Make documentation standalone and generalize examples

docs/code/nodes.md

@@ -132,9 +132,9 @@ storage; emitted frames alone are not recorded as run artifacts.
 ### Making clips playable on an LVGL panel
 
 A wall panel that renders dashboards natively, such as an ESP32-P4 running
-openpanel, decodes only H.264 Constrained Baseline (in software, about 29 fps
-at 640×360) and MJPEG. It refuses clips straight from a phone or camera, which
-are usually Main or High profile at 1080p or more. Re-encode those with a node
+openpanel, decodes H.264 Constrained Baseline and MJPEG. Clips straight from
+a phone or camera may use an unsupported profile or resolution, such as
+Main or High profile at 1080p or more. Re-encode those with a node
 that takes a `video` input port `clip` and has a `video` output port
 `panel_clip`:
 
@@ -288,19 +288,9 @@ downloads them again. Point `MODELS` at a directory under `/data` to keep them
 on the volume. An [attached worker](workers.md) starts a process per call and
 loads the model each time.
 
-What it costs, measured on a 2016 server CPU (Xeon E5-2683 v4):
-
-| | Size | Memory | 3 s of speech, one core | All cores |
-|---|---|---|---|---|
-| Packages | 150 MB download, 460 MB installed | | | |
-| Whisper `tiny` | 78 MB | 300 MB | 1.6 s | 0.7 s |
-| Whisper `base` | 148 MB | 375 MB | 2.9 s | 1.2 s |
-| piper `en_US-lessac-medium` | 63 MB | 250 MB | 0.6 s | 0.3 s |
-
-Each model downloads once, on its node's first call. Loading it takes about a
-second for Whisper and four seconds for piper. Memory is the worker's peak while
-it holds the model. These figures come from piper's own speech fed back into
-Whisper. A room microphone is harder, on `tiny` most of all.
+Models download on first use. Speed, memory use and recognition quality depend
+on your hardware, model and audio. Try a short clip from your own microphone
+before choosing a model; `tiny` is a useful first stop on a small CPU.
 
 ## Printing
 
@@ -391,7 +381,7 @@ access token; see [The HTTP API](api.md).
 managed environment can also install your project from a Modules manifest:
 
 ```text
--e /home/you/my-research
+-e /path/to/my-research
 numpy>=2
 ```