This is a first stab at solving
https://github.com/astral-sh/ty/issues/500, at least in part, with the
old solver. We add a new `TypeRelation` that lets us opt into using
constraint sets to describe when a typevar is assignability to some
type, and then use that to calculate a constraint set that describes
when two callable types are assignable. If the callable types contain
typevars, that constraint set will describe their valid specializations.
We can then walk through all of the ways the constraint set can be
satisfied, and record a type mapping in the old solver for each one.
---------
Co-authored-by: Carl Meyer <carl@astral.sh>
Co-authored-by: Alex Waygood <alex.waygood@gmail.com>
Fixes https://github.com/astral-sh/ty/issues/1787
## Summary
Allow method decorators returning Callables to presumptively propagate
"classmethod-ness" in the same way that they already presumptively
propagate "function-like-ness". We can't actually be sure that this is
the case, based on the decorator's annotations, but (along with other
type checkers) we heuristically assume it to be the case for decorators
applied via decorator syntax.
## Test Plan
Added mdtest.
## Summary
Add support for generic PEP 613 type aliases and generic implicit type
aliases:
```py
from typing import TypeVar
T = TypeVar("T")
ListOrSet = list[T] | set[T]
def _(xs: ListOrSet[int]):
reveal_type(xs) # list[int] | set[int]
```
closes https://github.com/astral-sh/ty/issues/1643
closes https://github.com/astral-sh/ty/issues/1629
closes https://github.com/astral-sh/ty/issues/1596
closes https://github.com/astral-sh/ty/issues/573
closes https://github.com/astral-sh/ty/issues/221
## Typing conformance
```diff
-aliases_explicit.py:52:5: error[type-assertion-failure] Type `list[int]` does not match asserted type `@Todo(specialized generic alias in type expression)`
-aliases_explicit.py:53:5: error[type-assertion-failure] Type `tuple[str, ...] | list[str]` does not match asserted type `@Todo(Generic specialization of types.UnionType)`
-aliases_explicit.py:54:5: error[type-assertion-failure] Type `tuple[int, int, int, str]` does not match asserted type `@Todo(specialized generic alias in type expression)`
-aliases_explicit.py:56:5: error[type-assertion-failure] Type `(int, str, /) -> str` does not match asserted type `@Todo(Generic specialization of typing.Callable)`
-aliases_explicit.py:59:5: error[type-assertion-failure] Type `int | str | None | list[list[int]]` does not match asserted type `int | str | None | list[@Todo(specialized generic alias in type expression)]`
```
New true negatives ✔️
```diff
+aliases_explicit.py:41:36: error[invalid-type-arguments] Too many type arguments: expected 1, got 2
-aliases_explicit.py:57:5: error[type-assertion-failure] Type `(int, str, str, /) -> None` does not match asserted type `@Todo(Generic specialization of typing.Callable)`
+aliases_explicit.py:57:5: error[type-assertion-failure] Type `(int, str, str, /) -> None` does not match asserted type `(...) -> Unknown`
```
These require `ParamSpec`
```diff
+aliases_explicit.py:67:24: error[invalid-type-arguments] Too many type arguments: expected 0, got 1
+aliases_explicit.py:68:24: error[invalid-type-arguments] Too many type arguments: expected 0, got 1
+aliases_explicit.py:69:29: error[invalid-type-arguments] Too many type arguments: expected 1, got 2
+aliases_explicit.py:70:29: error[invalid-type-arguments] Too many type arguments: expected 1, got 2
+aliases_explicit.py:71:29: error[invalid-type-arguments] Too many type arguments: expected 1, got 2
+aliases_explicit.py:102:20: error[invalid-type-arguments] Too many type arguments: expected 0, got 1
```
New true positives ✔️
```diff
-aliases_implicit.py:63:5: error[type-assertion-failure] Type `list[int]` does not match asserted type `@Todo(specialized generic alias in type expression)`
-aliases_implicit.py:64:5: error[type-assertion-failure] Type `tuple[str, ...] | list[str]` does not match asserted type `@Todo(Generic specialization of types.UnionType)`
-aliases_implicit.py:65:5: error[type-assertion-failure] Type `tuple[int, int, int, str]` does not match asserted type `@Todo(specialized generic alias in type expression)`
