I’m always searching for better ways to improve my workflow, increase productivity, and just generally learn new and exciting things. (Besides, it’s part of having a healthy growth mindset.)

We’ve had some big changes on our team during the past year and I’ve felt like I’ve needed to step up when it comes to reviewing code that my fellow colleagues write. While searching for some ideas on how to improve my code review skills, I discovered a blog post from 2018, entitled “Code Review from the Command Line“.

This blew my mind and really helped reframe how we engineers should approach code reviews:

When I ask that other people review my code, it’s an opportunity for me to teach them about the change I’ve just made. When I review someone else’s code, it’s to learn something from them.

Jake, the author of the above post, goes on to describe his setup and custom tooling for conducting an interactive code review.

He uses custom git alias to see which files have changed and how many changes there are, a custom script that visualizes how often the files within the pull request have changed over time, and another custom script that can visualize the relationship between changed files.

All of these go above and beyond the call of duty for reviewing code, but it’s stuff that decreases friction and can make a sometimes tedious process much more enjoyable.

I’ll be implementing some of these ideas into my own workflow in the near future.

“Talking to Strangers” examines how our internal and cultural biases affect our interactions and perceptions of the people around us.

Early in the book, Gladwell mentions a study that gets right to the heart of the book and was really eye opening for me.

It’s a study into the “illusion of asymmetric insight” and shows us how we think we’re good an interpreting what a stranger is thinking or their intentions (spoiler: we’re actually really horrible at it).

Participants were required to complete a set of words with missing letters and then asked what the results say about them.

For example, someone was given “TOU _ _”. Some people would fill in the blanks as “TOUGH” and others would put “TOUCH”.

Afterward, the participant was asked if they think these results reflect their beliefs and core values. Nearly every participant said their word choices were random and had no relation on how they view the world.

Then they were given a random set of words from other people and asked the same question: What do you think these word choices say about these strangers?

Everyone had a strong opinion within literally minutes of being asked the same question about themselves! “Whoever wrote these sounds pretty vain“, or “this person sounds very obsessed with money and power.“

Crazy, right?

The book is full of studies like this, as well as specific examples from real life interactions like this (some with very tragic consequences) and takes a deeper look into why things might have played out the way they did — from a traffic stop in Texas that went very wrong, broken diplomatic promises before World War II, a high-profile sexual assault that occurred at Stanford, and even looking into the motivations of people who try to commit suicide and our collective misunderstanding as to why and where these things happen.

I went into this book not knowing exactly what it was about. I thought, “Oh! New Malcom Gladwell book and it seems to be getting some good reviews, let’s go for it”. It took around a chapter or two for me to really get into the book and see where it was going, but when things finally clicked for me, this was fascinating and enlightening look at every day interactions.

You Look Like a Thing and I Love You is a great, quick read for anyone who is even remotely curious about what things like AI, machine learning or neural networks actually mean and how it affects your day-to-day life.

Whether it’s facial recognition, to self-driving cars, to the search results you get from Google, this book describes the different types of machine learning algorithms, often in a humorous way (the first few pages deal with the author trying to generate pick-up lines using a neural network — hence the title of this book).

It’s a quick read and dives into some of the limitations that machine learning algorithms are bound by and how machine learning algorithms can be tricked, return results outside the parameters of a problem, or even cheat (e.g., when one AI agent was given an objective to minimize the number of times a character died in a video game, it found out how to simply pause the game).

One particular section of the book talks about the recently released GPT-2 data model by OpenAI, which can be used to generate fairly intelligent and coherent sounding blocks of text. It prompted me to try and run the model on my own machine.

After giving the model a prompt of “machine learning can be scary,” the computer responded with the following block of text:

“The problem is, it’s easy to forget that we can’t avoid the problem with software. It’s often hard to avoid the problem, but knowing how to avoid it is not the same as knowing how to avoid it. The problem is, the problem is easy to avoid, but the problem is hard to avoid.”

Do we need to worry about robot overlords any time soon? Probably not.

This is a book that I’d recommend to both people who are tech savvy and to parents who might still call you with questions on how to turn on a computer… or at least anyone curious to how machine learning affects various aspects of our lives.

It’s hard to believe that I’ve been sending music data to Last.fm for nearly 15 years! Because of this, we can start doing some crazy things… like compiling a list of all top artists I listened to over this entire decade.

From 2010 to 2019, the list is:

1. Bob Dylan
2. Hot Water Music
3. Johnny Cash
4. State Radio
5. Dispatch
6. Daft Punk
7. Chuck Ragan
8. The Glitch Mob
9. Bad Religion
10. Frank Turner
11. Aesop Rock
12. DJ Shadow
13. Manu Chao
14. ait-J
15. Widespread Panic

Benson’s ears are ridiculous.

Well, actually!

Was stomping around the old neighborhood when I noticed the moon peeking out from behind Sutro Tower.

Read this in your best David Attenborough voice: “Here we see a rare sighting of a wild Benson in his natural habitat.”

I’ve long joked that “we’re all just algorithms in some engineer’s machine.”

But it’s kind of true.

I recently finished reading Homo Deus: A Brief History of Tomorrow by Yuval Noah Harari, which imagines what the lives of our children, grandchildren, and beyond will be like and how technology will affect them.

We generate copious amounts of data each day and give our personal electronic devices and social networks almost unfettered access to all of it. Everything from how long we sleep, how often we exercise, where we go each day to the types of songs, movies and books we like.

There was one passage from the book that I found both amazing and frightening:

A recent study commissioned by Google’s nemesis – Facebook – has indicated that already today the Facebook algorithm is a better judge of human personalities and dispositions than even people’s friends, parents and spouses. The study was conducted on 86,220 volunteers who have a Facebook account and who completed a hundred-item personality questionnaire.

The Facebook algorithm predicted the volunteers’ answers based on monitoring their Facebook Likes – which webpages, images and clips they tagged with the Like button. The more Likes, the more accurate the predictions. The algorithm’s predictions were compared with those of work colleagues, friends, family members and spouses.

Amazingly, the algorithm needed a set of only ten Likes in order to outperform the predictions of work colleagues. It needed seventy Likes to outperform friends, 150 Likes to outperform family members and 300 Likes to outperform spouses. In other words, if you happen to have clicked 300 Likes on your Facebook account, the Facebook algorithm can predict your opinions and desires better than your husband or wife!

This is one of the main reasons why both Google and Facebook have some of the largest (and most effective) advertising networks on the internet.

They fundamentally know who you are and what you like and know us better than we know ourselves.

Indeed, in some fields the Facebook algorithm did better than the person themself. Participants were asked to evaluate things such as their level of substance use or the size of their social networks. Their judgements were less accurate than those of the algorithm.

Excerpts from “Homo Deus: A Brief History of Tomorrow” by Yuval Noah Harari.