SE-AI Mind — Darwin 0.1

Build an AI that becomes better at being yours.

Open infrastructure for building persistent AI Minds that improve through governed experience. Intelligence should be optimized, not merely scaled.

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Core Packages

Modular architecture

Runtime Adapters

Ollama, MLX, llama.cpp, Local

Evolution Layers

From prompts to architecture

0.1

Darwin Version

First generation

Core Capabilities

Model ≠ Mind

A Mind combines model, memory, skills, tools, routing, evaluation, and evolution into a persistent system.

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Cheapest Inference is No Inference

Cognitive compiler checks cache, memory, skills, and tools before calling a model.

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Evolution with Guardrails

Sandbox → Benchmark → Security/Privacy/Cost Review → Human Approval → Promote/Rollback.

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Intelligence Efficiency

Metric: Verified Useful Outcome / (Compute + Memory + Latency + Energy + Cost)

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Core Distinction

Model ≠ Mind

A model predicts. A Mind persists. The distinction is fundamental.

Capability	Raw Model	SE-AI Mind
Neural Substrate (Weights)
Persistent Memory
Skills Library
Tool System
Knowledge Base
Model Routing
Evaluation Engine
Evolution Engine
Genome/Versioning
Hardware Awareness
Privacy Gates
Benchmark Infrastructure

Memory as Intelligence

10 memory types with lifecycle: capture → validate → score → consolidate → compress → index → retrieve → decay → archive

Skills as Intelligence

Composable, versioned procedures with inputs/outputs, prerequisites, tools, model requirements, and evaluators

Governance as Intelligence

Privacy gates, policy engine, capability permissions, audit trails — all enforced before execution

Evolution Engine

Governed Evolution Loop

Every improvement passes through 12 gates. No autonomous self-modification. Human approval required for promotion.

OBSERVE

Monitor task success, latency, cost, accuracy, hallucination rate, memory retrieval, skill reuse

Details

Task success rate tracking
Latency & cost monitoring
Accuracy & factuality metrics
Memory & skill utilization

IDENTIFY WEAKNESS

Detect regressions, capability gaps, quality drops, cost overruns, privacy risks

Details

Regression detection
Capability gap analysis
Quality threshold breaches
Cost anomaly detection

GENERATE CANDIDATES

Propose changes across 10 evolution layers: prompts, routing, memory, skills, knowledge, models, adapters, architecture

Details

Prompt optimization
Routing rule updates
Memory config changes
Skill creation/composition
Model selection policies
Adapter/LoRA training
Architecture modifications

SANDBOX

Isolated execution environment with resource limits, no network access, frozen dependencies

Details

Process/container/VM isolation
CPU/memory/disk limits
Network disabled
Frozen dependency graph
Deterministic execution

BENCHMARK

Run MindBench suite: warmup → measurement → cooldown. Compare against baseline genome.

Details

Warmup runs (2)
Measurement runs (10)
Cooldown periods
Statistical significance
Hardware profiling

REGRESSION TEST

Verify no capability loss on previous task suites. Automated pass/fail with thresholds.

Details

Full task suite re-run
Capability preservation check
Performance delta analysis
Automated pass/fail

SECURITY REVIEW

Capability permissions, threat signatures, sandbox escape attempts, privilege escalation checks

Details

Permission analysis
Threat signature scan
Sandbox integrity check
Escalation attempt detection

PRIVACY REVIEW

Data classification, cross-privilege access, external routing, redaction compliance

Details

Data flow analysis
Privacy gate verification
External routing audit
Redaction validation

COST REVIEW

Compute, latency, token, energy delta. Must be within budget or justified by quality gain.

Details

Compute cost delta
Latency impact
Token usage change
Energy estimation
Budget compliance

COMPARE

Statistical comparison: candidate vs baseline across all metrics. Minimum 5% improvement threshold.

Details

Multi-metric comparison
Statistical significance
5% minimum improvement
Pareto frontier analysis

PROMOTE

Human approval required. New genome created with lineage. Old genome preserved for rollback.

Details

Human-in-the-loop approval
Genome versioning
Lineage tracking
Instant rollback capability

MONITOR & ROLLBACK

Post-promotion monitoring. Auto-rollback on regression. Continuous evaluation.

Details

Real-time metric tracking
Automated rollback triggers
Canary deployment
Continuous evaluation

Safety First, Evolution Second

No candidate reaches production without passing all 12 gates. Every promotion creates a new genome with full lineage. Instant rollback is always one click away.

Security Review RequiredPrivacy Review RequiredCost Review RequiredHuman Approval RequiredInstant Rollback

Intelligence Efficiency

Measuring What Matters

Not benchmark scores. Real efficiency: verified useful outcomes per unit of resources consumed.

Verified Useful Outcome

Numerator

∑

Denominator

Compute (FLOPs)

Memory (GB)

Latency (ms)

Energy (Wh)

Cost ($)

MindBench Metrics (20 Dimensions)

Every benchmark run captures all 20 metrics. No cherry-picking. Full provenance recorded.

Task Success↑ Better

Accuracy↑ Better

Factuality↑ Better

Hallucination↓ Better

Latency↓ Better

Tokens/sec↑ Better

tok/s

RAM Peak↓ Better

CPU Avg↓ Better

GPU Util↑ Better

Model Load↓ Better

Compute↓ Better

FLOPs

Energy↓ Better

Cost↓ Better

Tool Calls↓ Better

Escalation↓ Better

Mem Retrieval↑ Better

Skill Reuse↑ Better

Adaptation↑ Better

Forgetting↓ Better

Regression↓ Better

Reliability↑ Better

Evidence-Based Comparison

Hardware-Aware

Benchmarks include full hardware profile: CPU, RAM, GPU, thermal, battery, load. Results tied to actual capacity.

Reproducible by Design

Fixed seeds, config snapshots, dependency hashes, hardware fingerprints. Anyone can re-run and verify.

System Architecture

Four-Layer Architecture

Clean separation of concerns. Each layer builds on the one below. Swap any component without breaking the stack.

FOUNDATION