This is the real answer.

There are still, in the year 2023, Cobal developers graduating and getting hired to work on software.

My alma mater’s website runs on PHP.

The investment to flip even a microservice from one language to another is REALLY high, and most companies won’t pay unless there’s a significant pain point. They might not greenfield new projects with it anymore - but it will still be around effectively forever.

I think sometimes the difference between strongly typed and loosely typed is easier to explain in terms of workflow. It’s possible (note: not necessarily a good idea, but possible) to start writing a function in vanilla JavaScript without any real idea of what it’s going to output. Is it going to return a string? Is it going to return null, because it’s performing some action to a variable? Or is it going to return that variable? And vanilla JS is good with that.

But that same looseness that can be kind of useful when you’re brainstorming or problem solving can result in unexpected behavior. For instance, I write a function that prepares a string for additional processing. For the purposes of this conversation, let’s say that it removes white spaces and any non-letter or number characters, and then returns the transformed string.

What happens if I feed a number (integer) to this function instead of a string? What about a number with a decimal (float)? Does my function (and/or JavaScript) treat them as literal strings? Does it remove the decimal from the float, since it’s a non-letter, non-number value?

Without Typescript, you have to either a) write your code in such a way that you ensure that the situation never comes up (i.e. by validating inputs, or by being very careful about when and how this function is used), or b) you have to handle those weird cases, which turns a cute little three line function into a 30 line monster with a switch case and typecasting. And truthfully, on any project ‘at scale’ you’re going to have to do both.

With Typescript, you specify that this function takes in a string, and returns a string, and that’s it. Then, when another dev (or you, in six weeks when you’ve forgotten all about this function that you wrote) tries to send in a boolean, or a float, or an array of strings, Typescript is going to blow up. Nope. Can’t do that. And they (it’s more fun to think of it as future you) will have to follow the rules that you put in place by writing the function the way you did, which just means being careful enough to cast a float or integer into a string before passing it, or concatenating an array, or whatever.

Strongly typed languages expect you to write functions with the end state you’re expecting in mind. You’ve already got the solution in your head before you start typing, at least enough to know what type of data goes in and what type of data goes out.

It also hides (but doesn’t totally get rid of!) some of the sillier things that JavaScript does because it’s not strongly typed. True + True should probably never equal 2.

As to OP’s question, though - if you know how to do things in JavaScript (.trim(), for example), then you know how to do a thing in Typescript. Manipulating data in JavasScript and manipulating data in TypeScript is identical - but TypeScript is also watching what the types of the different data types being used, and throwing up the BS flag when you get too loose with what you’re doing (or don’t think through the consequences of your design decisions).

Is it just an extreme difficulty spike at this point that I have to trial-and-error through, or am I doing anything wrong?

I would say this is the biggest ‘aha’ moment for pretty much any developer - the first time you go from “I built this myself” to “A team built this and has supported it for 10+ years”. Not only can a team of three or four write a lot of code in ten years - they’ll optimize the Hell out of it. It’s ten years worth of edge case bugs, attempts to go faster, new features, etc. And it’s ‘bumpy’ because some of it was done by Dev A in their own style, some of it by Dev B, and so on. So you’ll find the most beautiful implementation for problems that you haven’t even considered before next to “Hello World” level implementation on something else.

The biggest thing you can do to help yourself out is make sure you’re clear on their branching strategy. When you’re the only one working on your code, it’s cool to push to main and occasionally break things and no harm no foul. But for a mature code base, a butterfly flapping its wings on that obscure constructor can have a blast radius of ‘okay, we have to rebase to the last stable commit’. When in doubt, ‘feature/(what you’re working on)’; but there might be more requirements than that, and it’s okay to ask. Some teams have feature requests tracked by number, on a kanban board, some put it in their username, etc.

Get the code pulled down, get it running on your machine (no small task), git checkout -b from wherever you’re pulling a branch off of (hopefully main or master, but again, it’s okay to ask) and then, figure out what the team’s requirements are for PRs. Do they have any testing environments, besides building it locally? Do they use linting or some other process to enforce style on PR reviews?

And then…don’t move a button. (Unless that button actually needs moved!) But try to mimic something that already exists. Create a second button in the new location. Steal from the codebase - implement something small in a way that has been done before. After the new button works - then remove the old button and see what happens.

The longer you deal with a codebase (and the attendant issues and feedback) the more you’ll feel yourself drawn to certain parts of the code that you’re familiar with.

