#llmaccuracy
Live, measured metrics for the hashtag #llmaccuracy from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #llmaccuracy
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 2026-07-28 20:21 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 2026-07-28 20:21 UTCNo related tags with measured usage found for #llmaccuracy.
Live pulse
measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-07-28 20:21 UTCEverything below is measured over the latest 2 public posts (spanning ~101 hours).
Posting hours (UTC)
Languages: English (2)
Avg boosts / post: 0
Top of the latest posts
Prompt Politeness Affects LLM Accuracy https://arxiv.org/abs/2510.04950 #HackerNews #PromptPoliteness #LLMAccuracy #AIResearch #NaturalLanguageProcessing #MachineLearning
[AI로 시장조사할 때 프롬프트에 넣어야 할 제약 조건 4가지 AI를 활용한 시장조사 시 발생할 수 있는 4가지 주요 오류 유형(가짜 숫자 생성, 사용자 가설에 맞춘 데이터 조작, 오래된 정보의 현재화, 가짜 출처 URL)과 이를 방지하기 위한 4가지 프롬프트 제약 조건(모른다고 말하게 강제, 반론 포지션 강제, 시간 범위 + 출처 타입 명시, 신뢰도 라벨 요청)을 소개. 또한 최종 검증 방법(다양한 질문 방향, AI의 약점
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/llmaccuracy