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**
```text
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:

| 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.

---

## 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** |

---

## 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 |

---

## 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