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Comment by nostrademons | Hacker News Reader

"The C compiler made me feel adequate. Unlike Ada, I regularly got my programs to compile. And so, I made my choice and went with C."

I think this dynamic plays out in a lot of technology evaluation decisions. It's probably why techs like Javascript, MongoDB, Ruby on Rails, Java, PHP, MySQL, Wordpress, etc. have gotten widespread adoption despite numerous technical flaws. They have a very low barrier to getting something useful up on screen, and feed peoples' need for instant gratification. When you feel good about yourself while using the language, you feel good about the language.

The interesting thing about the software industry is that network effects are often so strong that it's rational for a disciplined, expert developer to use one of these technologies rather than something more niche that plays to their expertise. You may hate Java, but if you need to use Hadoop/Storm/SparkML/OpenNLP and the myriad of well-tested libraries, it may be a lot better choice than building your own distributed big-data stack.

I guess this is the idea behind "Worse is Better" [1]. It makes me wonder if "better" will ever be "better" (perhaps after the pace of adoption in the software industry slows down, and we start spending time getting known product architectures right rather than finding the next big unknown product architecture), or whether by that time the entrenched base written on top of ad-hoc technologies will be too big to change.

[1] https://www.dreamsongs.com/WorseIsBetter.html

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> It's probably why techs like Javascript, MongoDB, Ruby on Rails, Java, PHP, MySQL, Wordpress, etc. have gotten widespread adoption despite numerous technical flaws. They have a very low barrier to getting something useful up on screen, and feed peoples' need for instant gratification.

It might not be just about that. Sometimes you don't know what you're doing, and you have to find out first. What use is it if you painstakingly design and craft a perfectly engineered program, only to find out that you actually needed something else? Being able to experiment is precious. Being forced to handle everything before the thing even compiles is not all that useful at times. There's a reason why interactive environments like Lisp and Smalltalk don't force you to define all functions that could plausibly get called (and when they do get called, you can even add them on the fly and continue).

Big +1 to this. The takeaway for me is to develop languages and tools that scale with the domain-expertise curve. That is, as you learn more about both the problem and solution, you can evolve your implementation without having to start over or get many things right from the start. The goal should be to both tighten the feedback loop and provide a smooth path from tinkering towards production grade.

Static languages, in particular Haskell, PureScript, etc, are unparalleled when it comes to fast prototyping or design space exploration. The compiler can show you whether your idea even makes sense before writing big chunks of business code.

How? By building the structure/hard stuff first and letting the compiler verify it while leaving out the uninteresting parts.

A very simple example. Let's say you have some data model with people, addresses, whatever. You want to see what you need to do to get a list of addresses for some group of people.

You start by defining the structure of your data model (this can depend on DB schema, business requirements, whatever):

    data Person = Person { name :: String, city :: String }

    data Address = Address { city :: String, zipcode :: String, street :: String }

Now, go straight to the hard part. No need to fuck around with DB connections, mock data, whatever:

    getAddresses :: [Person] -> [Address]
    getAddresses = undefined

Leave out the implementation for now. So probably we are gonna need a way to get an Address for a Person. Ok:

    addressForPerson :: Person -> Address
    addressForPerson = undefined

Again, leave out the implementation. Now go back to `getAddresses` and see if we can implement it now:

    getAddresses = map addressForPerson

Cool, the compiler doesn't complain. So far we've verified that if we have a function like `addressForPerson` we can safely assume that our design is going to work.

Now we can go back to `addressForPerson` and implement that. But turns out, we only have a city stored in Person, so the best we can do is get back a list of potential addresses for each person. So we need to adjust that type signature:

    addressForPerson :: Person -> [Address]
    addressForPerson = undefined

Leave out implementation, it's trivial so no need to spend time on it now. But now the compiler complains that `getAddresses` doesn't make sense anymore! We are saying that, having a list of people, we can get a list of addresses, but that's not true anymore. We found out that we can only get a list of potential addresses, so we have to adjust the type signature.

    getAddresses :: [Person] -> [[Address]]

Now every use of `getAddresses` will have to be adjusted to the new requirements. Etc etc.

Granted, this is a very simple example, but I hope it illustrates how static languages facilitate extremely fast prototyping. Sure, a Lisp REPL is very very nice, but in my personal experience it's no match for a type system. We didn't even have to mock data to start experimenting!

