# Prolonging Temporaries in Rust

A colleague of mine learning Rust had an interesting type / borrow checker error. The solution needs a less-used feature of Rust (which basically exists precisely for this kind of thing), so I thought I’d document it.

The code was like this:

If you want to follow along, here is a full program that does this (playpen):

I’m only going to be changing the contents of main() here.

What’s happening here is that a non-Copy type, Foo, is returned in an Option. In one case, we have a reference to the Foo, and in another case an owned copy.

We want to set a variable to these, but of course we can’t because they’re different types.

In one case, we have an owned Foo, and we can usually obtain a borrow from an owned type. For Option, there’s a convenience method .as_ref() that does this1. Let’s try using that (playpen):

This will give us an error.

The problem is, thing.get_owned() returns an owned value. There’s nothing that it gets anchored to (we don’t set its value to a variable), so it is just a temporary – we can call methods on it, but once we’re done the value will go out of scope.

What we want is something like

but this will still give a borrow error – owned will still go out of scope within the if block, and we need the reference to it last as long as maybe_foo (outside the block) is supposed to last.

So this is no good.

An alternate solution here can be copying/cloning the Foo in the first case by calling .map(|x| x.clone()) or .cloned() or something. Sometimes you don’t want to clone, so this isn’t great.

Another solution here – the generic advice for dealing with values which may be owned or borrow – is to use Cow. It does incur a runtime check, though; one which can be optimized out if things are inlined enough.

What we need to do here is to extend the lifetime of the temporary returned by thing.get_owned(). We need to extend it past the scope of the if.

One way to do this is to have an Option outside that scope which we mutate (playpen).

This works in this case, but in this case we already had an Option. If get_ref() and get_owned() returned &Foo and Foo respectively, then we’d need to do something like:

which is icky since it introduces an unwrap.

What we really need is a way to signal to the compiler that it needs to hold on to that temporary for the scope of the enclosing block.

We can do that! (playpen)

We know that Rust doesn’t do “uninitialized” variables. If you want to name a variable, you have to initialize it. let foo; feels rather like magic in this context, because it looks like we’ve declared an uninitialized variable.

What’s less well known is that Rust can do “deferred” initialization. Here, you declare a variable and can initialize it later, but expressions involving the variable can only exist in branches where the compiler knows it has been initialized.

This is the case here. We declared the owned variable beforehand. It now lives in the outer scope and won’t be destroyed until the end of the outer scope. However, the variable cannot be used directly in an expression in the first branch, or after the if. Doing so will give a compile time error saying use of possibly uninitialized variable: owned. We can only use it in the else branch because the compiler can see that it is unconditionally initialized in that branch.

We can still read the value of owned indirectly through maybe_foo from outside the branch. This is okay because the storage of owned is guaranteed to live as long as the outer scope, and maybe_foo borrows from it. The only time maybe_foo is set to a value inside owned is when owned has been initialized, so it is safe.

1. In my experience .as_ref() is the solution to many, many borrow check issues newcomers come across, especially those involving .map()

# You’re Doing It Wrong

“You’re doing it wrong”

A common refrain in issue trackers and discussion forums everywhere. In isolation, it’s a variant of RTFM – give a non-answer when someone wants help, and bounce them back to a manual or docs which they probably have already read. Not very helpful, and not useful to anyone. Of course, one can accompany it with a nice explanation of how to do it right; “You’re doing it wrong” isn’t always a bad thing :)

Especially when it comes to programming languages, but in general in the context of any programming tool or library, “you’re doing it wrong” is almost always due to a “bad” mental model. The person, whilst learning, has built a mental model of how the tool works, but this doesn’t accurately reflect reality. Other times, it does reflect reality, but it does not reflect the mental model of the maintainers (there can be multiple valid ways of looking at something!), which leads to an impedance mismatch when reading docs or error messages.

In other cases, “doing it wrong” is a case of the XY problem, where the user has problem X, and think they can solve it with solution Y, and end up asking how they can achieve Y. This happens pretty often — folks may be approaching your technology with prior experience with related things that work differently, and may think the same idioms apply.

When I was at WONTFIX, someone who had done support work in the past mentioned that one thing everyone learns in support is “the user is always wrong …. and it’s not their fault!”.

This is a pretty good template for an attitude to approach “doing it wrong” questions about your technology on online forums as well. And this doesn’t just benefit the users who ask questions, this attitude can benefit your technology!

Back when I used to be more active contributing to the Rust compiler, I also used to hang out in #rust a lot, and often answer newbie questions (now #rust-beginners exists too, and I hang out in both, but I don’t really actively participate as much). One thing I learned to do was probe deeper into why people hit that confusion in the first place. It’s almost always a “bad” mental model. Rust is rarely the first programming language folks learn, and people approach it with preconceptions about how programming works. This isn’t unique to Rust, this happens any time someone learns a language with a different paradigm — learning C or C++ after doing a GCd language, learning a functional language after an imperative one, statically typed after dynamic, or one of the many other axes by which programming languages differ.

Other times, it’s just assumptions they made when reading between the lines of whatever resource they used to learn the language.

So, anyway, folks often have a “bad” mental model. If we are able to identify that model and correct it, we have saved that person from potentially getting confused at every step in the future. Great!

With a tiny bit more effort, however, we can do one step better. Not for that person, but for ourselves! We can probe a bit more and try to understand what caused them to obtain that mental model. And fix the docs so that it never happens again! Of course, not everyone reads the docs, but that’s what diagnostics are for (in the case of errors). They’re a tool to help us nudge the user towards the right mental model, whilst helping them fix their immediate problem. Rust has for a long time had pretty great diagnostics, with improvements happening all the time1. I think this is at least in part due to the attitude of the folks in #rust; always trying to figure out how to preempt various confusions they see.

It’s a good attitude to have. I hope more folks, both in and out of the Rust community, approach “You’re doing it wrong” cases like that.

1. Diagnostics issues are often the easiest way to contribute to the compiler itself, so if you want to contribute, I suggest starting there. Willing to mentor!

# I Never Hear the Phrase ‘INHTPAMA’ Anymore

Imagine never hearing the phrase ‘INHTPAMA’ again.

Oh, that’s already the case? Bummer.

Often, when talking about Rust, folks refer to the core aliasing rule as “that &mut thing”, “compile-time RWLock” (or “compile-time RefCell”), or something similar. Basically, referring to the fact that you can’t mutate the data that is currently held via an & reference, and that you can’t mutate or read the data currently held via an &mut reference except through that reference itself.

