- Prefix unused catch error variables with underscore in AIChat.tsx and Alerts.tsx
- Reduced lint warnings from 8 to 0
- Clean lint output with no warnings or errors
- Removed unused getPulsePort/getPulseWebSocketUrl imports
- Optimized polling to only run on proxmox tab (DiagnosticsPanel handles its own fetching)
- Remaining lint warnings are for legacy code that may need future refactoring
- Added dynamic security score calculation (0-100%)
- Added gradient header that changes color based on score:
- Green (>=80%): Strong security
- Amber (50-79%): Moderate security
- Red (<50%): Weak security
- Improved visual hierarchy with color-coded status cards
- Added 'Critical' labels for important missing features
- Changed icons to lucide-solid for consistency
- Created standalone DiagnosticsPanel component with modern visual design
- Gradient header with system health indicator
- Card-based layout for System Runtime, PVE Nodes, PBS, Discovery
- Status badges and improved visual hierarchy
- Export functionality (full and GitHub-sanitized)
- Reduced Settings.tsx from 8,035 to 5,956 lines (26% reduction)
- Removed ~2,079 lines of inline diagnostics code
- Improved maintainability by encapsulating diagnostics logic
- Improved AI Settings UX:
- Cost Controls: Gradient design, RECOMMENDED badge, $ prefix
- Shows daily/weekly budget equivalents when value is entered
- Auto-Fix: Streamlined acknowledgment with clear button instead of checkbox
- Better visual hierarchy throughout
- Ceph: Complete rework with summary cards (storage, clusters, services, pools),
search with type-to-search, proper loading/disconnected/empty states, cluster
overview table, pools table with filtering, health badges with tooltips
- Replication: Add summary cards (total/healthy/issues/next sync), status filter
buttons (All/Healthy/Warning/Error), search with type-to-search, countdown timers
for next sync with overdue/imminent highlighting, row highlighting for problem jobs,
error tooltips, loading state
- Mail Gateway: Add aggregate summary cards across all instances (mail/spam/virus/
quarantine/queue), visual threat rate progress bars, status badges with health
indicators, queue depth tooltips with breakdown, streamlined stats grid with
color-coded threat categories, improved cluster nodes table
All three pages now follow the same design patterns as Dashboard, Hosts, Docker,
and Storage using Card, EmptyState, and consistent styling.
- Add sortable headers to Clusters and Nodes views
- Add ColumnPicker dropdown for Pods view (toggle Namespace, Cluster, Ready, Restarts, Image, Age columns)
- Column visibility persists to localStorage
- Sorting persists across sessions for all views
- Add sortable table headers for Pod and Deployment views
- Click column headers to toggle sort direction
- Sort state persists across sessions
- Add namespace dropdown filter for Pods/Deployments views
- Auto-populates from available namespaces
- Include namespace filter in reset and active filters check
The set_resource_url tool had an incorrect example ID format ('pve1-delly-101')
which caused the AI to save URLs with wrong IDs that didn't match the actual
guest IDs used by Pulse ('instance-VMID' format like 'delly-150').
This fix updates the tool description to clearly document the correct format,
so URLs saved by the AI will now properly appear in the dashboard.
Backend:
- Seed OCI classification from previous state so containers never
'downgrade' to LXC if config fetching intermittently fails
- Prevent type regression in recordGuestSnapshot when OCI was previously detected
- Move metrics zeroing before snapshot recording for cleaner flow
Frontend:
- Add isOCIContainer() memo that checks both type and isOci flag
- Use isOCI helper in Dashboard.tsx for AI context building
- Include oci-container type in useResources container conversion
- Preserve isOci and osTemplate fields through legacy conversion
This ensures OCI containers retain their classification even when
Proxmox API permissions or transient errors prevent config reads.
The useResources.ts hook was hardcoding type: 'lxc' when converting
unified resources to legacy container format, causing OCI containers
to intermittently display as LXC when WebSocket updates occurred.
Now preserves the actual type from platformData (oci/lxc).
- Refactored enrichContainerMetadata to not return early when container is stopped
- Status API calls are still skipped for stopped containers (as expected)
- Config fetch now runs regardless of status, enabling OCI detection
- Added test for OCI detection on stopped containers
Discovered: Proxmox 9.1 requires VM.Config.Options permission to read
OCI container configs (not just VM.Audit). Document this in setup guides.
- Added isOCIContainerByConfig() to detect OCI containers by:
- Presence of 'entrypoint' field (only OCI containers have this)
- Combination of ostype=unmanaged, cmode=console, and lxc.signal.halt
- This is needed because Proxmox doesn't persist ostemplate after creation
- Now supports detection of already-created OCI containers (like the test alpine container)
- Frontend: Add ociImage memo to extract clean image name from osTemplate
- Frontend: Show OCI image name in type badge tooltip
- Frontend: Display OCI image in OS column when no guest agent info available
- Frontend: Include ociImage in AI context data for selected OCI containers
- Backend: Differentiate OCI containers as 'oci_container' type in AI context
- Backend: Add Metadata field to ResourceContext for extensibility
- Backend: Include oci_image in container metadata for AI analysis
- Backend: Update section heading to 'LXC/OCI Containers' in AI context
This follows Docker container patterns to avoid duplicating work.
