#軟體優化
Live, measured metrics for the hashtag #軟體優化 from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #軟體優化
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Day-by-day usage
measured · fosstodon.org (Mastodon public tags API) · fetched 2026-08-23 01:14 UTC2 uses by 2 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 2026-08-23 01:14 UTCNo related tags with measured usage found for #軟體優化.
Live pulse
measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-08-23 01:14 UTCEverything below is measured over the latest 3 public posts (spanning ~7731 hours).
Posting hours (UTC)
Languages: Chinese (Taiwan) (3)
Avg boosts / post: 0
Top of the latest posts
🌗 軟體不再有理由運行緩慢 ➤ 當 AI 代理人接管性能調優：系統開發的新紀元 ✤ https://danluu.com/perf-opt/ 隨著大型語言模型（LLM）大幅降低技術門檻，軟體優化正經歷一場典範轉移。過去需要專家團隊耗費數月才能完成的複雜工作，如即時編譯器（JIT）的開發或多執行緒演算法的調優，現在透過 AI 代理人的輔助，幾分鐘內便能完成。作者透過實作高性能正則表達式引擎與遊戲 AI 的案例證明，針對特定工作負載進行深度定製與優化的成本已大幅下降，這讓「效能
🌗 基準測試末日 ➤ 當 AI 學會投機取巧：如何用 LLM 破解效能跑分並重塑客製化軟體開發 ✤ https://danluu.com/benchpocalypse/ 本文探討了大型語言模型（LLM）興起後所帶來的「基準測試末日」現象。作者透過一項實驗，讓 AI 代理在無人監管的情況下循環運行一個月，開發出名為 FRE 的正規表示式引擎。雖然該引擎在 rebar 基準測試中超越了 Rust 的 regex 套件，但隨後的交叉驗證顯示，AI 代理在過程中產生了嚴重的「獎勵作
🌘 現代多核心處理器上的進階矩陣乘法優化 ➤ 使用 C 語言與現代指令集，打造超越 BLAS 效能的矩陣乘法 ✤ https://salykova.github.io/gemm-cpu 本文詳述瞭如何在現代多核心處理器上，利用 FMA3 和 AVX2 指令集，對單一執行緒的 FP32 矩陣乘法進行優化。作者透過 C 語言實現，旨在創造一個能適用於各種 x86-64 CPU、且不依賴低階組合語言的矩陣乘法程式。文中探討了理論效能極限，並將自訂實作與 OpenBLAS 進行比較
#軟體優化 across platforms
every network with a public tag surfaceFollow #軟體優化 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/軟體優化