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Based on SOC occupation classification
Karpathy LLM Wiki ํจํด ๊ตฌํ. ํ๋ก์ ํธ ๋ฌธ์(์คํ/ADR/ํ์๋ก)๋ฅผ wiki/raw/์ ๋ชจ์ผ๊ณ LLM์ด ์ปดํ์ผํ์ฌ wiki/์ ๊ต์ฐจ์ฐธ์กฐ ๋งํฌ๋ค์ด ์ํค๋ฅผ ์์ฑยท๊ฐฑ์ . qmd ์ค์น ์ ํ์ด๋ธ๋ฆฌ๋ ๊ฒ์ ์๋ ํ์ฑ, ๋ฏธ์ค์น ์ INDEX.md ๋ผ์ฐํ ์ผ๋ก graceful degrade. LanceDB ์ ํ์ ๋ฒกํฐ ์ธ๋ฑ์ค. Obsidian ํธํ. ํ์ ๋ช ๋ น - init, ingest, query, lint, sync, export, qmd-index, lancedb-sync.
Intent Document(INTENT.md) ์์ฑ ๋ฐ ๊ด๋ฆฌ ์คํฌ. ํ๋ก์ ํธ์ ์๋๋ฅผ Why/What/Not/Learnings๋ก ๋ฌธ์ํํ๊ณ , ํ๊ตฌ์ ํ์ต์ ํตํด ์๋๋ฅผ ์งํ์ํค๋ ์๋ช ์ฃผ๊ธฐ๋ฅผ ๊ด๋ฆฌํ๋ค. ๋ฐ๋์ ์ฌ์ฉํด์ผ ํ๋ ๊ฒฝ์ฐ: "intent document", "INTENT.md", "intent engineering", "ํ๋ก์ ํธ ์๋ ์ ๋ฆฌ", "์ ๋ง๋๋์ง ์ ๋ฆฌ", "ํ๋ก์ ํธ ๋ชฉ์ ๋ฌธ์ํ", "What/Why/Not ์ ๋ฆฌ", "ํ๋ก์ ํธ ๋ฐฉํฅ ์ก๊ธฐ", "ํผ๋ฒ", "ํ๋ก์ ํธ ์ข ๋ฃ ํ๋จ" ๋ฑ์ ์ธ๊ธํ๊ฑฐ๋, ์ ํ๋ก์ ํธ๋ฅผ ์์ํ๋ฉด์ ๋ฐฉํฅ์ ์ก๊ณ ์ถ๋ค๊ณ ํ ๋. ๊ธฐ์กด INTENT.md๋ฅผ ์์ ํ๊ฑฐ๋ ํ์ต ๊ฒฐ๊ณผ๋ฅผ ๊ธฐ๋กํ๊ณ ์ถ์ ๋๋ ์ด ์คํฌ์ ์ฌ์ฉํ๋ค.
AWS always-on ์ํคํ ์ฒ(EC2/ALB/ECS/RDS)๋ฅผ ์๋ฒ๋ฆฌ์ค+Spot ํจํด์ผ๋ก ์ดํํ ๋ ์ฌ์ฉ. ์ํฌ๋ก๋ ๋ถ๋ฅ, ํธ๋ ์ด๋์คํ ํ๊ฐ, ๋จ๊ณ๋ณ ์ดํ ๊ณํ ์์ฑ. ๊ตฌํ how-to๋ sagemaker-spot-training ๋ฑ ํ์ ์คํฌ๋ก ์์. ํธ๋ฆฌ๊ฑฐ ์ - "์๋ฒ๋ฆฌ์ค ์ดํ", "EC2์์ Lambda๋ก", "Spot ์ดํ", "serverless migration", "ALB์์ API Gateway", "๋น์ฉ ์ ๊ฐ ์ดํ".
Guide for running cost-effective ML training on SageMaker Managed Spot Training. Use this skill when setting up GPU training jobs, choosing instance types/regions, debugging Spot capacity issues, or optimizing SageMaker training costs.
AWS Well-Architected Cost Optimization Pillar review. Checks resource sizing, scaling, storage classes, and cost monitoring configurations in IaC code.
AWS Well-Architected Operational Excellence Pillar review. Checks monitoring, logging, deployment automation, and continuous improvement practices in IaC code.
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