-aliases_implicit.py:67:5: error[type-assertion-failure] Type `(int, str, /) -> str` does not match asserted type `@Todo(Generic specialization of typing.Callable)`
-aliases_implicit.py:70:5: error[type-assertion-failure] Type `int | str | None | list[list[int]]` does not match asserted type `int | str | None | list[@Todo(specialized generic alias in type expression)]`
-aliases_implicit.py:71:5: error[type-assertion-failure] Type `list[bool]` does not match asserted type `@Todo(specialized generic alias in type expression)`
```
New true negatives ✔️
```diff
+aliases_implicit.py:54:36: error[invalid-type-arguments] Too many type arguments: expected 1, got 2
-aliases_implicit.py:68:5: error[type-assertion-failure] Type `(int, str, str, /) -> None` does not match asserted type `@Todo(Generic specialization of typing.Callable)`
+aliases_implicit.py:68:5: error[type-assertion-failure] Type `(int, str, str, /) -> None` does not match asserted type `(...) -> Unknown`
```
These require `ParamSpec`
```diff
+aliases_implicit.py:76:24: error[invalid-type-arguments] Too many type arguments: expected 0, got 1
+aliases_implicit.py:77:24: error[invalid-type-arguments] Too many type arguments: expected 0, got 1
+aliases_implicit.py:78:29: error[invalid-type-arguments] Too many type arguments: expected 1, got 2
+aliases_implicit.py:79:29: error[invalid-type-arguments] Too many type arguments: expected 1, got 2
+aliases_implicit.py:80:29: error[invalid-type-arguments] Too many type arguments: expected 1, got 2
+aliases_implicit.py:81:25: error[invalid-type-arguments] Type `str` is not assignable to upper bound `int | float` of type variable `TFloat@GoodTypeAlias12`
+aliases_implicit.py:135:20: error[invalid-type-arguments] Too many type arguments: expected 0, got 1
```
New true positives ✔️
```diff
+callables_annotation.py:172:19: error[invalid-type-arguments] Too many type arguments: expected 0, got 1
+callables_annotation.py:175:19: error[invalid-type-arguments] Too many type arguments: expected 0, got 1
+callables_annotation.py:188:25: error[invalid-type-arguments] Too many type arguments: expected 0, got 1
+callables_annotation.py:189:25: error[invalid-type-arguments] Too many type arguments: expected 0, got 1
```
These require `ParamSpec` and `Concatenate`.
```diff
-generics_defaults_specialization.py:26:5: error[type-assertion-failure] Type `SomethingWithNoDefaults[int, str]` does not match asserted type `SomethingWithNoDefaults[int, typing.TypeVar]`
+generics_defaults_specialization.py:26:5: error[type-assertion-failure] Type `SomethingWithNoDefaults[int, str]` does not match asserted type `SomethingWithNoDefaults[int, DefaultStrT]`
```
Favorable diagnostic change ✔️
```diff
-generics_defaults_specialization.py:27:5: error[type-assertion-failure] Type `SomethingWithNoDefaults[int, bool]` does not match asserted type `@Todo(specialized generic alias in type expression)`
```
New true negative ✔️
```diff
-generics_defaults_specialization.py:30:1: error[non-subscriptable] Cannot subscript object of type `<class 'SomethingWithNoDefaults[int, typing.TypeVar]'>` with no `__class_getitem__` method
+generics_defaults_specialization.py:30:15: error[invalid-type-arguments] Too many type arguments: expected between 0 and 1, got 2
```
Correct new diagnostic ✔️
```diff
-generics_variance.py:175:25: error[non-subscriptable] Cannot subscript object of type `<class 'Contra[typing.TypeVar]'>` with no `__class_getitem__` method
-generics_variance.py:175:35: error[non-subscriptable] Cannot subscript object of type `<class 'Co[typing.TypeVar]'>` with no `__class_getitem__` method
-generics_variance.py:179:29: error[non-subscriptable] Cannot subscript object of type `<class 'Contra[typing.TypeVar]'>` with no `__class_getitem__` method
-generics_variance.py:179:39: error[non-subscriptable] Cannot subscript object of type `<class 'Contra[typing.TypeVar]'>` with no `__class_getitem__` method
-generics_variance.py:183:21: error[non-subscriptable] Cannot subscript object of type `<class 'Co[typing.TypeVar]'>` with no `__class_getitem__` method
-generics_variance.py:183:27: error[non-subscriptable] Cannot subscript object of type `<class 'Co[typing.TypeVar]'>` with no `__class_getitem__` method
