Daemon Active|v1.0.0

Autonomous CPU Brain Daemon

Self-healing reinforcement learning for Linux infrastructure. Monitor, optimize, and execute system tasks with sub-second daemon telemetry and safety-first policy enforcement.

99.99%System UptimeNominal
1,240Active DaemonsOptimized
<12msLatencyStable
Safety Locked
RL Optimized
brain://daemon-telemetry
LIVE
CPU Load
42.1%
Nominal
Memory
12.4GB
Cached
Disk I/O
4.2MB/s
Stable
CPU Core 0
Load: 42%
3.2 GHz
70% Max
CPU Core 1
Load: 38%
3.2 GHz
70% Max
CPU Core 2
Load: 45%
3.2 GHz
70% Max
Policy: Reinforcement Learning
Config
Daemon Telemetry

System Benchmark Telemetry

Real-time performance metrics for autonomous infrastructure governance and self-healing loops.

1.0s

Tick Latency

Daemon heartbeat frequency for real-time system governance

Daemon Logged
99.9%

Safe Execution

Syscall boundary integrity for all automated daemon actions

Daemon Logged
45ms

Inference Loop

Sub-second model reasoning for adaptive resource management

Daemon Logged
12k+

Memory Episodes

Vector-indexed state history for reinforcement learning

Daemon Logged
System Stack:
Linux Kernel(Syscall API)
PyTorch(Neural Cortex)
SQLite(Memory Store)
Rich CLI(Telemetry HUD)
Psutil(Daemon Telemetry)
SYSTEM TELEMETRY & RL

Autonomous daemon infrastructure.

Self-healing reinforcement learning and sub-second telemetry for Linux infrastructure.

RL: POLICY
Q-learning policy
Adaptive reinforcement learning for real-time daemon optimization.
EPSILON_DECAY0.999
Convergence:98.4%

Alpha

0.1

Gamma

0.9

CORTEX
Local LLM cortex
Reasoning engine for complex system threat mitigation.
THREAT: CPU_OVERLOAD

Renicing top process to stabilize system load at 70%.

Model: llama3.2:3bMitigated
Timeout:20s
Provider:Rule-based
DB: RECALL
Episodic recall
SQLite-backed memory for historical state analysis.
14:32 UTCEPISODE_ID

"Dropped caches to resolve memory pressure at 75% usage."

Memory committed to demo_memory.db

Recall K

8

Short-term

200

1.0s

Tick Frequency

70%

CPU Max Goal

85°C

Temp Threshold

90%

Disk Max Goal

COGNITIVE FEEDBACK LOOP

Autonomous system self-healing cycle

Continuous telemetry, episodic memory, and reinforcement learning converge to maintain infrastructure stability.

STEP01
STAGE 1
Daemon Active
Sensor telemetry ingestion

Capture real-time CPU, RAM, and disk metrics with sub-second precision for immediate daemon analysis.

Tick rate1.0s
Data sourcepsutil
CPU / MEM / DISK / TEMP
STEP02
STAGE 2
Vector Recall
Episodic memory retrieval

Recall past system states and successful outcomes using vector similarity search in local SQLite memory.

Recall K8 episodes
Storagedemo_memory.db
Similarity dot-product
STEP03
STAGE 3
Cortex Logic
Cortex decision synthesis

Evaluate current threats against system goals and generate optimal remediation actions via LLM logic.

Providerllama3.2:3b
Timeout20s
Threat-based reasoning
STEP04
STAGE 4
Policy Update
Actuator execution & RL

Apply system fixes, record rewards, and update Q-learning policy weights for future optimization.

Alpha0.1
Gamma0.9
Q-table convergence
REINFORCEMENT ENGINE

Deterministic policy convergence

Q-learning algorithms optimizing system health via local state-action rewards.

01 // STATE
Bucket Key
CPU|MEM|DISK state
02 // REWARD
Score Delta
Current – Previous
03 // POLICY
Q-Update
Alpha * (Target – Q)
04 // SAFETY
Rate Limit
Max actions per min
BRAIN Telemetry HUDLive Daemon

System State: Nominal (RL Active)

Policy Stable
IDDaemon TaskExecWait
0001
Cron Job: CleanupSYS-9402 • Oct 24
1.0s—
0002
Alert Bot: CPU SpikeSYS-2089 • Oct 24
—1.0s
0003
Cron Job: Log RotateSYS-9403 • Oct 23
0.5s—
0004
Alert Bot: Mem LimitSYS-9403 • Oct 23
—0.5s
0005
Cron Job: Disk CheckSYS-2090 • Oct 22
—2.0s
0006
Alert Bot: Temp WarnSYS-9404 • Oct 22
2.0s—
Total Latency3.5s3.5s
RL Governor Active
System Governance

Adaptive RL governors vs. fragile cron scripts.

Replace static cron jobs and rigid alert bots with autonomous reinforcement learning governors built for Linux infrastructure.

Adaptive RL governors

Self-Healing

System state evolves with workload. Static thresholds are replaced by dynamic, self-optimizing policy convergence.

Sub-second telemetry

High-Speed

Generate real-time system state snapshots and episodic memory logs with one click, complete with audit trails.

Distributed daemon sync

Sync-Ready

Allow multiple system governors to work within identical memory spaces without race conditions or lockouts.

Daemon: Observe Mode

Run Autonomous CPU Brain

Clone the repository and initialize your local daemon. Monitor system telemetry, optimize process priority, and let the reinforcement learning agent manage your infrastructure.

Daemon heartbeat active
No root-level lock-in
Live telemetry support

Autonomous CPU Brain is a self-healing reinforcement learning daemon for Linux infrastructure, providing sub-second telemetry and automated system optimization.

RL-Driven OptimizationKernel Safety

Navigation

System Architecture

  • Reinforcement LearningRL_CORE
  • Daemon TelemetryDAEMON_V1
  • Episodic MemoryMEM_DB
  • Safety ConstraintsSAFE_SYS
Daemon Status: Active Monitoring Loop
System metrics nominal

© 2026 Autonomous CPU Brain. Self-healing infrastructure.