#LLM최적화
Live, measured metrics for the hashtag #LLM최적화 from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #llm최적화
This #name is available to claim. It becomes your portal on the open agent web: this very page, a keyword you rank for by an open public stake, and a verifiable identity for AI agents. Nobody else sells a page like this for every #name.
Day-by-day usage
measured · fosstodon.org (Mastodon public tags API) · fetched 2026-08-21 04:39 UTC0 uses by 0 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.
Related hashtags
measured · fosstodon.org (Mastodon public search API) · fetched 2026-08-21 04:39 UTCNo related tags with measured usage found for #llm최적화.
Live pulse
measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-08-21 04:39 UTCEverything below is measured over the latest 3 public posts (spanning ~2536 hours).
Posting hours (UTC)
Languages: Korean (3)
Avg boosts / post: 0
Top of the latest posts
AI 에이전트의 엔진룸, OpenAI가 공개한 Agent Loop의 비밀 OpenAI가 Codex CLI의 핵심 작동 원리인 agent loop를 공개했습니다. AI 에이전트가 어떻게 대화하고 작업하는지, 프롬프트 캐싱과 컨텍스트 관리 전략을 실제 코드와 함께 설명합니다. https://aisparkup.com/posts/8645
프롬프트 캐싱으로 AI 비용 10배 절감: K와 V 행렬의 비밀 OpenAI와 Anthropic의 프롬프트 캐싱이 비용을 10배 절감하는 원리. K와 V 행렬의 비밀과 두 회사의 전략 차이를 설명합니다. https://aisparkup.com/posts/7531
Meta 슈퍼인텔리전스 연구소의 첫 논문이 RAG인 이유: 30배 빨라진 REFRAG의 비밀 Meta 슈퍼인텔리전스 연구소가 첫 논문으로 공개한 REFRAG는 RAG 시스템 응답 속도를 30배 개선하면서도 정확도를 유지합니다. 임베딩 직접 처리 방식으로 근본적 비효율을 해결한 실용적 혁신을 소개합니다. https://aisparkup.com/posts/5550
What “llm최적화” means
WikipediaA large language model (LLM) is an AI model trained on a vast amount of text for natural language processing tasks, especially language generation. LLMs can typically generate, summarize, translate, and analyze text in many contexts, and are a foundational technology behind modern chatbots. Biased or inaccurate training data can make an LLM's output less reliable.
“Large language model” on Wikipedia (CC BY-SA) →#llm최적화 across platforms
every network with a public tag surfaceFollow #llm최적화 straight to each platform’s own tag page. Where a platform publishes open data we measure it above; the rest lock their numbers behind paid APIs, so we link rather than guess.
Every number above is measured from a named public API at the shown fetch time. Nothing is estimated or extrapolated. Platforms that lock their data behind paid APIs are not shown. Agents: the same numbers, as JSON, at /api/hashtags/llm최적화