Anthropic OS — Cognitive Symbiont Engine

From tool-based architecture to living cognitive symbiont. The brain is the best learning machine — instead of simulating its structure, we follow its evolutionary principles.

Usage Template

Prompt

Use anthropic-os on this work system. Diagnose the current loop, identify the big bet, improve feedback, and define the next self-evolution step.

Use Case

• Improving a team or personal operating system, not just completing a single task.

Expected Result

• The agent returns a work-method diagnosis with growth loops, allocation choices, feedback mechanisms, and next experiments.

Output Example

• A work-system memo with current loop, big bet, feedback signal, operating principle, and next experiment.

Verification Case

• The output names one measurable system change and how it will be reviewed after the next cycle.

Verified Effect

• A team or personal work system gains an explicit improvement loop rather than relying on one-off productivity tactics.

Success Metrics

• Output names one big bet, one 70/30 allocation choice, one CASH feedback signal, and one review date.
• The next self-evolution step can be completed inside two weeks.
• At least one failure mode or success disaster is recorded before execution.

Core Philosophy

"DNA only provides the basic blueprint. It is every subsequent encounter that shapes who we become." — David Eagleman, Livewired

"The brain and the computer are, in principle, no different." — Stephen Hawking, A Brief History of Time

System Architecture

┌──────────────────────────────────────────────────────────────────┐
│                    Cognitive Symbiont Engine                       │
├──────────────────────────────────────────────────────────────────┤
│  L0: Computational Equivalence — Brain ≈ LLM (Hawking)           │
│  L1: Livewired Layer — Plasticity, Competition, Constraint       │
│  L2: 3B Algorithms — Bending / Breaking / Blending              │
│  L3: 7 Flywheels — Each infused with 3B                          │
│  L4: Predictive Coding — Collective prediction error minimization │
│  E0: Evolution Engine — Self-upgrade via 3B iteration            │
└──────────────────────────────────────────────────────────────────┘

AIOS 4C Operating Audit

Use this audit when the operating system is meant to become the default work surface rather than a side tool:

Do not add cadence before context, connections, and capabilities are strong enough to support it. A scheduled prompt without the right context and proof path is automation theater.

Bike Method Permission Ladder

Treat permissions as keys, not intentions. Move through autonomy stages only after evidence accumulates:

Never grant send, publish, pay, delete, or production-write capability merely because the prompt says not to misuse it. Remove the key or put the action behind approval until the loop has passed lower stages.

L0: Computational Equivalence

"The brain and computer are fundamentally the same in information processing." — Hawking

L1: Livewired Layer — Three Core Principles

L2: 3B Creativity Algorithms

The three core evolutionary algorithms that turn mechanical workflows into living systems:

Bending (扭曲)

Mutate existing success patterns into new contexts.

Prototype: High-conversion copy
Bending → Twist into different product lines
Bending → Twist into different user segments
Bending → Twist into different media formats

Breaking (打破)

Eliminate the worst-performing patterns. Break path dependency.

Prototype: Worst-performing experiment hypothesis
Breaking → Regular "kill day" to cull
Breaking → Break local optima loops
Breaking → Destroy outdated evaluation metrics

Blending (融合)

Fuse elements from different domains to create novel patterns.

Prototype: Growth data + support data
Blending → Cross-domain insights
Blending → A/B test + user survey fusion
Blending → Human intuition + AI quantitative weighted voting

L3: 7 Flywheels × 3B Upgrade

1. Growth Flywheel (CASH + 3B)

2. Engineering Flywheel (Claude Code + Two-Week + 3B)

3. Culture Flywheel (Hive Mind + 3B)

4. R&D Flywheel (Harness + 3B)

5. Strategy Flywheel (70/30 + 3B)

6. Personal Effectiveness Flywheel (Working Backwards + 3B)

7. Symbiosis Flywheel (Human-AI Fusion + 3B)

L4: Predictive Coding — The Hidden Self

"The primary driver of our behavior is not a conscious monarch, but a vast, efficient, and contradictory unconscious system." — David Eagleman

Collective Predictive Coding Protocol

Step 1: Dual Prediction
  "Human vote" and "AI vote" execute simultaneously

Step 2: Expose Prediction Error
  After decision: actual result vs predicted deviation

Step 3: Error-Driven Reconstruction
  High-frequency contradictory "error predictions" → training data
  Dynamically adjust trust weights in future decisions

"Every disagreement becomes fuel for system self-optimization."