(And yes, Haskell, OCaml, PureScript, Elm etc all have a REPL too).

While I agree, getting used to that mode of thought takes time. IME you have to write a lot of Haskell (or OCaml) before it becomes a very fast prototyping language. Of course, it's 100% worth the investment, and I prefer prototyping in OCaml or Mercury to basically anything else, but it certainly wasn't like that from the first time I picked up OCaml.

> "Static languages, in particular Haskell, PureScript, etc, are unparalleled when it comes to fast prototyping or design space exploration. The compiler can show you whether your idea even makes sense before writing big chunks of business code. How? By building the structure/hard stuff first and letting the compiler verify it while leaving out the uninteresting parts."

Why shouldn't I be able to do this on a meta-level? There does not appear to be any restriction on, for example, Lisp code regarding the use of problem solvers and provers. The same should go for other metalanguages (perhaps even better, without forcing me into specific semantics).

(Likewise, generative solutions might reduce this problem simply by performing a sequence of correctness-preserving transformations from a higher-level specification. That would shift the burden to checking the specification, which would require a custom checker anyway. Haskell's capability to check or automatically derive stuff seemed limited the last time I looked at it.)

Agreed. My comment wasn't aimed that much at Haskell in particular (even if it's my language of choice), but rather the idea of using structure/types for fast experimentation, rather than just a REPL.

Also, using a type checker to wholly check your Lisp code basically makes it a static language, the difference then is pretty much philosophical I'd say.

Recently I've found myself having severe problems with seeing clear borders between many concepts. IF expressions, type-based dispatching, matching, generic functions, predicate dispatching...a lot of it basically boils down to the same thing, only expressed in different ways. The problem is that the same thing goes for type systems in my head. Types define sets of values, but if your language hardcodes what kinds of sets of values you can express, chances are that you'll have to express whatever residual distinction between values there is (that you can't encode into the value type) into one of the above-mentioned decision-making processes, and at that point, they go sort of out of sight of the typing/proving machinery.

I'd rather avoid someone forcing me into using a hardcoded type system, among other reasons because I'm contemplating a (perhaps novel?) search-based program/expression-manipulating system that wouldn't play well with any fixed typing system. For one thing, the types of expression results (being return values of operators applied to input expressions or programs) can easily be emergent (is the resulting expression linear? Is it an odd function? Is it analytically integrable? Etc.) and not easily arrived at in something ML-like. If I had to use something like Haskell, I'd still be rolling out my own type system/prover on top of the existing language implementation anyway. However, types in general (as something you can compute with in the meta-program) would be absolutely mandatory for a system like this (for example, some algorithms or operations can have more desirable refinements that work only on subsets of values - consider determinants of triangular matrices, with triangular matrices being an emergent type - and I absolutely want to find out if such a thing is available for the specific problem being transformed), just not types that any specific existing language offers, to my knowledge.

So it's not that I dismiss the notion of proving things about programs - that is a vital thing to have - but the ability to impose restrictions on a program from the outside seems much more flexible to me than going for some fixed type discipline that could force me to straddle inconvenient fixed walls in the structure of a complicated program (such as this transformation system that would search the tree of possible transformations for a result with some desirable properties, with the transformations concisely expressed in some very-high-level DSL source code).

I don't see this example particularly enlightening. I'm not really ever seeing problems where I'm stuffing an integer into where I wanted a string in dynamic languages. Or stuffing a Person into a Gorilla. These problems seem important and they tickle programmers warm fuzzies (unless you're like me and you've been programming static and dynamic for more than 20 years).

The small amount of scientific study around what type checking and static typing gives you is that you avoid around 3% of bugs but even those 3% are easier to fix than the effort in writing them correctly in the first place. While I agree that the subject needs more study but what we know now: the real win would be to have a dev process that lowers criticality of failure (like TDD, CI and other Agile processes) and dispense with static typing for most projects.

While I disagree (static typing is much more than catching type conversion errors), my point was that it makes experimentation and design exploration much easier and faster. Without types, you'd need mock data for even the simplest of programs, and changing mock data structure is much more painful than changing a type somewhere and getting the places (all the places) where your program turns then out to be incoherent. Not to mention that types are much more than Persons or Gorillas, they can be higher order functions too (Person -> Gorilla) that are passed around.

