#自動化證明
Live, measured metrics for the hashtag #自動化證明 from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #自動化證明
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 · mastodon.social (Mastodon public tags API) · fetched 2026-08-23 22:07 UTC0 uses by 0 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.
Related hashtags
measured · mastodon.social (Mastodon public search API) · fetched 2026-08-23 22:07 UTCNo related tags with measured usage found for #自動化證明.
Live pulse
measured · mastodon.social (Mastodon tag timeline) · fetched 2026-08-23 22:07 UTCEverything below is measured over the latest 4 public posts (spanning ~11037 hours).
Posting hours (UTC)
Languages: Chinese (Taiwan) (4)
Avg boosts / post: 0
Top of the latest posts
🌗 反思形式驗證：五十年後的反對論點檢視 ➤ 當 AI 代理全面接管程式碼生成，半個世紀前對形式驗證的質疑是否依然成立？ ✤ https://ivan-gavran.github.io/0-social-processes-paper 隨著人工智慧輔助程式設計的興起，形式驗證在近年重新受到重視。本文在 2026 年的背景下，重新評估 1979 年反對形式驗證的經典論文。作者結合現代軟體工程的進展，說明現代規格語言如 Quint 如何透過互動檢驗解決規格偏差，並分析開發者如何
🌗 為什麼非得用 Lean？ ➤ 破除形式化數學的「Lean」迷思：回顧六十年的多元路徑 ✤ https://lawrencecpaulson.github.io//2026/04/23/Why_not_Lean.html 作者以資深研究者的視角，回顧了形式化數學近六十年的發展歷程。文中指出，儘管 Lean 目前在數學界聲勢浩大，但形式化數學的成功並非始於今日，早在 1968 年的 AUTOMATH 就已具備核心能力，隨後的 Boyer-Moore 計算邏輯、LCF 方法論
🌘 測試，而不僅是驗證 ➤ 當 AI 遇上形式邏輯：正式驗證的曙光與不可逾越的侷限 ✤ https://alperenkeles.com/posts/test-dont-verify/ 隨著人工智慧（AI）技術的突破，正式驗證（Formal Verification）正從學術殿堂走進主流開發視野。AI 不僅提升了自動化證明與規格撰寫的效率，更為「強化學習與可驗證獎勵」（RLVR）開拓了新路徑。然而，作者在本文中提出了冷靜的思考：儘管 AI 能輔助生成證明，但軟體界缺乏正式規
#自動化證明 across platforms
every network with a public tag surfaceFollow #自動化證明 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/自動化證明