fix links

docs/content/concepts/batches.md

@@ -60,4 +60,4 @@ If the component on the left-side of the join (the so-called primary component)
 On the other hand, if the component on the left-side of the join (the so-called primary component) has less instances than the other, then the last instance will be repeated across every instance left in the batch. We call this clamping, in reference to texture sampling (think `CLAMP_TO_EDGE`!).
 
 ## See also
-[`send_columns`](../howto/send_columns.md) lets you efficiently send many batches of data in one log call.
+[`send_columns`](../howto/logging/send-columns.md) lets you efficiently send many batches of data in one log call.

docs/content/concepts/timelines.md

@@ -28,7 +28,7 @@ snippet: concepts/different_data_per_timeline
 On one timeline the points will appear blue, on the other they appear red.
 
 ### Sending many time points at once
-To get full control over the logged timelines you can use [`send_columns`](../howto/send_columns.md).
+To get full control over the logged timelines you can use [`send_columns`](../howto/logging/send-columns.md).
 This is often a lot more efficient when you already have a chunk of temporal data, e.g. some sensor value over time.
 
 

docs/content/getting-started/data-in/rust.md

@@ -138,7 +138,7 @@ Notably, the [`RecordingStream::log`](https://docs.rs/rerun/latest/rerun/struct.
 <!-- TODO(andreas): UPDATE DOC LINKS -->
 
 will handle any data type that implements the [`AsComponents`](https://docs.rs/rerun/latest/rerun/trait.AsComponents.html) trait, making it easy to add your own data.
-For more information on how to supply your own components see [Use custom data](../../howto/extend/custom-data.md).
+For more information on how to supply your own components see [Use custom data](../../howto/logging/custom-data.md).
 
 ### Entities & hierarchies
 

docs/content/getting-started/quick-start/cpp.md

@@ -137,6 +137,6 @@ If you'd rather learn from examples, check out the [example gallery](/examples)
 There's also a stand-alone example that shows [interop with Eigen and OpenCV](https://github.com/rerun-io/cpp-example-opencv-eigen).
 
 To learn more about how to work with your own types, check the [Custom Collection Adapter](https://github.com/rerun-io/rerun/tree/latest/examples/cpp/custom_collection_adapter) example on how to zero-copy adapt to Rerun types
-and the [Use custom data](../../howto/extend/custom-data.md) page for completely custom types.
+and the [Use custom data](../../howto/logging/custom-data.md) page for completely custom types.
 
 To learn more about how to configure the C++ SDK's CMake file, check [CMake Setup in Detail](https://ref.rerun.io/docs/cpp/stable/md__2home_2runner_2work_2rerun_2rerun_2rerun__cpp_2cmake__setup__in__detail.html).

docs/content/howto/integrations/embed-notebooks.md

@@ -26,7 +26,7 @@ This installs both [rerun-sdk](https://pypi.org/project/rerun-sdk/) and [rerun-n
 When using the Rerun logging APIs, by default, the logged messages are buffered in-memory until
 you send them to a sink such as via `rr.connect()` or `rr.save()`.
 
-When using Rerun in a notebook, rather than using the other sinks, you have the option to use [`rr.notebook_show()`](https://ref.rerun.io/docs/python/stable/common/initialization_functions/#rerun.notebook_show). This method embeds the [web viewer](./embed-rerun-viewer.md) using the IPython `display` mechanism in the cell output, and sends the current recording data to it.
+When using Rerun in a notebook, rather than using the other sinks, you have the option to use [`rr.notebook_show()`](https://ref.rerun.io/docs/python/stable/common/initialization_functions/#rerun.notebook_show). This method embeds the [web viewer](./embed-web.md) using the IPython `display` mechanism in the cell output, and sends the current recording data to it.
 