- Backend: Add IsOCI and OSTemplate fields to Container model
- Backend: Add extractContainerOSTemplate() and isOCITemplate() detection functions
- Backend: Detect OCI containers via ostemplate config and set type to 'oci'
- Frontend: Add isOci and osTemplate to Container interface
- Frontend: Add 'oci-container' to ResourceType with distinct purple badge
- Frontend: Update Dashboard filters to include OCI containers with LXC
- Tests: Add comprehensive unit tests for OCI detection logic
OCI containers are detected by checking the ostemplate for patterns like:
- oci: prefix (e.g., oci:docker.io/library/alpine:latest)
- docker: prefix (e.g., docker:nginx:latest)
- Known registry URLs (docker.io, ghcr.io, gcr.io, quay.io, etc.)
- Local templates with oci- or oci_ filename patterns
- Add DOMPurify sanitization for AI chat markdown rendering (XSS fix)
- Configure DOMPurify to add target=_blank and rel=noopener to links
- Update system prompt to align with command approval policy
- Clarify safe vs destructive commands in prompt
- Improve patrol auto-fix mode guidance with safe operation list
- Add verification requirements for auto-fix actions
- Update observe-only mode to be clearer about read-only restrictions
UX improvement: The Auto-Fix Model dropdown was too far from the
Patrol Auto-Fix toggle, making it hard to find.
Now the flow is:
1. Scroll to 'AI Patrol Behavior' section
2. Check the acknowledgement checkbox
3. Enable 'Patrol Auto-Fix' toggle
4. Model selector appears RIGHT BELOW the toggle
The model dropdown only appears when auto-fix is enabled (since
it's irrelevant otherwise).
Add collapsible panel to display AI-learned intelligence:
Features:
- Failure predictions with time estimates
- Color-coded severity (overdue=red, <3 days=amber, etc.)
- Human-readable event types and confidence percentages
- Resource dependency/correlation display
- Shows source → target relationships with avg delay
- Expandable/collapsible design to save space
Styling:
- Purple gradient theme consistent with AI branding
- Responsive with dark mode support
- Clean card-based layout for predictions
- Badge showing total insight count
Ready to integrate into Alerts page or resource details.
Add configurable model specifically for automatic remediation actions:
Backend (internal/config/ai.go):
- Add AutoFixModel field to AIConfig
- Add GetAutoFixModel() getter with fallback chain:
AutoFixModel -> PatrolModel -> Model
Frontend (AISettings.tsx, types/ai.ts):
- Add auto_fix_model to AISettings types
- Add Auto-Fix Model dropdown (only shows when patrol_auto_fix enabled)
- Falls back to patrol model if not set
API (ai_handlers.go):
- Add auto_fix_model to response and update request
- Handle saving/loading the new field
Rationale:
- Auto-fix takes real actions, may warrant a more capable model
- Patrol observation can use cheaper models for cost savings
- Gives users granular control over model costs vs reliability
- Model hierarchy: Chat > AutoFix > Patrol > Default
Create internal/ai/correlation package:
1. Correlation Detector (detector.go):
- Tracks events across resources
- Detects when events on one resource follow events on another
- Calculates average delay between correlated events
- Confidence scoring based on occurrence count
- Persists to ai_correlations.json
2. Features:
- GetCorrelations() - All detected relationships
- GetCorrelationsForResource() - Relationships for one resource
- GetDependencies() - What resources depend on this one
- GetDependsOn() - What this resource depends on
- PredictCascade() - Predict what will be affected
- FormatForContext() - AI-consumable summary
3. Integration:
- Wire to alert history in router startup
- Map alert types to correlation event types
- Add correlation context to enriched AI context
Example AI context now includes:
'When local-zfs experiences high usage, database often follows within 5 minutes'
This enables the AI to understand infrastructure dependencies
and predict cascade failures.
All tests passing.
Connect alert system to failure prediction:
1. Add AlertCallback to HistoryManager:
- OnAlert() method to register callbacks
- Callbacks invoked when alerts are added
- Called outside lock to prevent deadlocks
2. Expose OnAlertHistory() on alerts.Manager:
- Pass-through to HistoryManager.OnAlert()
- Enables external systems to track alerts
3. Wire pattern detector in router startup:
- Register callback when pattern detector is created
- Convert alert types to trackable events
- Pattern detector now learns from production alerts
Now every alert (memory_warning, cpu_critical, etc.) is recorded as
a historical event for pattern analysis. The AI can predict:
'High memory usage typically occurs every ~3 days (next expected in ~1 day)'
All tests passing.
Create internal/ai/patterns package:
1. Pattern Detector (detector.go):
- Records historical events (high memory, OOM, restarts, etc.)
