AI 導入後，為什麼 Senior 反而更累？
導入 AI 後團隊速度沒變快，Senior 卻每天加班——junior 用 Cursor 一小時產出 300 行，Senior 要花兩小時 review、補測試、抓邊界。產出曲線往上，認知負擔曲線更陡。從 code review 結構、責任邊界、AI 信任分層三個視角拆，看為什麼瓶頸從「寫得慢」變成「審得慢」，以及怎麼把 Senior 從人肉 linter 救回來。
Tim
Why Senior Engineers End Up More Exhausted After AI Adoption
Your team adopted AI coding tools and shipped faster—but your seniors are burning out. Juniors push 300 lines an hour with Cursor; seniors spend two hours reviewing, patching tests, and chasing edge cases the AI didn’t see. Throughput went up, cognitive load went up steeper. Three lenses on why the bottleneck shifted from writing to reviewing—code review structure, ownership boundaries, AI trust tiers—and how to stop using your seniors as human linters.
DGX Spark：什麼樣的工作流值得自有 GPU？80% 的工程債在硬體後面
邊緣 AI 推理該買 DGX Spark 還是繼續付雲端費用？算過一次帳的人都知道，硬體不是 sticker price 的問題——是後面 80% 看不見的工程債：模型量化、推理框架選型、散熱、運維、跨機 RDMA。雲端是租金，自有 GPU 是房貸加裝修。三個自評問題判斷你的工作流值不值得 on-prem：日均 token 量、延遲敏感度、模型迭代頻率，以及 break-even 怎麼算才不會被 GPU spec 表騙。
DGX Spark: What Workflows Justify Owning a GPU? 80% of the Engineering Debt Comes After the Hardware
Should your edge AI inference run on a DGX Spark or stay on cloud APIs? Anyone who’s run the numbers knows the sticker price is the easy part—the hidden 80% is engineering debt: quantization, inference framework choice, thermals, ops, cross-node RDMA. Cloud is rent; owning GPUs is a mortgage plus renovation. Three self-assessment questions to decide if your workload deserves on-prem—daily token volume, latency sensitivity, model iteration cadence—and how to compute break-even without getting fooled by the GPU spec sheet.
CI Broke Again Over a Lint Error—Can AI Just Fix It?
Most CI failures are lint errors, typos, and formatting issues—anyone can fix them, but each round costs 10 minutes of waiting. Anthropic’s internal YOLO Push concept lets Claude auto-fix these mechanical failures, with a complete GitHub Action YAML example and safety boundary design.
CI 失敗→複製錯誤→手動修→再等 10 分鐘——這個循環可以自動化嗎？
CI 失敗最常見的原因是 lint error、typo、格式問題——任何人都能修，卻要等 10 分鐘。Anthropic 內部的 YOLO Push 概念讓 Claude 自動修復這類機械性失敗，含官方 GitHub Action YAML 範例和安全邊界設計。
Agent 很慢、LLM 不夠聰明——什麼時候該用多步驟處理？
LLM 一次回答不完整，Agent 又跑很久還常卡住——單次呼叫、CoT、RAG、Agent 到底怎麼選？一張光譜圖 + 5 個問題幫你判斷。
Single Call vs Agent: A Spectrum of LLM Strategies
Single LLM calls miss details. Agents take forever and get stuck. What are the options in between? A visual spectrum + 5 questions to help you decide.
AI Outputs ‘用户’ ‘调用’? One Command to Convert to Taiwan Chinese
LLM-generated Chinese often mixes terminology from different regions. Taiwan readers stumble over Mainland terms like ‘用户’ and ‘调用’. The zhtw tool lets you localize with one command—supporting CLI, Python integration, and batch processing.
AI 輸出『用戶』『調用』？一個指令轉成台灣用語
LLM 生成的中文內容常混合不同地區用語，台灣讀者看到『用戶』『調用』會出戲。zhtw 工具讓你一個指令完成在地化，支援 CLI、Python 整合、批次處理。