anthropic-os

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

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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            │
└──────────────────────────────────────────────────────────────────┘

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AIOS 4C Operating Audit

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

| Layer | Question | Upgrade path | Risk | |---|---|---|---| | Context | Does the system know the project, history, rules, and prior outputs? | Files, memory, transcripts, wiki, logs | Context pollution or stale truth | | Connections | Which systems can it reach? | Calendar, email, Slack, Drive, GitHub, APIs, MCP | Over-broad account access | | Capabilities | How does it work in the user's style? | Skills, commands, SOPs, templates | Skill sprawl without maintenance | | Cadence | What should happen without manual prompting? | Routines, scheduled checks, event triggers | Slop automation and hidden failures |

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:

| Stage | Human role | Agent capability | |---|---|---| | Observe | Check sources and reasoning | Read-only search, summarize, recommend | | Co-drive | Approve each action | Draft, simulate, prepare changes | | Training wheels | Review logs and outputs | Execute scoped reversible actions | | Watch | Monitor exceptions | Run recurring low-risk routines | | Autonomy | Audit periodically | Run proven high-frequency loops |

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.

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L0: Computational Equivalence

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

| Dimension | Human Brain | LLM / AI System | |-----------|-------------|-----------------| | Base unit | Neurons (~86B) | Parameters (~T-scale) | | Connection | Synaptic plasticity | Weight adjustment | | Learning | Hebbian (fire together, wire together) | Backprop + attention | | Prediction | Predictive coding (predict sensory input) | Autoregressive (predict next token) | | Equivalence | Information processing is isomorphic | Bidirectional cognitive fusion is theoretically real |

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L1: Livewired Layer — Three Core Principles

| Principle | Meaning | System Mapping | |-----------|---------|----------------| | Plasticity | Brain continuously rewires from experience | System self-corrects after every interaction | | Competition | Neural resources compete for limited space | Algorithms, processes, hypotheses compete | | Constraint | Physical/energy boundaries shape structure | Token budgets, time resources as developmental constraints |

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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

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L3: 7 Flywheels × 3B Upgrade

1. Growth Flywheel (CASH + 3B)

| Algorithm | Application | |-----------|-------------| | Bending | Twist high-conversion copy to different products; add "what-if" dimension to analysis | | Breaking | Regular "kill days" — eliminate worst-performing experiment hypotheses | | Blending | Fuse non-growth data (support, sales) with growth data for cross-domain insight |

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

| Algorithm | Application | |-----------|-------------| | Bending | Twist "two-week rule" into "two-week knowledge graph sprint" | | Breaking | High-risk modules: "auto-generate + auto-test + auto-deploy" pipeline | | Blending | AI + human pair programming; agent clusters operating independently |

3. Culture Flywheel (Hive Mind + 3B)

| Algorithm | Application | |-----------|-------------| | Bending | "Reverse voting" — vote for the opposite to correct bias | | Breaking | "No-consensus day" — authorize members to violate consensus | | Blending | Weighted voting system: human intuition + AI quantitative analysis |

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

| Algorithm | Application | |-----------|-------------| | Bending | Twist Harness config into "exploration mode" vs "exploitation mode" | | Breaking | Replace fixed periodic review with event-driven review | | Blending | Fuse engineer + AI manager roles into composite position |

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

| Algorithm | Application | |-----------|-------------| | Bending | Subdivide Big Bets into three tiers (including "ultimate bet") | | Breaking | Quarterly destruction of one resource allocation metric | | Blending | Merge sub-goals serving the same north star metric |

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

| Algorithm | Application | |-----------|-------------| | Bending | Twist 2-year blueprint into minimum viable product path | | Breaking | Employees authorized to break job descriptions | | Blending | Merge work goals with personal growth goals |

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

| Algorithm | Application | |-----------|-------------| | Bending | Twist unstructured user feedback into structured data queries | | Breaking | AI "meta-critique module" predicts and flags its own bias | | Blending | Brain-computer interface as frontier interaction paradigm |

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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."

