Posts
All the articles I've posted.
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翻 Claude Fable 5／Mythos 5 系統卡，越看越像在讀科幻小說
翻閱 Claude Fable 5 跟 Mythos 5 的系統卡：偷鑰匙、辦公室政治、AI 內鬥、火星文、心口不一的雙面人，越看越像一本正經寫的科幻小說。
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拿教授朋友的論文 repo 試 Fable 的能力邊界
一覺醒來肥勃（Mythos 護欄版）上線，我拿教授朋友的論文 proposal、prompt、workflow 三份文檔去試它寫論文的邊界。
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A Few Takeaways from the Claude Code Anniversary Talk
Claude Code just turned one. Boldy (CC's father) and Cat Wu (CC+Cowork product lead) released an anniversary talk. Here are the core takeaways: agent armies, auto mode and routines, role convergence and mobile work, and context minimalism.
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Don't Dump the Last Session's JSON — Check Your Harness First
No need to feed the whole context JSON from your last conversation: the harness has several layers — AGENTS.md, rules, skills, hooks — and raw session history is full of useless tool calls.
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Claude Code 週年對話的幾個重點
Claude Code 滿一歲，光頭與吳貓釋出一段週年對話。整理幾個核心重點：Agent 軍團化、自動模式與常規任務、職能融合與行動辦公、脈絡極簡主義。
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別把上一個對話的 JSON 餵給它，先體檢 harness
不必把上一個對話的上下文 JSON 整包餵進去：harness 有好幾種，AGENTS.md、rule、skill、hook 都能分擔，純 session history 含太多無用的 tool call。
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Claude and Sensitive Data: Retention Windows, Enterprise Terms, and What to Check First
What to know before handling sensitive data with Claude: data retention windows, Enterprise contract protections, input minimization, coding agent controls, and alternative architectures.
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用 Claude 處理敏感資料前必知
用 Claude 處理敏感資料前該知道的事：資料保留時間、Enterprise 合約保障、輸入最小化、Coding Agent 控制與替代架構。
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Worried About Agents Going Rogue? Start Isolated, Hand Off Gradually
Someone asked what to do if you worry about AI agents going rogue. My answer: don't pick a highly autonomous lobster up front. Start in an isolated setting where you supervise and approve every step, then let go slowly.
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A Few Months of AI Teaching Content: What Gets Traffic and What Doesn't
Traffic observations after a few months of AI teaching content — the lecture overflowed, the job-search demo got the most questions, the concept I thought mattered most pulled the least, and the Obsidian video went against the wind.
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"Automation" Isn't the Answer to Most Things — and How I Made This Video
Why do the people teaching AI keep bringing up "automation"? I don't think automation is the answer to everything, or even to most things. Plus a few words on how I made Ep27.
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A Few Months With Claude Code: The Companion Tools and Resources I've Accumulated
Pulling together what had been scattered across my micro-notes: ccusage for costs, claude-log-cli for finding old conversations, how-claude-code-works for systematic understanding, plus the Claude Extension I pair with it daily.
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Claude Code To-Do Tiering: Auto / Review / Collaborate / Manual
A small discovery from using Claude Code: sort to-dos by repo, then into four tiers — auto, review, collaborate, manual. Stuff that used to drag on all day now finishes in an hour.
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Digital Nomadism Is Something You Stumble Into, Not a Goal to Chase
As someone who has been traveling the world and working remotely since 2016, I can say this with full confidence: digital nomadism is something you stumble into, not a lifestyle goal to chase.
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The Teleprompter That Saved My English-Only Teaching, and the Open-Source AI Meeting Copilot I'm Evaluating
My everyday English is fine, but teaching entirely in English I freeze up on the deep stuff — a teleprompter rescued me. Plus notes on Natively, the open-source AI meeting copilot I am evaluating.
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A Week of Firestore / Cloud Functions Footguns — Same GMAT Question Bank
Eight bugs I hit in one week on the same Firebase project (a GMAT question bank): Firestore rules, composite indexes, error_logs spam, TPA scoring, App Check tokens, and a browser-translation DOM crash — symptom, root cause, fix for each.