It’s always bugged me that we really don’t have a name for this thing. It’s one of the core bits of Rust, and crops up often in discussions.

But we did have a name for it! It was “INHTPAMA” (which was later butchered into “INHTWAMA”).

This is a reference to Niko’s 2012 blog post, titled “Imagine Never Hearing The Phrase ‘aliasable, mutable’ again”. It’s where the aliasing rules came from. Go read it, it’s great. It talks about this weird language with at symbols and purity, but I assure you, that language is Baby Rust. Or maybe Teenage Rust. The lifecycle of rusts is complex and interesting and I don’t know how to categorize it.

The point of this post isn’t really to encourage reviving the use of “INHTWAMA”; it’s a rather weird acronym that will probably confuse folks. I would like to have a better way of refering to “that &mut thing”, but I’d prefer if it wasn’t a confusing acronym that carries no meaning of its own if you don’t know the history of it. That’s a recipe for making new community members feel like outsiders.

But that post is amazing and I’d hate to see it drop out of the collective memory of the Rust community.

# Use Signal. Use Tor.

I went to send a missive today
As I have done so oft before
But I forgot to employ that scrap of advice
“Use Signal. Use Tor.”

Intercepted of course the missive was
By a ferocious beast of lore
Because I failed to use that bit of advice
“Use Signal. Use Tor.”

The beast was strong; and formidable
He hated the amendments four
I should have remembered that piece of advice
“Use Signal. Use Tor.”

I tried to reason with the beast
but he only wanted war
Do not neglect that important advice
“Use Signal. Use Tor.”

Here I lie in the belly of the beast
I shall discount this advice no more
If I ever manage to leave this place
I’ll use Signal, and Tor.

Heed this advice, children.
It’s not something to ignore
Always, always, always, always
Use Signal. Use Tor.

# Why Quantum Computing Is Weird

I’ve been meaning to write about physics for a while. When I started this blog the intention was to write about a wide variety of interests, but I ended up focusing on programming, despite the fact that I was doing more physics than programming for most of the lifetime of this blog. Time to change that, and hopefully write about other non-programming topics too.

Quantum Computing. It’s the new hip thing that’s going to change the world1. Someday.

In it’s essence, where classical computing deals with “bits”, which are on/off states, quantum computing deals with “qubits”, which are probabalistic quantum states that are often a mixture of on and off. These have interesting properties which make certain kinds of so-far-hard computation very easy to perform.

The goal of this post is not to teach quantum computing, rather to garner interest. I come to praise quantum computing, not bury it2. As a result, this post doesn’t require a background in physics. Having worked with very simple logic circuits is probably enough, though you may not even need that.

I’m basically going to sketch out an example of a very simple quantum algorithm. One that’s very logic-defying. It’s even logic-defying for many who have studied quantum mechanics; it certainly was for me. When I learned this first I could understand why it worked but there was a lot of dissonance between that and my intuitive conviction that it was wrong.

## The algorithm

This is a quantum circuit (specifically, the circuit for the Deutsch-Jozsa algorithm). It’s used to find out the nature of a black-box function f(x), which takes in one qubit and outputs another3. For now, you can try to interpret this circuit as if it were a regular logic circuit. You’ll soon see that this interpretation is wrong, but it’s useful for the purposes of this explanation.

To run this algorithm, you first construct an “oracle” out of the black-box function. The oracle, given inputs x and y, has outputs x and y ⊕ f(x) (where ⊕ is the symbol for XOR, the “exclusive OR”).

As with logic circuits, data flow here goes from left to right. This circuit has two constant inputs, a zero and a one. This is similar to how we might have constant “true” and “false” inputs to logic circuits.

They are then passed through “Hadamard gates”. These are like NOT gates, in that applying them twice is a no-op (they are their own inverse), but they’re not actually NOT gates. I like to describe them as “sideways NOT gates” since that description somewhat intuitively captures what’s going on with the qubits. What’s important to note here is that they have one input and one output, so they’re unaffected by the goings-on in a different wire.

Once these inputs have been Hadamard’ed, they are fed to the oracle we constructed. The top input goes on to become the top output. It’s also passed through f(x) and XORd with the bottom input to make the bottom output.

The top output is then Hadamard’ed again, and finally we observe its value.

Here’s where the magic comes in. By observing the top output, we will know the nature of f(x)4.

Wait, what? The top output doesn’t appear to have any interaction with f(x) at all! How can that work?

In fact, we could try to rewrite this circuit such that the measured output definitely has no interaction with f(x) whatever, assuming that the Hadamard gate isn’t doing anything funky5 (it isn’t):

How in the world does this work?

## Why it works

Sadly, I can’t give a satisfying explanation to exactly why this works. This requires some quantum mechanics background6 to grasp.

However, I can give a hopefully-satisfying explanation as to why our regular intuition doesn’t work here.

First and foremost: The rewritten circuit I showed above? It’s wrong. If this was a logic circuit, we could always do that, but in quantum computing, T-junctions like the following can’t exist:

This is due to the “No Cloning theorem”. Unlike regular logic circuits, you can’t just “duplicate” a qubit. In some cases (like this one), you can try to create a similar qubit via the same process (e.g. here we could take another 0 and pass it through a Hadamard gate), but it’s not the “same” qubit. Unlike bits, qubits have a stronger notion of unique identity.

And it’s this sense of identity that fuels this algorithm (and most of quantum computing).

You see, while the top output of the oracle was x, it wasn’t exactly the same x. This x had been mixed with the lower output. This means that the upper and lower outputs are now entangled, with their state depending on each other. In fact, it’s really misleading to show the output as two wires in the first place – it’s really a single “entangled” state of two qubits that can’t be decomposed as a “top half” and a “bottom half”. Of course, this way of representing quantum circuits is still used because it’s a tidy way of visualizing these circuits, and physicists are aware of the caveats involved.

So what happens is that when you observe the top output, you are really doing a partial observation on the combined state of the two outputs, and this includes some information about f(x), which leaks out when you perform the observation.

These properties of qubits make quantum circuits work significantly differently from regular logic ones. On one hand, this severely restricts what you can do with them, but at the same time, new avenues of erstwhile-impossible operations open up. Most useful quantum algorithms (like Shor’s factorization algorithm) involve a mixture of a classical algorithm and a quantum circuit due to this reason. It’s pretty cool!

