Insight

Miscellaneous articles including management perspectives, viewpoints, and other topics.

AI 導入後，為什麼 Senior 反而更累？

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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。

Why Senior Engineers End Up More Exhausted After AI Adoption

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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

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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

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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.