-generics_variance.py:187:25: error[non-subscriptable] Cannot subscript object of type `<class 'Co[typing.TypeVar]'>` with no `__class_getitem__` method
-generics_variance.py:187:31: error[non-subscriptable] Cannot subscript object of type `<class 'Contra[typing.TypeVar]'>` with no `__class_getitem__` method
-generics_variance.py:191:33: error[non-subscriptable] Cannot subscript object of type `<class 'Contra[typing.TypeVar]'>` with no `__class_getitem__` method
-generics_variance.py:191:43: error[non-subscriptable] Cannot subscript object of type `<class 'Co[typing.TypeVar]'>` with no `__class_getitem__` method
-generics_variance.py:191:49: error[non-subscriptable] Cannot subscript object of type `<class 'Contra[typing.TypeVar]'>` with no `__class_getitem__` method
-generics_variance.py:196:5: error[non-subscriptable] Cannot subscript object of type `<class 'Contra[typing.TypeVar]'>` with no `__class_getitem__` method
-generics_variance.py:196:15: error[non-subscriptable] Cannot subscript object of type `<class 'Contra[typing.TypeVar]'>` with no `__class_getitem__` method
-generics_variance.py:196:25: error[non-subscriptable] Cannot subscript object of type `<class 'Contra[typing.TypeVar]'>` with no `__class_getitem__` method
```
One of these should apparently be an error, but not of this kind, so
this is good ✔️
```diff
-specialtypes_type.py:152:16: error[invalid-type-form] `typing.TypeVar` is not a generic class
-specialtypes_type.py:156:16: error[invalid-type-form] `typing.TypeVar` is not a generic class
```
Good, those were false positives. ✔️
I skipped the analysis for everything involving `TypeVarTuple`.
## Ecosystem impact
**[Full report with detailed
diff](https://david-generic-implicit-alias.ecosystem-663.pages.dev/diff)**
Previous iterations of this PR showed all kinds of problems. In it's
current state, I do not see any large systematic problems, but it is
hard to tell with 5k diagnostic changes.
## Performance
* There is a huge 4x regression in `colour-science/colour`, related to
[this large
file](https://github.com/colour-science/colour/blob/develop/colour/io/luts/tests/test_lut.py)
with [many assignments of hard-coded arrays (lists of lists) to
`np.NDArray`
types](83e754c8b6/colour/io/luts/tests/test_lut.py (L701-L781))
that we now understand. We now take ~2 seconds to check this file, so
definitely not great, but maybe acceptable for now.
## Test Plan
Updated and new Markdown tests
## Summary
Previously if an explicit specialization failed (e.g. wrong number of
type arguments or violates an upper bound) we just inferred `Unknown`
for the entire type. This actually caused us to panic on an a case of a
recursive upper bound with invalid specialization; the upper bound would
oscillate indefinitely in fixpoint iteration between `Unknown` and the
given specialization. This could be fixed with a cycle recovery
function, but in this case there's a simpler fix: if we infer
`C[Unknown]` instead of `Unknown` for an invalid attempt to specialize
`C`, that allows fixpoint iteration to quickly converge, as well as
giving a more precise type inference.
Other type checkers actually just go with the attempted specialization
even if it's invalid. So if `C` has a type parameter with upper bound
`int`, and you say `C[str]`, they'll emit a diagnostic but just go with
`C[str]`. Even weirder, if `C` has a single type parameter and you say
`C[str, bytes]`, they'll just go with `C[str]` as the type. I'm not
convinced by this approach; it seems odd to have specializations
floating around that explicitly violate the declared upper bound, or in
the latter case aren't even the specialization the annotation requested.
I prefer `C[Unknown]` for this case.
Fixing this revealed an issue with `collections.namedtuple`, which
returns `type[tuple[Any, ...]]`. Due to
https://github.com/astral-sh/ty/issues/1649 we consider that to be an
invalid specialization. So previously we returned `Unknown`; after this
PR it would be `type[tuple[Unknown]]`, leading to more false positives
from our lack of functional namedtuple support. To avoid that I added an
explicit Todo type for functional namedtuples for now.
## Test Plan
Added and updated mdtests.