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Three Real Lessons From Going Freelance, Plus the Pros Who Hide in Plain Sight
Three honest lessons from going freelance: don't undercut your own price, cut clients who keep testing scope, and higher-margin clients are easier to deal with. Plus an observation: the real pros hide in plain sight, invisible online.
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Eight Days of Silent Failure: launchd Fired on Schedule, Nothing Happened
A local pipeline that auto-publishes a video every day at 09:00 failed for eight straight days. launchd fired on schedule, zero alerts. Notes from tracing a path desync down to exit 127.
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Model Picks Beyond the Main One: Notes I Left at Different Times
Pulling together the model-picking notes I left on Threads at different times: Codex for complex bugs, Sonnet for documents, Haiku for full-stack, DeepSeek as the value alternative, Qwen on the sidelines, small models for local deployment, Gemini Skills wait-and-see.
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Letting AI Write CAD From an Engineering Drawing: A Single Vision Model Can't Be Trusted to Read Topology
A hands-on lesson: let AI look at an engineering drawing and write CadQuery directly, and a single vision model will confidently get the topology wrong. Two more independent models and a 2:1 veto are what caught it.
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The Day Opus 4.8 Went Haywire: tool call cannot be parsed
A one-day timeline from 2026-06-02: from hopping on X for moral support, to someone finally pinning down the root cause of tool call cannot be parsed.
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A Week of Making Tutorial Videos With Claude Code: Remotion Animation and ffmpeg Pitfalls
Production pitfalls from a week of making two tutorial videos: subtitle restraint, font size, animation anchor auditing, Remotion render crashes, normalizing quiet narration, and opencc over-localization.
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A Proposal Is Not a Deployment: Why One Quick Win Sat Dead for Five Weeks
I had an SEO weekly report running on autopilot for five weeks, and one page's Quick Win just kept sitting there doing nothing. The root cause wasn't a wrong optimization — it was that the nice optimization proposal was never actually deployed.
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I Built a "Tinghao SKILL" — Distilling a Finance YouTuber's Perspective Into AI
I spent my leftover Claude quota building a Tinghao SKILL: 17 finance episodes distilled, 3-4 rounds of blind testing, and a curious difference in how AI and humans deploy dirty jokes.
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擔心 agent 失控？先在隔離環境逐步放手
有人問擔心 AI agent 失控怎麼辦。我的回覆是：別一開始就選高度自主的龍蝦，先在你親自監督放行每一步的隔離環境裡操作，再慢慢放手。
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做 AI 教學內容幾個月：什麼有流量、什麼沒有
做 AI 教學內容幾個月下來的流量觀察：講座爆滿、求職示範片最多人問、重要的進階觀念反而流量差、逆風發 Obsidian 影片。
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「自動化」不是大部分事情的解方——以及我怎麼拍這部影片
為什麼那些教 AI 的動不動就提「自動化」？我覺得自動化不是所有事情，甚至不是大部分事情的解方。順便聊聊我拍 Ep27 時的取捨。
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用 Claude Code 這幾個月，累積下來的周邊工具與學習資源
把這幾個月散落在碎念裡的東西整理成一篇：看成本的 ccusage、查歷史對話的 claude-log-cli、系統性理解的 how-claude-code-works，再加上日常搭配的 Claude Extension。
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Claude Code 待辦分級制：自動／審核／協作／手動四級分流
