AI 블랙아웃 및 모델 변동성에 따른 기업용 엔지니어링 도입 영향 분석 

## **서론: 인공지능 기반 엔지니어링의 패러다임 전환과 새로운 리스크의 출현**

2026년에 접어들며 기업용 소프트웨어 산업은 인공지능(AI)에 의해 근본적으로 재편되는 파괴적 혁신기를 맞이하고 있다.[1] 대규모 언어 모델(LLM)과 지능형 에이전트의 결합은 소프트웨어 개발 생명주기(SDLC) 전반에서 생산성을 20%에서 30%가량 향상시켰으며, 특히 빌드와 테스트 단계에서는 50%에 육박하는 효율 개선을 달성했다.[1] 그러나 이러한 비약적인 발전은 역설적으로 기업의 기술적 기반을 인공지능이라는 외부 인프라에 강력하게 결속시키는 결과를 초래했다.

현대 기업의 엔지니어링 파이프라인은 이제 단순히 코드를 작성하는 도구를 넘어, 요구사항을 분석하고 아키텍처를 설계하며 실시간으로 인시던트를 관리하는 '디지털 워크포스'에 의존하고 있다.[2] 이러한 상황에서 발생하는 'AI 블랙아웃(AI Blackout)'—즉, AI 모델의 서비스 중단이나 접근 불능 상태—은 단순한 도구의 부재를 넘어 기업의 운영 지능 자체가 마비되는 '에이전틱 암네시아(Agentic Amnesia)'를 유발한다.[2] 인공지능 에이전트가 자율적으로 실행하던 복잡한 다단계 워크플로우가 중단될 경우, 이를 수동으로 복구하기 위해 필요한 조직적 지식과 숙련된 인적 자원이 이미 고갈된 상태일 가능성이 높기 때문이다.[2]

또한, 인공지능 모델은 고정된 상수가 아닌 변동하는 변수이다. 모델 제공업체의 미세한 업데이트나 프롬프트 반응성의 변화, 즉 '모델 변동성(Model Variability)'은 기존에 설계된 엔지니어링 파이프라인의 성능을 예기치 않게 저하시키고 막대한 기술적 부채를 발생시킨다.[3] 본 보고서는 이러한 AI 가용성 리스크와 모델 변동성이 기업용 엔지니어링 도입에 미치는 영향을 심층 분석하고, 이에 대응하기 위한 하네스 엔지니어링(Harness Engineering) 최적화 및 비즈니스 연속성 계획(BCP)의 구체적인 로드맵을 제시한다.

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## **AI 의존도 및 가용성 리스크 평가**

### **현재 개발 Flow에서의 AI 비중과 가용성 분석**

기업의 개발 프로세스 내에서 AI의 비중은 가파르게 상승하고 있다. 단순히 코드 완성을 보조하는 수준에서 벗어나, 이제 AI는 제품 아이디어를 구조화된 사양서로 변환하고, CI/CD 파이프라인 내에서 지능적으로 테스트 케이스를 생성하며, 보안 취약점을 사전에 탐지하는 역할을 수행한다.[4] 2025년 기준 대다수의 소프트웨어 기업이 AI 지원 도구를 채택하고 있으며, 고성능 조직일수록 AI 도입이 소프트웨어 전달 처리량(Throughput)의 증가와 밀접한 관련이 있는 것으로 나타났다.[6]

그러나 이러한 높은 의존도는 가용성 측면에서 치명적인 리스크를 내포한다. 단일 LLM 제공업체에 의존하는 애플리케이션의 경우, 해당 업체가 99.3%의 가용성을 제공하더라도 전체 시스템 가용성은 이보다 낮아지는 구조를 가진다.[7] 2025년 한 해 동안 발생한 단 한 번의 주요 LLM 서비스 중단 사태는 전 세계 수천 개 기업에 수십억 달러의 손실을 입혔으며, 복구까지 평균 7시간이 소요된 것으로 보고되었다.[7] 기업의 가용성 손실을 수치화하면 다음과 같은 상관관계가 성립한다.

![image.png](attachment:e7fd5b36-8b1a-4e6c-adf4-e4914459e207:image.png)

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)

여기서 $A_{LLM}$이 1.0(100%)이 아닐 경우, 내부 시스템이 아무리 견고하더라도 전체 서비스의 가용성은 외부 모델 공급자에게 종속된다. 특히 에이전트 기반의 워크플로우는 다단계 추론을 필요로 하므로, 특정 단계에서 모델의 응답 지연이나 오류가 발생할 경우 전체 프로세스가 연쇄적으로 붕괴하는 취약성을 지닌다.[2]

### **AI 블랙아웃 시 자동화 파이프라인의 중단 지점 식별**

AI 블랙아웃이 발생했을 때 하네스 엔지니어링 기반의 자동화 시스템이 멈추는 핵심 지점은 크게 세 가지로 분류된다.

첫째, **지능형 요구사항 해석 및 컨텍스트 매핑 단계**이다. 최신 개발 도구는 자연어 사양을 코드로 변환하기 위해 전체 저장소의 컨텍스트를 분석하는데, 이 과정에서 AI가 부재하면 개발자는 수천 개의 파일을 직접 탐색하며 영향도를 파악해야 하는 초기 단계의 병목에 직면한다.[8]

둘째, **자가 치유(Self-healing) CI/CD 파이프라인**이다. AI는 빌드 실패 시 로그를 분석하여 수정 제안을 하거나 자동으로 수정된 코드를 커밋하는 기능을 수행한다.10 블랙아웃 시 이러한 자동 복구 기능이 정지되면서, 과거에는 수 분 내에 해결되던 사소한 환경 설정 오류가 엔지니어의 수동 개입을 기다리는 수 시간의 가동 중단으로 이어진다.[8]

셋째, **동적 인프라 및 리소스 관리 지점**이다. AI는 실시간 수요에 따라 클라우드 리소스를 동적으로 확장하거나 비용을 최적화하는 역할을 수행하는데, 시스템 단절 시 고정된 인프라 구성으로 회귀하게 되어 갑작스러운 트래픽 급증에 대응하지 못하거나 불필요한 비용이 발생하게 된다.[5]

| **파이프라인 단계** | **AI 수행 역할** | **블랙아웃 시 영향도** | **복구 난이도** |
| --- | --- | --- | --- |
| 계획 및 분석 | 사양 정교화 및 백로그 생성 | 매우 높음 (전략적 명확성 상실) | 높음 |
| 코딩 및 구현 | 보일러플레이트 제거 및 로직 제안 | 중간 (개발자 직접 코딩 전환) | 낮음 |
| 테스트 및 QA | 테스트 케이스 자동 생성 및 버그 예측 | 높음 (테스트 커버리지 급감) | 중간 |
| 배포 및 운영 | 자가 치유 및 인시던트 관리 | 매우 높음 (다운타임 장기화) | 매우 높음 |

[1]

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## **모델 변동성 및 기술적 부채 분석**

### **모델 드리프트가 프롬프트 및 에이전트 성능에 미치는 영향**

모델 변동성(Model Variability)은 모델 제공업체의 API 업데이트나 가중치 변경으로 인해 동일한 입력에 대해 다른 결과를 내놓는 현상을 의미한다. 이는 '프롬프트 드리프트(Prompt Drift)'라고 불리는 심각한 성능 저하를 유발한다.3 연구에 따르면 GPT-4와 같은 주력 모델조차 6개월 이내에 지시사항 준수율에서 31% 이상의 변동성을 보였으며, 이는 기존에 신중하게 설계된 프롬프트가 더 이상 의도한 대로 작동하지 않음을 의미한다.[3]

이러한 변동성은 특히 다단계 에이전트 워크플로우에서 치명적이다. 'CACE(Changing Anything Changes Everything)' 원칙에 따라, 파이프라인의 첫 번째 단계에서 발생하는 미세한 출력 변화가 하위 단계의 입력으로 전달되면서 오차가 증폭(Amplify)되기 때문이다.3 예를 들어, 코드 리뷰 에이전트가 모델 업데이트 후 평소보다 완화된 기준으로 결과를 내놓기 시작하면, 이는 보안 검토 단계를 통과하는 취약한 코드의 증가로 이어져 시스템 전체의 안정성을 저해한다.[3]

### **모델 변동에 따른 수정 비용과 기술적 부채 측정**

모델 변동성으로 인해 발생하는 기술적 부채는 '침묵의 부채'이다. 이는 컴파일 오류처럼 즉각적으로 나타나지 않고, 사용자 경험의 미묘한 악화나 시스템 성공률의 완만한 하락으로 나타난다.3 인공지능 관련 기술적 부채(SATD) 분석 결과, 파이썬 기반 LLM 애플리케이션 파일의 약 54%가 OpenAI 통합 과정에서, 12.35%가 LangChain 프레임워크 사용 과정에서 부채를 축적하고 있는 것으로 확인되었다.[11]

구체적인 비용 요소는 다음과 같다.

- **프롬프트 부채(Prompt Debt)**: 특정 모델 버전에 하드코딩된 프롬프트나 동적으로 조정되지 않는 템플릿으로 인해 발생하는 유지보수 비용 (비중 6.61%).[11]
- **평가 침식(Eval Erosion)**: 모델 성능을 측정하는 테스트 데이터셋이 실제 운영 환경의 변화를 반영하지 못해 발생하는 잘못된 신뢰 비용.[3]
- **임베딩 진부화(Embedding Staleness)**: 모델 업데이트로 인해 기존 벡터 데이터베이스에 저장된 임베딩 값이 새로운 모델의 벡터 공간과 일치하지 않게 되어 발생하는 재색인(Re-indexing) 비용.[3]

| **부채 유형** | **주요 원인** | **비즈니스 영향** | **수정 전략** |
| --- | --- | --- | --- |
| 프롬프트 부채 | 모델-특수적 최적화 | 응답 품질 저하 및 환각 발생 | 프롬프트 라이브러리 버전 관리 |
| 하이퍼파라미터 부채 | Temperature 등 설정값 고정 | 출력의 일관성 및 창의성 상실 | 동적 튜닝 및 메타데이터 추적 |
| 프레임워크 부채 | LangChain 등 추상화 계층 의존 | 시스템 복잡도 증가 및 버그 추적 곤란 | 경량화된 전용 하네스 전환 |
| 비용 부채 | 토큰 사용량 최적화 실패 | 운영 예산 초과 및 ROI 악화 | 프롬프트 캐싱 및 sLLM 도입 |

[11]

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## **대체 인프라 및 Redundancy 전략 검토**

### **로컬 sLLM 및 다중 모델 전환의 유연성 분석**

특정 모델 제공업체의 장애에 대응하기 위해 가장 유망한 전략은 'sLLM(Small Large Language Model) 기반의 하이브리드 아키텍처'이다. 1B에서 13B 파라미터 규모의 소형 모델은 이제 NVIDIA RTX 4090과 같은 단일 소비자용 GPU에서도 50ms 미만의 지연 시간으로 구동이 가능할 정도로 발전했다.14 이러한 모델은 클라우드 API보다 10배에서 25배 빠른 응답 속도를 제공하며, 네트워크가 단절된 상황에서도 핵심 기능을 수행할 수 있는 회복 탄력성을 부여한다.[15]

성공적인 Redundancy 전략을 위해서는 'AI 게이트웨이' 도입이 필수적이다. Bifrost와 같은 엔지니어링 도구는 기본 모델(Primary Model)의 실패를 밀리초 단위로 감지하여 사전에 설정된 순서대로 백업 모델(Fallback Model)에 요청을 전달한다.7 이 과정에서 개발자는 애플리케이션 코드를 단 한 줄도 수정할 필요가 없으며, 게이트웨이 단에서 모델 간의 응답 형식 차이를 변환하고 관리한다.[7]

### **Minimal Viable Workflow(MVW) 및 데이터 전략**

물리적 블랙아웃 상황에서 기업은 모든 기능을 유지하려 하기보다, 생존에 필수적인 '최소 기능 동작 워크플로우(MVW)'로 전환해야 한다. 이는 다음의 원칙을 따른다.