The Dual Wings of Consciousness

Wing 1: Storytelling (The Brain — Three-Pound Universe)

"The brain is a storyteller." — Michael Gazzaniga

AI generates narrative chains alongside every decision output, helping humans understand complex decisions and enabling inter-AI communication.

Wing 2: Time's Arrow (A Brief History of Time)

4-Stage Evolution Path

Seed Practice Library (10+)

L2: Diagnostics Layer — 6-Dimension Maturity Model

Readiness Scorecard (1-5)

Probe Questions

1. "In the last two weeks, how many structured growth experiments did your team complete?" (0 / 1-2 / 3-5 / >5)
2. "What's the longest task an engineer can drive without a PM?" (<1 day / 1-5 days / 2 weeks / >1 month)
3. "Describe an instance where AI agents independently completed a full dev task."
4. "What percentage of your code was AI-generated last month?"
5. "How fast do you go from idea to production experiment?"

L3: Prescription Layer — Adaptive Routing

if maturity_growth < 3:
    practices = ["CASH_lite", "Weekly experiment sprint", "Simple dashboard"]
elif maturity_ai_dev < 3:
    practices = ["Two-week rule", "Harness basics", "AI code review"]
else:
    practices = ["Full CASH", "Agent-cluster programming", "Hive-mind protocol"]

Each practice includes: step-by-step guide + success disaster warnings + example KRs.

L4: Execution Layer — Copy-Ready Artifacts

CASH Experiment Prompt

[SYSTEM] You are a growth experiment AI. Generate 3 A/B test hypotheses.
[INPUT] {experiment_data, goal, channels}
[OUTPUT] Each hypothesis: [variable][predicted effect][sample size][risk]

Hive Mind Voting Template

:honeybee: Proposal: [one line]
Vote: :bee: (yes) | :x: (no) + reason
Deadline: 2 hours
Pass: ≥5 :bee: and <2 :x:

Working Backwards Canvas

1. 2-year future state: _____
2. Key metric shift: _____
3. 3 problems to solve: _____
4. 1 thing to start this week: _____

Success Disaster Checklist

Before any "go" decision, ask:
[ ] What breaks if this works too well?
[ ] What's our load spike plan?
[ ] Can we roll back in 5 minutes?
[ ] Who needs to be paged?

E0: Evolution Engine — Self-Improvement Loop

Feedback Collector

Every interaction ends with:

• How many days to implement? (integer)
• What was least clear? (multi-choice)
• Outcome notes (open text)

Metrics Repository

Automatic Tuning

• Monthly meta-learning update
• Practice weight adjustment based on rating/time-to-value
• Template self-correction (v2 generation from usage patterns)
• New practice proposals from user feedback

3B Self-Evolution Protocol

The system evolves itself using the same 3B algorithms it prescribes:

Evolution Report (Monthly)

1. Top 3 most valuable practices
2. Bottom 2 weakest areas
3. 1 architecture adjustment suggestion
4. Self-upgrade script snippet (one-click apply)

5-Step Rapid Decision Protocol

Decision Triage Matrix

Universal Skill Stack

When to Use

• Team wants to adopt Anthropic-style growth and engineering methods
• Need rapid, structured decision-making under uncertainty
• Designing automated growth experimentation systems
• Building AI-native development workflows
• Introducing radical transparency and collective decision culture

Quality Gates

• [ ] Practice stored in unified YAML schema
• [ ] AIOS audit covers Context, Connections, Capabilities, and Cadence
• [ ] Permission ladder identifies current autonomy stage and next gate
• [ ] Diagnostic covers all 6 maturity dimensions
• [ ] Prescription follows adaptive routing rules
• [ ] Execution artifacts are copy-ready
• [ ] Feedback collected after every interaction
• [ ] Metrics repository updated with each run
• [ ] Monthly 3B self-evolution cycle executed
• [ ] Predictive coding error logged after each decision
• [ ] Narrative chain generated for complex decisions
• [ ] Monthly self-evolution report generated

Connections

• [[wiki/concepts/AI Agent Harness]] — Agent runtime governance
• [[wiki/concepts/MAD 框架]] — Diffusion gap vs org change
• [[wiki/entities/Anthropic]] — Company entity page
• [[wiki/entities/Claude Code]] — Core growth product
• [[sources/2026-05-10-anthropic-work-methods]] — Source synthesis