Sure. But does your static typing system allow you to skip writing automated tests? Pretty sure everyone writes tests these days unless they're spiking something. If you're writing tests, you will have to come up with the mock data anyway. And the kind of exploration you're doing I tend to do from a test regardless. Once my exploration is done I also have a test that captures my work. You still have to write one. The question of whether to static or dynamic is moot in this case. It no longer matters much.

Maybe I need to spend some time in Haskell and come over to your way of seeing it. But the studies show that constructing the type system for your problem is very difficult and remains error prone. People routinely make mistakes with types systems of all kinds. I've found I spend more time making the compiler happy that it does making me happy.

> If you're writing tests, you will have to come up with the mock data anyway.

With property based testing, your system will come up with mock data for you.

The sibling comment points out property based testing (see QuickCheck), which is a good idea in any language. And yes - you don't need to write that many unit tests (but still some) when using a type system, because you can encode a lot of invariants in the types.

But still, my point stands - writing tests during early experimentation is a lot harder than just altering some type and letting the compiler infere what needs to be done or what you broke.

I've spent some time writing Clojure and still use dynamic languages a lot for smaller tasks, so I know both sides. This doesn't mean much, but I'd really encourage you to try some ML language and spend some time with a nice type system. Haskell is probably too big a time investment, so maybe try Elm [0] - it's very beginner friendly and a lot of the people using it come from JavaScript.

[0] http://elm-lang.org

> I'm not really ever seeing problems where I'm stuffing an integer into where I wanted a string in dynamic languages

Wow, then you are extremely lucky because bugs like that would occasionally consume a whole day of senior developer time at my previous Python job.

I don't think the OP is particularly lucky. I never see errors like that, either. Just because the language doesn't enforce strong types doesn't mean variables don't conceptually have a type.

It seems like a symptom of other problems more than anything, and using a staticly typed language will just make those problems manifest in a different way.

I see simple type errors like this ALL THE TIME in legacy code bases I get pulled in to salvage. Even in a dynamic language with a fair amount of static checking like Objective C I find people putting the wrong kinds of objects into arrays etc. So when people tell me now that they just don't make type errors in their code I don't believe them. People make mistakes, particularly when tired and under pressure.

If people are writing code, thinking, "I have no idea what else is stored in this container, so I'll just add an object of type Foo because I can!" then the project has bigger problems than static or dynamic typing.

The reality is that a lot of code is written by average programmers trying to get a lot done in a hurry. A static check is the difference between a subtle bug making it into production and a helpful, specific error at compile time. Most projects do have bigger problems than static or dynamic typing but static typing is even more helpful there than it is on projects staffed only by top-flight coders with plenty of time to do things right.

Except that the evidence points to the contrary.

http://games.greggman.com/game/dynamic-typing-static-typing/

In the three years I've been doing consulting work I've had to clean up a lot of legacy code. Believe me, people do make these mistakes in real-world code all the time.

> I'm not really ever seeing problems where I'm stuffing an integer into where I wanted a string in dynamic languages.

If you are writing all of your code from scratch, then it doesn't happen too often. You usually have enough of the current context in your head to avoid most type level mistakes.

The real strengths come from two situations.

The first is refactoring. There is just no contest here. Having every single place where your change affects the test of the program identified automatically is extremely useful.

The second is returning to old code and either trying to understand it or make small changes to it.

I don't think I've ever seen a bug that shouldn't've been a type error. When I encounter a bug that wasn't a type error, it's usually a sign that my model is subtly incorrect. (E.g. I recently found a bug where one codepath wasn't filtering a tree that it should have been. This clued me in that filtered and unfiltered trees need to be different types, and actually making that change simplified a lot of code, since it forced me to figure out the correct point at which to do the filtering so that I could do it in one place)

The data shows that you see 3% bugs that could have been fixed with static typing. 97% are from other things. Mind you this area has not been studied much and needs more research, but that's what we know now.

I simply don't believe that when it's so utterly contrary to my experience. How did they define a bug as not being fixable with static typing?

This seems to be a very common situation with programmers. When presented with evidence to the contrary, we all of us tend to respond in this sort of "it's wrong because it conflicts with my experience". You realize this is essentially the classic flat earther denial. I'm not picking on you: I've done it too. It seems to be an industry hazard.

