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This blog is about Clever Questions, Answers, Resources, Feeds, Discussions about Tech jobs and careers at MAANGM companies including:

• Meta (Facebook)
• Apple
• Amazon
• AWS
• Netflix
• Google (Alphabet)
• Microsoft

How to prepare for MAANGM jobs interviews

MAANGM Job interviews Q&A

Tips to succeed at FAANGM companies

Fortune 500 Good Jobs,

Recipes to succeed in corporate, how to navigate the job world.

Top-paying Cloud certifications provided by MAANGM:

According to the 2020 Global Knowledge report, the top-paying cloud certifications for the year are (drumroll, please):

• Google Certified Professional Cloud Architect — $175,761
• AWS Certified Solutions Architect – Associate — $149,446
• AWS Certified Cloud Practitioner — $131,465
• Microsoft Certified: Azure Fundamentals — $126,653
• Microsoft Certified: Azure Administrator Associate — $125,993

https://www.youtube.com/channel/UCjxhDXgx6yseFr3HnKWasxg

MAANGM Compensation

Legend – Base / Stocks (Total over 4 years) / Sign On

Google (Alphabet)

– 105/180/22.5 (2016, L3)
– 105/280/58 (2016, L3)
– 105/330/22.5 (2016, L3)
– 110/180/25 (2016, L3)
– 110/270/30 (2016, L3)
– 110/250/20 (2016, L3)
– 110/160/70 (2016, L3)
– 112/180/50 (2016, L3)
– 115/180/50 (2016, L3)
– 140/430/50 (2016, L3)
– 110/90/0 (2017, L3)
– 145/415/100 (2017, L3)
– 120/220/25 (2018, L3)

– 145/270/30 (2017, L4)
– 150/400/30 (2018, L4)
– 155/315/50 (2017, L4)
– 155/650/50 (2017, L4)
– 170/350/50 (2017, L4)
– 170/400/75 (2017, L4)

*Google’s target annual bonus is 15%. Vesting is monthly and has no cliff.

Facebook

– 105/120/120 (2016, E3)
– 105/150/75 (2016, E3)
– 105/100/50 (2016, E3)
– 105/240/105 (2016, E3)
– 107/150/75 (2016, E3)
– 110/150/75 (2016, E3)
– 110/235/75 (2016, E3)
– 115/150/75 (2016, E3)
– 110/150/110 (2017, E3)
– 115/160/100 (2017, E3)

– 160/300/70 (2017, E4)
– 145/220/0 (2017, E4)
– 160/300/100 (2017, E4)
– 160/300/100 (2017, E4)
– 150/250/25 (2017, E4)
– 150/250/60 (2017, E4)
– 175/250/0 (2017, E5)
– 160/250/100 (2018, E4)

– 170/450/65 (2015, E5)
– 180/600/50 (2016, E5)
– 180/625/50 (2016, E5)
– 170/500/100 (2017, E5)
– 175/450/50 (2017, E5)
– 175/480/75 (2017, E5)
– 190/600/70 (2017, E5)
– 185/600/100 (2017, E5)
– 185/1000/100 (2017, E5)
– 190/500/120 (2017, E5)
– 200/550/50 (2018, E5)

– 210/1000/100 (2017, E6)

*Facebook’s target annual bonus is 10% for E3 and E4. 15% for E5 and 20% for E6. Vesting is quarterly and has no cliff.

LinkedIn (Microsoft)

– 125/150/25 (2016, SE)
– 120/150/10 (2016, SE)

– 170/300/30 (2016, Senior SE)
– 140/250/50 (2017, Senior SE)

Apple

– 110/60/40 (2016, ICT2)

– 140/99/8 (2016, ICT3)
– 140/100/20 (2016, ICT3)
– 155/130/65 (2017, ICT3)
– 120/100/21 (2017, ICT3)
– 135/105/20 (2017, ICT3)

– 160/105/30 (2017, ICT4)

Amazon

– 95/52/47 (2016, SDE I)
– 95/53/47 (2016, SDE I)
– 95/53/47 (2016, SDE I)
– 100/70/59 (2016, SDE I)
– 103/65/52 (2016, SDE I)
– 103/65/40 (2016, SDE I)
– 103/65/52 (2016, SDE I)
– 110/200/50 (2016, SDE I)
– 135/70/45 (2016, SDE I)
– 106/60/65 (2017, SDE I)

