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See a satellite tonight (maybe)

I’ll admit, it’s been awhile since I’ve seen the International Space Station pass overhead (it’s something I loved to do in the past).

While reading about Elon Musk’s ambitious plan to build a constellation of over 40,000 satellites for providing Internet coverage, I discovered a pretty awesome website for tracking viewing opportunities for his Starlink constellation, as well as other notable objects in orbit (such as the ISS):

See a Satellite Tonight is a project put together by James Darpinian, that shows you which satellites will be visible in your location per the next few days. As an added bonus, you can view where in the sky the satellites will pass overhead using Google Streetview. It’s pretty neat!

On the off chance that the SpaceX Starlink constellation is passing overhead, check it out — you’ll see a train of (as of this writing) 60 satellites appear overhead.

Due to weather and lighting condition in Oakland, I haven’t been able to see it yet, but my dad sent me the following message after using the same website:

Made a point to get up early this AM to watch for Elon’s Starlink. Wow, what a sight! 60 satellites in a row. Just thinking about all the implications of this. Definitely a visual I’ve never seen in my lifetime! Kind of surreal, actually.

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Next level code review skills

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.

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Book Review: “Talking to Strangers” by Malcom Gladwell

“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.

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Book Review: “You Look Like a Thing and I Love You” by Janelle Shane

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.

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We are all just algorithms…

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.

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National Novel Generation Month, 2017 Edition

November is traditionally “National Novel Writing Month.” The goal is to write a short novel that is 50,000 words in length. I always have grand plans to attempt it and have started a number of times over the years but have never actually finished. (One day, I swear!)

Recently, I stumbled across a geekier take on it, called National Novel Generation Month. The goal of this particular project is to write code that can generate a 50,000 word novel instead. Hey, why not?

I published my code at the beginning of November for my project: The Complete Encyclopedia on 1,449.5 Random Ways to Make a Sandwich.

This book of 1,449.5 random sandwich recipes was created for NaNoGenMo (National Novel Generation Month) 2017. You can view the source code for this project on GitHub.

It uses data parsed from a 1909 book, entitled “The Up-To-Date Sandwich Book: 400 Ways to Make a Sandwich”, written by Eva Greene Fuller and now available for free in the public domain.

For this particular book, new sandwich recipes were generated using Markov chains created from the above text.

Please don’t try to actually make any of the sandwich recipes created with this process. However, if you do, please contact me and show me pictures.

Disclaimer: I cannot be held responsible for any health issues that may arise from eating any of these sandwiches.

There are definitely some interesting ones…

BUTTERED CHEESE AND OLIVE SANDWICH NO. 3

Use three slices of Swiss cheese, spread fresh butter and two tablespoonfuls of olive oil, the juice of two oranges and knead the mixture.

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Giving audiobooks a try (again)

I’ve tried to get into audiobooks in the past, but never found them enjoyable.

That changed earlier this summer ahead of our epic road trip to Wyoming to see the total solar eclipse (oh hey, I never wrote about that experience). I saw an offer for new Audible memberships that sounded like a pretty good deal, so I jumped on it.

The first book I ended up download was The Power Broker, which ended up totaling over 60 hours of audio! And you know what?

It. Was. Awesome.

I think I’ve finally found out why I could never get into audiobooks in the past. It’s because I listened to them at normal 1x speed. The narrators read the stories so so so slow. If I speed things up to 1.5x or 1.75x, it sounds much more interesting to me and I find that I’m able to keep focus.

Plus, it turns a 66 hour story into a 50 hour story. Saves time (for more audiobooks). I really recommend it. It’s been a nice break from listening to my standard array of podcasts, which have focused on depressing news as of late.

Since I subscribed in August, I’ve now listened to:

And I’m currently working through Washington (41 hours) by Ron Chernow (he wrote the recently popular biography about Alexander Hamilton, which the musical is based on).

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Unimaginable fires in the North Bay

At the moment, something like 3,000 homes have been lost in the North Bay. It’s hard to even fathom the thousands of tragedies unfolding in the North Bay this week and how people who’ve lost their homes, pets, friends, loved ones, or all of it are even coping right now. ❤️

—

In 2003, a meth addict trying to burn down a house started the Old Fire in the mountains near our house in Southern California. At the time, my dad worked for San Bernardino County and helped maintain their emergency communications system.

When the fires broke out, he was tasked with heading up the hill and bringing some emergency generators and other supplies to an *old* AT&T communications bunker on Strawberry Peak. It was built in the 1950’s and allegedly hardened to withstand a nuclear war. I ended up making the trip up with him.

For two days, we sat on top of the bunker and watched the fires slowly climb the mountain toward us. They were far enough away that we couldn’t hear trees burning, nor hear the bombers dropping Phos-Chek, nor smell smoke due to the wind blowing in a different direction, nor hear the sirens of firetrucks passing below on Highway 18.

At night, we watched the eerie glow of the flames play off the constantly changing patterns of smoke. Fortunately for us, the flames never reach the communications bunker.

Down below, 90,000 acres and 1,000 homes would ultimately be lost.

We had a few close calls growing up, but we were always lucky. I can’t even pretend to imagine the pain and suffering our friends and their families are going through right now.

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Want to learn to code?

A friend of mine recently asked for some suggestions on which language she should use to learn to code.

There are so many different routes to go! Python and JavaScript might be easiest because they’re dynamically typed languages (basically they’re more forgiving with how you use variables and pass them around — it’s one less thing to worry about as you start out).

I find Python to be super fun and easy to pick up. Plus there’s tons of neat libraries available for manipulating data. One bonus: down the road, you can start playing with some of the many machine learning libraries that are available. Then you can build a model that will predict names of Android phones.

I’m partial to JavaScript. At this point, it’s probably one of the most popular languages right now. People are building web apps, desktop apps, native mobile apps, and backend servers with it due to the abundance of tools available. Plus, there’s a really addictive feedback loop with it: when you’re first starting out, you code something, refresh your browser and boom, there it is!

I’ve played only a little bit with Swift. I like it and I think Apple is doing some good work trying to provide tools to help people learn. For now, you’re mostly going to be limited to building mobile apps, though there are more tools being built that expand its uses (e.g., servers).

Stay away from PHP.

There are tons of great resources. Codecademy, Code School, Udemy, free tutorials, etc. When I started out, I started learning by trying to build a JavaScript app that could solve Sudoku puzzles. Somehow, I eventually did it! I was pretty hooked!