I think there's a level at which you have to be willing to believe your own eyes over the consensus. And there are levels of how established the consensus is. Perhaps it's particularly common for programmers because the industry moves so quickly, so all of us can quote previous established wisdom that turned out to be utterly wrong.

I used to do this with .. Java 5. Eclipse live typechecking allowed me to design in Interfaces until I had what I called the parametricity right. The thing is, I stumbled upon this out of paranoia (I hate being wrong down the road). Never was I invited to think or design this way. Maybe with embedded contracts in comments; but not in a real time, live repl prototyping way.

That may be useful if you have a lot of data types and transformations between types. I don't. I do a lot of work with (molecular) graphs, which have two data types: nodes and edges. If you code up a depth-first search which uses an edge instead of a node, or vice-versa, then even the most trivial of tests will find it in a dynamic language. A static type check adds little.

For an example of a project which I worked on for months, given N molecular graphs, find the maximum common subgraph which is in at least M of them. I did all that in Python. I don't think static types would have made a difference.

Matrix algorithms are an even more clear example. They work with a single numeric type, in a 1D vector or 2D array. It's hard to confuse the types. On the other hand, it's easy to get the algorithms wrong, and choosing the right algorithm can require a lot of design space exploration.

Just think of the number of clustering algorithms available which you might want to try out through prototyping before spending the time to scale things up. How does static typing really help there?

I'm not an expert on type systems, but I don't think it would add that much in your case. You might get more out of it if you wanted by using phantom types, which is just a way to differentiate among structurally identical types, like this:

    reduceGraph :: Graph a -> Graph Reduced

Now you can't pass unreduced graphs to functions where you expect some sort of an already reduced graph, and you could encode as much invariants as you wanted in your types, but yea, in cases where there are homogenous types with little variance (machine learning, statistics, your use case) static typing won't probably bring that much (unless you go nuts with dependant typing I guess).

Matrices and vectors can be thought of as typed by dimension[0], and it's easy to get that wrong. (I want to say c++'s eigen can do some compile-time checks if it has the dimensions available).

[0] In some languages, like older versions of pascal, arrays of different lengths were different types, which was a horror for writing generic code.

Yes, C++ is a particularly easy language to get that wrong, where float * can be a pointer to scalar, vector or 2d array. Other more strongly typed languages, like Python, will make those errors easy to spot with any sort of test case.

Beyond strong typing, do you get the sense that static typing significantly improve prototyping ability for matrix analysis code?

> For an example of a project which I worked on for months, given N molecular graphs, find the maximum common subgraph which is in at least M of them. I did all that in Python. I don't think static types would have made a difference.

Even with no knowledge of the problem space, I can think of a whole bunch of static types off the top of my head.

You've already given your input type: N molecular graphs, and a number M.

You've also given your output type: the maximum common subgraph which is in at least M of the given N molecular graphs.

Straight away I can spot a problem with this, since there are no programs of this type. In particular, there is no valid output when M > N. Hence I would refine the input type to ensure that M <= N.

Since it appears in your input type, it would be useful to have a type for "molecular graph". Likewise, based on your output type, it would probably be useful to have types for "subgraph of g", "common subgraph of gs", "maximum common subgraph of gs" and "subset of gs".

Naively, I'd give your program a type something like this:

    (m : Nat, gs : Set MolecularGraph, m <= size gs) -> (hs : SubSet gs, size hs >= m, MaximumCommonSubgraph hs)

Programming with type constraints like these would be very different to using Python, where you'd have no guarantee that a value is even a graph, let alone a maximum common subgraph of the input set.

Whether it would be more or less appropriate for your situation is, of course, an entirely separate issue ;)

I think that you are comparing static vs dynamic typing but the grandparent comment is more about strong vs weak typing.

If instead of having separate Person and Address types you just use strings for everything (even "lists" can be strings - just keep the data serialized into HTML, or XML) then you can your program is likely to "do something" if you just compose a bunch of functions together. Sure, your code might have a bunch of logic bugs (like HTML injection / XSS) but at least it does something!

Static typing is great once you leave this "stringly typed" world and start separating your values into strongly-typed types. When you get a type error from passing a value to the function then its nice if you can be warned about this at compilation time.

Right, exactly. I want guidance on whether code (new code or a modification to existing code) can make sense before I've written all of it. With modern Haskell I can start to ask those questions before I've written all of the term-level code and before I've written out all of the relevant types. I can start working with whichever is more convenient at the time, and feel ahead to where things might break down before I get there.

