Expressive Syntax

A blog about software, culture, psychology and silly gifs.

[the-future-now-deactivated20170]

Lego announces new set themed around the women of NASA

Toy manufacturer Lego Group has announced it will be creating a set of Legos based around the women of NASA, Community Specialist Hasan Jensen wrote in a blog post for the company on Tuesday.

The idea for the project was originally pitched by Maia Weinstock, who submitted it to Lego through the company’s Lego Ideas program.

The set will include five women who contributed to NASA’s mission, including computer scientist Margaret Hamilton, mathematician Katherine Johnson, astronaut Sally Ride, astronomer Nancy Grace Roman and astronaut Mae Jemison, the first African-American woman to enter space in 1992. Read more (2/28/17 5:53 PM)

follow @the-future-now

[diversemovies]

20th Century Fox just released these retro posters for Hidden Figures.

[drmanahan]

OH OH HELL YEAH

[skyholic-deactivated20180115]

[expressivesyntax]

What is this anime?

[expressivesyntax]

Pros and Cons of Feature Flags

Imagine you have a building. Some spaces are open and interconnected and anyone can move freely between them. Other spaces have doorways between them that restrict access. You can open and close the doors to control who goes where. Some doors require a key to get in.  Feature flags are like doors in a building. It would be wasteful clutter to put them everywhere, but when they’re placed strategically and when they’re well designed, they can be helpful.

Pros

Dark deploys: push code without users seeing it

Partial rollouts: beta test risky things

A/B testing: try things and see what works

Easy rollback: did something break? Just turn it off! 

Easier merges: feature flags > long running branches 

User segmentation: custom feature sets for different users

Feature dashboards: let stake holders decide when to turn something on

Cons 

Extra process: get ready to add 2-4 toggle related tasks to your to-do list

Tech debt: the longer your toggles live in the code the more they cost

Ugly code: feature toggles always feel messy 

Non-core value: people go to the circus to see the trapeze, not to see the safety net

Easy to misuse and abuse: toggles are like beer. The first few taste good. You’ll probably regret the tenth

Leaky features: toggle logic is code that can contain bugs just like any other code

Exponential testing: if you have features A, B & C, how many combinations are there?

[curiosityandcode]

What are some tips and techniques for coding practice. I have just started learning some basic concepts by taking classes in C++ and HTML and CSS

The best way to learn is to actually code. Back when I was taking intro to cs, everyone thought that the course was simple. The prof did a good job of explaining the code and everyone knew what was going on in lecture. But I think the course average was like 58 or something. That is because seeing someone else code and actually coding yourself are different things. You might think you understand certain concepts but you won’t know for sure until you have to figure out a solution yourself. So code small things along the way. since you are just learning, they stuff you make does not need to be complex. 

If your course offers any practice problems, do them! Try to redo class examples on your own without looking at your notes. Try doing some of the challenges on sites like programmr or codewars. Alternatively, try learning using a site like code academy to learn (which requires you to code for each lesson).

It doesn’t matter what you use, just so long as you get hands on practice. Also, if you notice your struggling with a certain concept, then read up on this concept and do more practice problems relating to it. 

Good luck! 

[geostatonary]

Can we take a moment to appreciate that Emrakul’s pronouns are capitalized

[femininefreak]

By  Illustrator Genevieve Womack

[code-bug]

I was asked to make a masterpost on websites like Codecademy, so I’ve tried to compile a list of the best (and mostly free!!). In no particular order: 

1. Codecademy [x]

I couldn’t not include Codecademy! Just in case you haven’t checked it out, it’s pretty neat. It has courses for learning languages and web developer skills.

Languages: Python, Ruby, Java, PHP, jQuery, JavaScript, HTML/CSS

Extras: Git, SQL, Command Line, AngularJS, Ruby on Rails

2. Code School [x]

This is pretty similar to Codecademy with the in-browser coding and such. It also has additional features like teaching videos. A good introduction to programming languages.