– 130/88/62 (2016, SDE II)
– 127/94/55 (2017, SDE II)
– 152/115/72 (2017, SDE II)
– 160/160/125 (2017, SDE II)
– 178/175/100 (2017, SDE II)
– 145/120/100 (2018, SDE II)

– 160/320/185 (2018, SDE III)

*Amazon stocks have a 5/15/40/40 vesting schedule and sign on is split almost evenly over the first two years*

Microsoft

– 100/25/25 (2016, SDE)
– 106/120/20 (2016, SDE)
– 106/60/20 (2016, SDE)
– 106/60/10 (2016, SDE)
– 106/60/15 (2016, SDE)
– 106/60/15 (2016, SDE)
– 106/120/15 (2016, SDE)
– 107/90/35 (2016, SDE)
– 107/120/30 (2017, SDE)
– 110/50/20 (2016, SDE)
– 119/25/15 (2017, SDE)

Twitter

– 130/200/20 (2016, SWE1)
– 120/150/18.5 (2016, SWE1)
– 145/125/15 (2017, SWE1)

– 160/600/50 (2017, SWE II)

Uber

– 110/180/0 (2016, L3)
– 110/150/0 (2016, L3)

– 140/590/0 (2017, L4)

Lyft

– 135/260/60 (2017, L3)

– 170/720/20 (2017, L4)
– 152/327/0 (2017, L4)
– 175/480/0 (2017, L4)

Dropbox

– 167/464/10 (2017, IC2)
– 160/250/10 (2017, IC2)
– 160/300/50 (2017, IC2)

As a software engineer or programmer, what’s the dumbest line of code you’ve seen in a codebase?

It’s not actually a line of code, so to speak, but lines of code.

I work in Salesforce, and for those who are not familiar with its cloud architecture, a component from QA could be moved to production only if the overall test coverage of the production is 75% or more. Meaning, if the total number of lines of code across all components, including the newly introduced ones, is 10000, enough test classes must be written with appropriate test scenarios so as to cover at least 7500 lines of the lump. This rule is enforced by Salesforce itself, so there’s no going around it. Asserts, on the other hand, could be done without.

If the movement of your components causes a shift in balance in production and tips its overall coverage to below 75%, you are supposed to work on the new components and raise their coverage before deployment. A nightmare of sorts, because there is a good chance your code is all clean and the issue occurs only because of a history of dirty code that had already gone in over years to drag the overall coverage to its teetering edges.

Someone in my previous company found out a sneaky way to smuggle in some code of his (or hers) without having to worry about this problem.

So this is simple math, right? If you have got 5000 lines of code, 3750 must be covered. But what if I have managed to cover only 2500 (50%) and my deadline is dangerously close?

Simple. I add 5000 lines of unnecessary code that I can surely cover by just one function call, so that the overall line number now is 10000 and covered lines are 7500, making my coverage percentage a sweet 75.

For this purpose they introduced a few full classes with a lone method in each of them. The method starts with,

Integer i = 0;

and continues with a repetition of the following line thousands of times.

i++;

And they had the audacity to copy and paste this repetitive ‘code’ throughout a bulky method and across classes in such a reckless manner that you could see a misplaced tab in first line replicated exactly in every 100th line or so.

Now all that is left for you to do is call this method in a test class, and you can cover scores of lines without breaking a sweat. All the code that actually matters may lie untested in automated coverage check, glaring red if one should care to take a look at, but you have effectively hoodwinked Salesforce deployment mechanism.

And the aftermath is even crazier. Seeing the way hoards of components could be moved in without having to embark on the tedious process of writing test classes, this technique acquired a status equivalent to ‘Salesforce best practices’ in our practice. In almost all the main orgs, if you search for it, you can find a class with streams of ‘i++;’ flowing along the screen for as far as you have the patience to scroll down.

Well, these cloaked dastards remained undetected for years before some of the untested scenarios started reeking. More sensible developers fished out the ‘i++;’ classes, raised the alarm and got down to clean up the mess. Just removing those classes drove the overall production coverage to abysmal low, preventing any form of interaction with production. What can I say, that kept many of us busy for at least a month.