The conformance suite changes have to do with `ParamSpec`, so no
meaningful signal there.
The ecosystem changes appear to be the expected effects of having more
precise type information (including occurrences of known issues such as
https://github.com/astral-sh/ty/issues/1495 ). Most effects are just
changes to types in diagnostics.
Refs https://github.com/astral-sh/ty/issues/544
## Summary
Takes a more incremental approach to PEP 613 type alias support (vs
https://github.com/astral-sh/ruff/pull/20107). Instead of eagerly
inferring the RHS of a PEP 613 type alias as a type expression, infer it
as a value expression, just like we do for implicit type aliases, taking
advantage of the same support for e.g. unions and other type special
forms.
The main reason I'm following this path instead of the one in
https://github.com/astral-sh/ruff/pull/20107 is that we've realized that
people do sometimes use PEP 613 type aliases as values, not just as
types (because they are just a normal runtime assignment, unlike PEP 695
type aliases which create an opaque `TypeAliasType`).
This PR doesn't yet provide full support for recursive type aliases
(they don't panic, but they just fall back to `Unknown` at the recursion
point). This is future work.
## Test Plan
Added mdtests.
Many new ecosystem diagnostics, mostly because we
understand new types in lots of places.
Conformance suite changes are correct.
Performance regression is due to understanding lots of new
types; nothing we do in this PR is inherently expensive.
## Summary
We synthesize a (potentially large) set of `__setitem__` overloads for
every item in a `TypedDict`. Previously, validation of subscript
assignments on `TypedDict`s relied on actually calling `__setitem__`
with the provided key and value types, which implied that we needed to
do the full overload call evaluation for this large set of overloads.
This PR improves the performance of subscript assignment checks on
`TypedDict`s by validating the assignment directly instead of calling
`__setitem__`.
This PR also adds better handling for assignments to subscripts on union
and intersection types (but does not attempt to make it perfect). It
achieves this by distributing the check over unions and intersections,
instead of calling `__setitem__` on the union/intersection directly. We
already do something similar when validating *attribute* assignments.
## Ecosystem impact
* A lot of diagnostics change their rule type, and/or split into
multiple diagnostics. The new version is more verbose, but easier to
understand, in my opinion
* Almost all of the invalid-key diagnostics come from pydantic, and they
should all go away (including many more) when we implement
https://github.com/astral-sh/ty/issues/1479
* Everything else looks correct to me. There may be some new diagnostics
due to the fact that we now check intersections.
## Test Plan
New Markdown tests.
## Summary
Add support for `typing.Union` in implicit type aliases / in value
position.
## Typing conformance tests
Two new tests are passing
## Ecosystem impact
* The 2k new `invalid-key` diagnostics on pydantic are caused by
https://github.com/astral-sh/ty/issues/1479#issuecomment-3513854645.
* Everything else I've checked is either a known limitation (often
related to type narrowing, because union types are often narrowed down
to a subset of options), or a true positive.
## Test Plan
New Markdown tests
I don't know why, but it always takes me an eternity to find the failing
project name a few lines below in the output. So I'm suggesting we just
add the project name to the assertion message.
## Summary
Add support for implicit type aliases that use PEP 604 unions:
```py
IntOrStr = int | str
reveal_type(IntOrStr) # UnionType
def _(int_or_str: IntOrStr):
reveal_type(int_or_str) # int | str
```
## Typing conformance
The changes are either removed false positives, or new diagnostics due
to known limitations unrelated to this PR.
## Ecosystem impact
Spot checked, a mix of true positives and known limitations.
## Test Plan
New Markdown tests.
## Summary
Infer a type of unannotated `self` parameters in decorated methods /
properties.
closes https://github.com/astral-sh/ty/issues/1448
## Test Plan
Existing tests, some new tests.