1. **최소 기능 데이터셋(MVD) 활용**: 방대한 데이터 전체를 분석하기보다 의사결정에 가장 예측력이 높은 최소한의 고품질 데이터만을 활용하여 로컬 모델의 부하를 줄인다.[17]
2. **티어별 에스컬레이션 구조**: 간단한 작업(코드 자동 완성, 문법 검사)은 1차적으로 로컬 sLLM(Tier 1)이 처리하고, 복잡한 추론이 필요한 경우에만 클라우드 LLM(Tier 2)으로 요청을 보낸다.[19]
3. **오프라인 가용 모델 상시 대기**: Phi-3.5-mini나 Qwen2.5-0.5B와 같이 적은 자원으로도 구동 가능한 모델을 엔지니어의 로컬 머신이나 엣지 서버에 사전 설치하여 네트워크 단절 시에도 '지능형 자동 완성' 기능을 보존한다.[16]

| **구분** | **클라우드 전용 (Single-Stack)** | **하이브리드 Redundancy (Portfolio)** |
| --- | --- | --- |
| 가용성 목표 | 99.9% (외부 의존) | 99.99% (로컬 백업 포함) |
| 지연 시간 | 2-5초 (네트워크 경유) | 50-200ms (로컬 우선 처리) |
| 데이터 보안 | API 공급자 정책 종속 | 완전한 데이터 소버린티 확보 |
| 비용 구조 | 사용량 기반 가변 비용 (High) | 고정 인프라 + 필요시 클라우드 확장 |

[7]

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## **DevOps 하네스 엔지니어링 최적화 수준 진단**

### **하이브리드 자동화 파이프라인의 독립성 평가**

하네스 엔지니어링(Harness Engineering)은 인공지능 에이전트의 행동을 규정하고 결과물을 검증하는 '외골격'과 같은 시스템이다.21 진정으로 견고한 파이프라인은 AI가 부재하더라도 기존의 결정론적(Deterministic) 룰 기반 도구가 독립적으로 작동하여 최소한의 배포 안정성을 유지할 수 있어야 한다.

하이브리드 자동화의 핵심 구성 요소는 다음과 같다.

- **가이드(Guides)**: 시스템 프롬프트나 규칙 파일을 통해 AI의 행동 반경을 사전에 제약하는 순방향 제어 장치.[22]
- **센서(Sensors)**: AI의 출력물을 실시간으로 감시하고 에러를 포착하는 피드백 장치. 여기에는 정적 분석기, 린터, 타입 체크 도구가 포함된다.[22]
- **액션 게이팅(Action Gating)**: AI가 수정할 수 있는 디렉토리와 파일 유형을 제한하고, 특정 임계값을 넘는 변경 사항에 대해서는 반드시 인간의 승인을 요구하는 물리적 제어 계층.[23]

### **테스트 하네스의 모델 변화 민감도 및 검증 피라미드**

AI 에이전트의 결과물은 모델의 미세한 변화에 따라 급격히 달라질 수 있다. 이를 검증하기 위해 기업은 '검증 피라미드(Verification Pyramid)' 전략을 도입해야 한다.[24]

1. **계약 및 사양 기반 검증 (Base Layer)**: TLA+나 Dafny와 같은 형식 검증 도구를 사용하여 모델의 출력값이 수학적으로 정의된 사양을 준수하는지 확인한다. 이는 모델이 바뀌어도 변하지 않는 불변의 진리(Invariants)를 검증한다.[24]
2. **결정론적 시뮬레이션 테스트(DST)**: 결함 주입(Fault Injection)을 통해 모델이 생성한 코드가 비정상적인 상황에서도 의도한 대로 동작하는지 수천 번의 반복 테스트를 수행한다.[24]
3. **관측성 기반 피드백**: 모델 업데이트 후 배포된 시스템의 메모리 사용량, 네트워크 지연 시간 등을 실시간으로 수집하여, 이전 버전과 성능 차이가 발생할 경우 하네스가 자동으로 배포를 중단(Halt)한다.[23]

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## **비용 대비 효율성 및 비즈니스 연속성 계획(BCP) 수립**

### **개발 속도(Velocity)와 가동 중단 비용(Downtime Cost) 비교**

AI 도입의 ROI(투자 대비 수익)는 단순히 개발 속도 향상만으로 계산해서는 안 된다. AI가 도입된 SDLC에서는 인프라 및 토큰 비용이 전체 프로젝트 비용의 15%~20%를 추가로 점유하며, 제대로 관리되지 않은 AI 도구는 코드 리뷰 오버헤드를 30%까지 증가시킨다.[26]

비즈니스 가치는 다음 공식을 통해 산출될 수 있다.

