The main difference is that pytype is a static analyzer (i.e. it inspects the code and tries to figure out what types various things are), whereas PyAnnotate is a profiler hook, so you have to run your code and it observes types as your code runs.

Both have their pros and cons. While static analysis (in my personal opinion) would be ideal because you don't have to run your code, and in theory it can be much more complete, it's also much harder (often impossible) in Python. The runtime analysis of PyAnnotate has a lot of downsides (it doesn't give you types for code that it didn't observe run and it can't know if it has seen all the particular types for a parameter or return). The upside is that it was quick to implement something useful and it gets you quickly to pretty descent type annotations for your main code paths. Which is nice, because in a large untyped codebase it effectively lays down a rough draft of type annotations, making it a lot easier to fix up and fill in edge cases by hand.

Pytype is similar to mypy that it can do type checking with proper annotations. In addition to use annotations, pytype can also do inference based on static analysis.

I don't have much experience with mypy but the last time I used it, it cannot infer from `return x == y` that the function returns a bool. Pytype can correctly infer many simple forms of function argument types and return type, and even some more complex form.

From reading the project, PyAnnotate completely rely on runtime profiling info to _help_ you get to the first round of annotations. We also have similar project that gathers types from runtime and help people to annotate the code. The type information gathered this way has its limitations (PyAnnotate project called this out as well, that you should only use it on legacy code but not on newly written code).

To give an example: if PyAnnotate observe a function below to accept a list of ints and returns an int, it may conclude that the type of this function is `Callable[[List[int]], int]`

``` def foo(xs): ret = 0 for x in xs: ret += x return ret ```

But it can actually work on any iterable (because of the for-in loop), and the item in `xs` is number (because the `__iadd__` call on integer 0). With static analysis, the correct inferred type might be `Callable[[Iterable[Union[int, float]]], Union[int, float]]`

I just tried pytype and it basically did nothing but spit out some errors about imports not found. I didn't have time to try to investigate further and the documentation seems to be almost non-existent. Surprising with 1780 commits to the project.

I'm not sure why you started that sentence with "No". I actually agree with it.

There are many good ideas that have emerged separately in different languages, and are being combined in some of the new languages.

All I'm saying is, successful upcoming languages will probably be more statically typed than dynamically typed.

In other words, the dynamic typing paradigm is failing the real world test.

> Go is like a statically typed language with lots of dynamic features.

Go is statically typed.

The part that lacks static typing (no generics) is the worst part of the language that gets the most flak.

> Traditional statically typed OOP languages such as Java is what people want to get away from.

Java's problem is that it's just way too verbose.

    User user = new User(....); // This is not even that verbose

    // Maybe more like this:

    User user = new User(new UserProfile(....), UserManagerFactory.getDefaultUserManager());

It turns out to be the lack of support for functions as objects. So for many things you end up having to create dummy classes and objects just to wrap functions.

Even C supported passing function pointers around.

So in this regard, Java is less expressive than C.

"Please don't be an uninformed Rich Hickey talk"

<Clicks>

"Oh, it's an uninformed Rich Hickey talk"

Hold it right there. I've never seen anyone argue that static type systems prevent bugs.

I mean they do prevent silly bugs that occur from mistyping variable/property names but I've never seen anyone claim that they eliminate other classes of bugs.

The biggest benefit of static type checking is you know what all the variables are.

    def checkout_cart(customer, payment_methods, cart, session):
        # body

Of course, this kind of code is confusing in Java as well, but for a different reason: Java conventions encourage a kind of obtuse programming style where everything is hidden behind layers of abstractions of factories and managers, so that even when everything is typed, you're not sure what anything is doing because all the data that matters is private and so are all the methods that actually do anything useful. All you're left with is an abstract interface that can sometimes be just as bad as an untyped variable. But this is mostly a cultural problem. (I've digressed).

> Static typing make auto complete and refactoring tools easier, for sure, but it also slows down ease of experimentation

Java slows down ease of experimentation because it requires tons of boilerplate code for even the simplest tasks.

It's not the static type checking.

If anything, static type checking helps experimentation because you can change your mind quickly and the compiler will help you catch all the stupid mistakes that can occur from mismatching types or mistyping variable names. This removes a huge cognitive tax and makes programming more enjoyable. Although I will concede this is subjective.