles
  2. @NadiehBremer’s & @sxywu’s “Data Sketches” written book club Tue Aug 3
  3. Sir David Speigelhalter: Mathematical Concepts in the Age of Covid Tue 13 July 8:30

To read

  1. “Ministry For The Future”, Kim Stanley Robinson, via Tim O’Reilly
  2. “Bad Data Handbook”, Paul Murrell, via Jenny Bryan
  3. “Master of Fine Arts in Software”, Richard P. Gabriel, via Hadley Wickham
  4. “The Atlas of Disappearing Places”
    “Climate change is not just about melting ice caps and starving polar bears, and The Atlas of Disappearing Places brings that reality home.” –forewordreviews
  5. “The Cruelty is the Point”, Adam Serwer, via Alberto Cairo
    “Once malice is embraced as virtue, it is impossible to contain.”

To listen

  1. Ologies
  2. “Now and Then”, @HC_Richardson and @jbf1755while
  3. “UUID 0412a969-5b27-4c28-9662-85ef2c201e0c. Is this identifier unique? Can we be sure?. https://t.co/BF9zWLyPSv,” –aperiodical Jul 02 tweet_embed(“https://twitter.com/480493093/status/1410869445564768257”)
  4. “Great podcast on loneliness, and it’s 2nd order effects on society.” –matthewedanwoo Jun 29

For study

  1. Long-lasting racist impacts baked into the land: Shade inequality and Broadband access
  2. “Need to give your ML model 🤖 reliable uncertainty quantification? Check out our new Gentle Intro to Conformal Prediction tutorial + video. You get valid confidence sets with any model for any (unknown) distribution, no retraining. https://t.co/ZspxjfvaZuwith @ml_angelopoulos,” –stats_stephen Jul 02
  3. “You Don’t Understand Neural Networks til You Understand the Universal Approximation Theorem”, Andre Ye via sapinker
  4. “Rescues of cats vs other animals by @LondonFire for this week’s #TidyTuesday. Used stat_difference() of {ggh4x} for the first time and {geofacet} for the borough gridcode:. https://t.co/0MClOTLYYh#RStats #dataviz. https://t.co/jDmIQ1O7Oo,” –geokaramanis Jul 02
  5. “My favorite dplyr combo is group_split() and furrr::future_imap_dfr(), for easy parallelization of your commands (and easy maxxing out your multi-core processor 🔥🔥),” –skyetetra Jun 30
  6. “Variance Reduction in Monte Carlo Methods: Bilinear Strategies”, Sam Power

For thought

  1. Daily action, smiles as team bonding glue
    “I think hedgefunds are the most suited to remote work because there’s a lot of ‘daily action’, which provides alternative fuel for bonding, normally powered by Duchenne smiles. And the pre-PMF startups are the worst hit, because the coworker Duchenne is all they have.,” –danielgross Jun 29
  2. “We can’t keep regulating AI as if it..works. Most policy interventions start with the assumption that the technology lives up to its claims of performance but policymakers & critical scholars need to stop falling for the corporate hype and should scrutinize these claims more.,” –rajiinio Jun 28
  3. Inverting the problem.
    Examples from Charlie Munger and the CIA. “An important lesson from Charlie Munger from this article is that even if you can’t figure out the right solution to a problem, you can get very far by cataloging and avoiding all the wrong things.”
  4. “How One Company Got Employees to Speak Up and Ask for Help”, Joe Brown, via ttorres Jul 01
  5. “It is important to consider company stage when thinking about how to intervene to up your product game. What”works" in a big company will not work in a startup.At @Amplitude_HQ we tailor our coaching/feedback to meet companies where they are.(a quick morning activity ⬇️). https://t.co/UGMFiHRqEO," –johncutlefish Jun 28
  6. “Fundamental attribution error is our tendency to blame (or credit) the PERSON instead of the SITUATION. Everyone has high & low periods in their lives that are beyond their control. And even the most adaptable people struggle in some environments & easily succeed in others.,” –cindyalvarez Jul 02
  7. “@SahilBloom The work required to have an opinion. https://t.co/ISZ7VY23aW,” –farnamstreet Jul 02

Pretties

Absurdities