TimDevelopers often set a Skill’s responsibility scope too broadly, leading to overcomplicated architectures. This post breaks down the five levels of Skill capability boundaries and points out that its nature is a “knowledge base,” not an “automation engine.”
PM 丟 Figma，工程師回 API spec。起點選錯，後期重構成本可能翻倍。拆解 Design-First、API-First、Code-First 的取捨，並提供 AI 時代的混合實戰框架。
A junior engineer submitted a PR, and the AI review returned a 40-page report. Among 120 flags, a senior engineer spent 20 minutes reviewing them and found that 80% were style comments and 15% were false positives. When there are too many warnings, high-confidence risks get buried. PR review has two special structural properties that make it especially benefit from multi-agent cross-validation.
Junior 提交了 PR，AI review 吐出一份 40 頁的報告。120 個標記中,Senior 看了 20 分鐘，發現 80% 是風格，15% 是誤報。當警告太多，高信賴度風險就被淹沒了。PR review 有兩個特殊結構，讓它特別吃多 Agent 交叉驗證的紅利。
The board review is next month. Three AI documents are on the table: Agentic AI, AI + SDLC, DevSecOps. Leadership wants innovation. Engineers want fewer late nights. Where does the workshop start? Most teams treat this as a pick-one decision — and 60% of GenAI initiatives stall at PoC as a result. These aren’t parallel options; they’re dependencies. This post walks through a three-act workshop structure — Pain Discovery, Three-Lens Mapping, and Quick Win → Core Change → Strategic Leap — so leadership and engineers can sequence the work on the same map.
A PM drops a Figma link, and an engineer replies with an API spec. Pick the wrong starting point, and later refactor costs may double. This post breaks down the tradeoffs among Design-First, API-First, and Code-First, and offers a practical hybrid framework for the AI era.
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。
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.
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.
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。