Languages: Python, Ruby, Javascript, HTML/CSS

Extra: iOS development with Objective-C or Swift, Git, SQL

3. Learnaroo [x]

Has tutorials followed by mini challenges based on the concepts you’ve just learnt! It also has some reference pages called “Learn x by Example”. Looks pretty good.

Languages: Python, Java, Ruby, HTML/CSS

Extra: Algorithms, Mathematics, + a bunch of premium membership stuff

4. Learn … 

A set of related websites (see below) which have mini tutorials on language concepts followed by mini challenges, similar to Learnaroo.

Languages: Python, Java, C, C#,  JavaScipt, PHP, Shell

5. Code Combat [x]

Basically, it turns learning to code into a game! As you move through the levels, new concepts are introduced, so the code you need to write to play the game gets harder. 

Languages: Python, Java, JavaScript, CoffeeScript, Clojure, Lua

6. Codingame [x]

Similar idea to Code Combat, but a little more advanced. 

Languages: Python, Java, Ruby, C, C++, C#, Haskell, VB … there’s actually loads it supports.

7. Code Wars [x]

This is more for ‘training’ with a language you already know. It offers plenty of practice through challenges, so it’s really good for improving your coding skills. 

Languages: Python, Ruby, Java, C#, Haskell, JavaScript, CoffeeScript, Clojure

8. Coderbyte [x]

This offers some video tutorials and challenges for a few languages, plus special courses on specific algorithms and bootcamp/interview prep. You need a premium membership for quite a lot of things, though (e.g. I don’t think you can watch any of the videos with a free membership).

Languages: Python, Ruby, JavaScript

Extras: Algorithms, Bootcamp Prep, Job Interview Prep

9. Free Code Camp [x]

Mini tutorials followed by mini challenges. Has a number of courses on the languages below!

Languages: HTML/CSS, jQuery, JavaScript

Extras: Git, Algorithms, + a bunch of projects to complete

10. Programmr [x]

Has courses to learn various languages through doing little exercises/challenges in the browser, similar to previous examples. Also has ‘zones’ for more languages, where you can find challenges, example code and projects people have shared. 

Languages: Python, Java, C++, C#, PHP, jQuery

11. CheckIO [x]

Only offers Python, but I still think it’s really great. You move from level to level, getting challenges which are gradually trickier. Learn by doing!

12. Rails for Zombies [x]

For people who already know a bit of Ruby, but nothing about Ruby on Rails!  Each level begins with a short video followed by interactive challenges where you program Rails in the browser.

That’s all (for now!) Hope this is helpful! (ﾉ^∇^)ﾉﾟ

[madewithcode]

She’s FRAN-TASTIC! We talk with Disney Pixar Technical Director Fran Kalal in our latest Code Chats video: https://goo.gl/G4B0ge

[tiffanyachings]

10.12.1815

“I never am really satisfied that I understand anything; because, understand it well as I may, my comprehension can only be an infinitesimal fraction of all I want to understand about the many connections and relations which occur to me, how the matter in question was first thought of or arrived at, etc., etc.”

(art: Sydney Padua, “The Thrilling Adventures of Lovelace and Babbage” )

[todayinhistory]