## Summary
Infer a type of `Self` for unannotated `self` parameters in methods of
classes.
part of https://github.com/astral-sh/ty/issues/159
closes https://github.com/astral-sh/ty/issues/1081
## Conformance tests changes
```diff
+enums_member_values.py:85:9: error[invalid-assignment] Object of type `int` is not assignable to attribute `_value_` of type `str`
```
A true positive ✔️
```diff
-generics_self_advanced.py:35:9: error[type-assertion-failure] Argument does not have asserted type `Self@method2`
-generics_self_basic.py:14:9: error[type-assertion-failure] Argument does not have asserted type `Self@set_scale
```
Two false positives going away ✔️
```diff
+generics_syntax_infer_variance.py:82:9: error[invalid-assignment] Cannot assign to final attribute `x` on type `Self@__init__`
```
This looks like a true positive to me, even if it's not marked with `#
E` ✔️
```diff
+protocols_explicit.py:56:9: error[invalid-assignment] Object of type `tuple[int, int, str]` is not assignable to attribute `rgb` of type `tuple[int, int, int]`
```
True positive ✔️
```
+protocols_explicit.py:85:9: error[invalid-attribute-access] Cannot assign to ClassVar `cm1` from an instance of type `Self@__init__`
```
This looks like a true positive to me, even if it's not marked with `#
E`. But this is consistent with our understanding of `ClassVar`, I
think. ✔️
```py
+qualifiers_final_annotation.py:52:9: error[invalid-assignment] Cannot assign to final attribute `ID4` on type `Self@__init__`
+qualifiers_final_annotation.py:65:9: error[invalid-assignment] Cannot assign to final attribute `ID7` on type `Self@method1`
```
New true positives ✔️
```py
+qualifiers_final_annotation.py:52:9: error[invalid-assignment] Cannot assign to final attribute `ID4` on type `Self@__init__`
+qualifiers_final_annotation.py:57:13: error[invalid-assignment] Cannot assign to final attribute `ID6` on type `Self@__init__`
+qualifiers_final_annotation.py:59:13: error[invalid-assignment] Cannot assign to final attribute `ID6` on type `Self@__init__`
```
This is a new false positive, but that's a pre-existing issue on main
(if you annotate with `Self`):
https://play.ty.dev/3ee1c56d-7e13-43bb-811a-7a81e236e6ab❌ => reported
as https://github.com/astral-sh/ty/issues/1409
## Ecosystem
* There are 5931 new `unresolved-attribute` and 3292 new
`possibly-missing-attribute` attribute errors, way too many to look at
all of them. I randomly sampled 15 of these errors and found:
* 13 instances where there was simply no such attribute that we could
plausibly see. Sometimes [I didn't find it
anywhere](8644d886c6/openlibrary/plugins/openlibrary/tests/test_listapi.py (L33)).
Sometimes it was set externally on the object. Sometimes there was some
[`setattr` dynamicness going
on](a49f6b927d/setuptools/wheel.py (L88-L94)).
I would consider all of them to be true positives.
* 1 instance where [attribute was set on `obj` in
`__new__`](9e87b44fd4/sympy/tensor/array/array_comprehension.py (L45C1-L45C36)),
which we don't support yet
* 1 instance [where the attribute was defined via `__slots__`
](e250ec0fc8/lib/spack/spack/vendor/pyrsistent/_pdeque.py (L48C5-L48C14))
* I see 44 instances [of the false positive
above](https://github.com/astral-sh/ty/issues/1409) with `Final`
instance attributes being set in `__init__`. I don't think this should
block this PR.
## Test Plan
New Markdown tests.
---------
Co-authored-by: Shaygan Hooshyari <sh.hooshyari@gmail.com>
## Summary
Use the type annotation of function parameters as bidirectional type
context when inferring the argument expression. For example, the
following example now type-checks:
```py
class TD(TypedDict):
x: int
def f(_: TD): ...
f({ "x": 1 })
```
Part of https://github.com/astral-sh/ty/issues/168.
## Summary
Modify the (external) signature of instance methods such that the first
parameter uses `Self` unless it is explicitly annotated. This allows us
to correctly type-check more code, and allows us to infer correct return
types for many functions that return `Self`. For example:
```py
from pathlib import Path
from datetime import datetime, timedelta
reveal_type(Path(".config") / ".ty") # now Path, previously Unknown
def _(dt: datetime, delta: timedelta):
reveal_type(dt - delta) # now datetime, previously Unknown
```
part of https://github.com/astral-sh/ty/issues/159
## Performance
I ran benchmarks locally on `attrs`, `freqtrade` and `colour`, the
projects with the largest regressions on CodSpeed. I see much smaller
effects locally, but can definitely reproduce the regression on `attrs`.