1. What isn’t?

2. The abstruseness of physics lives after it; the coolness is oft interred with its bones.

3. This actually can be generalized to a function with n input and n output qubits, and the circuit stays mostly the same, except the top “x” line becomes n lines all initialized to 0 and passing through n parallel H gates.

4. Specifically, if the observation is 1, the function is a constant, whereas if the observation is 0, the function is “balanced” (gives a different output for inputs 1 and 0)

5. For Hadamard is an honorable gate. So are they all, all honorable gates.

6. If you do have this background, it’s relatively straightforward; the Wikipedia page has the equations for it.

# Understanding Git Filter-branch and the Git Storage Model

The other day Steve wanted git alchemy done on the Rust repo.

Specifically, he wanted the reference and nomicon moved out into their own repositories, preserving history. Both situations had some interesting quirks, the reference has lived in src/doc/reference/* and src/doc/reference.md, and the nomicon has lived in src/doc/nomicon, src/doc/tarpl, and at the top level in a separate git root.

As you can guess from the title of this post, the correct tool for this job is git filter-branch. My colleague Greg calls it “the swiss-army knife of Git history rewriting”.

I had some fun with filter-branch that day, thought I’d finally write an accessible tutorial for it. A lot of folks treat filter-branch like rebase, but it isn’t, and this crucial difference can lead to many false starts. It certainly did for me back when I first learned it.

This kind of ties into the common bit of pedantry about the nature of a commit I keep seeing pop up:

Git commits appear to be diffs, but they’re actually file copies, but they’re actually ACTUALLY diffs.

## So what is a git commit?

Generally we interact with git commits via git show or by looking at commits on a git GUI / web UI. Here, we see diffs. It’s natural to think of a commit as a diff, it’s the model that makes the most sense for the most common ways of interacting with commits. It also makes some sense from an implementation point of view, diffs seem like an efficient way of storing things.

It turns out that the “real” model is not this, it’s actually that each commit is a snapshot of the whole repo state at the time.

But actually, it isn’t, the underlying implementation does make use of deltas in packfiles and some other tricks like copy-on-write forking.

Ultimately, arguing about the “real” mental model is mostly pedantry. There are multiple ways of looking at a commit. The documentation tends to implicitly think of them as “full copies of the entire file tree”, which is where most of the confusion about filter-branch comes from. But often it’s important to picture them as diffs, too.

Understanding the implementation can be helpful, especially when you break the repository whilst doing crazy things (I do this often). I’ve explained how it works in a later section, it’s not really a prerequisite for understanding filter-branch, but it’s interesting.

## How do I rewrite history with git rebase?

This is where some of the confusion around filter-branch stems from. Folks have worked with rebase, and they think filter-branch is a generalized version of this. They’re actually quite different.

For those of you who haven’t worked with git rebase, it’s a pretty useful way of rewriting history, and is probably what you should use when you want to rewrite history, especially for maintaining clean git history in an unmerged under-review branch.

Rebase does a whole bunch of things. Its core task is, given the current branch and a branch that you want to “rebase onto”, it will take all commits unique to your branch, and apply them in order to the new one. Here, “apply” means “apply the diff of the commit, attempting to resolve any conflicts”. At times, it may ask you to manually resolve the conflicts, using the same tooling you use for conflicts during git merge.

Rebase is much more powerful than that, though. git rebase -i will open up “interactive rebase”, which will show you the commits that are going to be rebased. In this interface, you can reorder commits, mark them for edits (wherein the rebase will stop at that commit and let you git commit --amend changes into it), and even “squash” commits which lets you mark a commit to be absorbed into the previous one. This is rather useful for when you’re working on a feature and want to keep your commits neat, but also want to make fixup patches to older commits. Filippo’s git fixup alias packages this particular task into a single git command. Changing EDITOR=true into EDITOR=: GIT_SEQUENCE_EDITOR=: will make it not even open the editor for confirmation and try to do the whole thing automatically.

git rebase -x some_command is also pretty neat, lets you run a shell command on each step during a rebase.

In this model, you are fundamentally thinking of commits as diffs. When you move around commits in the interactive rebase editor, you’re moving around diffs. When you mark things for squashing, you’re basically merging diffs. The whole process is about taking a set of diffs and applying them to a different “base commit”.

## How do I rewrite history with git filter-branch?

filter-branch does not work with diffs. You’re working with the “snapshot” model of commits here, where each commit is a snapshot of the tree, and rewriting these commits.

What git filter-branch will do is for each commit in the specified branch, apply filters to the snapshot, and create a new commit. The new commit’s parent will be the filtered version of the old commit’s parent. So it creates a parallel commit DAG.

Because the filters apply on the snapshots instead of the diffs, there’s no chance for this to cause conflicts like in git rebase. In git rebase, if I have one commit that makes changes to a file, and I change the previous commit to just remove the area of the file that was changed, I’d have a conflict and git would ask me to figure out how the changes are supposed to be applied.

In git-filter-branch, if I do this, it will just power through. Unless you explicitly write your filters to refer to previous commits, the new commit is created in isolation, so it doesn’t worry about changes to the previous commits. If you had indeed edited the previous commit, the new commit will appear to undo those changes and apply its own on top of that.

filter-branch is generally for operations you want to apply pervasively to a repository. If you just want to tweak a few commits, it won’t work, since future commits will appear to undo your changes. git rebase is for when you want to tweak a few commits.

So, how do you use it?

The basic syntax is git filter-branch <filters> branch_name. You can use HEAD or @ to refer to the current branch instead of explicitly typing branch_name.

A very simple and useful filter is the subdirectory filter. It makes a given subdirectory the repository root. You use it via git filter-branch --subdirectory-filter name_of_subdir @. This is useful for extracting the history of a folder into its own repository.

Another useful filter is the tree filter, you can use it to do things like moving around, creating, or removing files. For example, if you want to move README.md to README in the entire history, you’d do something like git filter-branch --tree-filter 'mv README.md README' @ (you can also achieve this much faster with some manual work and rebase). The tree filter will work by checking out each commit (in a separate temporary folder), running your filter on the working directory, adding any changes to the index (no need to git add yourself), and committing the new index.

The --prune-empty argument is useful here, as it removes commits which are now empty due to the rewrite.

Because it is checking out each commit, this filter is quite slow. When I initially was trying to do Steve’s task on the rust repo, I wrote a long tree filter and it was taking forever.