Anyway, hope that advice helps! But most of all, don’t be scared. You will break things unintentionally. Your code will break things. If there’s not a process in place to catch it before it happens, that’s not your fault; that’s the senior dev/owners fault. But do try to limit the damage by using good branching strategies, only PRing after linting/testing, and otherwise following the rules.

The most compelling argument I heard is that WASM can’t manipulate the DOM and a lot of people don’t want to deal with gluing JS code to it, but aside from that

But other than that, Mrs. Lincoln, how was the play?

You’ve gotten several other answers that are true and correct - the pain of implementation at this point is greater than the pain points that WASM solves. But this is also a non trivial one - most of what Javascript should be doing on a webpage is DOM manipulation.

At some point, WASM will either come out with a killer feature/killer app/use case that Javascript (and all the libraries/frameworks out there) hasn’t figured out how to handle, and it will establish a niche (besides “Javascript is sort of a dumb language let’s get rid of it”), and depending on the use case, you might see some of the 17.4 million (estimated) Javascript developers chuck it for…what? Rust? Kotlin? C? C#? But the switching costs are non-trivial - and frankly, especially if you still have to write Javascript in order to manipulate the DOM…well, what are we solving for?

If you’re writing a web app where one of the WASM languages gives you a real competitive advantage, I’d say that’s your use case right there. But since most web applications are basically strings of api calls looped together to dump data from the backend into a browser, it’s hard to picture wider adoption. I’ve been wrong before, though.

For a second I thought my github was going viral. ;)

This is a hilarious (and interesting!) read.

As a young(er) and slightly shittier web developer, before I really understood or could implement promises effectively (or when my code would ‘race’ and fail to recognize that the DOM hadn’t been loaded yet, so I couldn’t attach event listeners yet), I was known to implement a 2 second timeout.

It wasn’t pretty, but it worked!

Junior-ish DevOps with some blue/green experience.

It’s a very thorny problem, and I think your willingness to put up with the trade offs really would drive what patttern of architectecture.

Most of our blue/green deployment types use a unitary database behind the backend infra. There’s a lot ways to implement changes to the database (mostly done through scripting in the pipeline, we don’t typically use hibernate or other functionality that wants to control the schema more directly), and it avoids the pain of trying to manage consistency with multiple db instances. It helps that most of our databases are document types, so a lot of db changes can be implemented via flag. But I’ve seen some SQL implementations for table changes that lend themselves to blue/green - you just have to be very mindful to not Bork the current live app with what you’re doing in the background. It requires some planning - not just “shove the script into source control and fire the pipeline.”

If we were using SQL with a tightly integrated schema and/or we couldn’t feature flag, I think we’d have to monkey around with blue/greening the database as well. But consistency is non trivial, especially depending on what kind of app it is. And at least one time when a colleague set up a database stream between AWS accounts, he managed a circular dependency, which….well it wasn’t prod so it wasn’t a big deal, but it easily could’ve been. The data transfer fees are really what kills you. We managed to triple our Dev AWS bill prototyping some database streams at one point. Some of it undoubtedly was inefficient code, but point stands. With most blue/green infra, your actual costs are a lot less than 2x what a ‘unitary’ infra would cost, because most infra is pay for use and isn’t necessary except when you go to deploy new code anyway. But database consistency, at least when we tried it, was way MORE expensive than just 2x the cost of a unitary db, because of the compute and transfer fees.

Like the SEAL who became a doctor and then went to space. Don’t feel like you have to limit yourself, you know?

So glad I Xited right after Musk’s purchase was finalized. My days are freer and my conscience is clear!

Tech doesn’t really self select for well balanced, socially confident, neurologically normal folks.

I’m sure those people are in tech and have success as well, but the stereotype of the “hacker nerd” didn’t spring out of nothing. The obsessiveness and desire to be right and know everything that make IT geniuses can also make those same folks really, really hard to be around.

People that are ostracized for their socially aberrant behavior usually (not always!) have sympathy for other outcast groups, whatever the reason.

And you’re right, too - writing code is sort of one of those ultimate bullshit tests - either it works, or it doesn’t. Computers don’t care about your pedigree or your appearance or even your personality. Nice guys who write shit code might have management or product team in their future, but they don’t usually write code for very long. But good devs are hard to find, so even the most straight laced companies are willing to bend a bit when it comes to talented developers.

My $.02, and worth every penny 😂

Just because it’s ‘the hot new thing’ doesn’t mean it’s a fad or a bubble. It doesn’t not mean it’s those things, but…the internet was once the ‘hot new thing’ and it was both a bubble (completely overhyped at the time) and a real, tidal wave change to the way that people lived, worked, and played.