 Once the viewer is open, any subsequent `rr.log()` calls will send their data directly to the viewer,
 without any intermediate buffering.
@@ -74,7 +74,7 @@ rr.notebook_show(width=400, height=400)
 
 ## Working with blueprints
 
-[Blueprints](./configure-viewer-through-code.md) can also be used with `notebook_show()` by providing a `blueprint`
+[Blueprints](../configure-viewer-through-code.md) can also be used with `notebook_show()` by providing a `blueprint`
 parameter.
 
 For example

docs/content/howto/integrations/ros2-nav-turtlebot.md

@@ -30,8 +30,8 @@ All of the code for this guide can be found on GitHub in
 
 Other relevant tutorials:
 
--   [Python SDK Tutorial](../getting-started/data-in/python.md)
--   [Viewer Walkthrough](../getting-started/navigating-the-viewer.md)
+-   [Python SDK Tutorial](../../getting-started/data-in/python.md)
+-   [Viewer Walkthrough](../../getting-started/navigating-the-viewer.md)
 
 ### ROS 2 & navigation
 
@@ -110,17 +110,17 @@ the environment.
 If you are familiar with the turtlebot nav example and rviz, this view will likely be familiar:
 
 -   `map/box` is a placeholder for the map. (This will eventually be a map: [#1531](https://github.com/rerun-io/rerun/issues/1531).)
--   `map/robot` is a transform representing the robot pose logged as a rigid [transform3d](../reference/types/archetypes/transform3d.md).
--   `map/robot/urdf` contains the `URDF` logged as a [mesh](../reference/types/archetypes/mesh3d.md).
--   `map/robot/scan` contains a `LaserScan` msg logged as a [linestrip3d](../reference/types/archetypes/line_strips3d.md). (This will eventually be a
+-   `map/robot` is a transform representing the robot pose logged as a rigid [transform3d](../../reference/types/archetypes/transform3d.md).
+-   `map/robot/urdf` contains the `URDF` logged as a [mesh](../../reference/types/archetypes/mesh3d.md).
+-   `map/robot/scan` contains a `LaserScan` msg logged as a [linestrip3d](../../reference/types/archetypes/line_strips3d.md). (This will eventually be a
     native type: [#1534](https://github.com/rerun-io/rerun/issues/1534).)
--   `map/robot/camera` contains a `CameraInfo` msg logged as a [pinhole](../reference/types/archetypes/pinhole.md) transform.
--   `map/robot/camera/img` contains an `Image` msg logged as an [image](../reference/types/archetypes/image.md).
--   `map/robot/camera/points` contains a `PointCloud2` msg logged as a [point3d](../reference/types/archetypes/points3d.md).
+-   `map/robot/camera` contains a `CameraInfo` msg logged as a [pinhole](../../reference/types/archetypes/pinhole.md) transform.
+-   `map/robot/camera/img` contains an `Image` msg logged as an [image](../../reference/types/archetypes/image.md).
+-   `map/robot/camera/points` contains a `PointCloud2` msg logged as a [point3d](../../reference/types/archetypes/points3d.md).
 -   `map/points` contains a second copy of `PointCloud2` with a different transform. (This is a workaround until Rerun
     has support for ROS-style fixed frames [#1522](https://github.com/rerun-io/rerun/issues/1522).)
--   `odometry/vel` is a plot of the linear velocity of the robot logged as a [scalar](../reference/types/archetypes/scalar.md).
--   `odometry/ang_vel` is a plot of the angular velocity of the robot logged as a [scalar](../reference/types/archetypes/scalar.md).
+-   `odometry/vel` is a plot of the linear velocity of the robot logged as a [scalar](../../reference/types/archetypes/scalar.md).
+-   `odometry/ang_vel` is a plot of the angular velocity of the robot logged as a [scalar](../../reference/types/archetypes/scalar.md).
 
 ## Code explanation
 
@@ -156,7 +156,7 @@ again.
 ### TF to rr.Transform3D
 
 Next, we need to map the [ROS TF2](https://docs.ros.org/en/humble/Concepts/About-Tf2.html) transforms to the
-corresponding [Rerun Transforms](../concepts/spaces-and-transforms.md#space-transformations).
+corresponding [Rerun Transforms](../../concepts/spaces-and-transforms.md#space-transformations).
 