The faster version is the index filter. However, this is a bit trickier to work with (which is why I tend to use a tree filter if I can get away with it). What this does is operate on the index, directly.

The “index” is basically where things go when you git add them. Running git add will create temporary objects for the added file, and modify the WIP index (directory tree) to include a reference to the new file or change an existing file reference to the new one. When you commit, this index is packaged up into a commit and stored as an object. (More on how these objects work in a later section)

Now, since this deals with files that are already stored as objects, git doesn’t need to unwrap these objects and create a working directory to operate on them. So, with --index-filter, you can operate on these in a much faster way. However, since you don’t have a working directory, stuff like adding and moving files can be trickier. You often have to use git update-index to make this work.

However, a useful index filter is one which just scrubs a file (or files) from history:

The --ignore-unmatch makes the command still succeed if the file doesn’t exist. filter-branch will fail if one of the filters fails. In general I tend to write fallible filters like command1 1>&2 2>/dev/null ; command2 1>&2 2>/dev/null ; true, which makes it always succeed and also ignores any stdout/stderr output (which tends to make the progress screen fill up fast).

The --cached argument on git rm makes it operate only on the index, not the working directory. This is great, because we don’t have a working directory right now.

I rarely use git update-index so I’m not really going to try and explain how it can be used here. But if you need to do more complex operations in an index filter, that’s the way to go.

There are many other filters, like --commit-filter (lets you discard a commit entirely), --msg-filter (rewriting commit messages), and --env-filter (changing things like author metadata or other env vars). You can see a complete list with examples in the docs

## How did I perform the rewrites on the reference and nomicon?

For the Rust Reference, basically I had to extract the history of src/doc/reference.md, AND src/doc/reference/* (reference.md was split up into reference/*.md recently) into its own commit. This is an easy tree filter to write, but tree filters take forever.

Instead of trying my luck with an index filter, I decided to just make it so that the tree filter would be faster. I first extracted src/doc/:

Now I had a branch that contained only the history of src/doc, with the root directory moved to doc. This is a much smaller repo than the entirety of Rust.

Now, I moved reference.md into reference/:

As mentioned before, the /dev/null and true bits are because the mv command will fail in some cases (when reference.md doesn’t exist), and I want it to just continue without complaining when that happens. I only care about moving instances of that file, if that file doesn’t exist there it’s still okay.

Now, everything I cared about was within reference. The next step was simple:

The whole process took maybe 10 minutes to run, most of the time being spent by the second command. The final result can be found here.

For the nomicon, the task was easier. In the case of the nomicon, it has always resided in src/doc/nomicon, src/doc/tarpl, or at the root. This last bit is interesting, when Alexis was working on the nomicon, he started off by hacking on it in a separate repo, but then within that repo moved it to src/doc/tarpl, and performed a merge commit with rustc. There’s no inherent restriction in Git that all merges must have a common ancestor, and you can do stuff like this. I was quite surprised when I saw this, since it’s pretty uncommon in general, but really, many projects of that size will have stuff like this. Servo and html5ever do too, and usually it’s when a large project is merged into it after being developed on the side.

This sounds complicated to work with, but it wasn’t that hard. I took the same subdirectory-filtere’d doc directory branch used for the reference. Then, I renamed tarpl/ to nomicon/ via a tree filter, and ran another subdirectory filter:

Now, I had the whole history of the nomicon in the root dir. Except for the commits made by Alexis before his frankenmerge, because these got removed in the first subdirectory filter (the commits were operating outside of src/doc, even though their contents eventually got moved there).

But, at this stage, I already had a branch with the nomicon at the root. Alexis’ original commits were also operating on the root directory. I can just rebase here, and the diffs of my commits will cleanly apply!

I found the commit (a54e64) where everything was moved to tarpl/, and took its parent (c7919f). Then, I just ran git rebase --root c7919f, and everything cleanly rebased. As expected, because I had a history going back to the first child of a54e64 with files moved, and a54e64 itself only moved files, so the diffs should cleanly apply.

The final result can be found here.

## Appendix: How are commits actually stored?

The way the actual implementation of a commit works is that each file being stored is hashed and stored in a compressed format, indexed by the hash. A directory (“tree”) will be a list of hashes, one for each file/directory inside it, alongside the filenames and other metadata. This list will be hashed and used everywhere else to refer to the directory.

A commit will reference the “tree” object for the root directory via its hash.

Now, if you make a commit changing some files, most of the files will be unchanged. So will most of the directories. So the commits can share the objects for the unchanged files/directories, reducing their size. This is basically a copy-on-write model. Furthermore, there’s a second optimization called a “packfile”, wherein instead of storing a file git will store a delta (a diff) and a reference to the file the diff must be applied to.

We can see this at work using git cat-file. cat-file lets you view objects in the “git filesystem”, which is basically a bunch of hash-indexed objects stored in .git/objects. You can view them directly by traversing that directory (they’re organized as a trie), but cat-file -p will let you pretty-print their contents since they’re stored in a binary format.

I’m working with the repo for the Rust Book, playing with commit 4822f2. It’s a commit that changes just one file (second-edition/src/ch15-01-box.md), perfect.

This tells us that the commit is a thing with some author information, a pointer to a parent, a commit message, and a “tree”. What’s this tree?

This is just a directory! You can see that each entry has a hash. We can use git cat-file -p to view each one. Looking at a tree object will just give us a subdirectory, but the blobs will show us actual files!

So how does this share objects? Let’s look at the previous commit:

If you look closely, all of these hashes are the same, except for the hash for second-edition. For the hashes which are the same, these objects are being shared across commits. The differing hash is d5672d in the newer commit, and d48b2e in the older one.