[](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABcQAAACYCAYAAADKveqlAAAQAElEQVR4AezdB5w1T1Xn/+GPiIpgdo2ogIJidkXFgJhBMSsGEAOKAcSccwQTCuiqgGLOioprQnZNgBFzVswuomIWFPF/3vO757Gefu6d6Xsn3fCdV52p6urq7qpPV1dXnTpd9/87yl8IhEAIhEAIhEAIhEAIhEAIhEAIhMC+E0j5QiAEQiAEQiAEikAU4gUhLgRCIARCIARCYJ8JpGwhEAIhEAIhEAIhEAIhEAIhEAIhcBOBKMRv4rCf/1OqEAiBEAiBEAiBEAiBEAiBEAiBEAiB/SeQEoZACIRACMwmEIX4bFRJGAIhEAIhEAIhEAIhsG0Ekp8QCIEQCIEQCIEQCIEQCIEQWIdAFOLr0EraENgeAslJCIRACIRACIRACIRACIRACIRACITA/hNICUMgBM6ZQBTi5ww0pwuBEAiBEAiBEAiBEAiBEDgPAjlHCIRACIRACIRACIRACJw/gSjEz59pzhgCIRACZyOQo0MgBEIgBEIgBEIgBEIgBEIgBEIgBPafQEp4JQSiEL8S7LloCIRACIRACIRACIRACIRACBwugZQ8BEIgBEIgBEIgBK6KQBTiV0U+1w2BEAiBEDhEAilzCIRACIRACIRACIRACIRACIRACITAFRK4JIX4FZYwlw6BEAiBEAiBEAiBEAiBEAiBEAiBELgkArlMCIRACIRACGw3gSjEt/v+JHchEAIhEAIhEAK7QiD5DIEQCIEQCIEQCIEQCIEQCIEQ2HoCUYhv/S3a/gwmhyEQAiEQAiEQAiEQAiEQAiEQAiEQAvtPICUMgRAIgX0gEIX4PtzFlCEEQiAEQiAEQiAEQuAiCeTcIRACIRACIRACIRACIRACe0IgCvE9uZEpRghcDIGcNQRCIARCIARCIARCIARCIARCIARCYP8JpIQhcDgEohA/nHudkoZACIRACIRACIRACIRACEwJZDsEQiAEQiAEQiAEQuCgCEQhflC3O4UNgRAIgf8mkFAIhEAIhEAIhEAIhEAIhEAIhEAIhMD+E0gJrycQhfj1PLIVAiEQAiEQAiEQAiEQAiEQAiGwHwRSihAIgRAIgRAIgRC4gUAU4jcgSUQIhEAIhEAI7DqB5D8EQiAEQiAEQiAEQiAEQiAEQiAEQmAZgf1SiC8rYeJCIARCIARCIARCIARCIARCIARCIAT2i0BKEwIhEAIhEAIbEohCfENwOSwEQiAEQiAEQiAEroJArhkCIRACIRACIRACIRACIRACIbA5gSjEN2eXIy+XQK4WAiEQAiEQAiEQAiEQAiEQAiEQAiGw/wRSwhAIgRC4UAJRiF8o3pw8BEIgBEIgBEIgBEIgBOYSSLoQCIEQCIEQCIEQCIEQCIGLJhCF+EUTzvlDIAROJ5AUIRACIRACIRACIRACIRACIRACIRAC+08gJQyBLSAQhfgW3IRkIQRCIARCIARCIARCIARCYL8JpHQhEAIhEAIhEAIhEALbQSAK8e24D8lFCIRACOwrgZQrBEIgBEIgBEIgBEIgBEIgBEIgBEJg/wnsTAmjEN+ZW5WMhkAIhEAIhEAIhEAIhEAIhEAIbB+B5CgEQiAEQiAEQmCXCEQhvkt3K3kNgRAIgRAIgW0ikLyEQAiEQAiEQAiEQAiEQAiEQAiEwI4RiEJ8gxuWQ0IgBEIgBEIgBEIgBEIgBEIgBEIgBPafQEoYAiEQAiGwfwSiEN+/e5oShUAIhEAIhEAIhMBZCeT4EAiBEAiBEAiBEAiBEAiBENhLAlGI7+VtTaE2J5AjQyAEQiAEQiAEQiAEQiAEQiAEQiAE9p9AShgCIXCoBKIQP9Q7n3KHQAiEQAiEQAiEQAgcJoGUOgRCIARCIARCIARCIAQOmEAU4gd881P0EDg0AilvCIRACIRACIRACIRACIRACIRACITA/hNICUPgJAJRiJ9EJ/tCIARCIARCIARCIARCIARCYHcIJKchEAIhEAIhEAIhEAKnEIhC/BRA2R0CIRACV0Tgeeu671byoiVxpxJIghAIgXMg8DZ1jjuVxF1P4LVq801K4kIgBEJgVwm8dmX8biVxIRACIRACIbAHBM5ehCjEz84wZwiBEAiB8ybwAnXCHy358pKblcSFQAiEwGUQeOe6yC+UvGVJ3E0E3qW8nyu5a0lcCIRACFwtgc2v/ip16P8p+cSSuBAIgRAIgRA4eAJRiB98FQiAEAiBLSTwmMrTm5a8d8nflsSFwKEQeL4qKCu29y3/C0oeVfKwknuW3LJk6m5eEZ9b8pIlcWcn8FF1it8t+d6S1yg5dPe6BeBbSp5Y8sUl5+Fetk7CEh/rr6rw15R8TMntSpY5ivgPWrYjcSEQAiGwBoHvrLSPLHlIyf1K4pYT0K8weWCC+FMrydeWaKvfr/xVX21qo7XVlSQuBEIgBEJgVwhEIb69dyo5C4EQOEwCn1TFfq+Szyj5mZI5Tlv+I5Xwl0ueWvIrC/nVhd/bfGl+qeJ/sYTV45PK/6GSjy+JAqwgxF06AYNPA8+fqCv/S4k6/M3lf3LJ/UsoDtXRv6zwvUvaPU8FvrXkU0qeVRJ3dgL/Xqd4z5L/KsH8pcs/VGeS5fuq8M8soTzCpIIbuZevo76w5Oklf17iC6CHlf9hJR9S8qUlf1jyuJKXKGlHwaJtf9WOiB8CO0RAvX5K5Vd/Q99D264fQvRPbIu3/+cr3ZNLfrrEBNR3lO/ZeJny486PwMfVqfA22fwWFY77bwKvX8FvLPmnEhPD2v/Pq/AHl2irv6F8bfjnl6//Ud6xe1D9f3TJKmV57ZrtkjAEQiAEQuASCVCiXOLlcqkQCIEQCIETCLCC1dH+8UrDgqe8WU4n/M6V8rYldyyx3i15zUX41cu/Q8krlbxCySuWsH6R5o0q7LpfVP6vlXxTyYuUxIXAZRB497rIH5QYeBqcU8g+vrYpQnwlwWrW2s0fWXF/XfLtJeoqJTqlOeXtr1fcP5bEnQ+BP6rTfEAJJa57casKX6DbylPfonL13SWsud+n/GeUbOJevA7Spj6tfJOdlOzqO2vzd6g4bbS2+D0q/L9K3rFEO3z78rXNlOG3rvDcydFKGhcCW0Og23C/S/A6lStf/+h3kK776rq+iT6KvsobVLq7l2jbfT2hPdIvev6Kizs7Ae9YbE0+f0+dLpNtR0fqozbWpMx9i4kv1UzkWFrmrWpbv1rd/cAKm7g3CW/pGV+tPbDiHl5iwpSBSQXjQiAEQiAEdoVAFOK7cqeSz/0ikNKEwI0EDAZZu1oz/CNq93NL5rq/qYSUV5Qv1h//wdpu988VeKmSFyx5oZIXW8htytfpt+26tXns7lP/DQycp4JxIXAhBNRVFoDfVWc3QVPeEcusl6vAvUpYr6mHFIk/W9uPKKEooaD1NQNLwrYW/6naF3e+BExQsGK2ZIivVc737Nt/NtbblHnfVln9yZJNnMme36oDtakmcP6iwr6EeOXyP6GEBb7JnN+vMOX7h5dvv7bavlaGU7Swmq3dcSGwUwTuUrk1CaROf3qFR2f5CZNt3S8xsW/y53krkUlQE57PrjClIwWkd8VolVu74jYk4L360Dr2hUseW3KozsSn95vfzXjjBQRfTpqsMSFp8p0C/Pdqny8avr78e5R8dok6ql2mDK/No9+of39XEhcCqwlkTwiEwNYRiEJ8625JMhQCIXCgBAz4DAwp/ChIzoJhtPihLKQwX3U+HXjrNbNO7DSvVgGf95cXFwLnToDVrU+2Wak5+R/Xv7ctsSzFSWvmswLvdfV92lyHHLv/e/w//86bwJctTsgC7n8swofgsVb1ebyy+mFj/rpi3VkKPMufUGizdPUVz/efcqIfqP3WzmeRaNKyNo8VLSc9F9JsrSRjIbAgwEp8ETx6TgVMupW31JkEZamrve8Eb18BE0nlxZ0DAW3Sv9Z5TFqYhK7gQTnKcO0x5bYw4xHLs1mminJ7FQzt+WfVzh8r0Q9hxFLBo/RDjvIXAiEQArtHIArx3btnyXEIhMB2E9gkd6xle+C3qQKmr8sai7V5b1uLs8Mn+X5sadzPUnHcTjgEzoOA5XhYvlq6x/lMAPlM3uDS9mli0Do+IwanJn1OOy771yfgnvxmHeZrEeu5V/AgnC909I9Z//m9hXULbb1Z6846zvIEfkDzQ2vjH0rmuK+sRNYtL+/YRdFyjCH/dpxAW+AqhglRbbnwSWJNfUt7dBoTVb626O34mxPQxrR1+OfUaVqxW8G9d8r6dVVK1t7lHbGY1w/5itqY+3WmH/Ou5Ndc2ulrKBIIgYMkkELvKAEd/h3NerIdAiEQAntDwHrJli/xSeZcBfaqwvuMc9xnncNxe1XYJ6PjPla8Pl0e4xIOgbMQsAas5XwMPJ3HlxC+ThgVHuJPE1ZdnYYl10lfQHS6+JsRsHSIIx9Q/7QJ5e21s4TDBy1K2GVfbM7yTCSOX9s8uI56Qsk6zpcQY7sdRcs69JL2EgnMvpS2o5fGctDcOm3C8z8csBBLajnXYjPeGQmYXMbY+tjvesZz7dLhX1KZtZRVeUfPqn/vVvInJes4S7r5wtIxGG66tJbjIyEQAiEQAldEIArxKwKfy4ZACITAgoA1MVk92fTja/yzyJsNB7MA+pVh+6Sgz0TH/RTkrBvHuIRDYFMCrPqsGd5WgpTgBuCUf+uecxy4jorDdc+T9KcT+PZKYnkDE3Ysn2tzrx0liXV1WXOPEy9zCq3ttea4ui69NfG/WmADsYyQw6JoQSGy6wTuNinAXIW4pZo8j+Ph2qNxO+HNCZiUtma2MzzIvwMQy+58zFBOv93w1GF7nWC3034LIstarUMuaUMgBEJgSwjMUohvSV6TjRAIgRDYRwLvUoVi9VTekXUz+WcRSpk+3lIScz//fLs+aOH7Qb1FMF4InJnAV9UZxnVKfRXBurui13aU6H+/OGquYmWRPN6aBExc+HLFYW/l355LK4WeUuWc23ZW0iO/22D9bxMHtjGzTIrwJvKni4OiaFmAiLeUgB8a1odYunNm5HtUunF979o8dzf2Syi05/Z1XmOSE18DPWMSl82zEeh74UckLY917Wx7GHifKtNDS9o9qgJfX7Kp63Y6/ZBNCea4EAiBELhiAlGIX/ENyOVDIAQOnsB7LQg8u/xfLjmL86OcrzWcYG4n/bZ1jIFCecfur+o/BWZ5cSFwZgLqJAV4n+jnK/CtJWdx/1kHU1ie52fKdcq4JQSetIjz42svuAjvo0f5dudFwbrMi81Tvc+vFNrf8o6dNdf/7Ti02T/125H5AgKFyCoC71Q7vqdkbF9rc7b7gkrp90OmS61V9Lm6USE+d/1wGZgq+311MS6hIk3kbAS6rbNE3pue7VRbfbTJSkuldCa1z5/UGxv6aac3BJfDQiAEQmBbCEQhvi134srykQuHQAhcMYHXX1zfIPGsS5QYdI7t+hyFuB/09ON5L7XIB8tbFmN/vdiOFwJnJfDpkxM8YrK9yeat66BfK+k1PCsYd0EEg9dwTAAAEABJREFUWmFyizq/Nqa8vXTdFitcl1n4NKFIt3Z4p7MMgR+O7e1NfPXbcVGIoxBZReAzasf3lVia54Hlr+O+uBKbuPmi8h9dclHOsie+oOjzz+mXSKu9eXeBheibPHwRjnd+BJ48nOoth/C+Be9fBXrpknYm5c/af9BO7+jEfGOIHwIhEAKHTWBUnBw2iZQ+BEIgBC6fwEvWJV++hFtHASP9MnnzIVJHn8JwiLoh6IeUKG7uuNhjCQufYPcntIvoeCGwMQEWt9YK7xOYaGGR2Nub+ga3h7Cm9aZ8zvO4sW3a3WVTTifyPxdJKDh6Xd1F1Inep9Xem5W0+8oKWPu7vI3d4+vI9yvRPpcXFwJLCVh+xFdmP1h7TTR+dPlzHEvZj6uEX1byiSUX6TZdP/xTK1P6SOUdeSZ9xTb+foT4yNkJ+CLwaYvT7Gv7zvp9Ws8fuSjzWbwvrYN9peH3eioYFwIhEAIhsGsEohDftTuW/IbAGgSSdOsJtAJGRlkV8s8i48DTGravVCe7/ULuUP6rlLBmNLD8mQr7IaHXK99g6AHlU5D/XvlxIXBeBKbKwq+tE5/1S4g6xdE31T9fVZQXd8EErJPayxSY4Ljgy13Z6bs9/n+Vg38qmeNYvo5WrNZcf+ycA09JY+1wddxSWqckze4DJ+DZ9FWXSRQK7o8/hQcl3sdWmoeV8Mu7UDdO1MurvsdpF7SW/6csEnmmPqDCP1wSdzEE/mBx2n1t39Wf/q0eRVUHf0XgjOLrSs/dGU+zM4f7asMSizuT4UPNaModAiEwn0AU4vNZJWUIhEAInDeBtsx23rNamFi/9nWcaCF3L98gp4XC/XcrjtX4t5T/xiV/W/JhJRTlFJW9HmJFXYmTl++vK7NQl9ezru9YpzqTG60+z3SiAz1YH2O6BuwuKjXuUffvu0t86k9ReR4W7nW6nXLdPlliaacyvkZmuz3uss45VP1Wzzsta/p/6I1L9E08sRJ2fZOa97nEa2/DpcY8HGK7bZLx3QrCD5VYAuUTyl/mKMM/pnZ8eQm/vAt3o0L8KXU1Cu7ybnAvUzEfVGKZIEujUL75UsNEvbXDa1fcBRHoNo8ltWVALugyV3Zaz8Z48V3sh4z5P2t4kzbyA+uif1mCnXHEK1b4JLfJNU4630Xu26W8XiSHnDsEDpLA2Ik/SAApdAiEQAhcIYEXHq7dA5Ih6qTgDfus7Tu26QaWb1OpfAI7yltX3NuXfF4J97/qH4tIP8p