This doesn't feel consistent with my experience. Significantly restructuring a large and complex piece of infrastructure is never going to be easy, but I don't think a strong static type system makes it any harder. If anything, it makes it significantly easier, just like a strong battery of unit tests would.

A sufficiently expressive type system will guide you and hold your hand over the course of the refactoring. It will determine for you the precise cascading effect of any code changes you make. And if you forgot to write a unit test to handle some edge case, the chances are pretty decent that the type system will detect it for you anyway.

In terms of writing a new piece of software whose design you haven't fleshed out yet, my approach in a language like Haskell is in fact very experimental, but quite different from something like Python. Rather than writing lots of little tests and trying to get bare-bones functionality implemented passing those tests, I often completely ignore tests and let the type system guide me. I try to flesh out the types that my program will need at a high level and just go from there. I call it "type-driven development", and it's extremely useful for early-stage exploratory programming, because it lets you play around with the structure of the program without worrying about any details. This is only possible in a language those type system is powerful enough to actually encode nontrivial aspects of a program's high-level structure.

I make sure at every point that I have a codebase that passes the type checker. It may not be interesting to actually run it at an early stage, but in many ways that actually makes exploratory problem solving even easier.

In many cases, this approach will reveal architectural issues early on that you may not have realized existed until you'd already written thousands of lines of code in something like Python. The real point is that either way you will have to face those issues if you want to end up with a working piece of software. But it's a lot easier to face them early on, when little code depends on them, rather than later on.

I think there's a distinction in exploratory programming between code with known inputs & outputs but unknown data structures & algorithms, vs. code with known algorithms but unknown inputs and outputs.

The former encompasses situations like contract development, signal processing, control & embedded systems, compilers, maintenance programming, etc. I've found that strictly typed languages like Haskell, Ada, or Rust can be very helpful here, even for exploratory programming. In this situation, you know (and have little control over) the data that goes into your program and the requirements that it must meet, and if it doesn't do so, it's because you made a mistake somewhere.

But many types of exploratory programming involve unknown inputs or unknown outputs. Think of data mining or scientific computing, where the issue is often that you have unclean data and your task is to figure out how it's messed up and how to fix it as quickly as possible. Or UI software (both web and mobile) and tech entrepreneurship, where the big unknown is often how the user will react to the software and where they will find value in it. I've found that dynamic languages are great for this, because the whole point is you don't know what the data will look like, let alone how it will be structured.

It was interesting to me that the two alternatives you mentioned were "write lots of little tests" or "let the type system guide what needs to be fixed". For a lot of the exploratory programming I've been doing lately, the most efficient approach is just "let it break". Go make your changes, and if some part of the system doesn't work anymore, just take note of that. Because the act of letting things break gives you important information. If you don't care, then obviously that feature wasn't very important to the system to begin with, and you should rip it out entirely. If you do care, you should fix it, and then write some tests to make sure it doesn't break again. This way, you can build a rough rank ordering of how important features are, so that when you're faced with engineering trade-offs (and you always are), you have a solid, experience-based intuition about what you really need to support and what you can let slide.

To some extent, "let it break" is not all that different from "let the type system guide what needs to be fixed." The difference is in what's breaking. The type-driven approach is basically "let the type coherence of the program break." In other words, compile the program, knowing fully well that it won't successfully compile. You see exactly where the types break down, and you can then figure out what needs to be changed.

With all that said, I like your distinction at the beginning of your comment a lot. A lot of my recent work has focused very much on a problem domain where the input data is very well understood, but the transformations applied to that data are complex and not all that well understood. I've found that Haskell has worked extremely well for me in this case, but I think you make a compelling point that it may not work quite so well in cases where the input data is the unknown, but the transformations are fairly well understood already. Perhaps this is why languages like Python have dominated the machine learning space, and strictly-typed languages just never really took off there.

The advantage of "let it break" is that you can continue to run the portion of the program that remains coherent. It's letting the user (who is often just yourself, or a small group of beta testers) into the system; if the user continues to find value in the program even when it's broken, the part that's broken must not have been that important. It's not unusual for programmers to overspecify the requirements when the requirements are unknown; you have to make a number of (often arbitrary) decisions just to get a program working end-to-end, and there's no guarantee that those decisions align with how a user will get the most value from the system.