Let’s look at the objects:

Again, these are the same, except for that of src. src has a lot of files in it, which will clutter this post, so I’ll run a diff on the outputs of cat-file:

\$ diff -U5 <(g cat-file -p f9fc05a6ff78b8211f4df931ed5e32c937aba66c) <(g cat-file -p 3f8db396566716299330cdd5f569fb0a0c4615dd)
--- /dev/fd/63  2017-03-05 11:58:22.000000000 -0800
+++ /dev/fd/62  2017-03-05 11:58:22.000000000 -0800
@@ -63,11 +63,11 @@
100644 blob ff6b8f8cd44f624e1239c47edda59560cdf491ae   ch14-02-publishing-to-crates-io.md
100644 blob c53ef854a74b6c9fbd915be1bf824c6e78439c42   ch14-03-cargo-workspaces.md
100644 blob 3fb59f9cc85b6b81994e83a34d542871a260a8f0   ch14-04-installing-binaries.md
100644 blob e1cd1ca779fdf202af433108a8af6eda317f2717   ch14-05-extending-cargo.md
100644 blob 3173cc508484cc447ebe42a024eac7d9e6c2ddcd   ch15-00-smart-pointers.md
-100644 blob 14c5533bb3b604c6e6274db278d1e7129f78d55d   ch15-01-box.md
+100644 blob 29d87933d6832374b87d98aa5588e09e0c1a4991   ch15-01-box.md
100644 blob 47b35ed489d63ce6a885289fec01b7b16ba1afea   ch15-02-deref.md
100644 blob 2d20c55cc8605c0c899bc4867adc6b6ea1f5c902   ch15-03-drop.md
100644 blob 8e3fcf4e83fe1ce985a7c0b479b8b16701765aaf   ch15-04-rc.md
100644 blob a4ade4ae8bf5296d79ed51d69506e71a83f9f489   ch15-05-interior-mutability.md
100644 blob 3a4db5616c4f5baeb95d04ea40c6747e60181684   ch15-06-reference-cycles.md


As you can see, only the file that was changed in the commit has a new blob stored. If you view 14c553 and 29d879 you’ll get the pre- and post- commit versions of the file respectively.

So basically, each commit stores a tree of references to objects, often sharing nodes with other commits.

I haven’t had the opportunity to work with packfiles much, but they’re an additional optimization on top of this. Aditya’s post is a good intro to these.

# What Are Sum, Product, and Pi Types?

See also: Tony’s post on the same topic

You often hear people saying “Language X1 has sum types” or “I wish language X had sum types”2, or “Sum types are cool”.

Much like fezzes and bow ties, sum types are indeed cool.

These days, I’ve also seen people asking about “Pi types”, because of this Rust RFC.

But what does “sum type” mean? And why is it called that? And what, in the name of sanity, is a Pi type?

Before I start, I’ll mention that while I will be covering some type theory to explain the names “sum” and “product”, you don’t need to understand these names to use these things! Far too often do people have trouble understanding relatively straightforward concepts in languages because they have confusing names with confusing mathematical backgrounds3.

## So what’s a sum type? (the no-type-theory version)

In it’s essence, a sum type is basically an “or” type. Let’s first look at structs.

Foo is a bool AND a String. You need one of each to make one. This is an “and” type, or a “product” type (I’ll explain the name later).

So what would an “or” type be? It would be one where the value can be a bool OR a String. You can achieve this with C++ with a union:

However, this isn’t exactly right, since the value doesn’t store the information of which variant it is. You could store false and the reader wouldn’t know if you had stored an empty string or a false bool.

There’s a pattern called “tagged union” (or “discriminated union”) in C++ which bridges this gap.

Here, you manually set the tag when setting the value. C++ also has std::variant (or boost::variant) that encapsulates this pattern with a better API.

While I’m calling these “or” types here, the technical term for such types is “sum” types. Other languages have built-in sum types.

Rust has them and calls them “enums”. These are a more generalized version of the enums you see in other languages.

Swift is similar, and also calls them enums

You can fake these in Go using interfaces, as well. Typescript has built-in unions which can be typechecked without any special effort, but you need to add a tag (like in C++) to pattern match on them.

Of course, Haskell has them:

One of the very common things that languages with sum types do is express nullability as a sum type;

Generally, these languages have “pattern matching”, which is like a switch statement on steroids. It lets you match on and destructure all kinds of things, sum types being one of them. Usually, these are “exhaustive”, which means that you are forced to handle all possible cases. In Rust, if you remove that None branch, the program won’t compile. So you’re forced to deal with the none case, somehow.

In general sum types are a pretty neat and powerful tool. Languages with them built-in tend to make heavy use of them, almost as much as they use structs.

## Why do we call it a sum type?

Here be (type theory) dragons

Let’s step back a bit and figure out what a type is.

It’s really a restriction on the values allowed. It can have things like methods and whatnot dangling off it, but that’s not so important here.

In other words, it’s like4 a set. A boolean is the set $$\{\mathtt{true}, \mathtt{false}\}$$. An 8-bit unsigned integer (u8 in Rust) is the set $$\{0, 1, 2, 3, …. 254, 255\}$$. A string is a set with infinite elements, containing all possible valid strings5.

What’s a struct? A struct with two fields contains every possible combination of elements from the two sets.

The set of possible values of Foo is

$\{(\mathtt{x}, \mathtt{y}): \mathtt{x} \in \mathtt{bool}, \mathtt y \in \mathtt{u8}\}$

(Read as “The set of all $$(\mathtt{x}, \mathtt{y})$$ where $$\tt x$$ is in $$\mathtt{bool}$$ and $$\tt y$$ is in $$\mathtt{u8}$$”)

This is called a Cartesian product, and is often represented as $$\tt Foo = bool \times u8$$. An easy way to view this as a product is to count the possible values: The number of possible values of Foo is the number of possible values of bool (2) times the number of possible values of u8 (256).

A general struct would be a “product” of the types of each field, so something like

is $$\mathtt{Bar = bool \times u8 \times bool \times String}$$

This is why structs are called “product types”6.

You can probably guess what comes next – Rust/Swift enums are “sum types”, because they are the sum of the two sets.

is a set of all values which are valid booleans, and all values which are valid integers. This is a sum of sets, $$\tt Foo = bool + u8$$. More accurately, it’s a disjoint union, where if the input sets have overlap, the overlap is “discriminated” out.

An example of this being a disjoint union is:

This is not $$\tt Bar = bool + bool + u8$$, because $$\tt bool + bool = bool$$, (regular set addition doesn’t duplicate the overlap).

Instead, it’s something like

$\tt Bar = bool + otherbool + u8$

where $$\tt otherbool$$ is also a set $$\tt \{true, false\}$$, except that these elements are different from those in $$\tt bool$$. You can look at it as if

$\tt otherbool = \{true_2, false_2\}$

so that

$\mathtt{bool + otherbool} = \{\mathtt{true, false, true_2, false_2}\}$

For sum types, the number of possible values is the sum of the number of possible values of each of its component types.

So, Rust/Swift enums are “sum types”.

You may often notice the terminology “algebraic datatypes” (ADT) being used, usually that’s just talking about sum and product types together – a language with ADTs will have both.