From looking at the profiling results (on Codspeed), it seems that we
simply do more type inference work, which seems plausible, given that we
now understand much more return types (of many stdlib functions). In
particular, whenever a function uses an implicit `self` and returns
`Self` (without mentioning `Self` anywhere else in its signature), we
will now infer the correct type, whereas we would previously return
`Unknown`. This also means that we need to invoke the generics solver in
more cases. Comparing half a million lines of log output on attrs, I can
see that we do 5% more "work" (number of lines in the log), and have a
lot more `apply_specialization` events (7108 vs 4304). On freqtrade, I
see similar numbers for `apply_specialization` (11360 vs 5138 calls).
Given these results, I'm not sure if it's generally worth doing more
performance work, especially since none of the code modifications
themselves seem to be likely candidates for regressions.
| Command | Mean [ms] | Min [ms] | Max [ms] | Relative |
|:---|---:|---:|---:|---:|
| `./ty_main check /home/shark/ecosystem/attrs` | 92.6 ± 3.6 | 85.9 |
102.6 | 1.00 |
| `./ty_self check /home/shark/ecosystem/attrs` | 101.7 ± 3.5 | 96.9 |
113.8 | 1.10 ± 0.06 |
| Command | Mean [ms] | Min [ms] | Max [ms] | Relative |
|:---|---:|---:|---:|---:|
| `./ty_main check /home/shark/ecosystem/freqtrade` | 599.0 ± 20.2 |
568.2 | 627.5 | 1.00 |
| `./ty_self check /home/shark/ecosystem/freqtrade` | 607.9 ± 11.5 |
594.9 | 626.4 | 1.01 ± 0.04 |
| Command | Mean [ms] | Min [ms] | Max [ms] | Relative |
|:---|---:|---:|---:|---:|
| `./ty_main check /home/shark/ecosystem/colour` | 423.9 ± 17.9 | 394.6
| 447.4 | 1.00 |
| `./ty_self check /home/shark/ecosystem/colour` | 426.9 ± 24.9 | 373.8
| 456.6 | 1.01 ± 0.07 |
## Test Plan
New Markdown tests
## Ecosystem report
* apprise: ~300 new diagnostics related to problematic stubs in apprise
😩
* attrs: a new true positive, since [this
function](4e2c89c823/tests/test_make.py (L2135))
is missing a `@staticmethod`?
* Some legitimate true positives
* sympy: lots of new `invalid-operator` false positives in [matrix
multiplication](cf9f4b6805/sympy/matrices/matrixbase.py (L3267-L3269))
due to our limited understanding of [generic `Callable[[Callable[[T1,
T2], T3]], Callable[[T1, T2], T3]]` "identity"
types](cf9f4b6805/sympy/core/decorators.py (L83-L84))
of decorators. This is not related to type-of-self.
## Typing conformance results
The changes are all correct, except for
```diff
+generics_self_usage.py:50:5: error[invalid-assignment] Object of type `def foo(self) -> int` is not assignable to `(typing.Self, /) -> int`
```
which is related to an assignability problem involving type variables on
both sides:
```py
class CallableAttribute:
def foo(self) -> int:
return 0
bar: Callable[[Self], int] = foo # <- we currently error on this assignment
```
---------
Co-authored-by: Shaygan Hooshyari <sh.hooshyari@gmail.com>
## Summary
This PR adds support for unpacking `**kwargs` argument.
This can be matched against any standard (positional or keyword),
keyword-only, or keyword variadic parameter that haven't been matched
yet.
This PR also takes care of special casing `TypedDict` because the key
names and the corresponding value type is known, so we can be more
precise in our matching and type checking step. In the future, this
special casing would be extended to include `ParamSpec` as well.
Part of astral-sh/ty#247
## Test Plan
Add test cases for various scenarios.
## Summary
I played with those numbers a bit locally and `sample_size=3,
sample_count=8` seemed like a rather stable setup. This means a single
sample consistents of 3 iterations of checking pydantic multithreaded.
And this is repeated 8 times for statistics. A single check took ~300 ms
previously on the runners, so this should only take 7 s.
The benchmark is currently very noisy (± 10%). This leads to codspeed
reports on PRs, because we often exceed the trigger threshold. This is
confusing to ty contributors who are not aware about the flakiness.
Let's disable it for now.
Douglas Creager
Carl Meyer
Ibraheem Ahmed
David Peter
Micha Reiser
Alex Waygood
Dhruv Manilawala