I wouldn’t call the ‘developers’ that put this code in dumb. I would rather go for ‘wicked’. The higher heads and testers who didn’t care to look while this passed under their noses do qualify as dumb.

And the code… Man, that’s the dumbest thing I’ve ever seen.

Would Elon Musk pass the Google, Amazon, or Facebook technical interview for the software engineer position?

I think it is likely he will pass the interview if the job description includes the following text:

The successful candidate will have built a brand new car and launched it into interplanetary orbit, using a rocket that they also built. 

But will he even want the job?

FREE AWS, Azure, GCP Training & Certification Preparation Videos. FAANG IT SDE – Architect job interviews Prep

https://www.youtube.com/channel/UCjxhDXgx6yseFr3HnKWasxg

How difficult is it to find highly talented software developers?

Dev: Alright, let the competition begin!
Startup A: We will give you 50% of the revenue!
Startup B: To hell with it, we will give you 100%!
Startup A: Eh… we will give you 150%!

TL;DR: Nearly impossible. If you are a Google-sized company, of course. Totally impossible in other cases.

I run an outsourcing company. Our statistics so far:

• 500 CVs viewed per month
• 50 interview invitations sent per month
• 10 interviews conducted per month
• 1 job offer made (and usually refused) per month

And here we are looking for a mid-level developers in Russia.

Initially we wanted to hire some top-notch engineers and were ready to pay “any sum of money that would fit on the check”. We sent many invitations. Best people laughed at us and didn’t bother. Those who agreed – knew nothing. After that we had to shift our expectations greatly.

Still, we manage to find good developers from time to time. None of them can be considered super-expert, but as a team they cooperate extremely effectively, get the job done and all of them have that engineering spirit and innate curiosity that causes them to improve.

This is as good as an average company can get.

What is something worth knowing that people working at Google know and others don’t?

It takes constant human effort to keep sites like Google and Gmail online. Right now a Google engineer is fixing something that no one will ever know was broken. Some server somewhere is running out of memory, a fiber link has gone down, or a new release has a problem and needs to be rolled back. There are careful procedures, early warnings, and multiple layers of redundancy to ensure that problems never become visible to end users, but.

Sometimes problems do become visible but not in a way that an individual user can attribute to the site. A request might not get a prompt response, or any at all, but the user will probably blame the internet or their computer, not the site. Google itself is very rarely glitchy, but services like image search do sometimes have user visible problems.

And then of course, very rarely, a giant outage brings down something giant like YouTube or Google Cloud. But if it weren’t for an army of very smart, very diligent people, outages would happen much more often.

What do 10x software developers understand that other programmers don’t?

It’s what they don’t understand. 10x software engineers don’t really understand their job description.

They tend to think all these other things are their responsibility. And they don’t necessarily know why they’re doing all these other things. They just sense that it’s the right thing to do. If they spot something is wrong, they will just fix it. Sometimes it even seems like they’re not in control of what they do. It’s like a conscientiousness overdose.

10x engineers are often all over the code base. It is like they had no idea they were just part of one eng team.

Why don’t big tech companies like Google, Microsoft, and Facebook care about work experience and previous projects when interviewing software engineering candidates and rely completely on programming problems?

Thanks for the A2A.

I don’t think the premise behind the question is entirely true. These companies rely completely on programming problems with junior candidates that are not expected to have significant experience . Senior candidates do, in fact, get assessed based on their experience, although it might not always feel like it.

Let me illustrate this with an interview process I went through when interviewing for one of the aforementioned companies (AFAIK it’s typical for all the above). After the phone screen, there was a phone site interview with 5 consecutive interviews – 2 whiteboard coding + 2 whiteboard architecture problems + 1 behaviour interview. On the surface, it looks like experience doesn’t play a part, but, SURPRISE, experience and past projects play part in 3 interviews out of 5. A large part of the behavioural interview was actually discussing past projects and various decisions. As for the architecture problems – it’s true that the problem discussed is a new one, but those are essentially open ended questions, and the candidates experience (or lack thereof) clearly shines through. Unlike the coding exercises, these questions are almost impossible to solve without tackling something similar in the past.