In fact, you can even have exponential types! The notation AB in set theory does mean something, it’s the set of all possible mappings from $$B$$ to $$A$$. The number of elements is $$N_A^{N_B}$$. So basically, the type of a function (which is a mapping) is an “exponential” type. You can also view it as an iterated product type, a function from type B to A is really a struct like this:

given a value of the input b, the function will find the right field of my_func and return the mapping. Since a struct is a product type, this is

$\mathtt{A}^{N_\mathtt{B}} = \tt A \times A \times A \times \dots$

making it an exponential type.

You can even take derivatives of types! (h/t Sam Tobin-Hochstadt for pointing this out to me)

## What, in the name of sanity, is a Pi type?

It’s essentially a form of dependent type. A dependent type is when your type can depend on a value. An example of this is integer generics, where you can do things like Array<bool, 5>, or template<unsigned int N, typename T> Array<T, N> ... (in C++).

Note that the type signature contains a type dependent on an integer, being generic over multiple different array lengths.

The name comes from how a constructor for these types would look:

What’s the type of make_array here? It’s a function which can accept any integer and return a different type in each case. You can view it as a set of functions, where each function corresponds to a different integer input. It’s basically:

Given an input, the function chooses the right child function here, and calls it.

This is a struct, or a product type! But it’s a product of an infinite number of types7.

We can look at it as

$\texttt{make_array} = \prod\limits_{x = 0}^\infty\left( \texttt{fn()} \mathtt\to \texttt{Array<bool, x>}\right)$

The usage of the $$\Pi$$ symbol to denote an iterative product gives this the name “Pi type”.

In languages with lazy evaluation (like Haskell), there is no difference between having a function that can give you a value, and actually having the value. So, the type of make_array is the type of Array<bool, N> itself in languages with lazy evaluation.

There’s also a notion of a “sigma” type, which is basically

$\sum\limits_{x = 0}^\infty \left(\texttt{fn()} \mathtt\to \texttt{Array<bool, x>}\right)$

With the Pi type, we had “for all N we can construct an array”, with the sigma type we have “there exists some N for which we can construct this array”. As you can expect, this type can be expressed with a possibly-infinite enum, and instances of this type are basically instances of Array<bool, N> for some specific N where the N is only known at runtime. (much like how regular sum types are instances of one amongst multiple types, where the exact type is only known at runtime). Vec<bool> is conceptually similar to the sigma type Array<bool, ?>, as is &[bool].

## Wrapping up

Types are sets, and we can do set-theory things on them to make cooler types.

Let’s try to avoid using confusing terminology, however. If Rust does get “pi types”, let’s just call them “dependent types” or “const generics” :)

Thanks to Zaki, Avi Weinstock, Corey Richardson, and Peter Atashian for reviewing drafts of this post.

1. Rust, Swift, sort of Typescript, and all the functional languages who had it before it was cool.

2. Lookin’ at you, Go.

4. Types are not exactly sets due to some differences, but for the purposes of this post we can think of them like sets.

5. Though you can argue that strings often have their length bounded by the pointer size of the platform, so it’s still a finite set.

6. This even holds for zero-sized types, for more examples, check out this blog post

7. Like with strings, in practice this would probably be bounded by the integer type chosen

# Mitigating Underhandedness: Fuzzing Your Code

This may be part of a collaborative blog post series about underhanded Rust code. Or it may not. I invite you to write your own posts about underhanded code to make it so!

The submission deadline for the Underhanded Rust competition has been extended, so let’s talk more about how to keep your code working and free from bugs/underhandedness!

Now, really, underhanded bugs are just another form of bug. And how do we find bugs? We test!

We write unit tests. We run the code under Valgrind, ASan, MSan, UBSan, TSan, and any other sanitizer we can get our hands on. Tests tests tests. More tests. Tests.

But, there’s a problem here. You need to write test cases to make this work. These are inputs fed to your code after which you check whatever invariants your code has. There’s no guarantee that the test cases you write will exercise all the code paths in your program. This applies for sanitizers too, sanitizers are limited to testing the code paths that your test cases hit.

Of course, you can use code coverage tools to ensure that all these code paths will be hit. However, there’s a conflict here – your code will have many code paths that are not supposed to be hit ever. Things like redundant bounds checks, null checks, etc. In Rust programs such code paths generally use panics.

Now, these code paths are never supposed to be hit, so they’ll never show up in your code coverage. But you don’t have a guarantee that they can never be hit, short of formally verifying your program. The only solution here is writing more test cases.

Aside from that, even ignoring those code paths, you still need to manually write test cases for everything. For each possible code path in your code, if you want to be sure.

Who wants to manually write a million test cases?

Enter fuzzing. What fuzzing will do is feed your program random inputs, carefully watching the codepaths being taken, and try to massage the inputs so that new, interesting (usually crashy) codepaths are taken. You write tests for the fuzzer such that they can accept arbitrary input, and the fuzzer will find cases where they crash or panic.

One of the most popular fuzzers out there is AFL, which takes a binary and feeds it random input. Rust has a library that you can use for running AFL, however it currently needs to be run via a Docker image or needs a recompilation of rustc, since it adds a custom LLVM pass. We’re working on making this step unnecessary.

However, as of a few weeks ago, we now have bindings for libFuzzer, which uses existing instrumentation options built in to LLVM itself! libFuzzer works a bit differently; instead of giving it a binary, you write a function in a special way and give it a library containing that function, which it turns into a fuzzer binary. This is faster, since the fuzzer lives inside the binary itself and it doesn’t need to execute a new program each time.

Using libFuzzer in Rust is easy. Install cargo-fuzz:

Now, within your crate, initialize the fuzz setup:

This will create a fuzzing crate in fuzz/, with a single “fuzz target”, fuzzer_script_1. You can add more such targets with cargo fuzz add name_of_target. Fuzz targets are small libraries with a single function in them; the function that will be called over and over again by the fuzzer. It is up to you to fill in the body of this function, such that the program will crash or panic if and only if something goes wrong.

For example, for the unicode-segmentation crate, one of the fuzz targets I wrote just takes the string, splits it by grapheme and word boundaries, recombines it, and then asserts that the new string is the same.

The other targets ensure that the forward and reverse word/grapheme iterators produce the same results. They all take the byte slice input, attempt to convert to UTF8 (silently failing – NOT panicking – if not possible), and then use the string as an input testcase.

