Make documentation standalone and generalize examples

docs/code/agents.md

@@ -26,11 +26,11 @@ invalidates its access. MCP uses a separate signing key at
 `OAUTH_PRIVATE_KEY_FILE`; rotating that key invalidates all MCP tokens.
 
 An instance connected to a [portal](../interface/portal.md) also answers
-through it, at `https://hub.fluksio.com/i/<instance-id>/mcp` (the portal's
-own `hub.` host for a custom one). The portal forwards the endpoint and its
-OAuth, and the instance checks the agent's token as it does locally. The
-consent page opens in the instance as the portal serves it, so open the
-instance from the portal first if this browser has not recently.
+through the portal's API address, for example
+`https://portal.example.com/i/<instance-id>/mcp`. The portal forwards MCP and
+OAuth requests; the instance checks the agent's token as it does locally.
+Open the instance from the portal first so the browser can show its consent
+page.
 
 ## Available tools
 

docs/code/cli.md

@@ -57,7 +57,7 @@ that instance's running engine; plain mode refuses to start a duplicate.
 | `--admin-email ADDR` | `admin@example.com` | First admin account |
 | `--admin-password PW` | Generated | First admin password |
 | `--enroll CODE` | Unset | Enroll before starting, if not already paired |
-| `--portal URL` | `https://hub.fluksio.com` | Portal used by `--enroll` |
+| `--portal URL` | Built-in portal address | Portal used by `--enroll` |
 | `--max-runs N` | `4` | Concurrent batch runs |
 | `--max-cascades N` | `4` | Concurrent live cascades |
 | `--max-workers N` | `4` | Local Python worker processes |
@@ -106,7 +106,7 @@ from the instance's project directory, or select it explicitly.
 
 | Option | Purpose |
 |---|---|
-| `--portal URL` | Use another portal instead of `https://hub.fluksio.com` |
+| `--portal URL` | Use another compatible portal |
 | `--as EMAIL` | Choose the local superuser represented by portal sessions |
 | `--data-dir PATH` | Select instance storage |
 | `--global` | Select `~/.fluksio` |

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
 ```
 

docs/code/workers.md

@@ -38,11 +38,12 @@ export FLUKSIO_WORKER_TOKEN='<token from the engine>'
 fluksio-worker \
   --url wss://fluksio.example.com/api/v1/workers/attach \
   --labels gpu,cuda12 \
-  --python /opt/torch-venv/bin/python
+  --python /path/to/node-venv/bin/python
 ```
 
-Use your engine's hostname. `--python` selects the environment containing the
-node's dependencies, such as PyTorch or a device SDK. If you install the worker
+Replace the address and interpreter path with your own. `--python` selects
+the environment containing the node's dependencies, such as PyTorch or a
+device SDK. If you install the worker
 in that environment, you can omit the option. An engine installation also
 provides the same agent as `fluksio worker`.
 
@@ -55,7 +56,7 @@ provides the same agent as `fluksio worker`.
 | `--python` | worker's interpreter | Python used for node code |
 | `--parallel` | `1` | concurrent calls |
 | `--artifact-url` | derived from `--url` | artifact endpoint, if hosted separately |
-| `--service-host` | address the engine sees | host in a service's url, when the engine reaches this machine elsewhere, such as through a tunnel |
+| `--service-host` | address the engine sees | reachable hostname or IP for services, when the connection address is unsuitable |
 | `--cpus` | detected | advertised CPU count |
 | `--gpus` | detected | advertised GPU count; explicit `0` means none |
 | `--ram-mb` | detected | advertised memory in MB |
@@ -183,7 +184,7 @@ in `$DATA_DIR/provisioners.json`, replacing the example addresses and paths:
 [{
   "type": "slurm",
   "name": "hpc",
-  "login": "me@login.cluster",
+  "login": "user@login.cluster.example.com",
   "ssh_key": "/secrets/hpc_ed25519",
   "engine_url": "wss://fluksio.example.com/api/v1/workers/attach",
   "max_idle_s": 300,
@@ -195,7 +196,7 @@ in `$DATA_DIR/provisioners.json`, replacing the example addresses and paths:
     "ram_mb": 65536,
     "labels": ["gpu"],
     "sbatch": ["--partition=gpu", "--gres=gpu:1", "--time=04:00:00"],
-    "prerun": ["module load cuda/12", "source ~/venvs/fluksio/bin/activate"]
+    "prerun": ["module load cuda", "source /path/to/node-venv/bin/activate"]
   }]
 }]
 ```