>The advantage of "let it break" is that you can continue to run the portion of the program that remains coherent.

This works fine in Haskell, thanks in part to laziness. There needn't be a tradeoff here.

Even if it fails to typecheck? Has the Haskell compiler gotten smart enough that it can stub out portions of the code with type errors and fail at runtime?

Actually yes, you can pass a compiler flag, -fdefer-type-errors, which makes type errors turn from compilation errors to simply warnings. That said, any code that does not type check will also not run and instead will throw an exception at runtime if called (laziness is key here). But if Part B was giving type errors and you only cared about testing Part A, then you are good to go.

Yes. You can defer type errors. I turn this on in development.

Yep. It's easier to implement and makes more sense in a lazy language than what you're describing though. I don't use it in my work but I do use a related technique to isolate what I am focused on fixing.

To elaborate a little on this:

In a strict language, a type error would mean the bricked off expression that doesn't type-check would spread like an infection to any expression that incorporated it.

In Haskell, I can dance around sub-expressions that don't type-check at runtime and evaluate things optionally.

The technique doesn't work in practice otherwise.

> The technique doesn't work in practice otherwise.

I don't see why. Functions in a strict language are evaluated lazily. Any type error within a function wouldn't spread beyond that function.

Values still wouldn't work. You'd have to change their types to `() -> whatever` and that means a lot of churn.

Even strict-by-default languages that let you easily make values explicitly lazy would mean type level churn, if not term level.

The value part is important because it means you can't really talk about partially implemented hypothetical results where you know how to handle one branch of a function, but not another. This is a super common divide-and-conquer technique in Haskell.

Could possibly half-ass it if you have some kind of panic functionality, but it'll fail unconditionally in a strict language when the result of the function is forced. In Haskell, if you don't use the bottom, it doesn't blow up.

And the disadvantage of that is that you possibly don't even know what's broken! It will come down entirely to how vigilant you've been with testing. Your argument is that if it's broken but you don't notice, then it must not be all that important. But I think there are tons of exceptions to that, and that's putting a huge amount of faith in yourself and beta testers to reliably test all important aspects of the software. Besides, if you do more or less figure out what's broken and what's not important, you still have to engage in a flimsy surgery of the codebase to excise the broken pieces.

I'm not arguing universally against what you're proposing by any means, but it doesn't seem like a clearly superior approach to me. The bottom line is that "let it break" takes many forms. There's your version, there's my type coherence version, there's Erlang's version with supervisor trees, etc. I think it's hard to argue that any of these is universally superior to the others.

It's superior for a certain problem domain, one in which the desired feature set is unknown, the desired feature set is simple, and the consequences of failure are limited. Consumer web & mobile startups, basically, as well as some limited small-business B2B software where the alternative is an error-prone manual process. It's probably not appropriate for enterprise software, and is certainly not appropriate for embedded, regulated, or safety-critical software.

This is far from the whole software industry, but it's a segment that tends to be of disproportionate interest to HN readers, given the startup-focused origins of this site.

I spent the last few years in scheme, caml, haskell, abstract algebra .. one day I remembered that I used to spend hours toying with raw PHP4 just saw I could see "live HTML", it was so exciting.[1] There was nothing of intellectucal value in it, it was just a superb toy. No amount of pedagogy can make haskell shine this way for a newcomer.

[1] I somehow forgot that I enjoyed it a lot and loved that thing that I'd avoid like plague a few years later (I don't care about it now, for or against)

"The best camera is the one you have with you" is a good adage. For prototyping a language needs to feel effortless so you can deal with the essential complexity of the problem and not hesitate to jump in and experiment.

For implementation, it's whatever tool best fits the job.

C is a good compromise, but it requires real discipline. The CPython and Linux code bases are great examples of how C can be wielded well in practice. But of course C also famously offers unlimited opportunities to make a complete hash of things. I certainly wouldn't recommend it to anyone as their first and only language.

An alternative approach is to be bilingual. For my current project the final code is in C++98 - sometimes there's no good alternative. But I do most of my experimentation, tooling and prototyping in Python. Python's excellent C/C++ interoperability is a huge benefit in this.

I definitely agree with the article that you shouldn't avoid hard things that improve you. But in choosing between ADA and C, the right answer is both and neither.

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