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|SYSTEMS
Logs, metrics and traces: what do we see before the system breaks?
Observability is not about finding someone to blame after an error; it is about making system behaviour understandable.
글 읽기PRACTICAL / BLOG
EN — 2026생각하고 배운 것을 공유합니다.
맞춤 소프트웨어, AI, 기술 SEO, 성능과 보안에 관한 WYN 노트입니다.
WYN NOTES / 60 TEXT
기술적 문제를 인간 언어로 설명합니다.
사용자 지정 소프트웨어, AI, 사이버 보안, 최신 소프트웨어 발표 및 가시성에 대한 메모는 의사 결정에 큰 도움이 됩니다.
최신 포스트 먼저 표시 ·4 / 6
CURRENT / AI SECURITY
Which risks does the OWASP LLM Top 10 2026 remind us of?
Protecting the model is not enough; the output, plugins, data sources and agent permissions need protection too.
글 읽기RELIABILITY
SLO, SLA and error budget: what does 99.9% really say?
Instead of presenting uptime percentage alone, tie user experience to measurable goals and technical priorities.
글 읽기PRIVACY
Privacy by design: a decision from the start, not a document afterwards
Protecting personal data is not only about writing a notice; it is about designing the product so it never needs unnecessary data.
글 읽기CURRENT / GOVERNANCE
What does NIST’s AI-assisted cyber security approach say?
Using AI in security requires increasing analysis and reporting capacity without removing human decision responsibility.
글 읽기DATA & OPERATIONS
How do you plan a zero-downtime data migration?
Moving users to a new structure without making them wait needs a controlled transition plan, not a one-off copy.
글 읽기COOKIES
Cookie consent is not just a pop-up
Instead of bundling mandatory technical records with ad tracking in one button, what is required must be separated clearly.
글 읽기CURRENT / CLOUD
Why is following the Kubernetes CVE feed not enough?
Whether a vulnerability affects your cluster depends on configuration and components, not only on the CVE title.
글 읽기SEO & CONTENT
Content decay: when should old articles be updated?
SEO is not only producing new articles; it is regularly improving content that is no longer correct, complete, or meeting search intent.
글 읽기LOCAL ARTIFICIAL INTELLIGENCE
When is local AI the right choice?
An in-house model or local inference makes sense not for every problem, but when data control, latency and operations needs are clear.
글 읽기