>The first problem every startup solves is scalability.
I don't think this is true at all. The first problem they solve is typically finding product market fit, which startups will do by sacrificing scalability and quality for speed of execution.
These seems like a misstep that I've seen in a few other compiler implementation courses. For some reason these programming language professors always insist on completing the project in their personal favorite language (Haskell, OCaml, Standard ML, etc).
As a student this makes the project significantly more difficult. Instead of just learning how to implement a complier, you're also learning a new (and likely difficult) programming language.
Learning a new programming language isn't that hard of a task, every decent programmer will learn a dozen or maybe even dozens over their career.
Also, neither OCaml nor SML are hard to learn. Haskell is more challenging, but that's because it's become, in a sense, multiple languages. The core of Haskell is no harder than OCaml or SML to learn, except for reasoning about lazy evaluation and some of its consequences. All the things people use on top of Haskell, though, does make it more to learn but what you'd need to reach equivalent utility as SML or OCaml for a compilers course is not that hard to learn.
Many universities that use OCaml in upper-division courses also use it in lower-division; my university requires all CS majors to take a course that is taught in OCaml, and covers higher-order programming, "advanced" (Hindley-Milner) type systems, equational reasoning, etc., typically in their sophomore year.
The compilers class can then be taught in it without worrying about that problem much.
Algebraic data types are superb for compilers. Case exhaustiveness checks are really helpful in this domain, especially when doing any sort of semantic analysis.
Frankly, I try to avoid languages that don’t have ADTs as much as possible. They are incredibly useful for specifying invariants via your design, and their constraints on inputs lend themselves to easier implementation and maintenance.
You have to pick some language and some amount of students are likely to be new to it, so you might as well pick one that excels at the task of writing compilers as ML-family languages are.
In defense of such compilers professors, part of the purpose of a good undergraduate computer science program is to expose students to different programming language paradigms. Computing professionals are expected to grasp new languages, APIs, and tools quickly. Additionally, certain problems are easier to express in certain paradigms. For example, I would use a language like C or perhaps Rust in an operating systems class since we need to operate at a level that is much closer to the hardware. If I were teaching a course on machine learning, I’d use Python for its excellent ecosystem of libraries, though R and Julia are good alternatives. A course on relational databases should teach basic SQL.
Back in the 2000s there were some CS undergraduate programs that attempted to use Java in the entire curriculum, from introductory courses all the way to senior-level courses such as compilers. There was even an operating systems textbook that had Java examples throughout the text (https://www.amazon.com/Operating-System-Concepts-Abraham-Sil...).
I think using only one language for the entire undergraduate CS curriculum is a mistake. Sure, students don’t have to spend time learning additional languages. However, everything has to fit into that one language, depriving students the opportunity to see how languages that are better suited to specific types of problems could actually enhance their understanding of the concepts they are learning. In the case of Java, it’s a good general-purpose programming language, but there are classes such as computer organization and operating systems where it’s important to discuss low-level memory management, which conflicts with Java’s automatic memory management.
When it comes to writing compilers, it turns out that functional programming languages with algebraic data types and pattern matching make working with abstract syntax trees much easier. I learned this the hard way when I took compilers in 2009 at Cal Poly. At the beginning of the course, we were given two weeks to write an AST interpreter of a subset of Scheme. My lab partner and I didn’t like Dr. Scheme (now known as Racket), which we “endured” the previous quarter in a class on implementing programming language interpreters, and so we set about writing our interpreter in C++. It turned out to be a big mistake. We got it done, but it took us 40 hours to implement, and we had a complex class hierarchy to implement the AST. We realized that Dr. Scheme’s features were well-suited for manipulating and traversing ADTs. We never complained about Dr. Scheme or functional programming again, and we gladly did our other compiler assignments in that language.
16 years later, I teach Haskell to my sophomore-level students in my discrete mathematics class at a Bay Area community college that uses C++ for the introductory programming course.
The minimal element of a set which has no elements is 0.
Yes, every language has its warts, and anecdotes like this aren't going to change anyone's mind. I only wrote these up because they're hitting me right now in one of those languages that companies use "because anyone can learn them". I like harder languages better than easy languages, because they're easy.
Compared to Netflix and even YouTube, TikTok has an extremely tight dopamine feedback loop. Any buffering of a video after a scroll event could result in the user's attention being lost and the session ending. My guess is that they've spent a lot of time optimizing video load because of how correlated with their revenue it is. For Netflix doing this optimization might not be worth the increased infrastructure cost.
This looks really cool, but I'm a little bit confused about why this needs to be decentralized. You could theoretically just implement this network as a saas instead of on top of a blockchain. You could even still do all the financials with ether but just maintain the state on servers somewhere.
The big fear I have is immutability of blockchain data in the case that something goes horribly wrong. If there's a problem with the ethereum core protocol, solidity compiler, wallet software, colony contracts you could lose everything. Vitalik already said that there probably wouldn't be anymore hard forks.
It's a little annoying when companies put framework names in their job titles. Asking for a "react engineer" or a "boost programmer" will likely turn away a lot of people who would actually be a good fit for the role. My suggestion is to just be open with the company and tell them what you do know and how quickly you can get up to speed. Something like: "I haven't used React on a paid project before, but I have a lot of experience with other frontend frameworks like x/y."
You have to get past ignorant gatekeepers. Do some google stalking to find out who's in charge of the team you want to join, and reach out on social media to let them know you'll be applying. Then include "so and so is expecting my application" when you send it to HR. Or something like that.
People have wildly different experiences there. Some hate it and burn out after a year. Others really like the work environment and get promoted quickly.
One good thing I'll say about Amazon is that it's a great way to break into the "big four" tech companies. If you didn't get into Google or Facebook initially but got into Amazon you can pretty much guarantee you'll get recruiter spam from lots of top companies.
I don't think this is true at all. The first problem they solve is typically finding product market fit, which startups will do by sacrificing scalability and quality for speed of execution.