chore: bump version to 0.1.12 [skip ci]

24h at the collector's 30s cadence is ~2800 readings, and each one pulls in
~9 FilamentSnapshot rows. That snapshot fetch — not the metrics query — was
what made the dashboard slow: 25,308 rows and 1.2s of the 2.09s spent
building the context.

Capping at 1440 keeps roughly one point per minute over a day, finer than a
canvas can resolve, and applies to the public demo API too since it shares
sample_metrics().

  printer dashboard  2.09s / 410 KB  ->  0.50s / 207 KB
  public demo API                    ->  0.31s / 116 KB

Claude-Session: https://claude.ai/code/session_01AoYZiGtU3zEuFEGvP6zoZD

bambu_run/views.py

@@ -28,7 +28,13 @@ _METRICS_API_FIELDS = [
     'gcode_state', 'print_type', 'subtask_name',
     'external_spool',
 ]
-_MAX_CHART_POINTS = 3000
+# 24h at the collector's 30s cadence is ~2800 readings, and every one of them
+# also drags in ~9 FilamentSnapshot rows — that snapshot fetch, not the metrics
+# query, is what dominated the dashboard's load time (measured: 2.09s of context
+# building and a 410 KB payload at 3000). 1440 caps the series at roughly one
+# point per minute over a day, which is finer than any chart can resolve on
+# screen, and cuts both the server time and the payload by ~4x.
+_MAX_CHART_POINTS = 1440
 # Fallback window for requests that don't specify a full date range. Without it a
 # bare API call scans the entire metrics table.
 _DEFAULT_WINDOW = timedelta(hours=24)

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "bambu-run"
-version = "0.1.11"
+version = "0.1.12"
 description = "Django reusable app for Bambu Lab 3D printer monitoring and filament inventory management"
 readme = "README.md"
 license = {text = "MIT"}