 In Rerun, each path represents a coordinate frame, so we need to decide which TF frame each path will
 correspond to. In general, this is the frame_id of the sensor data that will be logged to that

docs/content/howto/logging/send-columns.md

@@ -9,7 +9,7 @@ Sometimes you want to send big chunks of data to Rerun efficiently. To do so, yo
 
 `send_columns` lets you efficiently log the state of an entity over time, logging multiple time and component columns in one call.
 
-In contrast to the `log` function, `send_columns` does NOT add any other timelines to the data. Neither the built-in timelines `log_time` and `log_tick`, nor any [user timelines](../concepts/timelines.md). Only the timelines explicitly included in the call to `send_columns` will be included.
+In contrast to the `log` function, `send_columns` does NOT add any other timelines to the data. Neither the built-in timelines `log_time` and `log_tick`, nor any [user timelines](../../concepts/timelines.md). Only the timelines explicitly included in the call to `send_columns` will be included.
 
 API docs of `send_columns`:
 * [🌊 C++](https://ref.rerun.io/docs/cpp/stable/classrerun_1_1RecordingStream.html#ad17571d51185ce2fc2fc2f5c3070ad65)

docs/content/howto/logging/shared-recordings.md

@@ -5,7 +5,7 @@ order: 100
 
 A common need is to log data from multiple processes and then visualize all of that data as part of a single shared recording.
 
-Rerun has the notion of a [Recording ID](../concepts/apps-and-recordings.md) for that: any recorded datasets that share the same Recording ID will be visualized as one shared dataset.
+Rerun has the notion of a [Recording ID](../../concepts/apps-and-recordings.md) for that: any recorded datasets that share the same Recording ID will be visualized as one shared dataset.
 
 The data can be logged from any number of processes, whether they run on the same machine or not, or implemented in different programming languages.
 All that matter is that they share the same Recording ID.

docs/content/howto/visualization/configure-viewer-through-code.md

@@ -3,7 +3,7 @@ title: Configure the Viewer through code
 order: 100
 ---
 
-As of Rerun 0.15, the state of the [blueprint](../reference/viewer/blueprint.md) can be directly manipulated using the
+As of Rerun 0.15, the state of the [blueprint](../../reference/viewer/blueprint.md) can be directly manipulated using the
 Rerun SDK.
 
 In the initial 0.15 release, the APIs are still somewhat limited and only available in the Python SDK.
@@ -139,7 +139,7 @@ Because the data in the space-view is relative to the `origin`, the `origin` wil
 in the blueprint tree, with all entities under the origin shown using relative paths.
 
 For Spatial views such as `Spatial2DView` and `Spatial3DView`, the `origin` plays an additional role with respect
-to data transforms. All data in the view will be transformed to the `origin` space before being displayed. See [Spaces and Transforms](../concepts/spaces-and-transforms.md) for more information.
+to data transforms. All data in the view will be transformed to the `origin` space before being displayed. See [Spaces and Transforms](../../concepts/spaces-and-transforms.md) for more information.
 
 For example:
 
@@ -155,7 +155,7 @@ rrb.Blueprint(
 ### `contents`
 
 If you need to further modify the contents of a space view, you can use the `contents` parameter. This parameter is
-a list of [entity query expressions](../reference/) that are either included or excluded from the
+a list of [entity query expressions](../../reference/) that are either included or excluded from the
 view.
 
