fix a few broken links in docs (#465)

Author: bddap

CHANGELOG.md

@@ -92,7 +92,7 @@ Beneficial fallouts include our ability to remove `RootTimestamp`, as dataflows
 - The `Root` type has been renamed `Worker` and is found in the `::worker` module. The methods of the `ScopeParent` trait are now in the `::worker::AsWorker` trait.
 - The communication `Allocate` trait's main method `allocate` now takes a worker-unique identifier to use for the channel. The allocator may or may not use the information (most often for logging), but they are allowed to be incorrect if one allocates two channels with the same identifier.
 - A `CapabilityRef<T>` now supports `retain_for(usize)` which indicates a specific output port the capability should be retain for use with. The `retain()` method still exists for now and is equivalent to `retain(0)`. This change also comes with the *inability* to use an arbitrary `Capability<T>` with any output; using a capability bound to the wrong output will result in a run-time error.
-- The `unary` and `binary` operators now provide `data` as a `RefOrMut`, which does not implement `DerefMut`. More information on how to port methods can be found [here](https://github.com/frankmcsherry/timely-dataflow/pull/135#issuecomment-418355284).
+- The `unary` and `binary` operators now provide `data` as a `RefOrMut`, which does not implement `DerefMut`. More information on how to port methods can be found [here](https://github.com/TimelyDataflow/timely-dataflow/pull/135#issuecomment-418355284).
 
 
 ### Removed

mdbook/src/chapter_0/chapter_0_1.md

@@ -2,7 +2,7 @@
 
 Timely dataflow means to capture a large number of idioms, so it is a bit tricky to wrap together one example that shows off all of its features, but let's look at something that shows off some core functionality to give a taste.
 
-The following complete program initializes a timely dataflow computation, in which participants can supply a stream of numbers which are exchanged between the workers based on their value. Workers print to the screen when they see numbers. You can also find this as [`examples/hello.rs`](https://github.com/frankmcsherry/timely-dataflow/blob/master/examples/hello.rs) in the [timely dataflow repository](https://github.com/frankmcsherry/timely-dataflow/tree/master/examples).
+The following complete program initializes a timely dataflow computation, in which participants can supply a stream of numbers which are exchanged between the workers based on their value. Workers print to the screen when they see numbers. You can also find this as [`examples/hello.rs`](https://github.com/TimelyDataflow/timely-dataflow/blob/master/examples/hello.rs) in the [timely dataflow repository](https://github.com/TimelyDataflow/timely-dataflow/tree/master/examples).
 
 ```rust
 extern crate timely;
@@ -44,7 +44,7 @@ We can run this program in a variety of configurations: with just a single worke
 To try this out yourself, first clone the timely dataflow repository using `git`
 
 ```ignore
-    Echidnatron% git clone https://github.com/frankmcsherry/timely-dataflow
+    Echidnatron% git clone https://github.com/TimelyDataflow/timely-dataflow
     Cloning into 'timely-dataflow'...
     remote: Counting objects: 14254, done.
     remote: Compressing objects: 100% (2267/2267), done.

mdbook/src/chapter_4/chapter_4_4.md

@@ -208,4 +208,4 @@ which just goes on and on, but which should produce 50 lines of text, with five
 
 There are several sorts of things you could capture into and replay from. In the `capture::events` module you will find two examples, a linked list and a binary serializer / deserializer (wrapper around `Write` and `Read` traits). The binary serializer is fairly general; we used it up above to wrap TCP streams. You could also write to files, or write to shared memory. However, be mindful that the serialization format (abomonation) is essentially the in-memory representation, and Rust makes no guarantees about the stability of such a representation across builds.
 
-There is also [an in-progress Kafka adapter](https://github.com/frankmcsherry/timely-dataflow/tree/master/kafkaesque) available in the repository, which uses Kafka topics to store the binary representation of captured streams, which can then be replayed by any timely computation that can read them. This may be a while before it is sorted out, because Kafka seems to have a few quirks, but if you would like to help get in touch.
+There is also [an in-progress Kafka adapter](https://github.com/TimelyDataflow/timely-dataflow/tree/master/kafkaesque) available in the repository, which uses Kafka topics to store the binary representation of captured streams, which can then be replayed by any timely computation that can read them. This may be a while before it is sorted out, because Kafka seems to have a few quirks, but if you would like to help get in touch.