December 10th 1815: Ada Lovelace born

On this day in 1815, British mathematician Ada Lovelace was born in London. Born Ada Gordon (she later married William King, Earl of Lovelace) she was the daughter of famed poet Lord Byron, who left the family just a few weeks after Ada’s birth. Lady Byron insisted that her daughter would undertake a strict education regimen of mathematics and science, despite this not being the convention for women in this period. Lovelace demonstrated a remarkable aptitude for the sciences, and as a teenager became friends with mathematician Charles Babbage, who became her mentor. It was through this connection that Lovelace was able to begin studying at the University of London. Babbage is best known for designing plans for the first forerunner to the computer, an Analytical Engine which would perform complex mathematical calculations. While translating an article on the device from French, Lovelace added her own notes and ideas on how to develop the machine; her work was published in a science journal in 1843. This article, which introduced theories on computer coding and included the first algorithm, has led historians to consider Lovelace the first computer programmer. This remarkable woman - whom Babbage called ‘The Enchantress of Numbers’ - passed away in 1852, aged just thirty-six, suffering from uterine cancer. Ada Lovelace is one of many pioneering scientists who have not received their due from history simply because of their gender. However, since her work was rediscovered in the mid-twentieth century by computer scientists like Alan Turing, efforts have been made to reassert Lovelace into her proper place in the history of computing. This endeavor is spearheaded by the Ada Lovelace Day campaign, who are dedicated to celebrating the achievements of women in science, technology, engineering, and maths.

200 years ago today

[stuffmomnevertoldyou]

HAPPY BIRTHDAY, ADA!!!

[expressivesyntax]

Code.org - Project for Awesome 2015

I made a video for the Project for Awesome!  Check it out!

 https://www.youtube.com/watch?v=ElU638X5sJ0

[brontosaurus-flex]

How to code for starters

In the past 4 years, I have learned myself to code. Coding actually is really fun, if you know what you can do with it. But for many people it is hard, just as it is for me. That’s why I want to give all the people who are interested in coding some tips. - Make mistakes. Many of them. Just like math, you learn from solving problems. Try to find the source of your mistake. Step by step. - Take breaks. Just to clear your mind. It’s just like you’re solving a puzzle. Break it up in little pieces and every time you finished one piece, take a little break. Stretch, have a snack or go for a little walk around the corner. - Know your limits. If something is too hard, just stop. Ask for someone to help you, or search for something on the internet, just don’t be too hard on yourself. - Do it every day. Break it up in pieces. While you are working on a project, try to work on a piece everyday. That way, you will actually understand what you’re doing. If you try to read code you wrote like 3 months ago, it will be much more difficult to understand than to read code where you have been working on every day for like a week. - Start slow. Begin with something easy, like HTML. Then work your way up to Javascript, PHP, Mysql, AJAX and so on. You won’t understand difficult languages without any knowledge of the basics. - Keep it fun! Reward yourself. Go and buy that thing you have wanted for so long. And don’t forget to take care of yourself. If you really like webdesign, learn CSS, learn how to create dynamic webpages. If you want to make an app, create it the way you want it to. Excuse me for any grammar/spelling mistakes, I’m dutch. Happy coding!

[jay-the-tiger]

Good points tbh! Would like to add:

- Make use of commenting, it will make reading your code from 3 months ago that much easier (everybody does it, trust me)

Plus I’d like to say I don’t think the “start slow” point is particularly true… HTML for example has nothing to do with coding and you should start with it only if your goal is to design websites. JavaScript and PHP on the other hand is almost the same as most higher languages. My advice for starting with a simple language would be Delphi, but you can also just pick up C++ or C#, Java, whatever you want, there’s loads of tutorials out there for either of these. My university starts with Java for example.

[expressivesyntax]

An Open Letter to Startup Institute

I'm a big fan of Startup Institute (SI). I'm proud to be an alumni and I love being a teacher there and helping people learn how to code.  

I also care about diversity and inclusiveness in the tech community.   So I wanted to write and say that I have some mixed feelings when I read that 41% of SI's graduates are female.  I've heard that number quoted a couple of times in the context of SI and diversity in tech.

On the one hand it's awesome to hear people in the community talking about diversity.  It's great to know that we share those values.

On the other hand, it seems a little misleading.  I'm not seeing those women in the programming classes that I teach.  The last Startup Institute Boston (SIB) class had 0 women in the developer track. Only 13% of my Ruby RampUp students have been female.

To be clear, I'm not disputing the 41% number and I'm not saying we should stop tooting that horn.   I just want to point out that most of the women who graduate from SIB are in the marketing, sales and design tracks, not the web development track. 