Now, these targets will panic if the test fails, and the fuzzer will try and force that panic to happen. But also, these targets put together exercise most of the API surface of the crate, so the fuzzer may also find panics (or even segmentation faults!) in the crate itself. For example, the fuzz target for rust-url doesn’t itself assert; all it does is try to parse the given string. The fuzzer will try to get the URL parser to panic.

To run a fuzz script:

This will start the fuzzer, running until it finds a crash or panic. It may also find other things like inputs which make the code abnormally slow.

Fuzzing can find some interesting bugs. For example, the unicode-segmentation fuzzers found this bug, where an emoji followed by two skin tone modifiers isn’t handled correctly. We’d probably never have been able to come up with this testcase on our own. But the fuzzer could find it!

The Rust Cap’n Proto crate ran cargo-fuzz and found a whole ton of bugs. There are more such examples in the trophy case (be sure to add any of your own findings to the trophy case, too!)

cargo-fuzz is relatively new, so the API and behavior may still be tweaked a bit before 1.0. But you can start taking it for a spin now, and finding bugs!

# Clarifying Misconceptions About SHAttered

This week Google published a SHA-1 collision.

There’s a lot of confusion about the implications of this. A lot of this is due to differences of opinion on what exactly constitutes a “new” collision. I tweeted about this. The webpage for the attack itself is misleading, saying that the answer to “Who is capable of mounting this attack?” is people with Google-esque resources. This depends on what exactly you mean by “this attack”.

So I’m seeing a lot of “oh well just another anti-milestone for SHA, doesn’t affect anyone since its still quite expensive to exploit” reactions, as well as the opposite “aaaaa everything is on fire” reaction. Both are wrong. It has practical implications for you even if you are certain that you won’t attract the ire of an entity with a lot of computational power. None of these implications, however, are likely to be disastrous.

TLDR: Now anyone, without needing Google-esque resources, can generate two colliding PDFs with arbitrary visual content in each.

(In fact, there’s already a PDF collision-generator up where you can upload two images and get a PDF with collisions in it)

## Okay, back up a bit. What’s a hash? What’s SHA-1?

I explained this a bit in my older post about zero-knowledge-proofs.

In essence, a hash function takes some data (usually of arbitrary size), and produces a value called a hash (usually of fixed size). The function has some additional properties:

• In almost all cases, a small perturbation in the input will lead to a large perturbation in the hash
• Given an input and its hash, it is computationally hard to find an alternate input producing the same hash
• It’s also hard to just find two inputs that has to the same value, though this is usually easier than the previous one

when two inputs hash to the same value, this is called a collision. As mentioned, is easier to find a collision, over finding a colliding alternate input for a known input.

SHA-1 is one such hash function. It’s been known for a while that it’s insecure, and the industry has largely moved off of it, but it’s still used, so it can still be a problem.

## What did the researchers do?

They found a hash collision for SHA-1. In essence, they found two strings, A and B, where SHA1(A) == SHA1(B).

However, given the way SHA-1 works, this means that you can generate infinitely many other such pairs of strings. And given the nature of the exact A and B they created, it is possible to use this to create arbitrary colliding PDFs.

Basically, SHA-1 (and many other hash functions), operate on “blocks”. These are fixed-size chunks of data, where the size is a property of the hash function. For SHA1 this is 512 bits.

The function starts off with an “initial” built-in hash. It takes the first block of your data and this hash, and does some computation with the two to produce a new hash, which is its state after the first block.

It will then take this hash and the second block, and run the same computations to produce a newer hash, which is its state after the second block. This is repeated till all blocks have been processed, and the final state is the result of the function.

There’s an important thing to notice here. At each block, the only inputs are the block itself and the hash of the string up to that block.

This means, if A and B are of a size that is a multiple of the block size, and SHA1(A) == SHA1(B), then SHA1(A + C) == SHA1(B + C). This is because, when the hash function reaches C, the state will be the same due to the hash collision, and after this point the next input blocks are identical in both cases, so the final hash will be the same.

Now, while you might consider A+C, B+C to be the “same collision” as A, B, the implications of this are different than just “there is now one known pair of inputs that collide”, since everyone now has the ability to generate new colliding inputs by appending an arbitrary string to A and B.

Of course, these new collisions have the restriction that the strings will always start with A or B and the suffixes will be identical. If you want to break this restriction, you will have to devote expensive resources to finding a new collision, like Google did.

## How does this let us generate arbitrary colliding PDFs?

So this exploit actually uses features of the JPEG format to work. It was done in a PDF format since JPEGs often get compressed when sent around the Internet. However, since both A and B start a partial PDF document, they can only be used to generate colliding PDFs, not JPEGs.

I’m going to first sketch out a simplified example of what this is doing, using a hypothetical pseudocode-y file format. The researchers found a collision between the strings:

• A: <header data> COMMENT(<nonce for A>) DISPLAY IMAGE 1
• B: <header data> COMMENT(<nonce for B>) DISPLAY IMAGE 2

Here, <header data> is whatever is necessary to make the format work, and the “nonce”s are strings that make A and B have the same hash. Finding these nonces is where the computational power is required, since you basically have to brute-force a solution.

Now, to both these strings, they append a suffix C: IMAGE 1(<data for image 1>) IMAGE 2(<data for image 2>). This creates two complete documents. Both of the documents contain both images, but each one is instructed to display a different one. Note that since SHA1(A) == SHA1(B), SHA1(A + C) = SHA1(B + C), so these final documents have the same hash.

The contents of C don’t affect the collision at all. So, we can insert any two images in C, to create our own personal pair of colliding PDFs.

The actual technique used is similar to this, and it relies on JPEG comment fields. They have found a collision between two strings that look like:

By playing with the nonces, they managed to generate a collision between A and B. In the first pdf, the embedded image has a comment containing only the nonce. Once the JPEG reader gets past that comment, it sees the first image, displays it, and then sees the end-of-file marker and decides to stop. Since the PDF format doesn’t try to interpret the image itself, the PDF format won’t be boggled by the fact that there’s some extra garbage data after the JPEG EOF marker. It simply takes all the data between the “begin embedded image” and “end embedded image” blocks, and passes it to the JPEG decoder. The JPEG decoder itself stops after it sees the end of file marker, and doesn’t get to the extra data for the second image.

In the second pdf, the jpg comment is longer, and subsumes the first image (as well as the EOF marker) Thus, the JPEG decoder directly gets to the second image, which it displays.

