As part of improving the learnability of Rust, I propose:

Use Cases

Something I've really appreciated about Rust community is taking seemingly opposing principles and finding a way to bring them in harmony, for example:

One I've been contemplating for a while is "Learnability with control". We've made incremental improvements to learnability, for example changes in the 2018 and 2021 editions, but I feel like we've only been working to a local maxima. I appreciate the work boats did in sketching out an ideal which, even if incompatible with other goals in Rust, helps lift our sights past our local maxima. More recently, Niko encouraged us to be bold and creative.

In considering "learnability", we should also keep in mind the related "expert" workflows:

I'll use Python as our point of comparison in exploring these ideals in Rust due to its popularity and my familiarity with it.


Some parts of Python that stick out to me include:

  1. Low friction exploratory programming (e.g. REPL or notebooks)
  2. Low friction to experimenting (e.g. just open a ".py" file)
  3. Low friction discovery of "good enough" packages (i.e. batteries-included)
  4. Reads as pseudo-code
  5. Small, easy to remember surface area (e.g. just use [] instead of a collection of subset/indexing functions)
  6. Design out error cases (e.g. no out of bounds errors on [])

(yes, there are counter-examples)

For Rust, we also want: Scale to control, when needed. We don't want a split ecosystem with a high porting cost between them.

Breaking It Down

MC hammer: break it down

Exploratory programming comes in two parts

For API discoveability, Python has to deal with dynamically generated APIs, which is less of a concern for Rust. Rust does have traits that need to be pulled in and crate features that need to be enabled. For those in an IDE, I'm assuming rust-analyzer handles these cases. For the rest of us, we have rustdoc which has high friction in being adapted to your live code. I'm assuming us non-IDE users are enough of a minority that we don't need to invest in alternatives.

This still leaves us verifying assumptions on behavior of an API. We can invest in a REPL or a Notebook just for this use case but I'm thinking we'll get a "good enough" solution faster by first investing in the next part.

Python gets you quickly experimenting on your ideas by allowing you to have just a .py file. Python doesn't scale too well though, running into problems with relative imports and requiring packages to be installed globally or learning about venvs.

I think we can do better in Rust. We already have cargo-script as a starting point for writing Rust code in a single file, pull in third-party dependencies, and have unit tests. The one additional piece I think is needed is turning a .crs file into a full project with Cargo.toml for those times throw-away code becomes maintained code.

(Note: cargo-script isn't maintained anymore, there are a variety of replacements, like cargo-eval or cargo-play).

This helps us with exploratory programming by being able to quickly write throw-away code. I know cargo-script also has expression evaluation but I do not have experience with that and assume its too limited to help with the REPL case.

Another benefit to adopting cargo-script is it'd make it easy to share reproduction cases in bug reports.

For discovery of packages, Python takes the approach of "batteries-included", meaning nearly everything you need is a part of the stdlib. This comes at the cost of everything having to maintain the same compatibility guarantees, release on the same cadence, and original authors moving on from their package. The community sometimes leaves behind the standard library and you have to be in-the-know of what the replacement is.

With that said, a lot of the community still relies on the stdlib, whether because

In Rust, we've had debates in the past about batteries-included, batteries-adjacent, documentation, or algorithms.

I think batteries-adjacent gets us most of the way there. It ticks almost all the boxes of batteries included while not being limited in its evolution. For being universally available, this is important in python mostly due to the dependency management pain in Python which we have solved with cargo. This just leaves availability from isolated networks which is a minority case (though a big pain for those that do have to live with it).

For breaking changes, we can mitigate this by the batteries-adjacent library being composed of crates, rather than having logic of its own, This makes it easier to narrow down what sections broke compatibility and allows people to depend on an older version of that subset, just adding a dependency and changing their use statements. If we maintain a certain level of friction to the process of crates being added (required level of maturity, RFC process), we are also likely to reduce the number of breaking changes.

I think a lot of Rust is almost there for reading as pseudo-code. At its best, Rust almost feels like Python. Lifetimes and complex where clauses stand out to me as pain points. I think a learning focused standard library-alternative that limits the use of lifetimes would both be easier to grok and help encourage writing code that borderlines on pseudo code. When needing generics, favoring impl would also help to reduce some of the noise, despite its downsides.

For a small surface area, if we take this standard library alternative, and apply Python's "there is only one way to do it" mantra, I think we can reduce quite a few functions at the cost of some runtime performance (for non-optimal choices) and occasional extra work by the developer.

This standard library-alternative could design out errors as we re-examine why different APIs might error or panic and look to generalize it. For example, instead of text[range] panicing on out-of-bounds, we re-define the behavior to return at most the start and end bound.

A sticky question is whether such a library's string type should stay focused on bytes or be focused on chars, avoiding panics when not aligned with character boundaries. Working on chars is safer and more familiar but it will surprise people when a developer uses "lower level" Rust.

With this learning-focused API, we can even leverage people's muscle memories to smooth out the transition to rust by providing familiar functions.

With all of this, we can still scale to give users control by providing interop with the standard library. If an optimizer points out a hot loop involving it, a developer can drop down to the standard library version and make things faster. This is also important for interop with the ecosystem and allowing gradual migration away from this standard library-alternative.


Being easy-to-use doesn't mean performance has to be abysmal as Lily Mara's touched at RustConf.

For example, cargo-script re-uses dependency builds across scripts, speeding up builds at the risk of contention.

For eztd, we can make eztd::String moderately performant with a no-lifetime API with

We can also reduce the build time overhead of eztd by

To further speed up build times, maybe we should consider sharing dependency builds across all builds, not just cargo-scrupt.


I've started eztd to showcase the ideas in this post and with the hope that it encourages others to help make this possible.

Be sure to check out eztd's design guidelines for some additional details on designing for ergonomics.

Just as important are the goals are the non-goals:

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