I think it's great that the SI community is celebrating its diversity.   But I want to dive a little deeper.  Why is the dev track still mostly men?  How can we improve?

First of all, diversity is important because when people create software to solve problems, we tend to only solve the problems that we are aware of. Until the experiences of all software developers begin to mirror the experiences of all software users, real problems will remain unsolved and real needs will remain unmet. Our differences are our strength.

The reasons for the gender gap are many, complex, and hard to split apart. They range from historical to cultural to perceptual to organizational. 

Here are some things that I think we could improve: The primary recommendation made by the authors in Unlocking the Clubhouse is to "pay ferocious attention to the quality of the student experience... Good teaching is especially important because failures in pedagogy or in curriculum integration affect women disproportionately."   Since SI relies on mostly volunteer teachers, this is a real challenge.  We haven't invested in teacher training.  It doesn't make sense when each teacher will only deliver a day or two worth of material.  However if we're serious about helping women become great software engineers, we need to do a better job of coaching teachers. Pedagogical theory and practices are important. Ramp Up would be a good place to pilot pedagogical improvement programs since instructors teach multiple sessions.

We also need to do a better job helping students overcome financial obstacles. Launch Academy, Hacker School, and The Turing School value diversity and put their money where their mouth is.  They offer scholarships, discounts, and grants for women and minorities. As far as I know, SI has never done anything like that.  Why not?  

It also helps to clarify why and how we value and encourage diversity. For example, conferences have mission statements and codes of conduct.   Recurse does well with their 4 social rules: 1. No feigning surprise, 2. No well-actually's, 3. No back-seat driving, and 4. No subtle -isms. "These rules are designed to help all of us build a pleasant, productive, and fearless community."  #CodeNewbie sets expectations with these guidelines: be nice, be honest, be helpful.  What are our rules?  What do we do to support an awesome and inclusive community? 

I want to close by pointing out one of the things I think we’re doing really right. The authors of Unlocking the Clubhouse observe that "prior experience did not predict eventual success, it seemed clear that admissions policy should not give a strong preference to highly experienced students." As SIB CEO, Diane Hessan, pointed out, the prior experience requirement of many other bootcamps and internships is a big barrier.  SI stands apart by aiming at an admissions process that focuses on enthusiasm and aptitude and that does not exclude beginners. And that’s awesome.

I hope this sparks new thoughts and discussions. I’d love to hear what other people think. 

[expressivesyntax]

Top 6 awesomely nerdy data sets & APIs

Are you looking for some sweet data to play with? Check out these super nerdy, super awesome APIs.  Do some machine learning stuff, make a twitter bot, or just take your geek trivia to the next level.

1. Magic: the Gathering - Download all of the stats for every card in the 20-year history of this epic fantasy game.  There are over 13,000 of them!

2. Marvel’s social graph - Find out how many comics Jean Grey has been in. Discover which issues featured both Thor and Ms. Marvel.  Play 5 degrees of Spider Man. It’s like facebook for super heroes! 

3. Star Wars API - This api has all the star wars data you could need. From ships to people to planets.

4. NASA API - The photos are beautiful, the data is rich and interesting.  NASA is the Champagne of nerdy data sets. 

5. The Open Movie Data Base - Have a new favorite film? Find out who wrote it and who directed it.  You can also get ratings, awards, and plot summaries.  It’s like IMDB in json form. 

6. Echo Nest Song Data - This incredibly comprehensive database of music related information is for all you hipsters, band geeks and music nerds out there. 

[expressivesyntax]

Debunking the talent myth

A while ago I was talking with one of my coworkers about what it was like to teach programming.  I had just started teaching an evening intro to Ruby class and he had just taught a workshop called Programming for the Non-programmer.  When I asked him how it went, he said that some of his students really struggled.  I asked him what happened, hoping it would lead to a conversation about pedagogy and programming.  Instead he told me that they just didn’t get it.  He brought up a theory that some programmers were more naturally talented than others and said that there were just some people who couldn’t learn to code.  To me, that seemed wrong.  At the time I couldn’t quite put my finger on why I didn’t buy it. I asked him for more details and he told me that there was a scientific study proving the idea.  He said it was something to do with variable assignment and mental models and aptitude of college freshmen.