Now, here a few reasons to why the emphasis is still on solving new problems and not diving into the candidates home territory, in no particular order:

• Companies do not want to pass over strong candidates that just happen to be working on some boring stuff.
• Most times companies do not want to clone a system that the candidate has worked on, so the ability to learn from experience, and apply it to new problems is much more valuable.
• When the interviewer asks different candidates to design the same system, they can easily compare different candidates against one another. The interviewer is also guaranteed to have a deep understating of the problem they want the candidate to solve.
• People can exaggerate (if not outright lie) their role in working on a particular project. This might be hard to catch-on in one hour, so it’s to avoid in the first place.
• (This one is a minor concern, but still) Large companies hire by committee, where interviewers are gathered from the whole company. The fact that they shouldn’t discuss previous projects, removes the need to coordinate on questions, by preventing a situation where two interviewers accidentally end up talking about the same system, and essentially doing the interview twice.

I hope that adds some clarity.

As a teenager, what can I do to become an engineer/entrepreneur like Elon Musk? What skills can I start learning to succeed as an engineer/entrepreneur?

Originally Answered: What can I, currently 17 years old, do to become an engineer/entrepreneur like Elon Musk?

This is a quick recap of my earlier response to a similar question on Quora:

I would recommend that you take a close look at the larger scheme of things in your life, by spending some time and effort to design your life blueprint, using Elon Musk as your inspiration and/or visual model.

By the way, here’s my quick snapshot of his beliefs and values:

1) Focus on something that has high value to someone else;

2) Go back to first principles, so as to understand things more deeply and widely, especially their implications;

3) Be very rigourous in your own self analysis; constantly question yourself, especially on the practicality of the idea(s) you have;

4) Be extremely tenacious in your pursuits;

5) Put in 100 hours or more every week, as sweat equity of intense efforts and focused execution count like hell;

6) Constantly think about how you could be doing better, faster, cheaper and smarter;

7) Relentlessly and ruthlessly think about how to make a better world;

Again, here’s my quick snapshot of his unique traits and characteristics:

1) Be a voracious reader.

2) Be intrinsically driven.

3) (F)ollow (o)ne (c)ourse (u)ntil (s)uccess. That’s Focus!

4) Develop a steadfast problem solving attitude.

5) Employ a physics-mind or first principles in problem solving.

6) Work doubly hard, and a lot, and diligently.

7) Welcome negative feedback.

Nonetheless, here is a simple template:

1) First and foremost, know exactly what you want, in terms of compelling, inspiring and overarching long-range goals and objectives:

a) what do I want to be?

b) what do I want to do?

c) what do I want to have?

d) what do I want to improve?

e) what do I want to change?

in tandem with the following major life dimensions in your life:

i) academic pursuit;

ii) mental development;

iii) career aspirations;

iv) physical health;

v) financial wealth;

vi) family relationships;

vii) social networking;

viii) recreational ventures (including hobbies, interests, sports, vacations, etc.);

ix) spiritual development (including contributions to society, volunteering, etc.);

2) Translate all your long-range goals and objectives in (1) into specific, prioritised and executable tasks that you need to accomplish daily, weekly, monthly, quarterly and even annually;

3) With the end in mind as formulated in (1) and (2), work out your start-point, endpoint and the developmental path of transition points in between;

4) Pinpoint specific tasks that you need to accomplish at each transition point till the endpoint;

5) Establish metrics to measure your progress, or milestone accomplishments;

6) Assign and allocate personal accountability, as some tasks may need to be shared, e.g. with team members, if any;

7) Identify and marshal resources that are required to get all the work done;

[I like to call them the 7 M’s: Money; Methods; Men; Machines; Materials; Metrics; and Mojo!]

8) Schedule a timetable for completion of each predefined task;

9) Highlight potential problems or challenges that may crop up along the Highway of Life, as you traverse on it;

10) Brainstorm a slew of possible strategies to deal with (9);

This is your contingency plan.

11) Institute some form of system, like a visual Pert Chart, to track, control and monitor your forward trajectory, as laid out in your systematic game plan, in conjunction with all the critical elements of (4) to (10);

12) Follow-up massively and follow-through consistently your systematic game plan;

13) Put in your sweat equity of intense effort and focused execution;

14) Stay focused on your strategic objectives, but remain flexible in your tactical execution;

Godspeed to you, young man!