11Z1C1mNPr/y8YQkl/VV9Hk3pZyDu+repvKxyt1q1I/HHBHyhcMvj0E3/TLich1XWTWc7+f95/vijz/Ut9UIx8+p1WXW0vINy3T55Nvax4DevQrUiqMtaUac6FuJjol8aNy4x7AfjtN9vVNd85RIToeXtlZvT3rLOd//2+ccBV91USnHLU1GKP7QSTZXirQynbJ67tEqd5kxuun64H+/09YV7ZFk364L/c11B3v+ifGuZu3c/WWFroku/DXV5Tt2rLO+scz868/vYxm9LO92Mr9L3TP1xZYDxSXmznEmtR1VKPx7tHedLU89nRS1129IOz3luP7xKoC26jK9l6lJxIXBWAjn+vAmMypPzPnfOFwIhEAIhcDKB81SI67D21Qxuvr42frzkJybyhNr+3yV+6JDimXLvxWr7a0q+o+QqncGwTmmvv/vTV5QZVp93qWuzul/1nnzP2m/tzfcvP245gekglNX/vy5Peq6xfizOM3C7czrrj9Z5DJosHVLBo5/y78AET0XWVvD3TUx8USgrV5dV+DSZ1vFfPu2AC9rPQryXyvCMUVpc0KWu5LRz2lsTAfer3Gm3n6f8Q3QUy8uU4q0Mt27ygy8RzNgvcdkn1j9f2zyufAq57ynfjxt6939Fha2bb91wx1mL3xrpR0e144rcC9R1Waz/Zvn77MY2b9/aeJPj43Ip7uNVtdOufdXiS0zLnozjj9Py9DmVQF9YX+jeFeb+0L8lsi3t8Jx3huz7rQIs9AFsR0IgBA6MgMbtwIqc4oZACITA1hDQCevMjAOSjlvHt0RKp6dIbqVyxy3zWV599rDDOqQUf0PUpQdZr7DW/KO68p+XXIUzUP+CuvBblLBgK+8Gx/qORemq/TccsE7EhmlZ7VgH+bzFgHKTLN1pctBlKOkow1g/qkPneW9YzbEOV6RDVIg3SxZXrJFx2ER8kn/e9dP5/BbCJvnpYzZti3uZlT7PZdTxvtbU95WQuCfXP1/blLc3bk57631mYsCa7pSYe1P4NQsyVYprryyP4muwj1zzXGdNPvZLnMuXaNZzbvElmzj9jo+qBNbNZ61Zwa1w+gCU893+bUWmLiATY/m8685yiW3vh/xZFe4ZJYfq/M4ABfd7zwTwUpXOEouWAvvtClM0GytYZrE2b3Db0g7PeWfIvP6ituchNiIhEAKHR2CbFOKHRz8lDoEQOHQCo6LRZ8Sb8niROvC1Str53LjDp/k/Mklw1RbPBp+ytE4ZpD9PsTwGq5FVyq0XrYtZr92kg8mH2rxyZ4kZ6zr+QuXkvOUz65ybuFeYHCR/k6i1N1nvU+ysOpAChjL8NyrBeSpW1EsWxKzcfVVRpz8o949DaUfl8RA9K8iC97zrp/P5gVVLQc3KxJJEm7TFLCnH4yz9YCJvyelnR7Fs/uZKrR6Xt5ZTRx1wlW2n65+3zG1vtcc+6Wd1fN552LXzUYqbGLDmryVIPB8fUYXAqLxLc10nXdAXNmd9PpznMsVyc65nUoG/r3Je7fsm/RDt9xzZpn6Icvqy4eV3sELo12KpbZiTfcss0hf96iIxgxFfeVgCbxF1naeNuep2eO47Q8b9JoyvU/7NRiQEQuDwCGjgDq/UKXEIhEAIbAeBcRAyKlbWzR3LwLE9X2fw5oc1x+ux7qH4G+MuM9wDaD9geJnXXedaFAx4+4HFKb91znOeaf2Q3tvWCd/pAsSPtNVp13Z/Mzli1Y+pTZKduMmy6fYnpLjbYt95W4i2gvIc6uUih7vlje3TWe4jJcJF1NF3LJzTyb2Kmu02aYtZVY5KAUuVzL7gioTYvG/te3bJuq7bzn1TiG9je7vuvbns9N5PJg79UKVJPF9RsJ6/zHy49vgFxS62nRf1PrnM+zDnWufVvh9KP+RBBfUeJec56V6n20rXX189dStztzxTeWcs55LYEAiBJQR0mJZEJyoEzplAThcCIbCMwKhMPYvVpc96+/wszdfpuN65D1z4rLhYeCw2r3l+HMsAwJqa1yIXAettWzdwsbnU877xo4QsRF+/UrDiLe86ZwmS113EzFFoss55n0pPhCt4onv+2sviyzqlb1Bh1pjlXXPy5IdIp0wksFSEdRcJxbM4FjOsoMU5VhwxocCqhhJVucVN5RYVMR5Tm2d2P1Zn+IELkE0/L/YjaZWda27OPbqWeEnAcjqWLXnEZB9rIPeAtFKQEsi9oZSZJL9u05cV6qR1d09aQ9K9dOBU2WiJFsrYk451nHpwWv23xIxPkV/aAQtRl6xT7TNlP1K6iD7Vs376O1eqe5awZC7vTM5XKE5AAcwSWngToUy/iDr6g5UZeStvI7dJW+x6fpC4L2ipgbMsJ+M81ne1vu2TbJwgrsMivuuLuuPT9mfVMX4QuLylTrujLmsDtYXTNrAPMslKGaj+ddzou/64TaEmL5bEGeO1cequT+49A73Pc6ON1JaOP7zb+9dpbx3jix3P/ngN8S3eLSymPUsdx9c+eE6UdZp37Yl9zruKk3OMgov0963IO5RchcPgMXVhX3t9efneZ99SvslEXz5V8FKcejZeaM47fUx/Wtj7xLufCC9LL1691H9Ztl/ctE+jTrj3nqnXlKBEv0jcSW2pdlt7SxnnOavDZjnPmHeDfHqG5xzkHjvGu2tVf2rOeTpNt++2LY3B31T2vR+ibfJ8f10B8j4r7zrn3mg/X+262Js2tJPabe3iTTHL/+vT6Fdos15iSRLH64+PE06SaVO16/bbHkUbaMJ1Th3TV1bf9aH0753Hu8629dhXtYc3tsOOXC2MG7C8VyV52ZJVzvNEMa+ftiqd+yLPxHvFuVb10e1TDoynz799y0Rboq0hwsvSaPPcF/e593uvGG+8W0WMz1ltxoVACGwDAY32NuQjeQiBEAiBQyQwWtCepaM0KsR/pkBS1JQ3y+nAjgkdP24Lf1b982NYfqzQchFjR/8tax8FjCUqKDlq8zpH2fEZFaOsPrOkLGC59lsVN01PaaKj7bNq6zxWkqXOgJPS/ym19x1KrP/3J+UboOh8VvA6p3P6JRUjD8oiz4+qbWXtAa6OPuumT6p4lt8UChW85vxIqWsQSis7dOT/uALiekLA4NaPllqPUDmdU5kq2TWnw/4rtYVHeXvr1JlxcuWsCiLKnKcVrR8qGd3v1IZ7QCgJavPID7K5N5SLtqfyoRWhjrFYNoh5WG2r25YVqOB1zo+8tSKtrRwNGP0QLatk9UCdce+vO7A25tb/96q031jyKSXKw6LTZ9k+b/7qirNUjGdMvVk1IKxkRxTz1uH1I3AUYtYO9uOvBnH2byrdPrGkHu/ppufbtuMo+dsqu8s6J49+tLjT6VPfrjc28N+wjtGWTyd8KvqaU8esjeo50A5Zi9m9/uRFCm1il2MRdexR1PosnFLj8yrGxJ+lRSj6TDRV1LHTfj6+Qp4H9dvEkriKuuYouE2SjWvQ+rE1n56PX5Ooi+Ip+kwkqZcYfWKdSbvoeOmdq/Nfu47d3PbW86W91fYrjzVup+2td5z3hecBK2tWy5tn3/NMKfLtdVXWnibcMJZH7wfvF+8KbYP1rivZUkfJpHzOoUzu4xMqpfOWd2kOX+8uZaUMtz6u/gDFKaW4ez9lfVGZ066O5z4vhfjc9//b1cW1/764MDl7/9qeOu9pRgT93tC3kda7RF3C0zGWnBHnvWJ7FO2FZ0WfRjvrd1nUK5MpllSjWB/Tj2F1Sv3XP6Ewo8CTT3XavjGtsPo+tz8l/VxRhk7ry5cO74Pvxx/1BbosZ+2HPLBORLm8rC7oR/5+7dcn1dZol2rzmvvhCmlf/UZNBW9wlL6+8NQ30RehENdWaus7sWur155pfQLtjjqmzsjbx1ZC8V13a/PIs+Cd4d3ht3m0D+JXiXZCfcftjRaJvrB82/Kmr16b15x66frKu6odvpa4AiYM1PVuY7XJ+jYfWPumzgSCZ0s/iOLceEP6h1dC66GXd+zmvjMktmSM8yiTJRK7f2ffVOa2N96Zxg/fUCfwOx6eKRNkuH98xcmz94h+//QdVbvjQiAErorA2FheVR5y3RAIgd0mkNxvToCCto/WeerwOj7rERZgfYzOdIdP8yn1dNzHdN8xblSYMoO1hU6xziPl8gMqvp3rC1OaUDwIt7C00umlXKC4MRDhs2iyxikFZ6flU5bzDSb5y8QxBtYGq6xLDEBZsOiIOjdlxHicAYq1q1lE6kxT/khH6dhWG96FlKzKR+R5VzMAABAASURBVJFhAELJSRHa5zKAYLVD8U0h+Nza8Yol4tw756vNI4MUPypkYCOf8qhDb1/LvStg4P3M8vfZmdgY6yNl06blpcA2qPu4OgH25V1z6pX74B6JVOdsE/zFtbAiMmAzmDWY8uw4jvU15eYjKyFFRnnXHOtRG+oF5TJlGotkAyrHutcmOdQN6Vrm1n/PjgEy5Zs66PyU4wbPD66TuT7loQkpFu2ep4pe6gxIKc/VTeVgLfbYSukHsFhZVXAj11+w7JuyZITR7bHneYw/KWwQPu635NS4PTesXbV2uAkQitplx7H6M9h+aO000aG+GHDb1r5V9NGyttPzYY3e+xwdHVG6UWYIq5/qBKW1cO0+oggxgFentV/qtHbTvhZtrfyOlqSeK/vHdwDlBkW0d4hnR1tNUeBZc177TQLIgzVne3LSeTxXnl/pTmpvKX1Z9/uy6LvqQJNIuFTwmmtFhTadshuvn6+9WL1q+fLh+VKGR9c2hZXysb6knPRekhcTCt4VleQ6551h4uCuFSsf3kfeMTh7Ft2j2nXhTt6myvC+6KgU19ZQjPW+i/Jx6XN7F1Cw9fam/tz3PwWdttQ7Xn/FszP9QVH1jgIRN+26POlXuNfqookQcd4V4oj3u7gWE03eBSY09ZE8V8qtvN9XifQ3KDAreJ1zTYpM1/BO0047h7yKY2Wq7zIe5Bn1btP+q1+ea/6q/tR47Gnhbt+l27c2Xvuh3VQ2os960hcD0qwS719fWqgT7vGYzj1VF0w4sWTWD9TmuF6n8xWRsHrJH8UzqV0yOetrA/1udYIiVTvX7yX1xUSLffqd6pzj1PcPrhOaMKcg1/7U5pH2Vr9eXdT+exam/X7pRtHGu15PFD2ndpr08wyoKyY4K+qak7/T2uFObELH8RTg6rD+sL6NfHn/dDo+i3ZflJlc7HbVZK5JY8vWaKOlI3PfGa6HmWt/bh2o3ffcVfAGN7e9wUn777ml8NbX9aWEukChbmLD/fTll3tgEvaGi+1gRLIcAntBQOO9FwVJIUIgBEJgBwnoQHa2dT47vI5v8GWQ38foaHb4NN/AmMKh07FS0onrbb4OIatqlqE6fOLGH59hAfe9IkvGgRSlHutZHVZKPT/EU0mOnQEKqyznPI5Y/KN4EKSI4U+FxZQOM6sSyhXLA3Qag09hitMegFJos1I2IKD0EJaG0oqSRtighsLHAJpyS4ffchM65wYB0hDrA1NgU2TjTclugC+uy+26BhIGrI4xAOFP37UGv+J/1r89FwraLiLlU3PvuDk+a+kvrYSs+bqu1eY1px65DwarItUf22T6STMrI4NV9YiiTHpCyd511MBUXEvXS88WRYl0Jl6UzUBH/aJMNQDuY9ap/wZIFJYsldQ/5zBYo3wx8LVN1FW+6/Gngq9zYcQSyX4KQko9Vl3/IWJD6fZpLOOGp9raw7o97rLOyag60fdFeu2ltkN4rkhPCabOqJsmC5cdS5FLwaL+fduQwCRO31v5GXYdUexS0higq6uUwL3fs6G998k4xSCFmzZ22n41F8dR6Bj8e14M7sURygh5YHFuWxupLfaM2O56zRr2wyvCM1vekXcJZaT0JnHEkTntrfYdD+8n7xSKQccK84n3mzbb+4GiSByFjnyZJKIoFtf3kKLKj819ekXKW3lHLIbdk1sdHR25prgWS2o4N4YmPimoel+Xsf2OvwgfP8p894FyimX49DrKqk1hKa4usVqcpjmvbfdybKe0yWc9t7qp3Z7z/tfnoJhyX/s9zHp/zIN7TRlmsqbbS3XHe0Na7x3pf7z+iSPj1xeYm7ikeGRxOt57k5B12JF6TPEnPIoJLUo5z3Gntf9x9a+ft/H50jas25+qU8123eap8+r/7AN3JCHG7m1nl1K2w3N9bZ/7ZSKcFfb0OMpbbaovZSiP9SNdc6wzvlbQr+w+Y5/Dc+uZZIFtEm1sM4T1dfs8ngNtl/rXS3f4fQB9BeMB/VvXYFXt/CaC9AnUz2VtpDRT0ZbLo+fYPsd6RjwD40SofdrE09ph6QhuvqDAUJ8cL/HaK3XQ82qbUICbHPZ+0FZhKV65TAAIj8+IZ03+tPfYLOujO8b7TTuirp/EA2fp5rQ3LPjVMe/Fftfh4n0+lqnDY9soT5EQCIErJDAdpF9hVnLpEAiBEDg4AqzKutA6zx1ex7f8R6enZKZY6+1Vvs/1dL4NGjuNzjRrht7mv9jR0RFFH6UJpQ1lm3gWtvwWyhwKA9ZVHefcOuYUaJQPHW/dPp1cAwYWuh1PccMq0faotLFNKPtYhQjrVPNH8VmsbYptShBh1h+4svgj4ogONeWqASkluXxSgFAYsLphOabMBsXSjzIqR8d4YRZilCM65vJLCeWeGMjaT5y7B+iYi9tnMWEyKv4pkimP5pYZb/fIoISy8aTjpLV/qhQURww63XN1mjWVuFHUVdvuG7+lz2vwRhlp0kP9sN8kiueGpavBkDiyTv1npd7PCGsix7OYUmbhFlZfwj555k+l97Okp/ii+DMAE+5JmOkxc7ZZq3mupJ3Tvki3i9LtsbZpbh01SGdt1/deW8Kib275