- Detects recurring failure patterns
- Calculates average interval between occurrences
- Computes confidence based on pattern consistency
- Predicts when failures will occur again
- Persists to ai_patterns.json
2. Event types tracked:
- high_memory, high_cpu, disk_full
- oom, restart, unresponsive
- backup_failed
3. Integration:
- Wire PatternDetector into router startup
- Add to AI context in buildEnrichedContext
- FormatForContext generates failure predictions
Example AI context now includes:
'OOM events typically occurs every ~10 days (next expected in ~3 days)'
This enables proactive alerts before problems recur.
All tests passing.
Phase 4 - Remediation logging integration:
1. logRemediation hook after tool execution:
- Only logs run_command tools (main remediation action)
- Records resourceID, resourceType, findingID
- Extracts problem summary from user prompt
- Truncates output for storage (max 1000 chars)
- Distinguishes automatic (patrol) vs manual (chat) actions
2. buildRemediationContext for system prompts:
- Shows 'Past Successful Fixes for Similar Issues' section
- Uses keyword matching to find relevant past fixes
- Shows 'Remediation History for This Resource' section
- Includes timestamps and outcomes
This enables the AI to say things like:
- 'This worked before: apt clean to free 6GB (resolved)'
- 'Last time on this resource: restarted nginx (resolved)'
All tests passing.
Complete Phase 3 integration:
- Initialize ChangeDetector and RemediationLog in StartPatrol
- Add SetChangeDetector/SetRemediationLog to handler chain:
Router -> AISettingsHandler -> Service -> PatrolService
- Persist change history to ai_changes.json
- Persist remediation log to ai_remediations.json
- Both use the Pulse config directory for storage
Operational memory is now fully integrated:
- Change detector tracks infrastructure changes on each patrol
- Recent changes (24h) are appended to AI context
- Remediation log ready for command execution logging
All tests passing.
Integrate operational memory into patrol context:
- Add changeDetector and remediationLog fields to PatrolService
- Add SetChangeDetector and SetRemediationLog methods
- Integrate change detection into buildEnrichedContext
- Convert state to ResourceSnapshots for change tracking
- Append recent changes summary to AI context
The AI now sees a 'Recent Infrastructure Changes (24h)' section
showing events like:
- VM 'web-server' status changed: running → stopped (2h ago)
- 'db-server' migrated from node1 to node2 (4h ago)
- 'web-server' memory increased: 4 GB → 8 GB (1d ago)
All tests passing.
Phase 3 of Pulse AI differentiation:
Create internal/ai/memory package with:
1. Change Detection (changes.go):
- Tracks infrastructure changes: creation, deletion, config changes
- Detects status changes (started, stopped)
- Detects VM/container migrations between nodes
- Detects CPU/memory configuration changes
- Detects backup completions
- Persists change history to ai_changes.json
- GetChangesSummary for AI context
2. Remediation Logging (remediation.go):
- Records actions taken to fix problems
- Tracks command, output, and outcome
- Links to AI findings via findingID
- GetSimilar finds past similar problems
- GetSuccessfulRemediations for learning
- Persists to ai_remediations.json
3. Type exports (memory_exports.go):
- Clean re-exports from ai package
This enables the AI to say things like:
- 'This VM was migrated 2 hours ago'
- 'Memory was increased from 4GB to 8GB yesterday'
- 'Last time this happened, restarting nginx resolved it'
All tests passing.
When there's only 1 day of AI usage data, the sparkline was invisible
because a single point draws at x=0 with no width. Now draws a
horizontal line across the full width so users can see the value.
This happens when AI has just been enabled and there's only one
day of recorded usage so far.
Complete Phase 2 baseline integration:
- Add baseline_exports.go for clean type aliasing
- Wire baseline store initialization into StartPatrol
- Implement startBaselineLearning background loop
- Runs initial learning after 5 min delay
- Updates baselines every hour from metrics history
- Learns from 7 days of data for nodes, VMs, containers
- Add SetBaselineStore methods throughout the chain
(Router -> AIHandler -> Service -> PatrolService)
- Persists baselines to data directory as JSON
The baseline learning loop:
1. Starts automatically when AI patrol starts
2. Queries metrics history for all resources
3. Computes mean, stddev, percentiles for cpu/memory/disk
4. Saves baselines to disk for durability
5. Anomaly detection uses these baselines in context builder
All tests passing.
Phase 2 of Pulse AI differentiation:
- Create internal/ai/baseline package for learned baselines
- Implement statistical baseline learning with mean, stddev, percentiles
- Add z-score based anomaly detection with severity classification
(low, medium, high, critical based on standard deviations)
- Integrate baseline provider into context builder
- Wire baseline store into patrol service with adapters
- Add anomaly enrichment to resource contexts
Key features:
- Learn computes baseline from historical metric data points
- IsAnomaly and CheckAnomaly detect deviations from normal
- Persists baselines to disk as JSON for durability
- Formatted anomaly descriptions for AI consumption
Example: 'Memory is high above normal (85.2% vs typical 42.1% ± 8.3%)'
The baseline store needs to be initialized and triggered to learn
from metrics history. Next step is adding the learning loop.
All tests passing.