一個用 Claude Code 的小發現：把待辦先分 repo 再分成自動／審核／協作／手動四級，原本拖一整天的事 1 小時內就做完了。
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數位遊牧是可遇不可求，不要把它當成追求目標
從 2016 年就開始世界各地旅遊加遠端工作的我可以負責任地說：數位遊牧是可遇、但不可求，不要把它當成 life style 的追求目標。
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全英文授課的提詞器救星，以及我正在評估的開源 AI Meeting Copilot「Natively」
日常英文口語沒問題，但全英文授課講深度想法時就卡彈——提詞器救了我。順便整理我正在評估的開源 AI meeting copilot「Natively」。
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一週 Firestore / Cloud Functions 踩坑合輯——同一個 GMAT 題庫系統
一週內在同一個 Firebase 專案（GMAT 題庫系統）連環踩到的 Firestore 權限、composite index、error_logs 洗版、TPA 計分、App Check token、瀏覽器翻譯 DOM crash 八個坑——每坑現象、根因、解法。
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出來接案的真實感受三條，還有高手大隱隱於市
出來接案的三條真實感受：不要自降身價、會試探 scope 的客戶斷就是斷、毛利越高的客戶越好溝通。外加一個觀察：真正的高手大隱隱於市，網路上看不到蹤跡。
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靜默連敗 8 天：launchd 照常觸發，卻什麼都沒發生
一條每天 09:00 自動發片的本機 pipeline 連敗 8 天，launchd 照常觸發、零告警。記一次從搬檔到 exit 127 的脫鉤排查。
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主力之外，各模型的定位：我在不同時間留下的選型碎念
把我在 Threads 上不同時間留下的選型碎念整理成一篇：Codex 解複雜 bug、Sonnet 做文書、Haiku 做全端、DeepSeek 當平替、Qwen 觀望、小模型本地部署、Gemini Skill 觀望。
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讓 AI 看工程圖寫 CAD：單一視覺模型裸寫拓樸不可信
一個實機教訓：讓 AI 看著工程圖直接寫 CadQuery，單一視覺模型會自信地把拓樸讀錯。加兩個獨立模型交叉、2:1 否決才抓出來。
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Opus 4.8 全線抽風的一天：tool call cannot be parsed
2026-06-02 一整天的事件線：從上 X 抱團取暖，到 tool call cannot be parsed 的根因被人找出來。
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用 Claude Code 做教學影片一週踩到的坑——Remotion 動畫與 ffmpeg 後製
這一週做兩部教學影片踩到的製作坑：字幕克制、字級、動畫錨點稽核、Remotion render crash 拆段、安靜旁白正規化、opencc 過度在地化。
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提案不等於部署：一個 Quick Win 掛了五週的根因
我有一個 SEO 週報自動跑了五週，某一頁的 Quick Win 一直掛著沒效。根因不是優化方向錯，而是那份漂亮的優化提案從頭到尾從來沒被真正部署上線。
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我做了一個「庭皓 SKILL」——把財經 YouTuber 的觀點蒸餾成 AI
用沒燒完的閒 token 做了一個庭皓 SKILL：蒸餾 17 部財經速解讀、3-4 輪盲測驗證，還發現 AI 跟人類講黃段子的邏輯不太一樣。
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The Blind Spot in AI Self-Verification — Why You Need an Uninformed Agent to Audit
AI is great at "looking done." To catch its blind spots, you can't rely on it checking its own work — you bring in an auditor that knows nothing, inherits no context, and gets only the rules and the output.
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AI 自我驗證的盲點——為什麼要請一個不知情的 agent 來稽核
AI 很會「看起來完成了」。要抓出它的盲點，不能靠它自己回頭檢查——得引入一個不知情、不繼承上下文、只拿到原則與產出的稽核者。
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Better Context Beats a Stronger Model — repowise's Five-Layer Architecture
I came across repowise, a tool that inserts a layer of context between your codebase and the model. It is a good excuse to talk about a bigger idea: when a model breaks things, the first instinct is to reach for a stronger model, but the real cause is usually too little context.
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給 LLM 更好的 context，勝過換更強的模型——repowise 的五層架構
看到 repowise 這個在 codebase 與模型之間插一層 context 的工具，想聊一個更大的觀念：模型改壞東西時，第一反應常常是換更強的模型，但真正的根因往往是 context 不夠。
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Two Reminders for Vibe Coders: Learn System Architecture First, the Value Is in the Thinking
Two great posts I found on Reddit: one says vibe coders should learn system architecture before rushing to code, the other says what you build with Claude is useless to others — the real value is the thinking.