fR3Aeo+y2lIkJi6WHWuChGLDPsoBfgtFtnaFwsSSKR3PpyxXL7R/y5SSY52Xd4qWafs1Kh186k7ZwFKVwsQ1CKUfa/B+B1A+CxPtMYtMz4/nr1k5TrvvnSBM8cwfpZ+9Zc80a3RLVWj7KUFYZ8sTi/c+h3jvJ207xaV47ZF2SLhFHoU9W5YqEG6hvHCfKKVaWdn7TKpRVrL2pTQSb2xlck777n0wfY6lOU9xP5SRtbB2g3Jp1flx8DWBrxEwYG26Ku1Z4qftzbL7t875vU/Xef+bvHdPXMNzxR/rhe1e0sV9sj0K5Zx76Pletl9avE3wmgTynIlr0e8Q9kUE5aJwi7qmb2JbP4zfwprYpKtnZHzu1nmf9LnW8dVx6T3DlKnC+yQUzSbVu0wsgrV3vX2ar41SDzzr7vn0njpev6GvoT3XDphModC2n1jqxFdA2kvbhCGFSSxhymLtuLCvDxhr6C9T4mqXtfX6r5S92jbLDmpXtduO8aWjtCZ7uq1SFyl27ae45uuX8U8Tbaw0Y35tjyLNae2w9JTrvgQS1sZqi4SJr2/0ybVNtrVpJha8hzFhNS++RZmEl7UrJ70zKKu9I7R/xjS+DHKeKY912xv3xWSWc3UfjiGCCWBxLd7Fwt4z/EgIbC+BA8qZxvqAipuihkAIhMBWEdCxZf0gUwZw/HVE59wnen2MDvtJgxmdS5+TS/cJdZBOp4GXgbEf45keq0NtYGtQyKfUMLCYKsUoy1hsdEdeR1OHty5xxALL2oKWzmABa0DgHDrsOvjSEMoKinoKmmWdRdaGlnixLqMBhWNGYanX2/LBcoaFhzif0vNbDGYoMVnKKb/BQneIKQ+lGz+xtd3Sne1lSntKUueVtjvaPvdkiSKOKLf7YC1flmni9llMlFi6g9JKOQ2K1JXTBqMGgxTPlg1h4crSxr1yjmVC+dsKDHVtWZpWFFFAUm5N0zhenH2eDWEKCgNPYYpPy0n0PnGs6Qzkunzi1q3/rLtaWWN5FOcwsOWP4hm0vazuifdseFblmbWS59enxhguUzY6Zo6MbdP02Z9z/K6kwUpetUNtOWb7NFE/x0k6ijuDePXgpGMpcN1L95x1sfCq9CxQKb3VM2vUjukcb5syXN0VJuoBBbTwqvas67w2Ux3s9svEoOOUzTMsTPpay/JKwUFRI502nKWjcB9DwUcZIa6Fgl25KJO1iR3f/kntrfw29263p8+2CayeSOt8eB/1+dvvfZZNGstrfz93FOneHeIIBRiLTGH1RfvP8pECTD78hkTnS5qLEtfxrvPFl/f6adfRjsoXhfFpCvTTzrVsv3pPITju0+aP2+uG133/e+dQVJtwoVR0vWk/oOuW/pD9R8M/Si1jZPV52cS4pL5S41N6TvtOjrdv2XNCoer9ZrLS+aVr6XpIAakeicdz3f6U4+YKBbwviaTf5/adotgElXJiSpk6raf2TUUaExv6cdorltLTNLZNcvgqQZglOH9a58TpL491zrNIsW2f59h7SL1g7WzCyrl82Wa//oYlQ4S7jjlXt/sMMPTn9bOlISzXvTdM8liqw/NPIWzfadLvBxMlq9LOaYcd692i7PpQ0z6858R+z4S0xgO9ZNBoUGIfZTgFtPuxrF3p59o5pR/Fs6zN9q7C0WS/d492e0y3bnujjTGWM3Etb8Y20+sbr7F6dx11jx8JgRDYAgJe9luQjWQhBEIgBA6WgAG4whtQU8QIrxKKOJakFH46qBTLLFc6vc46pYgOMmtA52ZdKyyegoyCgzWrY3SSHWOgQCEibpW0gmRqHS49ZToFgDBhHUh5LswK3EDBJ/mU1j4j9MOBU8uJkzqx3lUGkc4n//yptNKCUoVikBLPoEfHd5lycXq8bVYhOqw+CW1rD/Eto9IV445f5jevkYt0bZXmHtq+Srmsa7PIpvDrwQ6LaAPvr6oM+OrAgLyCRwZs7qP6YZBiKQSflhsQsoKSZpWox+rJr1cCg57yrnMsc9Q/kafVIc8MxbK0raAwiGOZq34YHBsEmuyRZiqb1H/nYDnoWIMpfMS1iPcFBYUcBUzHj74JFpaErdDDQ/5ZB6/K63j8qrBnqfdN89Xx++BrL7scY5k77iSfhZ+JEcoG7Y5P8g2yKTQovt0LYnKFAsRknB/qc04WdpSpwqtE/bZvOsgW123ndJDt2ZIXyjqTItKOYl8fO5ZdGuus8qftl/okfpoPCh0KYs+O/aN0mzfNnzSUNfxlbfTc9lY5WvFvIsj5lknnfVk+msOyfavy6L66jraCYt49twyAZ1V7w2rytPeq488ilEisXbWVrfifcz711BczvnDxju77PefYZWmUXT9DnbaUmPVyx3Tql+eBb2Jx3Hda2HOz7vu/z0kBaCKdAs5ke8ez+PdFl+1l7+KT6oNj9GM6zfRZsP+k57UVmcvq/LJnRX1atz8lD3NlbOv2uX1X57UT/f7U36CwpiSniG4reZMVDBf0WX3dIY32E6fuw5zE1qS/PrZ3tQm5Ma2vj0xUjnWm2xE/hGni0zOtvmv/tFlThXCfzz7h8Vy2V4k+KYW08s+dIPducj7jDf5Jclo73M/ElMmyc/ZXHcYo06+m+jwmWLuv0+fAjBJfm3xaH13757jpZPFZ2pt+fuXN/Xf+Fn1gjLRFxikdHz8EQuCKCXjorzgLl335XC8EDp4Apao1QA3grDnpUztrR1NEnaaQPXh4FwDAANFpKax05IRXCQsDHWWddRa2lBA6fp2eNZR9luQwWGMJwdeBpEyjGHE9FqksMAzaWb318Sf5lmGwfzqI08GWp1FxotMvLTHYcA0WLyd1AuVT+mUKCWs3suSwX0eTPxX1V1wrZJTXNsWqiQDh06Q7yMrSFjfjMTh6bxq4nHROAyvKTZ111j7jOboVsB1GAAAQAElEQVTDPHcQMx67y2HLUZhwoCBkJareWHeRRZXJETwpsg3WTNAYgLqHrB/nlPuk+uP4VoZ7Xii0xY3CSrUHmJ6R3tfnNSjVTpq4Up9ZF1HWd7rR36T+O76vJX/TwZTBrLonHzhJPxVfHrBO9ryYpPL5tmdem9B1e3rMnO1WHJlUmzuQnnPebUtj8E2ZJ1+UH/x1hCKDAqGfefWXBa4BMMs9YrKO0pYlGUWmOsea+qTrUNz40VZplrWPXW+mbUrXeW3vsgklddk7xHnHOu9afvtB3emySCOt944wRRG/Rfur7TWZ03Htd/6meffMsb6UzrIA/FHmtreeRVZ5JoTU0fEcHWYBKw2rwqnSjwLMRJe00zz6xN75tRs+4ZempZ9zz4QJDdd2f02odpqL9lmCWpPdFwnrXouCkCLQJNocRd9J5zf5oj7hrB/CulU7pa1X73Gk+Ou6cNK5pvu0Z+u+//scrZiffhUgrxTl7p3Jzk7ffudzWh96v/aVcsv2NA2FKOtWdW1qwaoN1/dy3LK+zLL+Qdczx8ztT0k7V7p99+xOOc09xw6kO86iMnqefUGgvRApbCzkGVBv1VfPsuWQTObr+1oGa1mf0PFT6TrnPe58435LgujzMNTo+L6/vgrQhmuf9JdOm0zz7nAOE1H806T7AFMF8KrjvHfUY23fHIU4rtpYXPEbz6tN0DaI03/inyT95ev02XJMP5tY2R5l7jtD2Ux0agOn1vJnaW86b8vy3V+ULHvXjWVIOARC4JIJeDFf8iVzuRA4OAIsEljNGLjoVOj0jCLegFnHWQfKQNPg1EvaGoWsQ84DmgELRZ9lK+TD2oU6HZSZlOMUUT4FZIHcg491rsvqUzm7LDr7Zuh11pSHGDDrKCira69z/n1Ni0uX7TQljIG29QYpE01eGJBpx01yrBL7paN8cCzFhXX83B8Kj772ST5rqO60qztjWoMJHXhKzY73WWeHp9YdHc+XJ77JgP4BK/VDfj0j8mv/OHigtBI3imdER1jnltWxfVjxp8sLiGtxHXxsC3tWhXvAwOKTxZ84sqyzy3ILE8dLQ1oR5dk2KBZHRqu0ZZ15afZZTBBQ0lIGYm2pGtZX2gwDqK+swrOe1f5QllMuVdQst+ze3LOO1J6Vd9R13QC3P0UX32IpIYM2n72OX0GoV9L0/XIspbQ4n+7yKdSsldz1ed367xyky6DNtD0KhbjtHkx5N2jDxWkLWDlSPlH0GNA/4ejoyOQD3tK0old4HdHWUOw6hrKdv8/SCmBKIm3qumWlfKYsYwXKEtan7u6nNbd9dfI5dUITnyYprPs8nfio3Tc4bUgrY9znMcHta0P759nyDKnDvpLQrnWdX9Zm1mFHPUD3tcPYTnqX2+/5o0ASJiZQnZcCn7JTXIvn1rPc2+2bGKQkcR7v/Y7nU6BQlFL+aCu1j9oC7xv7+3nwTrBNlrW3U0WPZ7mfe8eQPhdlDFbiWtxr9dwSG5RiHc/Xb/Jc68tQnpoUsNSZvPZz7jhpl4n3AmbL9p1HnHs3d9Jw2fUoux5eO7zDy9vYqdPqnkl7Vpruqy9/KJ7EmazDEQ9fU6xzoU3e/33+fhe77x3Hb2Wi+2qbcoylvLD8Kw824/NmSRoTCNKoC3xf6U37OK3UZhGvrlGAd7utj9LtCsWhc7ToY5k0lma8btcz6abXEteCb4fX8e+6SOyrlakCd7Fr7zxWyr5o0X/1RZofR9V+6ssJGxOZGLTfM7YOAHVHem0afxTvbWO7Ma7v70ntiHf8eAzlsn6StbWXXWdMK6xemfTTf/EeEqfum/gUXiYmd8VrE/UthE+Sk9phyn3i+PFdY3uULmc/X+Nz0On6+ZJ/cZYlbObdzp/2ztCf0uYbf3mfeecwdHG+s7Q3fX3vfOdq0aaYKPFsG4eL9/73BYtwJARC4AoJXGQn7QqLlUsfKoEtLTdrbOsZG7jqnHtxj6KjYh/RQTNQZtlrmQCDOi9rL21WJ5sUkbUqBQ6rBC9fHUGKIgMUA2kDFkoUn/cauOiwGbh2Z2juNZVTHnXUWAIZZI6WysqkI0PxicfY6Zh7jX1MZ4DDGlbZdNL42yY63TpyFCw61J0/CmvW5hTsHcdXZykWhduiULhFp5flJGWROM+DOEo7PHQqDZwp9uxnPUxRIux54Y9Ckcray9qJPfBtnwJlTNthHVTW7hSz4iiwKG58Vm2yQNl8tvm9di5E3RU0acUnlObWHx/rs7LY10osYWIygvLUxBMFqrhDFPfT4NsPmWKs/fE5qU+UDUYpz9bhgrc65Ji+N9o39cEXMOINUNRfihqDH3Et7jWlkDpO4dHKPm2jNlm6cWBG0SOOMoRvoKPetHJz3frvHKSVNCYObbfIM6W0/BmcezY8d103ta3qLw6dhz6WksVz1Fw6fq5vHWjKP193LFN4zj3PrqTr+uIee143zTfefijMxI77Q+nkne6TeErzdc6rHXE+x1DS8VsoboS9synOPUcUGOoKq2X7lrWZ2jKWxZ5FdV66llZ+UHB2HF9/ge/ZGpVvFH6W7tIO2j9K12n5pxwc95k4sN3vPwp6E/ZEvDzyHcsn0/ZWe0qZiYs1saXx5QYrfOGWntiaPlv2e9/wl+2jtLev84gZwwVl8U5yXcvgeCalG4WloeP6HTPuS3g+AXUUa0csq8vL3v/SEm0i3zPEJxTS+sLCXbf0vbqN1381PjZRYwJVOvfY74L0VwL6CN5T7r/9LfohD1psqCeC7v844dAKzPE9pI3VB5fe5Fk/g7Y3fZ849jTx3Hp+pTuECU/lbPGuNB7yA9sm9IxZtNXaQ5Mj3Yft9HP9NsaYTi4YW+njMhwaz9V1w/hwjO+wsZj+ovFVx43tqn5Nx6/ye0kk9dd7Qnuo/Or4qmOM6ezrZ094lZzWDruWd5TjV/XJfS3U12KgJe30+fKcmqjQ38bNpIDlaX5T4pI574xKdtQ82vjFmuHY2Ldpe8PQwxjC11hdVucj3jHemd79lP3G+95bPQEvTSQEQuCKCHjhX9Glc9kQOBgCrPR0AFgWdSemC+8zPdaxlHMG4BQzOsaUGPetRJQs5R3ppHmRsua2PUcMBFzbrDeFjRlzSqP718EGaQZ0FTx2Omg6BDok4nWSDRTWseKm4DIAVAYDZp2V45Mv/smDQaPy6sT0YH2x+6A9ikEAKE1YcQhvkxj4GdypU60gEGb1RIlg3zS/lg8Q98H1T2e5vCMDUQpGEzw6+Z1G3daJ1GmWzoByavVmjVSdY5+umriRjsLSM6RjaZ1ylj7iicEtyxHKVp1/cURepNdhpThSb8XrBPNZCHk3+lzWAHEcmFJkS2OCQFkoo3SCKb7Et1BaUuwro+dBvHxYr1WYcpYfOR8CBoR9/9QjbQzW2jlWX65icMoyURukfvHFU7BIpy5R/gqLJwbI7rNldyiExZEexJgcUlfUYwp1+1rECc+p/9KZuKRwpLyetp3y6tnwab9yUMp5N7BAdCylDQUoi2RfVogjFLDKpL6P9di+udI/REpZIw9zj9vVdNqFXmO+y74NZdGmyEcr8vQTtJEsGMWrN3wK+McIlJgI1zZrc71/K+rYUZqbTFGfKMMM0I93LP5pj7V1Bu3eSaKte6tNVOdN0JhUF+8rCfEmsygpxI1iosa2Np8/ivbcNiWIc1oqiRWuOCIP/JPaW8++uu0LJW2uJQmw0P47tuWkfHS/bFkePVfOoT9EKWqZG5O54iipKFT03yg9tRXiWR56T+pXUTj1O8a+yGYE1n3/91X6Weg+AKXUI2unvnB5R94XDDQYovitBXEmZNRN9c62/aw6ta/qljj9ZO9x7bbnSxxFnSVH9N9tU4zq1/tiYZwsUhb1Sp9bX9u19VWkc5zz8kdZ930yHntS2LtQX874wJclJ6XNvnkE9Iml1ObqHwirfwxHtJO2R9FX8bUAg6GuS/YbM2rPtePaxZ6At4+xEX9ZXRE/FX0Ecfq3+jqWf/ObKOJWiUl4+7Rj/JNkTjtsEt/ElDIZI/b5fEXkHaJd1b8X3+87zwSG+kAs0OXbtZyHeIZY3Itz3Jx3hnQjD4p/hhn9NZ39nlFtgPzMGW84pt8x3j3TvpJzSeNdpzwmdpXZ2Ep8JARC4AoJeCiv8PK5dAgcHAGfHI+FZrWtYzzGeXF6qXvJU9b0II1ijdWTQeqYfllYJ5wFiw6GjgdrB50tna5l6TuOtUR32BxH2cjv/XN9ZZL3Tk+hRAEuvuPW9Pc6uU5Sf8a4TUqYEbrOqHxSpFg+gvLBp4+r8ktRoONrYkdax7C8ZYVBkcP60sDTNQw8Lf/AuoICh0KhrcftJwar8sB6iwKHBQmlIIWMuu1HvTw70hJ1jTWJ4wxSDSqIvHxEJdDRpVhsJY7BIAtYCiflpFRleVZJrzkKcNZa4ikt5YVyScf8WqIKKI/zU9L6DNaAQue3dh27HjAdb+TfuRBg1UW5oZ5RglNWTQd8lAraRIo3g0v3wb1Rjw2KKCXGzJjssG3ih99isKZOOY/lE2yrj72fv079l77bdQqcHtyJJ5R96g/FjHpHGWkpB/sIi0PPmeU4WKApl2eD8o7V7dxBs3ONQllqgs7gbvo8jun2LdxtibVjp+/sqyqr+0+RZ/LNRIh2SNuljlK4srYW7x3f9Vg9etfKsPe6to8yRH1nSe3rM5N0FLaV5DrnOTKR4r1NEaxNFtZGO8Y1XNMn6yaHPFP6G9edZLGhXqu/3huLqGue94B6SlEkX54n5+wEc9pbZZQvyiOTGd5H2vc+B98kqMkmz4nriBuFIlM7PU6G9X7cscZe20LxjUXv9w6xTBellrbA+ZXX5CwDAwpH96nTx9+MgPf4Ou//vopJbUtguR/e65YP8n72JaMJcdbBng1tpbrkOEYk4inNtZ2eJwoyz5H9LfKjTjivZ0T9ojj3VZxzqM/6vY4drY2dUxuuv+P5k0fPtfeQc9vPH2Xd98l47KqwPo5+iv3TCV1xkc0I6Auob/qH7q/+iPenvqX2bnpWkyv6w9oO/VBtqeP0UUxI6ptoX8bjul3tccO4b1nYeNI5/fCueqpPo94uS9txrRA3WdJxq3zPzmntsHeJSXrPledD3pVX/8lyKp4b7zDXwILVvv61vpx+u2NZg2tTfYmhL/SWldjzVd6xm/POkNDzRimuzy8f3hnee/aRTdob90RfaXqv+nyea4p3ZXYvtDH2RU4lkAQhcLEEohC/WL45ewhMCVAwdJwXpw5Cby/zdTIMXnsf5bRZ9t5e5lOGG6QauNpPGa6TMCoLxa8SSncDQPt12A0whdcRHanxc2sdxHWOP8S0PqV3jwyiKb62jYGOvDqlM0thpJPu03SK51V5NQhgjaKzrw5SSrCyG62l+lgW3r6kYIXly4Rl57WcBosqlrt4sSzx9YRBaZ9n9H0aSrHD+lCn2TX8SBDrvWXH6KzKny8xfB5toDKej9KE8oRVJiW8DjqLyTFNhw2yQXPurgAAEABJREFUdZBZf0lPKG0808sUL31c/M0IsOrV9lFssMx3v6dnwv4TKtKXLBTK6iTLV58yq9+16zrHKlpbZq3zcYfzGKjZp26ri+P+Dq9T/ymxWYetam8/qU7qy5oeJFLqVNQ1Z4DLUpWVknKZkGSVZlmga4nWDHReTACNFvJrnmbnkrOyp8Bl8as92oYCUKpSBGhP3Rd1xeQ1CzNKWpaq3h2t4Oo8u2/aHnXnIytSm2WpBs8Hy+qKWuoo+KwlbkLAxJJ2k+KXMty6s9pe7wH5oFRYepKK9Gm6urjs+XIu52EF6FmieKxDrrm57S2ltfKxFvTJfPdf+kQmLClbtMXL3ismfbT7JjD7mPYps5Tdc6c9n/bZvGMoU7Ttfm/CxJT3jU/kKXX6POv5Sb2MwLrvf+fw3Hgu3BP3Sf+FopqCzWSSvoD+xrSPSnHI0tsz531Cqe58o1Co6QdYysIzom+izVePLH/Bitd+k5zjccKUgOodpTSfcpKSXn5ZmEozFef2/HvnaONP6k9Nj1227b3nnaGeTpX9y9Inbj4Byk71R9usXVIfcF51BvVRm+EYfRT32L3WnoyTKX18t6va0I47ydfWa8v1W7SDvpY7KT0DLPXR+uYUzyel7X2ntcPSea8yYtE3MRFkwta2/s20T+N5Vz9N6vuKx6Svtt1z4HnG1jM29sHnvjNMvBrfega855Yp/V3fMz13vMHq27tx2fvQc22sov0x0X7aOB6rSAiEwCURiEL8kkDnMiGwIODlvQge6WSMM9IdP/V1lMY4nedxewxTmFOGU1aK92LWcRaeKxT14wy3zpmO2dzjpdOx47e0lXtvx7+RgM/UfRbp81WfBd6Y4upjKOxZMRmw+YphTo4ola2JacBHebP0mEWkDnFbiCyibvBYkrAuYUXD4vyGBEsi5IFiw7rip+WB1dgy5U2fFgMKs/50uuPbZxXEepjSSpwBi068DjDLd1aRtu2LnC8BdUM9o7A+6cwGUKxJWcyeVB+cjzWVe77sfL5UOO1eqntz6j9FnR8xXHadjlPfWaFK23FT34BQubTj033rbFPkmEwyUKTsWefYfUjr+aWIYAXa79NtKJe6q+3rr2s6T+rhtK/Q+/gU45QALNNOez6kJ+qZ9osSWNssrgUbFm8nPT/SekYoCIVXCYs5SwEt2+/ZO6m97WMc71mleOi40WdluKrcFJsn9cVw8AyvavNdxz6TCCddR7rI2Qhok9d9/7uiiQt9APXENlG31D1tpu2paG9daxo/3fYseEZMvPQ+59ZWe147jm/cbfJEeBTKPhNw3kvTY8Z0c98n4zHLwowPKP3147Tzq56bZccmbh4BS+boz2q/VrU90zO5H+rSSW25Y+a0q9JNRV6m745O40sahgLqBuMX9dFEibrcaU7zPV8ntcN9vPeVcaHxRMct8/Vj9Omm6bDV1i47Rn6VU5u8bH/HObfn7aS2f532xjvSMmR9/mW+/afd22XHJS4E9pbANhTMi3kb8pE8hMAhEDAbzSKly7rMQrX3jT7rqnHbp2Lj9hhmAduDdxYBLMLG/XPDBs2d1vppOke9Pcc3m9/pdLQpVXo7/moC1hL/ttrNUpmlawXjdoyAZStYnLHg6ayz3rR8jAE2y8yOjx8C20jAgJjFuUGrryXmDua3sSyb5smg9V51sLJb5onFXG3GhUAIhMBGBHzFYLLd5JRwn8RY3Nd2tn2Nw79IcT1LHvmiivW8dv4ir5dz7wYBXy5axtMXb6yiKZbHL5R3oxTJZQiEQAisScBLcc1DkjwEQmBDAj4RGw9lXTVurwr7xGrcxzp33O4wy/Hx826foOl49/51fOvQjul91jxunxT24yejJTxrRcr5k47JvpsI6ID6hI+FiE8afWZ505783xUCvqbw3PWzaHLIhBAFmy8nKMV3pSznlM+cZocI6BdShpsItdSG9ZB3KPvnmlWWaZZOslSGz7TP9eQ5WQiEwEER0J6aHPdVRfeJGbhYptCXZXyK6ouGwvDC2suWg9HXvOjr5fzbT8CXqQyffA3jSyIGHY+qbPvqpby4EAiBENhfAgY++1u6qyxZrh0CNxIYFeI+e/TZ842pro+hlNAx6VgKUz/u1Nvt36IC1insZ9onnH40q6I3cqzixgOtJTdunxS2FuhoTTdX8X/SOQ9pn0/0DJx8IugHmqwxeUjl3/WysvQyKfSlVRDrklIu8g02fJ5d0XEhsLUEPrtyZh3y9yvfZGZ5B+2sTe+HE/1Arx9uPWgYKXwIhMDGBKyRT8Hoq0nLGVI8Wr6CMpwhBNn45DMPtDSLtf/9tpDfyJh5WJLNJrCbCY35rOlteSvLbFqS5BCXStvNu5dch0AInIlAK8/OdJIcHAIhMIsA69BOaN0yViK9vcq/f+2gFC/v2FmOYdkPs3xy7fXjVuUdu6+t/9ZHK28jN37O6QR+VIo/R8ZySj93aRhpIzcR0CllZf8Vtclyo7y4HSFgzUg/xOMHvB5cefbDVxRp1laszbgQ2GoCv1O504Yv+xG52nWjO4AYiqO3rXL6fYHy4kIgBEJgbQLWfb5rHfUaJSYeP6V8P+in7846vDYv3DG48COFlJ4XfrFcYKcIGEf6IvlDKtcMuE5ay76SxIVACITAfhCIQnw/7mNKcbkENrmaNVnHtcDnWE2zEv784WJfXGGd6PKucz65tDxKR1KEG8D39ib+VCF+0o+OTM9PmdJxLGHySWbTWM/XGfWZPkuN9Y5M6qsm4AsQP+rjR7WuOi+5fgisQ8An+3O+XlrnnPuQ9glViDnv7UoWFwIhEAIrCfjhxF+ovSzEV/2gZ+2+EGfJRV8eXsjJc9KdJ+DrYj+K6WvknS/MBRQgpwyBENhDAlGI7+FNTZG2koB1hMeMWVN43B7DfhTT54x+3OT5aof1vN+1/E8oWdZJuXvFU4qXd+x+tv7r1JS3sfMJ53jw3HVkLRXBsrmP9ck9xW5vxw+BEAiBEAiBENgJAslkCIRACIRACIRACIRACOwngSjE9/O+plTbR2C0mpY7n6RZ8/vRteFXvK0n+BMVpvymRP7oCv9hiSUXrN/9fRVe5e4x2eE8k6i1N6cKfJ/RzznJdP3wi1guxY9NPq0yczFydPRrde6XKIkLgRAIgRAIgRAIgRAIgRAIgRAIgRDYVwIp18ESiEL8YG99Cn7JBEaF+N/VtV+pxDqC1mvjE5bVt614zjIjr1mBh5dYfqG8le6ekz1nVYi/TJ3vDiWjm6vYHsvp+LnHSTtX/CgRPuuKz1N9Lso6n8ibT+CfWBcmuPVn8bFqLyhxIRACIRACIRAC+0kgpQqBEAiBEAiBEAiBQyYQhfgh3/2U/bIIvFRdaFw//JG1fZeFvH75lki5U/kvWfL4Eo5y/AcrcKuSk9wda+ftStpRnlubsLc38d9+ctCf1fZcC3HLt1TyY3dR64db//C+dYV15f3qmPuVvP9CPqD8Dyzxg4fED5h+cG2zyvfDQxU8N2epm8jR0VUzyPVzD1IHUgdSB1IHUgdSB1IHUgdSB1IHdq0OnNvANCcKgQMicGJRoxA/EU92hsC5EJhaTbNOXnZiP55orfBWPr91JXpIyUmOhfm4/69r4zklZ3EUxePxc3+g83nqIIr88o6dHxVUpuON/AuBEAiBEAiBEAiBEAiBELhoAjl/CIRACIRACITAaQSiED+NUPaHwNkJjApxFtyW/Fh11v+oHT9e0u5tOrDCp4Qed1GIj9vrhlmqv9Fw0LMq/LUlc5z1w19wSGhJkmHzoIM3q9JHjo7CIAwurg6EbdimDqQOpA6kDqQOpA6kDqQO7GcdOMpfCITA+RKIQvx8eV762XLBnSAwLiNiOZN/OyXX/zrsf5UKW0qlvKXuTyexZ1WIP3Byvi+u7WeUzHF3mySKQnwCJJshEAIhEAIhEAIhEAIhEAIhsCmBHBcCIRACIXA+BKIQPx+OOUsIrCLwsrWDUru8Y7dquZTjnYt/r7vw27MWd4en/m9OIl5gsr3OpvXIHzAc8AcV/oKSuW5UiD+3DvIjluXFhUAIhEAIhMCZCOTgEAiBEAiBEAiBEAiBEAiBEDg3AlGInxvKnCgElhIYrcMlOM1q2hIod5Xw6Oj4v/XA/+44tPzfMyv6L0vaUcB3eF2fNbjrO+7Z9e9+JZZMKe9Ud/NKMa4f/qu1fVHrh79VnfuLLlA+r859m5K4EAiBEAiBEAiBEAiBEAiBEAiBELgEArlECITAZRKIQvwyaedah0jgLYZCWx/8ScP2suBdKvJWJe0ow/+rN1b4vzHEv3yFb12yrmMZfq/hoPtX+LS8VpJr7nUqNCqR51jC1yEbOYr6B9eRFyUfWud+oZK4EAiBEAiBEAiBiyaQ84dACIRACIRACIRACITAJROIQvySgedyB0dgVIj/YpX+X0pOcuMPcEr39/6dIl857H/eCr9XyTruzSrxI0rafW4FvrlkHTe1hL9Ihfh9K2O3vEB58Tr3n5VcqMvJQyAEQiAEQiAEQiAEQiAEQiAEQiAE9p9ASrh9BKIQ3757khztD4E7VFFeoaTdaculSPfS/g0yVYg/f+373ZJ3L2n3AxX41pJ2D6oAhXF5pzrLjzyuUt2ixFrln1z+Z5Ss60aFOIv2rB++LsGkD4EQCIEQCIEQCIH9IpDShEAIhEAIhEAIhMBWEohCfCtvSzK1JwTuPSnHkyfbyzb/aBI5Lp9il+U8/Ejnn9sY5CMr/Ncl3GvUv68uOcndrHZ+VMmPlLxIyd+UvF3JQ0rWdS9VB4yW8H6M828rLi4EDpRAih0CIRACIRACIRACIRACIRACIRACIbCtBM5PIb6tJUy+QuDyCNyzLvUTJT9XworbjzNW8Jp7TIV+oeSJJX4UsrwbHGvtUZl8u0rR1t6vWWEW3N9X/lNKRueYd64Iyujyjt7/6OjoR0scU94151z3qS3rjj+s/OeWPLrEGuBPKH+Ok6cfq4SswJ9aPiW+81bw2L1y/Xd++/GYTgzU7rgQCIEQCIEQCIEQCIEQCIGdJZCMh0AIhEAIhMAOE4hCfIdvXrK+dQQoxN+wcvVqJaymLXfiRzGJsPW971z77lYyKpBr85p7WoWsI/708jlLpIj7ttqgaP/T8v2oZHk3OBborMNZeT+n9r5Nya+W/GXJT5X8fok1zL+pfErtR5Z/+5IPLplanFfUSnfX2kMo0Z3n2bX9zBLl5CvrbWvbD4SyHHfN2owLgRAIgd0nkBKEQAiEQAiEQAiEQAiEQAiEQAjsNoEoxHf7/l1W7nOdeQQeWMkscXLr8l+oxFIkL1Y+EX7hCr9Ayc1LHlyyyrGupmx+30rAkvzXyre2+KeX/6Yl/1Syyj2rdrAiv2P5LMEpx3+pwtYIf1L5n1ryTiXWNrfW+J9VeF33zXXACy6ky/mita2cfGW9TW1T+lua5fEVjguBEAiBEAiBELgaAr4I80XXL9blfWH2s+X/ZIkv1nzJxfc7Jz9TcXXsiQsAABAASURBVL5UKy8uBEIgBELggAmk6CEQAiGw9wSiEN/7W5wC7iiBv6p8+6HMTyzf2t6sxr+kwv9WMsdZxuRbKiHl+L3Kf6MSluUPLd+PcPZ647UZFwIhEAIhEAIhcHS0twx8rfWKVTpfqb1B+b7yerPy715iH/+NK2y/yewKxoVACIRACIRACIRACITA/hKIQnx/721KFgLzCCRVCIRACIRACITAPhN4rSqcr7csw/YuFR7dj9SGfb4k49+jtuNCIARCIARCIAT2lUDKFQIhcEwgCvFjDPkXAiEQAiEQAiEQAiEQAntP4E6TEj62tv32R3n77VK6EAiBEAiBEAiBEAiBEGgCUYg3ifghEAIhsH8EUqIQCIEQCIEQGAncbdj4rwpbQ7y8uBAIgRAIgRAIgRAIgR0nkOyvQSAK8TVgJWkIhEAIhEAIhEAIhEAI7CiB56l8Wyu8vGP3q/X/b0riQmDHCST7IRACIRACIRACIbAegSjE1+OV1CEQAiEQAiGwHQSSixAIgRBYj8DrVvJbl7R7Ygfih0AIhEAIhEAIhEAIhMAhEdg5hfgh3ZyUNQRCIARCIARCIARCIATOicCbT84ThfgESDZDIAS2j0ByFAIhEAIhEAIXQSAK8YugmnOGQAiEQAiEQAiEwOYEcmQIXASBUSH+n3WBny6JC4EQCIEQCIEQCIEQCIGDIxCF+MHd8m0ucPIWAiEQAiEQAiEQAiFwAQRuXud8k5J2v1yBfyyJC4EQCIEQCIErIpDLhkAIhMDVEYhC/OrY58ohEAIhEAIhEAIhEAKHRuBqyvt6ddlx/fD/W9txIRACIRACIRACIRACIXCQBKIQP8jbnkKHwOUTyBVDIARCIARCIASujMC4XIpM/KR/kRAIgRAIgRAIgRC4CAI5ZwhsO4EoxLf9DiV/IRACIRACIRACIRACIXA2AqNCPOuHn43lSUdnXwiEQAiEQAiEQAiEwA4QiEJ8B25SshgCIRAC200guQuBEAiBENhiAs9TeRvXD39qbWf98IIQFwIhEAIhEAIhEAIhsC6B/Ugfhfh+3MeUIgRCIARCIARCIARCIASWETjr+uE3W3bSc4w7r/Of13kUjUX9d1XAWuu/Uf7jSuIOnUDKHwIhEAIhEAIhsDcEohDfm1uZgoRACIRACITA+RPIGUMgBHaewN0nJZi7fviL13E/V/InJbcpuQj32DrpM0vetGSVu2XtYOVe3kr34bXn70o+tuS83F/Vie5UcueSp5fEhUAIhEAIhEAIhEAI7AmBKMSX38jEhkAIhEAIhEAIhEAIhMA+ELjbUIh11g9/lzruLiUvVHIRY4ZXrvPer8T5Vym8KaN/u9I8uuQk96m184VLzktxzzL8I+t8f1rC/bR/kRAIgb0lkIKFQAiEQAgcGIGL6NweGMIUNwRCIARCIARCIAR2kUDyfAAEbl5lfOOSdr9SgX8omeO+vxJ9QclblPx9yXm7P6gTflrJu5f8n5Jl7gMq8pVKTrv+R1eajyp5SMl5uVvXiV63hJtrVS9tJARCIARCIARCIARCYMsJRCG+5Tco2bsAAjllCIRACIRACIRACBwGAQpdit0uLcvnDp/m/3UlYHn9S+VfhPuvOunnl3xPySr3VosdP7XwV3nfWTu+ouTfSs7L+SFSEwpPqxP+WUlcCIRACITALhJInkMgBEJgCYEoxJdASVQIhEAIhEAIhEAIhEAI7DKBRd7ffOG3d5piudNtg/8ilYnXLKE4vwoL7Wa3ziRCZTcuBEIgBEIgBEIgBEJg2wlEIb7tdyj5C4EQWIdA0oZACIRACIRACPw3gXH98OdW9Jy1sFlFv02ltX53eUudH+q8R+15wZJ2z1uBNyh5txLK7PJWutepPRTO07GItcRfvvbdtuReJTcr+f0SVu7iblXh0b1cbbxjyQuUnOZuUQler+RdS1625DQnf9KsWs7FvkgIhEAIhEAIhMDVEciVQ2BjAtNO6MYnyoEhEAIhEAIhEAIhEAIhEAJbQ4Bi+02H3Px6hZ9ZcpJ7hdr5eyWfVCL9Y8qfuodVhP3fUP6TSyi/71m+pUU+vvwPKXl6yXeXUHCXd83dskL/u+TrSiyV4gczxzSO9UOWf1L7nb+8o1epf84t7r4VbveZFfi5ki8ssbzLq5a/yr197fitkq8uoWh3nGs9vLa/rGTqKOAtNyN+Cy3EZSsSAiEQAiEQAiEQAiGwKYEoxDcll+NCIARCIAQul0CuFgIhEAIhsA4B1tC3GQ44bbkU44IfqvQUzH5Ik2KcAvolK67da1fgriWswym8X73CP1byySWU734g820r/KQSluKstyt4zX15hf62hLL5u8qn7H6H8ts9ugIvWvJiJRTY5R29T/0TRx5VYe7e9e8+JXco+dwSluMPKH+Ze6eK/IGSJ5TcpcQPdbKAf0SFH1Ryx5Kp80OkFPV/VDuyfnhBiAuBEAiBEAiBEAiBSyVwwRfT8b3gS+T0IRACIRACIRACIRACIRACl0yAInm85FPGjSVhltMsuL++9lE+3658VubPKb/dh1XgsSWWX5GmgkcU5pYhoTy2TTr8SjYWIh0F+2fVtnXBX798TphP/r3+sWJ3/jtV2L4fKV8c+c8Kc59Y/z6vxI9oLjtP7Tp2FODfWqG/KmG97nwVPKJs/3mBEsr78q5zWS7lOhzZuEwCuVYIhEAIhEAIhMDFE4hC/OIZ5wohEAIhEAIhEAInE8jeEAiBsxP4jDqF9a4pvv+4wg8uGR2LaEuFSMMSfNwn/Br179NLKJ3fq3xrbrP+/rsKt3uZCjyuhGMRzr9//XtGyegos23/uX8LsW7411T4D0terYSV+N+X/6MlU/dmFWGc8mvlU4SXd81RxDvumyuGwv69y+e+3b9BrD/+nbVtffHPLv+fS0ZH4W972Q92tkI8y6UgFAmBEAiBEAiBEAiBPSOgo7lnRdql4iSvIRACIRACIRACIRACIXAuBCixWURTNr9wnZEimTKbUCBTHlvihCL7f9T+qWNx3Url+y129jrei80jVuSsrS1VQjn+r7VjqjS2TIt81K6jUdlM8f2xIkv6/N9R4WeVTN1JCmnlsaQLxb2lT166Dv6dEsr+8q45y55YE10EK3N+C2W4/LMwb0vx3ueHQi03Y3taNnGREAiBENiQQA4LgRAIgRDYFgJRiG/LnUg+QiAEQiAEQiAEQmAfCaRMl0WAItxa2hTSFOIsqa3FTV6kMiHOfmtj/8/aXuVetXZQaP9D+W0NXsHr3N0XW5Yb+Y9FuL23rwDl+y+V//9Kps4+63+Lt/wKfyqtEB8V6tM0tt/fv5Kp4r6ijt7TvxIW6dN1wFmg164j+bdMi3ALRTpGf1ARo4V7bcaFQAiEQAiEQAiEQAjsA4EoxPfhLqYMW0kgmQqBEAiBEAiBEAiBHSTQSuZV1tuKdJLCutcut3a3tFNh1c062492Wt5lup/i/rUq0nrfJ/0QKCW/H8y03rjlU+qQ65wf/hSxTKne+V9mAd77lh3nfJEQCIEQCIEQuIFAIkIgBHaLQBTiu3W/ktsQCIEQCIEQCIEQCIEQuCgCo/V2W10/pC72gSWja6Wx9cjHeMrst60ISmoK9QoePar+vWNJu1a49/nfoXZ8XUk71tvGKL9ZEX9b0o7yutcmF2eJmFtW4CdKWHK/ZPm/UdLuLxaBZUr1tnD/6UWaHyifEr68oy5bK8Qfc3R09JYlcSEQAiEQAiEQAiEQAntCQGdzT4qSYoRACITAZRHIdUIgBEIgBEJgLwlYX5z1tuVCLCdy6yqlpUe+t/x2vX74v1TEdP1tymQ/xunHMCmkb19pWHE/oXzu+eqfbdbf31Rh7mPq37h0Si/n8jMV386a49b7tlZ4x917EWjF+ofWth/RLO/Y/fjx/6Mj11oEjz2K9NtWyBrkv1C+NcjfsHwK+PKOev1wynLriSv/qGiXJhICIRACIRACIXAwBFLQfSQQhfg+3tWUKQRCIARCIARCIARCIATWJ9AW2U+tQ40THlb+I0r8KGd5x47SXIDl9XT98FY+Wz/c8azLncOPbzrmOfXPuZ5RvnW936/8fypxrvKO3dOP/x8d/fPCv1v5n1nywJLR/c1iQ15fu8IU119afjs/4inMAl1eWL+zTv+SipQPP+ZJKNItuSKudh092b8SZbt/+T9c0nmq4AG5FDUEQiAEQiAEQiAE9pSAzuGeFi3FCoEQCIEQCIH1CeSIEAiBEDhgAr9eZf+uEkucUGr7Ec6vrO3RsfqmLP76MXIRfnz5llGhnHau363tLyxpR+n80Np4oRLW5R9d/keUjO5rauOJJQ8osWzJZ5cvP34cs4LX3FdUiFJcfv34p/OwWq/oY/eL9Z9luR/5/O0KU8C/b/mswR9U/vOXsBC3HMpnVbjdF1XAj4H+YPmU5fJYwbgQCIEQCIEQCIEQCIF9IdAK8X0pT8oRAiEQAiEQAiEQAiEQAiGwOQHK7Ferw9+j5L1L/r1kdKy+71gRFNHlXecsQ/IWFWM97rcr/9NKpo7FuGVKKJstj2L97zENpTklNatvCmzLsCxbssSSJpZ3+YA6+M4llOflXee+sbasa+6HPlmav3Vtu95Xl/+iJfcpeZOSfyxp90MVeLmSDyuRB0u/VDAuBPaKQAoTAiEQAiEQAgdNIArxg779KXwIhEAIhEAIHBKBlDUEQmAmgT+qdNYRL+8GZymTp90Qe32E/Syyr4/9761nVvCXSyjQy1vq5IHyeunORSRLdZbmo2X4Ytc1TxrW7r9/LeamwD+Ux3K8vBucfP1cxVpSpby4EAiBEAiBEAiBEAiBfSIQhfg+3c1VZUl8CIRACIRACIRACIRACIRACIRACITA/hNICUMgBEIgBE4lEIX4qYiSIARCIARCIARCIARCYNsJJH8hEAIhEAIhEAIhEAIhEAIhMIdAFOJzKCVNCGwvgeQsBEIgBEIgBEIgBEIgBEIgBEIgBEJg/wmkhCEQAudEIArxcwKZ04RACIRACIRACIRACIRACFwEgZwzBEIgBEIgBEIgBEIgBM6PQBTi58cyZwqBEAiB8yWQs4VACIRACIRACIRACIRACIRACIRACOw/gZTwUglEIX6puHOxEAiBEAiBEAiBEAiBEAiBEAiBJhA/BEIgBEIgBEIgBC6bQBTil0081wuBEAiBEAiBo6MwCIEQCIEQCIEQCIEQCIEQCIEQCIEQuAICl6wQv4IS5pIhEAIhEAIhEAIhEAIhEAIhEAIhEAKXTCCXC4EQCIEQCIHtJBCF+Hbel+QqBEIgBEIgBEJgVwkk3yEQAiEQAiEQAiEQAiEQAiEQAltLIArxrb01u5ex5DgEQiAEQiAEQiAEQiAEQiAEQiAEQmD/CaSEIRACIbDLBKIQ3+W7l7yHQAiEQAiEQAiEQAhcJoFcKwRCIARCIARCIARCIARCYMcJRCG+4zcw2Q+ByyGQq4RACIRACIRACIRACIRACIRACIQvzC6wAAAC/UlEQVRACOw/gZQwBPafQBTi+3+PU8IQCIEQCIEQCIEQCIEQCIHTCGR/CIRACIRACIRACITAQRCIQvwgbnMKGQIhEAKrCWRPCIRACIRACIRACIRACIRACIRACITA/hNICW8iEIX4TRzyPwRCIARCIARCIARCIARCIARCYD8JpFQhEAIhEAIhEAIhcI1AFOLXUCQQAiEQAiEQAvtGIOUJgRAIgRAIgRAIgRAIgRAIgRAIgRAYCeynQnwsYcIhEAIhEAIhEAIhEAIhEAIhEAIhEAL7SSClCoEQCIEQCIE1CUQhviawJA+BEAiBEAiBEAiBbSCQPIRACIRACIRACIRACIRACIRACKxPIArx9ZnliKslkKuHQAiEQAiEQAiEQAiEQAiEQAiEQAjsP4GUMARCIAQuhEAU4heCNScNgRAIgRAIgRAIgRAIgU0J5LgQCIEQCIEQCIEQCIEQCIGLIhCF+EWRzXlDIATWJ5AjQiAEQiAEQiAEQiAEQiAEQiAEQiAE9p9AShgCV0ggCvErhJ9Lh0AIhEAIhEAIhEAIhEAIHBaBlDYEQiAEQiAEQiAEQuBqCUQhfrX8c/UQCIEQOBQCKWcIhEAIhEAIhEAIhEAIhEAIhEAIhMD+E9j6EkYhvvW3KBkMgRAIgRAIgRAIgRAIgRAIgRDYfgLJYQiEQAiEQAiEwC4QiEJ8F+5S8hgCIRACIRAC20wgeQuBEAiBEAiBEAiBEAiBEAiBEAiBHSEQhfgZblQODYEQCIEQCIEQCIEQCIEQCIEQCIEQ2H8CKWEIhEAIhMD+EIhCfH/uZUoSAiEQAiEQAiEQAudNIOcLgRAIgRAIgRAIgRAIgRAIgb0iEIX4Xt3OFOb8CORMIRACIRACIRACIRACIRACIRACIRAC+08gJQyBEDg0AlGIH9odT3lDIARCIARCIARCIARCAIFICIRACIRACIRACIRACBwggSjED/Cmp8ghcOgEUv4QCIEQCIEQCIEQCIEQCIEQCIEQCIH9J5AShsAyAv8/AAAA//9O7CYoAAAABklEQVQDAAIjMU5xWTB5AAAAAElFTkSuQmCC)