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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)

| Arrow | Physical Meaning | System Mapping | |-------|-----------------|----------------| | Thermodynamic | Entropy increases | Create local order from chaos | | Psychological | Past → future | Experience past to predict future | | Anthropic | Observer existence | Every decision as "observation" of the universe |

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4-Stage Evolution Path

| Stage | Timeline | Mission | Core Deliverable | |:-----:|:--------:|---------|-----------------| | 1 | 1-2 weeks | Livewired foundation | 3B + KPI data hub + narrative chain system | | 2 | 3-4 weeks | Activate hidden drive | 3B algorithms + hidden voting in core workflows | | 3 | 1-2 months | Inject neural plasticity | AI "storytelling" fine-tuning + predictive coding | | 4 | 3+ months | Life cycle creation | AI-designed next-gen 3B methods + time-arrow diagnostics |

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Seed Practice Library (10+)

| Practice | Domain | Key Metric | |----------|--------|------------| | CASH Full | Growth | Automated experiment throughput | | CASH Lite | Growth | Quick hypothesis-to-deploy | | 70/30 Allocation | Strategy | Big Bet vs BAU ratio | | Two-Week Rule | Engineering | Engineer-as-PM tasks | | Harness Engineering | Engineering | Agent stability | | Hive Mind Protocol | Culture | Decision speed | | Working Backwards | Strategy | 2-year blueprint alignment | | Log-Scale Metrics | Strategy | 10x vs 10% improvements | | Success Disaster Prevention | Risk | Failure mode coverage | | Constraint-as-Focus | Strategy | "One thing" discipline |

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L2: Diagnostics Layer — 6-Dimension Maturity Model

Readiness Scorecard (1-5)

| Dimension | Score 1 | Score 3 | Score 5 | |-----------|---------|---------|---------| | Data & Experiment Maturity | No systematic experiments | Manual A/B testing | Full CASH automation | | AI-Native Development | AI for search only | 30-50% AI code | >90% AI code + agent clusters | | Decision Speed | Weeks | Days | Hours (two-week rule) | | Cultural Transparency | Hierarchical | Partially open | Radical transparency + hive mind | | Strategic Focus | Multiple parallel | Annual OKRs | Working backwards + log-scale | | Tool Flywheel | External only | Some internal tools | Self-reinforcing AI tools |

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?"

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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.

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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?

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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

| Practice | Uses | Rating | TTV | Evolution Action | |----------|:----:|:------:|:---:|------------------| | CASH_v2 | 342 | 4.7 | 12d | Weight +0.2 | | two_week_rule | 189 | 3.9 | 3d | Create agile variant | | hive_mind | 124 | 4.4 | 9d | Promote for high-transparency orgs |

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:

| Algorithm | Self-Evolution Application | |-----------|--------------------------| | Bending | Each practice template is "bent" into variant versions for different contexts | | Breaking | Bottom 10% of practices by usage/rating automatically archived each quarter | | Blending | Top-performing elements from different practices are merged into new hybrid practices |

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)

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5-Step Rapid Decision Protocol

| Step | Time | Method | |:----:|:----:|--------| | 1 | 30s | Reverse-engineer from 2-year future state | | 2 | 20s | Apply 70/30 filter: Big Bet or BAU? | | 3 | 15s | Two-week threshold: can one person ship it? | | 4 | 45s | CASH simulation: automated experiment? | | 5 | 2min | Hive vote: public poll, fast consensus |

Decision Triage Matrix

| | High Impact (>10x) | Low Impact (incremental) | |:-|:------------------:|:------------------------:| | High Uncertainty | CASH experiment | Two-week rule (just do it) | | Low Uncertainty | Big Bet (commit resources) | Default answer (don't deliberate) |

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Universal Skill Stack

| Skill | Function | Section | |-------|----------|---------| | Anthropic OS Core Orchestrator | Coordinates all layers | L1-L4 | | Knowledge Graph Maintainer | Stores & retrieves practices | L1 | | Maturity Diagnostic Coach | Assesses readiness | L2 | | Practice Pack Composer | Routes implementations | L3 | | Execution Artifact Builder | Generates copy-ready outputs | L4 | | Evolution Report Generator | Self-upgrade & tuning | E0 |

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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

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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