This coworker was a friend.  He was a skilled programmer and an enthusiastic manager. He was someone who I admired and respected. He seemed so sure and confident.  Nonetheless, I was skeptical.  I didn’t want to believe that there were some naturally talented programmers and other hopelessly unteachable non-programmers. But I didn’t have an avenue to argue.  I needed to learn more.

I spent the rest of the afternoon reading and researching.  I found the paper that he was talking about and the blog post that made it famous.  The more I researched the more I felt vindicated.  I was right to be skeptical.  Not only is the talent myth wrong, it’s also harmful. 

The original paper is called “The Camel Has Two Humps.”  The Authors, Dehnadi and Bornat, claimed to “have found a test for programming aptitude.” They say that their test “can predict success or failure even before students have had any contact with any programming language.” The title comes from the shape of the graphs in the paper.  They say that when you measure aptitude using their test, and plot it on a graph, there are two humps: Programmers and non-programmers.  

The paper was never published.  Attempts to reproduce the findings failed.  The authors issued multiple retractions.  In their formal retraction they say that the original paper “was very misleading and, because web documents persist, it continues to mislead to this day.”  

Despite being unpublished, unreproducible, and retracted, the ‘Double Hump’ paper continues to be cited and quoted.  My coworker friend read about it on Jeff Atwood’s blog, Coding Horror, in a post called ‘Separating Programming Sheep from Non-Programming Goats’.  I’m not sure why this blog post still exists. We should know better by now.  I’m pretty sure Jeff knows better. In the comments of the blog post, Jeff quotes the official retraction: “There wasn’t and still isn’t an aptitude test for programming”

Programming is weird and complicated and unlike anything humans have ever done before.  It would be incredibly surprising if anyone was naturally good at it. Learning to program, is like learning to fly an airplane.  There are skilled programmers and there are skilled pilots. But the distribution is a bell curve and getting better has a lot more to do with feedback, practice, perseverance, pedagogy, attitude, and environment than it has to do with talent or genes or any innate factors. 

The naturally gifted programmer does not exist.  

Even if there there were a way to predict programming aptitude, it would still be in our best interest to emphasize the importance of effort and downplay the importance of innate talent. 

In their paper “Expectations of brilliance underlie gender distributions across academic disciplines” Sarah-Jane Leslie and Andrei Cimpian, explain how implicit cultural beliefs about talent affect diversity. It’s a thought provoking and well written paper.  (If you click one link today, it should be this one.)  They start with the observation:

“Recently, women have earned approximately half of all Ph.D.’s in molecular biology and neuroscience in the United States, but fewer than 20% of all Ph.D.’s in physics and computer science. The social sciences and humanities exhibit similar variability.”  

They hypothesize that the computer science gender gap is, in part, caused by a belief in talented programmers:

“Individuals’ beliefs about what is required for success in an activity vary in their emphasis on fixed, innate talent . Similarly, practitioners of different disciplines may vary in the extent to which they believe that success in their discipline requires such talent. Because women are often negatively stereotyped on this dimension, they may find the academic fields that emphasize such talent to be inhospitable.”

They conclude by recommending that we downplay innate giftedness and instead focus on effort:

“The extent to which practitioners of a discipline believe that success depends on sheer brilliance is a strong predictor of women’s and African Americans’ representation in that discipline. Our data suggest that academics who wish to diversify their fields might want to downplay talk of innate intellectual giftedness and instead highlight the importance of sustained effort for top-level success in their field. We expect that such easily implementable changes would enhance the diversity of many academic fields.”

I love helping people learn to code.  As a teacher, my first task is to believe in my students. 

[whatshouldwecallstartupinstitute]

What my friends think I do.