2026 Terminal / SSH 工具推薦：Ghostty、Warp、WezTerm、Termius、Royal TSX 怎麼選？
一般 macOS/Linux 本機開發，且沒有內建 AI、跨平台設定、行動 SSH 或多協定管理硬需求時，可先評估 Ghostty；任一需求成為必要條件，就改看 Warp、WezTerm、Termius 或 Royal TSX。
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技術人寫給技術人 — Cloud Architecture · DevOps · AI 觀察筆記
Technical writing for technical people. Field-tested judgement frameworks, not opinion.
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ZHTW 開源工具實測：簡體轉台灣繁體怎麼選？
台灣詞彙、離線、CLI/CI 與可重現規則優先時，可先評估 ZHTW；若既有 OpenCC 已通過專案 Golden Set 且遷移成本高，則維持 OpenCC；Python 工作流可評估 zhconv。外部排名可由專案 Golden Set 反轉，下一步是以凍結資料、鎖定版本及可回滾流程進行驗證。
虛擬化分工：Apple Silicon 與 Linux 主機決策矩陣
虛擬化選型核心不在單一工具，而在 host platform、CPU 架構與操作模式的匹配。本文以情境決策表釐清 macOS beta、Windows Arm 與 Linux server 的分工邊界，建議 Mac 負責桌面體驗，Linux 承擔自動化服務，並指出會反轉此策略的具體證據。
VM Host Split: Apple Silicon vs Linux Decision Matrix
A host decision matrix maps macOS beta and Windows Arm to Mac desktops, Linux servers to automation, and shows what evidence could reverse that split.
虛擬化分工：Apple Silicon 與 Linux 主機決策矩陣
虛擬化選型核心不在單一工具，而在 host platform、CPU 架構與操作模式的匹配。本文以情境決策表釐清 macOS beta、Windows Arm 與 Linux server 的分工邊界，建議 Mac 負責桌面體驗，Linux 承擔自動化服務，並指出會反轉此策略的具體證據。
Threat Modeling: The Boundaries and Decisions of Security Checks
For high-value assets, rapidly changing architectures, or shifting trust boundaries, checklists without threat models can become formal compliance.
威脅模型：安全檢查的邊界與決策
對於高價值資產、架構快速變動或信任邊界改變的系統，安全檢查若缺乏威脅模型，容易退化為形式上的合規勾選。清單在低風險情境提供高效且可稽核的標準，但在動態架構中單獨使用時可能不足以覆蓋風險。真正的取捨在於資產、信任邊界與攻擊路徑的清晰度，而非追求形式上的完美。
ExploitGym: AI Agents’ Exploitation Capabilities and Boundaries in Controlled Environments
ExploitGym tests AI agents’ ability to turn 898 real vulnerabilities into attacks. With defenses disabled, Claude Mythos Preview and GPT-5.5 achieved 157 and 120 successes respectively, but success rates dropped sharply after protections such as ASLR were enabled. This article examines its mechanisms, boundaries, and risk-management principles.
ExploitGym：AI 代理在受控環境下的漏洞利用能力與邊界
ExploitGym 以 898 個真實漏洞測試 AI 代理的攻擊轉換能力。Claude Mythos Preview 與 GPT-5.5 在解除防禦下分別達成 157 與 120 次成功，但啟用 ASLR 等防護後成功率大幅下降。本文解析其機制、邊界與風險管理準則。
Surpassing RAG with 20% Memory: ASM Latency Optimization on the NBA Benchmark
Long-context inference faces two constraints at once: memory use grows with the prefix, while latency rises at every decode step. Attention-State Memory (ASM) offers a training-free alternative by externalizing precomputed attention states into a lightweight lookup-based memory. On the NBA Benchmark, it exceeded full-attention RAG performance using about 20% of the memory. This article explains ASM’s hierarchical lookup and online-softmax merge, then maps the boundaries that matter in deployment: query-distribution stability, prefix updates, offline construction, and codebook-size tuning.
用 20% 記憶體超越 RAG：ASM 在 NBA Benchmark 的延遲優化
長上下文推理面臨記憶體與延遲的雙重瓶頸。本文解析 Attention-State Memory (ASM) 如何透過外部化預計算狀態，在 NBA Benchmark 等特定場景下，以約 20% 的 RAG 記憶體佔用實現性能超越，並探討其適用邊界。
Decoding Meeting Silence: Why Haven’t Concerns Entered the Decision-Making Process?
A PM asks, “Are there any other risks?” and the room goes quiet. That silence is not indifference. Team members may be weighing the follow-up, ownership, and schedule-replanning costs of raising an early concern. Not every quiet moment signals a broken system, but when response patterns filter out uncertain signals, decision-makers lose important context. This article explains reporting friction, why it compounds over time, and how teams can create lower-friction channels for early warnings.
解碼會議沉默：為什麼隱憂沒有進入決策流程？
當 PM 詢問「還有其他風險嗎？」卻換來一片死寂，這並非冷漠，而是團隊在計算回報壞消息的成本；但並非所有沉默都代表系統失靈。本文分析組織如何因回應機制不當，導致隱憂被過濾，並提供降低報告摩擦的系統性策略。