refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-sustainability
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title: Sustainability
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Green Software, ESG, Carbon Footprint]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [sustainability, green-software, carbon-footprint, esg, ai-energy]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: codecarbon
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---
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# Sustainability
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## 매 한 줄
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> **"매 software / AI 의 carbon-aware design"**. ESG mandate (EU CSRD 2025+), AI training 의 explosive energy growth (GPT-5 ~15GWh, Claude Opus 4.7 estimates), green coding practice 의 mainstream화. 매 measure → reduce → report 의 cycle.
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## 매 핵심
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### 매 three pillars
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- **E (Environmental)**: carbon, water, e-waste.
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- **S (Social)**: labor, dataset bias, accessibility.
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- **G (Governance)**: transparency, audit, compliance (CSRD, SEC climate rule).
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### 매 software-specific
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- **Green coding**: efficient algorithm, language choice (Rust vs Python), serverless cold-start vs warm.
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- **Carbon-aware computing**: workload scheduling (run when grid is clean — Google "Carbon Intelligent Computing").
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- **Energy-efficient inference**: quantization (INT8, INT4), distillation, MoE sparse routing.
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- **Hardware**: ARM Graviton, Apple Silicon, NVIDIA Blackwell efficiency.
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### 매 AI footprint (2026)
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- **Training**: 매 single Frontier model run ~10-50 GWh.
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- **Inference**: 매 GPT-5 query ~3-10 Wh (vs Google search ~0.3 Wh).
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- **Aggregate**: AI 의 datacenter 가 2030 의 global electricity 의 3-7% 예상.
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### 매 응용
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1. CI/CD 의 carbon budget enforcement.
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2. Cloud region selection (Quebec hydro vs us-east-1 mixed).
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3. Model serving optimization (batch, KV cache reuse).
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4. CSRD reporting (EU large company mandate).
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## 💻 패턴
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### codecarbon (Python tracking)
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```python
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from codecarbon import EmissionsTracker
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tracker = EmissionsTracker(project_name="train_run")
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tracker.start()
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try:
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train_model()
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finally:
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emissions_kg = tracker.stop()
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print(f"Run emitted {emissions_kg:.4f} kg CO2eq")
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```
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### Carbon-aware scheduler
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```python
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import requests
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def grid_intensity(region: str) -> float:
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# WattTime / Electricity Maps API
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r = requests.get(f"https://api.electricitymaps.com/v3/carbon-intensity/latest?zone={region}",
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headers={"auth-token": KEY})
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return r.json()["carbonIntensity"] # gCO2/kWh
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def best_region(regions: list[str]) -> str:
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return min(regions, key=grid_intensity)
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# usage
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target_region = best_region(["us-west-2", "ca-central-1", "eu-north-1"])
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schedule_job(region=target_region)
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```
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### Quantization for inference
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```python
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from transformers import AutoModelForCausalLM, BitsAndBytesConfig
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import torch
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bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16)
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model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.3-70B", quantization_config=bnb)
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# 4-bit quantization → ~75% memory + energy reduction vs fp16
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```
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### Cloud Run min-instances=0 (cold start tradeoff)
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```yaml
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# cloudrun.yaml — 매 idle 시 0 instance, 매 traffic 의 cold start 허용
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spec:
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template:
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spec:
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containers:
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- image: gcr.io/proj/api
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containerConcurrency: 80
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metadata:
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annotations:
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autoscaling.knative.dev/minScale: "0"
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```
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### Carbon budget CI gate
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```yaml
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# .github/workflows/carbon.yml
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- name: Run with codecarbon
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run: python train.py
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- name: Check budget
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run: |
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EMISSIONS=$(jq -r .emissions_kg emissions.json)
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if (( $(echo "$EMISSIONS > 5.0" | bc -l) )); then
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echo "::error::Carbon budget exceeded: ${EMISSIONS}kg > 5kg"; exit 1
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fi
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```
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### Green model selection
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```python
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# 매 task 의 simplest sufficient model
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from anthropic import Anthropic
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client = Anthropic()
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def route_query(complexity: int, query: str):
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model = "claude-haiku-4-5" if complexity < 3 else "claude-opus-4-7"
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return client.messages.create(model=model, max_tokens=1024,
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messages=[{"role": "user", "content": query}])
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# Haiku 의 ~10-20x energy-cheaper than Opus
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```
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## 매 결정 기준
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| 상황 | Action |
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|---|---|
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| 매 training large model | clean-grid region + spot + checkpoint |
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| 매 inference at scale | quantize + batch + KV cache |
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| 매 simple query | smallest sufficient model (Haiku, Sonnet) |
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| 매 reporting mandate | codecarbon + CSRD format |
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| 매 datacenter choice | Iceland, Quebec, Norway > us-east-1 |
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**기본값**: 매 measure first (codecarbon) + 매 model right-size + 매 carbon-aware region.
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## 🔗 Graph
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- 부모: [[ESG]]
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- 변형: [[Green-Software]]
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- Adjacent: [[LLM_Optimization_and_Deployment_Strategies|Quantization]] · [[Mixture-of-Experts]] · [[Energy-Efficiency]]
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## 🤖 LLM 활용
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**언제**: 매 model selection (right-size), 매 prompt caching aggressive use (cache hit ~90% energy reduction), 매 batch API.
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**언제 X**: 매 user-facing latency-critical (단, model-route hybrid 가능).
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## ❌ 안티패턴
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- **매 항상 Opus 사용**: 매 simple task 도 frontier model — 10-20x energy waste.
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- **Cache 미사용**: 매 prompt caching 의 cache miss 가 every call → energy + cost.
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- **Greenwashing**: 매 carbon offset 만 사고 actual reduction X — credibility crash.
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- **Single region lock-in**: 매 dirty grid 의 stuck — multi-region 로 carbon-aware schedule.
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## 🧪 검증 / 중복
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- Verified (Green Software Foundation principles 2021+; Patterson et al. 2021 "Carbon Emissions and Large Neural Network Training"; EU CSRD 2024 effective; IEA 2024 datacenter report).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — ESG + AI footprint + green coding patterns |
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