Why may a software engineer struggle in a Google/Facebook onsite interview despite solving most of the LeetCode questions?

For a whole bunch of reasons.

You aren’t so stressed and nervous when you are practicing LeetCode, because your career doesn’t depend on how well you do while solving LeetCode.

When solving LeetCode, you aren’t expected to talk to the interviewer to get clarifications on the problem statement or input format. You aren’t expected to get hints and guidance from the interviewer, and to be able to pick them up. You aren’t expected to be able to communicate with other human beings in general, and to be able to talk about technical details of your solution in particular. You aren’t expected to be able to prove and explain your idea in clear, structured way. You aren’t expected to know how to test your solution, how to scale it, or how to adjust it to some unexpected additional constraints or changes. You may not be able to simply get constraints on input size and use them to figure out what is the complexity of expected solution. You have limited amount of time, so if you slowly got through most of the LeetCode, you may still struggle to get stuff done in 45 minutes. And many more… For all these things, you don’t need them to solve LeetCode, so you usually don’t practice them by solving LeetCode; you may not even know that you need to improve something there.

To sum it up: two main reasons are:

1. Higher stakes.
2. Lack of skills that are required at typical Google/Facebook interview, but not covered by solving LeetCode problems on your own.

You should also keep in mind that LeetCode isn’t the list of problems being asked at Google or Facebook interviews. If anything, it is more of a list of problems that you aren’t going to be asked, because companies ban leaked questions 🙂 You may get a question that is surprisingly different from what you did at LeetCode.

And sometimes you simply have a bad day.

I failed all technical interviews at Facebook, Google, Microsoft, Amazon, and Apple. Should I give up the big companies, keep improving my algorithm skills, and try some small startups?

Originally Answered: I failed all technical interviews at Facebook, Google, Microsoft, Amazon and Apple. Should I give up the big companies and try some small startups?

Wanted to go Anonymous for obvious reasons.

Reality is stranger than Fiction.

In 2010: After graduation, I was interviewed by one of the companies mentioned above for an entry level Software Engineering Role. During the interview, the person tells me: ‘You can never be a Software Engineer’. Seriously? Of-course I didn’t get hired.

In 2013: I interviewed again with the same company but for a different department and got hired.

Fast Forward to 2016 Dec: I received 2 promotions since 2013 and now I am above the grade level of the guy who interviewed me. I remember the date, Dec 14 2016, I went to his desk and asked him to go out for a coffee. Initially he didn’t recognize me but later he did and we went out for a coffee. Needless to say, he was apologetic for his behavior.

For me, it felt REALLY GOOD. Its a story I’ll tell my Grandkids! 🙂

I have 3 years of experience as a software developer. Should I expect algorithms at an interview at FAANG + Microsoft?

Big tech interviews at FAANG companies are intended to determine – as much as possible – whether you’ve got the knowledge and attributes to be a successful employee. A big part of that for software developers is familiarity with a good set of data structures and algorithms. Interview loops vary, but a good working knowledge of common algorithms will almost always come in handy for both interviews and the job.

Algorithm-related to questions I was asked in my first five years, or that I ask people with less than 5 years: sorting, searching, applying hashes correctly, mapping, medians and averages, trees, linked lists, traveling salesman (I was asked this a couple times, never asked it), and many more.

I never recommend an exhaustive months-long review before an interview, but it’s always a good idea to make sure you’re current on your basics: hash tables and sets, string operations, working with arrays and vectors and lists, binary trees, and linked lists.

For more information on how interviews work and what to expect for big tech interviews, you may want to watch some of my videos in this playlist: Big Tech InterviewsVideos about interviewing at the big tech companies like Microsoft, Google/Alphabet, Amazon, and Facebook.

How true is it that learning Python programming language first will make it harder to learn other programming languages later down the line?

Compared to other modern languages, python has two features that make it attractive, and then also make learning a second language difficult if you started with python. The first is that, despite some minor steps to allow annotation, python is loosely and dynamically typed. The second is that python provides a lot of syntactic sugar; this is shorthand, like a map function, where you can apply a function to each element in a data structure.

