- Step 1: Generate API token (with blue circle)
- Step 2: Installation commands (with green circle when unlocked)
- Shows locked preview of Step 2 before token generation, making it clear
that installation commands will appear after generating a token
- Addresses user feedback about the unclear agent creation flow
When a user deletes an API token that was migrated from .env, track
the hash in a suppression list to prevent it from being re-migrated
on the next restart.
Changes:
- Add SuppressedEnvMigrations field to Config
- Add env_token_suppressions.json persistence
- Check suppression list during env token migration
- Record suppressed hash when deleting "Migrated from .env" tokens
- Update RemoveAPIToken to return the removed record
Related to #871
- Add validateAIFindings() that cross-checks AI findings against actual metrics
- Filter out low-confidence findings (CPU <50%, memory <60%, disk <70%)
- Always allow critical findings, backup issues, and reliability findings through
- Update AI system prompt with stricter thresholds and explicit noise examples
- Add 'before creating a finding' checklist for AI (the 3am test)
- Update AI.md docs with clear value proposition and expectations
- Add comprehensive tests for the validation layer
This ensures paying users get immediate value without noise.
PBS datastores are now displayed in the Storage overview alongside PVE
storage. Each PBS datastore is converted to a Storage entry with:
- type: 'pbs'
- content: 'backup'
- shared: true
- active: based on PBS instance status
This provides a complete picture of all storage resources in one view
while keeping detailed PBS info in the dedicated PBS section.
Closes#869
- Fix PVE nodes: buildNodeUrl in ProxmoxNodesSection.tsx now prioritizes
guestURL over host (was ignoring guestURL entirely)
- Add PBS support: GuestURL field added to PBSInstance config, model,
and API handlers
- Add PMG support: GuestURL field added to PMGInstance config, model,
and API handlers
- Update NodeSummaryTable to use guestURL for PBS nodes
- Frontend types updated for PBS/PMG guestURL support
The Guest URL setting in node configuration now works correctly across
all node types. When set, it takes priority over the Host URL when
clicking on node names to navigate to the Proxmox/PBS/PMG web UI.
Closes#870
- Add IsMockResource() helper to detect mock data resources by naming patterns
- Filter out heuristic findings from mock resources when PULSE_MOCK_MODE is off
- Mock findings still appear when demo mode is enabled
- Ensures users don't see mock data findings in production
The AI demo/mock findings were using a non-existent MOCK_ENABLED
env var. Changed to PULSE_MOCK_MODE which is the actual env var
used by the mock data system.
The staging images in preflight are intentionally amd64-only for speed,
but the publish workflow was just copying them instead of building
multi-arch. Now builds linux/amd64,linux/arm64 from source at publish.
Related to #868
- Add AnalyzeKubernetes to PatrolConfig and enable by default
- Add analyzeKubernetesCluster() for K8s heuristic analysis
- Detect offline clusters, nodes not ready, CrashLoopBackOff pods
- Detect failed/pending pods and unavailable deployments
- Add K8s clusters to LLM context in buildInfrastructureSummary()
- Add kubernetes_cluster as valid resource type in AI prompt
- Enhance analyzeDockerHost() for Docker/Podman
- Podman-aware messaging based on runtime
- Add unhealthy container detection (health checks)
- Add exited container with error detection
- Add high CPU detection (>90%)
- Add stale host detection (10+ minutes)
- Tiered severity for restarts (>10 = critical)
- Add 20 new tests for K8s and Docker analysis
New commands:
pulse mock enable - Enable mock mode
pulse mock disable - Disable mock mode
pulse mock status - Show current status
Makes it easy to toggle between mock and real data without
manually editing config files.
When MOCK_ENABLED=true, Pulse now injects realistic AI patrol
findings to showcase the AI features without requiring actual
LLM API calls. This enables the demo instance to demonstrate:
- Critical/warning/info findings with realistic content
- Patrol run history
- Actionable recommendations
Also includes refinements to dismissal logic from earlier work:
- Only 'not_an_issue' creates permanent suppression
- 'expected_behavior' and 'will_fix_later' just acknowledge
When a Docker host has a custom URL configured (e.g., Portainer link),
the host name in the container table's group headers is now a clickable
link that opens the URL in a new tab. A link icon appears next to the
host name to indicate it's clickable.
Related to #860
1. Fixed TestNewConfigPersistenceFailsWhenEncryptedDataPresentWithoutKey
- Test was picking up real encryption key from /etc/pulse during migration
- Now temporarily moves system key during test for proper isolation
- Uses t.Cleanup to ensure key is restored even on failure
2. Cleaned up console.log statements in production code
- Dashboard.tsx: replaced console.log with logger.debug for metadata events
- CompleteStep.tsx: removed verbose agent detection debug logs
These changes reduce log noise in production while maintaining debug
capability in development mode.
Addresses #866 - agents were logging 'WebSocket connection failed' warnings
even during normal reconnection scenarios (server restart, network blip, etc).
Changes:
- Normal close errors (1000, 1001, connection reset) now log at Debug level
- Only log Warning after 3+ consecutive failures
- Changed 'Connecting to Pulse' from Info to Debug to reduce noise
- Successful connections still log at Info level
The WebSocket is only used for AI command execution, not metrics, so
transient disconnections don't affect monitoring functionality.
User feedback fields (DismissedReason, UserNote, TimesRaised, Suppressed, Source)
were not being saved to disk, causing 'expected behavior' dismissals to be lost
after Pulse restarted.
- Add missing fields to AIFindingRecord in persistence.go
- Update FindingsPersistenceAdapter to save/load these fields
- Add comprehensive tests for dismissal persistence round-trip
Fixes issue where Frigate storage warning kept reappearing despite being
marked as expected behavior.
Since watch/info findings are filtered from the UI and never shown
to users, don't include them in the patrol run status summary.
This makes the summary consistent with what users actually see.
The LLM was confusing VMIDs because they weren't included in the
context. Now the formatted context shows:
### Container: ollama (VMID 200) on minipc
This prevents the AI from referencing the wrong VMID when generating
findings and recommendations.
When the service restarts, it now checks if a patrol ran within the
last hour. If so, it skips the initial patrol to avoid wasting API
tokens during development/maintenance when the service is restarted
frequently.
The scheduled patrol runs (every 6 hours) are not affected.
100 samples was causing 326k+ input tokens which is expensive.
24 samples (hourly resolution) still provides good pattern visibility
while significantly reducing token cost.
Estimated reduction: ~75% fewer metric tokens.
When AI patrol fails due to API issues like insufficient balance, invalid
API key, or rate limiting, we now create a finding that appears in the
AI Insights tab. This makes the issue visible to users rather than hidden
in logs.
The finding includes:
- Clear description of the issue (e.g., 'Insufficient API credits')
- Recommendation for how to fix it
- Evidence showing the actual error message
When a patrol run encounters errors (e.g., LLM call failed), don't
display 'All healthy' in the summary as that's misleading - the
analysis didn't complete properly.
Now shows 'Analysis incomplete (N errors)' instead, which correctly
explains why the status badge shows red/error.
Modern LLMs have 100k+ token contexts. 100 samples over 24h gives
~15 minute resolution while adding minimal token overhead.
This lets the LLM see fine-grained patterns, short spikes, and
accurately distinguish anomalies from normal behavior.
The in-memory MetricsHistory only retains 24 hours of data, not 7 days.
Changed computeGuestMetricSamples to use trendWindow24h instead of
trendWindow7d, and reduced sample count from 24 to 12 points.
This ensures the LLM actually receives metric samples in the context,
which wasn't happening before because the 7-day query returned empty data.
rcourtman