Since the actual images are not part of the original collision (A and B), you can substitute any pair of jpeg images there, with some length restrictions.

## What are the implications?

This does mean that you should not trust the integrity of a PDF when all you have to go on is its SHA-1 hash. Use a better hash. Anyone can generate these colliding PDFs now.

Fortunately, since all such PDFs will have the same prefix A or B, you can detect when such a deception is being carried out.

Don’t check colliding PDFs into SVN. Things break.

In some cases it is possible to use the PDF collision in other formats. For example, it can be used to create colliding HTML documents. I think it can be used to colide ZIP files too.

Outside the world of complex file formats, little has changed. It’s still a bad idea to use SHA-1. It’s still possible for people to generate entirely new collisions like Google did, though this needs a lot of resources. It’s possible that someone with resources has already generated such a “universal-key collision” for some other file format1 and will use it on you, but this was equally possible before Google published their attack.

This does not make it easier to collide with arbitrary hashes – if someone else has uploaded a document with a hash, and you trust them to not be playing any tricks, an attacker won’t be able to generate a colliding document for this without immense resources. The attack only works when the attacker has control over the initial document; e.g. in a bait-and-switch-like attack where the attacker uploads document A, you read and verify it and broadcast your trust in document A with hash SHA(A), and then the attacker switches it with document B.

1. Google’s specific collision was designed to be a “universal key”, since A and B are designed to have the image-switching mechanism built into it. Some other collision may not be like this; it could just be a collision of two images (or whatever) with no such switching mechanism. It takes about the same effort to do either of these, however, so if you have a file format that can be exploited to create a switching mechanism, it would always make more sense to build one into any collision you look for.

# Mitigating Underhandedness: Clippy!

This may be part of a collaborative blog post series about underhanded Rust code. Or it may not. I invite you to write your own posts about underhanded code to make it so!

Last month we opened up The Underhanded Rust competition. This contest is about writing seemingly-innocuous malicious code; code that is deliberately written to do some harm, but will pass a typical code review.

It is inspired by the Underhanded C contest. Most of the underhanded C submissions have to do with hidden buffer overflows, pointer arithmetic fails, or misuse of C macros; and these problems largely don’t occur in Rust programs. However, the ability to layer abstractions on each other does open up new avenues to introducing underhandedness by relying on sufficiently confusing abstraction sandwiches. There are probably other interesting avenues. Overall, I’m pretty excited to see what kind of underhandedness folks come up with!

Of course, underhandedness is not just about fun and games; we should be hardening our code against this kind of thing. Even if you trust your fellow programmers. Even if you are the sole programmer and you trust yourself. After all, you can’t spell Trust without Rust; and Rust is indeed about trust. Specifically, Rust is about trusting nobody. Not even yourself.

Rust protects you from your own mistakes when it comes to memory management. But we should be worried about other kinds of mistakes, too. Many of the techniques used in underhanded programming involve sleights of hand that could just as well be introduced in the code by accident, causing bugs. Not memory safety bugs (in Rust), but still, bugs. The existence of these sleights of hand is great for that very common situation when you are feeling severely under-plushied and must win a competition to replenish your supply but we really don’t want these creeping into real-world code, either by accident or intentionally.

Allow me to take a moment out of your busy underhanded-submission-writing schedules to talk to you about our Lord and Savior Clippy.

Clippy is for those of you who have become desensitized to the constant whining of the Rust compiler and need a higher dosage of whininess to be kept on their toes. Clippy is for those perfectionists amongst you who want to know every minute thing wrong with their code so that they can fix it. But really, Clippy is for everyone.

Clippy is simply a large repository of lints. As of the time of writing this post, there are 183 lints in it, though not all of them are enabled by default. These use the regular Rust lint system so you can pick and choose the ones you need via #[allow(lint_name)] and #[warn(lint_name)]. These lints cover a wide range of functions:

• Improving readability of the code (though rustfmt is the main tool you should use for this)
• Helping make the code more compact by reducing unnecessary things (my absolute favorite is needless_lifetimes)
• Helping make the code more idiomatic
• Making sure you don’t do things that you’re not supposed to
• Catching mistakes and cases where the code may not work as expected

The last two really are the ones which help with underhanded code. Just to give an example, we have lints like:

• cmp_nan, which disallows things like x == NaN
• clone_double_ref, which disallows calling .clone() on double-references (&&T), since that’s a straightforward copy and you probably meant to do something like (*x).clone()
• for_loop_over_option: Option<T> is iterable, and while this is useful when composing iterators, directly iterating over an option is usually an indication of a mistake.
• match_same_arms, which checks for identical match arm bodies (strong indication of a typo)
• suspicious_assignment_formatting, which checks for possible typos with the += and -= operators
• unused_io_amount, which ensures that you don’t forget that some I/O APIs may not write all bytes in the span of a single call

These catch many of the gotchas that might crop up in Rust code. In fact, I based my solution of an older, more informal Underhanded Rust contest on one of these.

## Usage

Clippy is still nightly-only. We hook straight into the compiler’s guts to obtain the information we need, and like most internal compiler APIs, this is completely unstable. This does mean that you usually need a latest or near-latest nightly for clippy to work, and there will be times when it won’t compile while we’re working to update it.

There is a plan to ship clippy as an optional component of rustc releases, which will fix all of these issues (yay!).

But, for now, you can use clippy via:

If you’re going to be making it part of the development procedures of a crate you maintain, you can also make it an optional dependency.

If you’re on windows, there’s currently a rustup/cargo bug where you may have to add the rustc libs path in your PATH for cargo clippy to work.

There’s an experimental project called rustfix which can automatically apply suggestions from clippy and rustc to your code. This may help in clippy-izing a large codebase, but it may also eat your code and/or laundry, so beware.

## Contributing

There’s a lot of work that can be done on clippy. A hundred and eighty lints is just a start, there are hundreds more lint ideas filed on the issue tracker. We’re willing to mentor anyone who wants to get involved; and have specially tagged “easy” issues for folks new to compiler internals. In general, contributing to clippy is a great way to gain an understanding of compiler internals if you want to contribute to the compiler itself.

If you don’t want to write code for clippy, you can also run it on random crates, open pull requests with fixes, and file bugs on clippy for any false positives that appear.

There are more tips about contributing in our CONTRIBUTING.md.

I hope this helps reduce mistakes and underhandedness in your code!

..unless you’re writing code for the Underhanded Rust competition. In that case, underhand away!