![image.png](attachment:2088bc5d-20cd-466a-951a-7862dff9f995:image.png)

여기서 $B_{velocity}$는 단축된 개발 시간의 가치, $C_{tokens}$는 모델 사용료, $C_{downtime}$은 중단 시 손실액, $P_{outage}$는 발생 확률, $C_{verification}$은 '검증 세(Verification Tax)'이다. 고도로 자동화된 하네스를 갖춘 '거버넌스 기반 AI 개발' 팀은 22%~30%의 순이익을 달성하지만, 그렇지 않은 팀은 속도 향상의 이점이 리뷰 백로그와 인시던트 대응 비용으로 인해 2%~8% 수준으로 상쇄된다.[26]

### **AI 비상 대응 매뉴얼 및 단계별 도입 로드맵**

기업 엔지니어링 리더십은 인프라 불안정성에 대비하여 'AI 비상 대응 매뉴얼'을 수립하고 이를 주기적으로 훈련해야 한다. 이는 Netflix의 Chaos Monkey와 유사한 'AI Chaos Monkey'를 통해 AI 서비스를 강제로 중단시키고 시스템의 복구 능력을 테스트하는 과정을 포함한다.[2]

### **AI 엔지니어링 도입 로드맵**

| **단계** | **목표** | **핵심 과제** | **성공 지표** |
| --- | --- | --- | --- |
| **1단계: 가시성 확보** | AI 의존도 전수 조사 | 모델별 RTO/RPO 설정, AI 자산 인벤토리 구축 | AI 중단 시 영향도 매핑 완료 |
| **2단계: 하네스 강화** | 하이브리드 파이프라인 구축 | 액션 게이팅 도입, 정적 검증 도구와 AI 결합 | AI 오류 자동 탐지율 > 90% |
| **3단계: 탄력성 완성** | 멀티 모델 Redundancy 구현 | AI 게이트웨이 및 로컬 sLLM Fallback 활성화 | 블랙아웃 시 복구 시간 < 5분 |
| **4단계: 지능형 운영** | InferenceOps 고도화 | 자가 학습 루프 및 동적 로드 밸런싱 적용 | 모델 변동 시 성능 저하 0% |

[2]

---

## **결론: 지속 가능한 AI 엔지니어링을 향한 제언**

본 분석을 통해 밝혀진 바와 같이, 인공지능은 현대 엔지니어링의 핵심 동력이자 동시에 가장 부서지기 쉬운(Fragile) 기반 시설이다.[2] AI 블랙아웃은 이제 '만약'의 문제가 아니라 '언제'의 문제이며, 이에 대비하지 않은 기업은 기술적 전문성을 상실한 채 외부 인프라의 붕괴와 함께 침몰할 위험이 크다.

따라서 기업은 다음의 세 가지 전략적 기조를 견지해야 한다. 첫째, **하네스 중심의 사고**이다. 모델이 아무리 똑똑해지더라도 이를 제어하고 검증하는 하네스 시스템이 부재하면 그 결과물은 신뢰할 수 없다. 둘째, **모델 불가지론(Model-Agnostic) 아키텍처**를 지향해야 한다. 특정 모델에 종속되지 않는 게이트웨이와 로컬 sLLM의 하이브리드 운영은 가용성 리스크를 관리하는 유일한 해법이다. 셋째, **인간 전문성의 재정의**이다. AI가 코드를 쓸 때 인간은 그 코드를 검증하는 '하네스'를 설계하는 고도의 아키텍트 역할을 수행해야 하며, 블랙아웃 상황에서도 시스템을 이끌 수 있는 핵심 도메인 지식을 보존해야 한다.[6]

인공지능은 도구일 뿐, 엔지니어링의 본질은 여전히 신뢰할 수 있는 시스템을 구축하는 데 있다. 모델 변동성과 블랙아웃이라는 파고를 넘어서기 위한 하네스 엔지니어링의 고도화는 2026년 이후 기업의 생존을 결정짓는 가장 핵심적인 경쟁력이 될 것이다.

---

### **참고 자료**

1. Enterprise Software Faces AI-Driven Disruption as Development Productivity Gains Fail to Materialize - ERP Today, 4월 22, 2026에 액세스, https://erp.today/enterprise-software-faces-ai-driven-disruption-as-development-productivity-gains-fail-to-materialize/
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11. PromptDebt: A Comprehensive Study of Technical Debt Across LLM Projects - arXiv, 4월 22, 2026에 액세스, https://arxiv.org/html/2509.20497v1
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14. Small Language Models: Phi-4 vs Gemma 3 vs Llama 3.3 — Enterprise Edge AI [2026], 4월 22, 2026에 액세스, https://www.meta-intelligence.tech/en/insight-slm-enterprise
15. SLMs vs Cloud Giants: AI Battle Reshaping Enterprise Tech - ThoughtMinds, 4월 22, 2026에 액세스, https://thoughtminds.ai/blog/slms-vs-cloud-monsters-the-ai-battle-reshaping-enterprise-technology
16. Transform Your AI Applications with Local LLM Deployment - Microsoft Community Hub, 4월 22, 2026에 액세스, https://techcommunity.microsoft.com/blog/azuredevcommunityblog/transform-your-ai-applications-with-local-llm-deployment/4462829
17. Start with an AI Initiative with a Minimum Viable Dataset | by hj barraza - Medium, 4월 22, 2026에 액세스, https://medium.com/@hjbarraza/start-with-an-ai-initiative-with-a-minimum-viable-dataset-3343fb617dd6
18. Minimal Viable Data Strategy for Early-Stage Startups: Leveraging AI and Big Data Without a Formal Corporate Structure - OASK Publishers, 4월 22, 2026에 액세스, https://oaskpublishers.com/assets/article-pdf/minimal-viable-data-strategy-for-early-stage-startups-leveraging-ai-and-big-data-without-a-formal-corporate-structure.pdf
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27. Emerging Trends in AI Infrastructure and How Enterprise Teams Can Stay Ahead - BentoML, 4월 22, 2026에 액세스, https://www.bentoml.com/blog/emerging-trends-in-ai-infrastructure-and-how-enterprise-teams-can-stay-ahead
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