There are already several other outstanding comments, and I’m far from a prolific user of AI like some folks, but - it allows you to tap into some of the more impressive capabilities that computers have without knowing a programming language. The programming language is English, and if you can speak it or write it, AI can understand it and act on it. There are lots of edge cases, as others have mentioned below, where AI can come up with answers (by both the range and depth of its training data) where it’s seemingly breaking new ground. It’s not, of course - it’s putting together data points and synthesizing an output - but even if mechanically it’s 2 + 3 = 5, it’s really damned impressive if you don’t have the depth of training to know what 2 and 3 are.

Having said that, yes, there are some problematic components to AI (from my perspective, the source and composition of all that training data is the biggest one), and there are obviously use cases that are, if not problematic in and of themselves, at very least troubling. Using AI to generate child pornography would be one of the more obvious cases - it’s not exactly illegal, and no one is being harmed, but is it ethical? And the more societal concerns as well - there are human beings in a capitalist system who have trained their whole lives to be artists and writers and those skills are already tragically undervalued for the most part - do we really want to incentivize their total extermination? Are we, as human beings, okay with outsourcing artistic creation to this mechanical turk (the concept, not the Amazon service), and whether we are or we aren’t, what does it say about us as a species that we’re considering it?

The biggest practical reasons to not get too swept up with AI is that it’s limited in weird and not totally clearly understood ways. It ‘hallucinates’ data. Even when it doesn’t make something up, the first time that you run up against the edges of its capabilities, or it suggests code that doesn’t compile or an answer that is flat, provably wrong, or it says something crazy or incoherent or generates art that features humans with the wrong number of fingers or bodily horror or whatever…well then you realize that you should sort of treat AI like a brilliant but troubled and maybe drug addicted coworker. Man, there are some things that it is just spookily good at. But it needs a lot of oversight, because you can cross over from spookily good to what the fuck pretty quickly and completely without warning. ‘Modern’ AI is only different from previous AI systems (I remember chatting with Eliza in the primordial moments of the internet) because it maintains the illusion of knowing much, much better.

Baseless speculation: I think the first major legislation of AI models is going to be to require an understanding of the training data and ‘not safe’ uses - much like ingredient labels were a response to unethical food products and especially as cars grew in size, power, and complexity the government stepped in to regulate how, where, and why cars could be used, to protect users from themselves and also to protect everyone else from the users. There’s also, at some point, I think, going to be some major paradigm shifting about training data - there’s already rumblings, but the idea that data (including this post!) that was intended for consumption by other human beings at no charge could be consumed into an AI product and then commercialized on a grand scale, possibly even at the detriment of the person who created the data, is troubling.

This was my first thought.

I do this for a living and it’s literally built into Linux.

Set their permissions carefully, ensure that the permission set does what you want (and not a bunch of stuff you don’t want), and keep on keeping on.

I hated everything about this comment, thanks.

There’s probably a bit of a disparity, but it’s not nearly as much as you’re making it out to be.

In the US it depends greatly on the industry and company - I don’t know anybody making 200k-400k in software development (CTO? sure. Devs writing code? Nah), but I also don’t know or work with anybody that’s in FAANG world. Those are the companies paying $100k+ for a junior.

I live and work in a lower cost of living area, for a company that’s not ‘software first’ and our juniors come in between $50k-75K. And that’s not the lowest I’ve seen for junior engineers starting out.

That said - it’s also not unusual for mid-career folks to be in the $100-150k range, and seniors/leads moving up from there.

So with all that in mind - some of it is market forces (are there more devs and/or fewer dev jobs in Europe than in the US? Potentially less mobility?) but one of the bigger causes (I’d guess, anyway) is the lack of FANG type “Master of the Universe” companies. Part of the reason juniors and seniors command that kind of pay in the US is because the rates that the FANGs pay tend to ‘trickle down’. The average senior/mid career dev may not be interested (or capable!) of working at a FANG - but if the other people in their hiring pool are, they’re still going to command that kind of salary.

As a point of comparison - my understanding is that financial services is sort of the same thing. Most Euro bankers/stockbrokers/finance bro types are pretty heavily underpaid compared to their US counterparts. Some of that is regulatory, but a lot of it is that there are more higher paying jobs in the US, mostly at the big multinantional conglomerates you can think of off the top of your head (Goldman Sachs, Bank of America, Citibank, JP Morgan), and that tends to drag the scale up throughout the whole system. A rising tide raises all boats.

Anyway - I don’t have any research or statistics to back any of these suggestions up - hopefully Cunningham’s Law gets us a ‘real’ answer. :)