Do these features make it harder to switch to another language that is strongly and statically typed? For some people, yes, and for others, no.

Some programmers are naturally curious what’s happening under the hood. How are data being represented and manipulated? Why does an operation produce one type of result in one situation, and another type of result in another situation? If you are the kind of person who asks these questions, you are more likely to have an easier time transitioning. If you are a person who finds these questions uninteresting or even distasteful, transitioning to another language can be very painful.

As a software engineer, how do you make your resume stand out from the crowd?

I have excellent skills and experience on my resume, which makes it stand out.

Seriously, there is no magical spell that will make a crappy resume attractive to recruiters. Most people give up believing in magic after they are 5 or 6 years old. A software engineer who believes in magic is not a good candidate for hire.

What are some secrets about working for big tech companies that you didn’t know before joining those companies?

All those complaints you have about their products? The people working there complain about the same exact things. Microsoft employees complain about how slow Outlook is. Google employees complain about everything changing all the time. Salesforce employees complain about how hard our products are to use.

So why don’t we do something about it? There are a few possible answers:

1. We are actively doing something about it right now and it will be fixed soon.
2. The problem is technically difficult to fix. For example, it’s currently beyond the state of the art to change the wake word (“Alexa”/”OK Google”) to a user-selected word. A variation of this is the problem that’s more expensive to fix than the amount of annoyance saved.
3. The team responsible for that functionality has problems. Maybe they have a bad manager or have been reorged a lot, and as a result they haven’t been doing a good job. Even once the problem is solved, it can take a long time to catch up.
4. The problem is related to making money. For example, Microsoft used to have a million different versions of Office, each including different programs and license restrictions. It was super confusing. But the bean counters knew how much extra money the company made from these bundles, compared to a simpler scheme, and it was a lot. So the confusion stayed.
5. The problem is cultural. For example, Google historically made its reputation by offering new features constantly. Everything about the culture was geared towards change and innovation. When they started making enterprise products, that cultural became baggage.

But none of that keeps the employees from complaining.

I can’t understand the solution of LeetCode. Can I recite and write from my memory them to achieve the effect of learning?

That’s perhaps the first stage of learning, recitation.

Using the four-stage model of learning that goes

1. Unconscious Incompetence
2. Conscious Incompetence
3. Conscious Competence
4. Unconscious Competence

that’s maybe a 2 to 2.5 there. You know you haven’t really understood why you are doing things that way and without detailed step-by-step, you don’t yet know how you would design those solutions.

You need to step back a bit, by reviewing some working solutions and then using those as examples of fundamentals. That might mean observing that there is a for() loop, for example – why? What is it there for? How does it work? What would happen if you changed it? If you wanted to use a for loop to write out “hello!” 8 times, how would you code that?

As you build up the knowledge of these fundamental steps, you’ll be able to see why they were strung together the way they were.

Next, practice solving smaller challenges. Use each of these tiny steps to create a solution – one where you understand why you chose the pieces you chose, what part of the problem it solves and how.

As a software engineering hiring manager, would you be concerned if the candidate who has applied for a position has changed 3 jobs in 4 years?

Early 2020 has been a very rough period for many companies who laid off tons of good people, many of which have bounced to a company who was not a good fit and eventually went to a third one. Forced remote work was also difficult for many folks. So in the current context, having changed 3 jobs in the last 4 years is really a non-event.

Now more generally, would my hiring recommendation be influenced by a candidate having changed jobs several times in a short period of time?

The assumption here is that if a candidate has switched jobs 3 times in 4 years, there must be something wrong.

I think this is a very dangerous assumption. There are lots of things that cause people to change jobs, sometimes choice, sometimes circumstances, and they don’t necessarily indicate anything wrong in the candidate. However, what could be wrong in a candidate can be assessed in the interview, such as:

• is the candidate respectful? Is the candidate able to disagree consrtuctively?
• does the candidate collaborate?
• Does the candidate naturally support others?
• Has the candidate experience navigating difficult human situations?
• etc, etc.

There are a lot of signals we can detect in the interview and we can act upon them. Everything that comes outside of the interview / outside of reference check is just bias and should be ignored.

The hiring decision should be evidence-based.