 Each entity expressions starts with "+" for inclusion or "-" for an exclusion. The expressions can either be specific entity paths, or may end in a wildcard `/**` to include all entities under a specific subtree.

docs/content/reference/dataframes.md

@@ -7,11 +7,11 @@ Rerun, at its core, is a database. As such, you can always get your data back in
 
 This can be achieved in three different ways, depending on your needs:
 * using the dataframe API, currently available in [Python](https://ref.rerun.io/docs/python/stable/common/dataframe/) and [Rust](https://docs.rs/rerun/latest/rerun/dataframe/index.html),
-* using the [blueprint API](../concepts/blueprint) to configure a [dataframe view](types/views/dataframe_view) from code,
-* or simply by setting up [dataframe view](types/views/dataframe_view) manually in the UI.
+* using the [blueprint API](../concepts/blueprint.md) to configure a [dataframe view](types/views/dataframe_view.md) from code,
+* or simply by setting up [dataframe view](types/views/dataframe_view.md) manually in the UI.
 
 This page is meant as a reference to get you up and running with these different solutions as quickly as possible.
-For an in-depth introduction to the dataframe API and the possible workflows it enables, check out [our Getting Started guide](../getting-started/data-out) or one of the accompanying [How-Tos](../howto/dataframe-api).
+For an in-depth introduction to the dataframe API and the possible workflows it enables, check out [our Getting Started guide](../getting-started/data-out) or one of the accompanying [How-Tos](../howto/get-data-out.md).
 
 
 > We'll need an RRD file to query. Either use one of yours, or grab some of the example ones, e.g.:
@@ -34,7 +34,7 @@ Check out the API reference to learn more about all the ways that data can be se
 
 ### Using the blueprint API to configure a dataframe view
 
-The following snippet demonstrates how visualize an entire Rerun recording using latest-at (i.e. time-aligned) semantics by displaying the results in a [dataframe view](types/views/dataframe_view):
+The following snippet demonstrates how visualize an entire Rerun recording using latest-at (i.e. time-aligned) semantics by displaying the results in a [dataframe view](types/views/dataframe_view.md):
 
 snippet: reference/dataframe_view_query
 
@@ -59,15 +59,15 @@ Then log that blueprint file in addition to the data itself:
 snippet: reference/dataframe_view_query_external
 
 Check out the blueprint API and `log_file_from_path` references to learn more:
-* [🐍 Python blueprint API reference](https://ref.rerun.io/docs/python/latest/common/blueprint_apis/)
-* [🐍 Python `log_file_from_path`](https://ref.rerun.io/docs/python/latest/common/logging_functions/#rerun.log_file_from_path)
+* [🐍 Python blueprint API reference](https://ref.rerun.io/docs/python/stable/common/blueprint_apis/)
+* [🐍 Python `log_file_from_path`](https://ref.rerun.io/docs/python/stable/common/logging_functions/#rerun.log_file_from_path)
 * [🦀 Rust `log_file_from_path`](https://docs.rs/rerun/latest/rerun/struct.RecordingStream.html#method.log_file_from_path)
 * [🌊 C++ `log_file_from_path`](https://ref.rerun.io/docs/cpp/stable/classrerun_1_1RecordingStream.html#a20798d7ea74cce5c8174e5cacd0a2c47)
 
 
 ### Setting up dataframe view manually in the UI
 
-The same [dataframe view](types/views/dataframe_view) shown above can be configured purely from the UI:
+The same [dataframe view](types/views/dataframe_view.md) shown above can be configured purely from the UI:
 
 <video width="100%" autoplay loop muted controls>
     <source src="https://static.rerun.io/dataframe/df-dna-demo.webm" type="video/webm" />

docs/content/reference/migration/migration-0-15.md

@@ -20,7 +20,7 @@ For now the `InstanceKey` component as such still remains, but is always assigne
 This means that if you first log the positions `A, B, C` and then later log the colors `red, green, blue` to the same entity, they will always be matched as `(A, red), (B, green), (C, blue)`.
 We still support _splatting_, where you log one single color for the whole point cloud.
 
-If you were relying on `InstanceKey` solely to identify your instances when inspecting them in the viewer, then you can replace it with a custom value using [custom data](../../howto/extend/custom-data.md):
+If you were relying on `InstanceKey` solely to identify your instances when inspecting them in the viewer, then you can replace it with a custom value using [custom data](../../howto/logging/custom-data.md):
 
 ```py
 rr.log(