anthropic-os

name	anthropic-os
description	Improve a personal or team operating system with self-evolving loops, CASH allocation, 3B creativity, predictive coding, and diagnostics. Use when the user wants to redesign a work method, learning loop, or cognitive operating system.
version	1.2
updated	2026-06-01
assumes	The user wants to improve a work system rather than solve a single isolated task.
conflicts_with	Do not use as a replacement for startup-evaluation, daily-okr, or project-flow-ops when the user asks for those narrower workflows.

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:

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 → Human intuition + AI quantitative weighted voting

---

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

---

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)

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

---

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

---

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

---

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?"

---

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

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)

---

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)

---

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

---

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