What does it feel like to have an IQ of 140?

My IQ was around 145 the last time I checked (I’m 19).

I feel lots of gratitude for my ability to deeply understand and comprehend ideas and concepts, but it has definitely had its “downsides” throughout my life. I tend to think very deeply about things that I find interesting and this overwhelming desire to understand the world has led me to some dark places. When I was around 9 or 10, I discovered the feeling of existential panic. I had watched an astronomy documentary with my father (who is a geoscience professor) and was completely overwhelmed with the fact that I was living on an unprotected orb, orbiting around a star at speeds far faster than I could even comprehend. I don’t think anyone in my family expected me to really grasp what the documentary was saying so they were a bit alarmed when I spent that whole night and most of the next week panicking and hyperventilating in my bedroom.

I lost my mom to suicide when I was 11 which sent me into a deep depression for several years. I found myself thinking a lot about death and the meaning of human existence in my earlier teenage years. I was really unmotivated to do school work all throughout high school because I found no meaning in it. I didn’t understand why I was alive, or what being alive meant, or if there even was any true meaning to life. I constantly struggled to see how any of it truly mattered in the long run. What was the point of going to the grocery store or hanging out with my friends or getting a drivers license? I was an overdeveloped primate forced to live in and contribute to a social group that I didn’t ask to be in. I was living in a strange universe that made no sense and I was being expected to sit at a desk for 8 hours every day? Surrounded by people who didn’t care about anything except clothing and football games? No way man, count me out. I spent a lot of nights just sitting in my bedroom wondering if anything I did really mattered. Death is inevitable and the whole universe will one day end, what’s the point. I frequently wondered if non-existence was inherently better than existence because of all of the suffering that goes hand in hand with being a conscious being. I didn’t understand how anyone could enjoy playing along in this complex game if they knew they were all going to die eventually.

Heavy stuff, yeah.

When I was 18 I suddenly experienced what some people label as an “ego death” or a “spiritual awakening” in which it suddenly occurred to me that the inevitably of death doesn’t mean that life itself is inherently meaningless. I realized that all of my actions affect the universe and I have the ability to set off chain reactions that will continue to alter the world long after I’m gone. I also realized that even if life is inherently meaningless, then that is all the more reason to enjoy being alive and to experience the beauty and wonder of the world while I’m still around. After that day I began meditating daily to achieve a deeper awareness of myself and try to find inner peace. I began living for the experience of being alive and nothing else. All of this has brought me great peace and has allowed me to enjoy learning again. For so long learning was terrifying to me because it meant that I was going understand new information that could potentially terrify me. Information that I could not unlearn. I have become a very emotionally sensitive person after the death of my mother, so I simply could not handle the weight of learning about existential concepts for a while. Now that I’ve been able to find a state of peace within myself and radically accept the fact that I will die one day (and that I do not know what occurs after death) I have begun to enjoy learning again! I read a lot of nonfiction and fiction alike. I enjoy traveling and seeing the world from as many different perspectives as possible. Talking to new people and attempting to see my world through their eyes is very enjoyable for me. Picking up new skills is generally very easy for me and I spend a lot of my free time pondering philosophical issues, just because it’s fun for me. I’m not a very social person, I like having a few close friends, but I mostly enjoy being alone.

So all in all, I think having an IQ of 140+ is a very turbulent experience that can be very beautiful! When you are able to truly understand deep concepts, it can seriously freak you out, especially when you’re searching for meaning and answers to philosophical problems. If I hadn’t embraced a way of life that revolves around radically acceptance, I don’t think I would have the guts to look as deeply into some things as I do. However, since I do have that safety cushion, I’m able to shape my perception of the world with the knowledge that I learn. This allows me to see incredible beauty in our world and not take things too personally. When I have a rough day, all I need to do is sit on my roof for half an hour and look at the stars. It reminds me that I am a very small animal in a very big place that I know very little about. It really puts all of my silly human problems in perspective.

If no-code is the future, is a CS major even worth it?

If you can explain to me how “no-code is the future”, maybe there’s a useful response to this.

As far as I can tell, “no-code” means that somebody already coded a generic solution and the “no-code” part is just adapting the generic solution for a specific problem.

Somebody had to code the generic solution.

As to the second part, “is a CS major even worth it?” I’ve had a 30+ year career in software engineering, and I didn’t major in CS. That hasn’t kept me from learning CS concepts, it hasn’t kept me from delivering good software, and it hasn’t stopped me from getting software jobs.

Is a CS major even worth it? Only the student knows the answer to that.

How can we solve the issue of English speakers advantage in software programming and computer related fields over other languages speakers, considering the fact that programming languages are mostly English based?

IT’S NOT ABOUT THE PROGRAMMING LANGUAGE:

People have written no-English versions of many programming languages – but they aren’t used as much as you’d think because it’s just not that useful.

Consider the C language – there are no such English words as “int”, “bool”, ”enum”, “struct”, “typedef”, “extern”, or “const”. The words “auto”, “float” and “char” are English words – but with completely different meanings to how they are used in C.

This is the complete list of C “reserved words” – things you’d have to essentially memorize if you’re a non-English speaker…

auto, else, long, switch, break, enum, register, typedef, case, extern, return, union, char, float, short, unsigned, const, for, signed, void, continue, goto, sizeof, volatile, default, if, static, while, do, int, struct, double

…but very few of those words are used in their usual English meanings…and you have to just know what things like “union” mean – even if you’re a native english speaker.

But if you really think there is an advantage to this being your native language then:

#define changer switch

#define compteur register

#define raccord union

…and so on – and now all of your reserved words are in French.

I don’t think it’s going to help much.

IT”S ABOUT LIBRARIES AND DOCUMENTATION:

The problem isn’t something like the C language – we could easily provide translations for the 30 or so reserved words in 50 languages and have a #pragma or a command to the compiler to tell it which language to use.

No problem – easy stuff.

However, libraries are a much bigger problem.

Consider OpenGL – it has 250 named function, and hundreds of #defined tokens.

glBindVertexArray would be glLierTableauDeSommets or something. Making versions of OpenGL for 50 languages would be a hell of a lot more painful.

Then, someone has to write documentation for all of that in all of those languages.

But a program written and compiled against French OpenGL wouldn’t link to a library written in English – which would be a total nightmare.

Worse still, I’ve worked on teams where there were a dozen US programmers, two dozen Russians and a half dozen Ukrainians – spread over two continents – all using their own languages ON THE SAME PIECE OF SOFTWARE.

Without some kind of control – we’d have a random mix of variable and function names in the three languages.

So the rule was WE PROGRAM IN ENGLISH.

But that didn’t stop people from writing comments and documentation in Russian or Ukranian.

SO WHAT IS THE SOLUTION?

I don’t think there actually is a good solution for this…picking one human language for programmers to converse in seems to be the best solution – and the one we have.

So which language should that be?

Well according to:List of languages by total number of speakers – Wikipediahttps://en.wikipedia.org/wiki/List_of_languages_by_total_number_of_speakers

There are 1.3 billion English speakers, 1.1 billion Mandarin speakers, 600 million Hindi speakers, 450 Spanish speakers…and no other language gets over half of that.

So if you have to pick a single language to standardize on – it’s going to be English.

Those who argue that Mandarin should be the choice need to understand that typing Mandarin on any reasonable kind of keyboard was essentially impossible until 1976 (!!) by which time using English-based programming languages was standard. Too late!

SO – ENGLISH IT IS…KINDA.

Even though we seem to have settled on English the problems are not yet over.

British English or US English – or some other dialect?

As a graphics engineer, it took me the best part of a decade to break the habit of spelling “colour” rather than “color” – and although the programming languages out there don’t use that particular word – the OpenGL and Direct3D libraries do – and they use the US English spelling rather than the one that people from England use in “English”.

ARE PROGRAMMERS UNIQUE IN THIS?

No – we have people like airline pilots, ships’ captains.

ICAO (International Civil Aviation Organization), require all pilots to have attained ICAO “Level 4” English ability. In effect, this means that all pilots that fly international routes must speak, read, write, and understand English fluently.

However, that’s not what happened for ships. In 1983 a group of linguists and shipping experts created “Seaspeak”. Most words are still in English – but the grammar is entirely synthetic. In 1988, the International Maritime Organization (IMO) made Seaspeak the official language of the seas.

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