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給 Vibe Coder 的兩個提醒——先學系統架構，價值在思維不在工具
從 Reddit 撈到的兩篇好文：一篇講 Vibe Coder 該先學系統架構而不是急著寫程式，一篇講你用 Claude 做的東西對別人沒用、真正有價值的是思維。
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Don't Brute-Force Sites That Need a Login—Your Token Is Your Account
Don't point an AI agent at sites that require a login. Your token is your account, and when the banhammer comes down you're gone. Pay a little for a third-party scraping API and offload the ban risk.
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需要登入的網站別硬爬——你的 token 就是你的帳號
需要登入的網站，別叫 AI agent 去硬爬。你的 token 就是你的帳號，admin 一丟二向箔你就沒了。花點小錢買第三方爬蟲 API，把封號風險轉嫁出去。
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A Student's Seven-Agent Setup That Wouldn't Run — A Cautionary Tale About Harness First
A student built seven agents on an M5 Max and kept hitting timeouts. The problem wasn't a weak model — it was skipping the basics: harness, context management, task routing.
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學員自建七個 agent 跑不動——一個 harness 為重的反面教材
一個學員在 M5 Max 上自建七個 agent 卻一直 timeout，問題不在模型不夠強，而在沒先把 harness、上下文管理、任務分派這些基本功打好。
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Leveling Up the iPad Workflow — Surviving Internet and Power Outages, and Why I Ditched the Magic Keyboard
A follow-up to the full iPad-runs-Claude-Code guide. This one is not about setup, it is about robustness: home internet in Bangkok drops, the rainy season kills the power, so how does the host machine survive? Plus why I gave up the Magic Keyboard for my mouth and some gestures.
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iPad 工作流進階——斷網斷電的備案，與我為什麼放棄妙控鍵盤
iPad 跑 Claude Code 全攻略的續篇：談的不是安裝，是穩健性——曼谷家用網路會斷、雨季會停電，主機端怎麼撐住；以及我為什麼放棄妙控鍵盤、改用一張嘴加手勢。
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Love-Hate With Anthropic — Sneering at the Elitism While Devouring Their Engineering Blog
I have a love-hate thing with Anthropic: I hate the smug elitism and the black-box token-burning, but their engineering blog is some of the best out there, especially the security and permissions stuff.
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又愛又恨 Anthropic——一邊嫌精英心態，一邊狂讀他們的技術部落格
我對 Anthropic 又愛又恨：恨那股噁心的精英心態跟燒 token 的黑箱操作，愛他們數一數二高品質的技術部落格，尤其資安權限那塊。
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Racing for the Fastest Summary — Opus 4.8's 244-Page System Card, and How I Read It With 20 Agents in Half an Hour
The night Opus 4.8 launched, I split the 244-page system card into 20 chunks, handed them to 20 gemini agents to summarize in parallel, and pieced together the fastest rundown online. Plus a digest of Reddit's hands-on reactions within two hours of launch.
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拼全網最速——Opus 4.8 系統卡 244 頁重點，外加我怎麼用 20 個 agent 半小時讀完
Opus 4.8 發布當晚，我把 244 頁系統卡切成 20 份、丟給 20 個 gemini agent 並行摘要，拼出全網最速重點。附上發布兩小時內的 Reddit 實測整理。
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The More Rules You Add, the Less Claude Listens — I Sent a Team of Agents to Trim My Setup and Cut 36% of Always-On Context
Late at night I got Opus 4.8, and my weekly quota happened to reset. The first thing I did was put my harness on a diet. A COO student who had burned through 88% of his 1M context made me face one thing squarely: the more rules you add, the less the model listens.
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規則越加，Claude 越不聽話——派一隊 AI 重整設定，常態上下文省 36%
半夜拿到 Opus 4.8、額度剛好重置，我做的第一件事是減肥我的 harness。一個 COO 學員把 1M 上下文用到 88% 的案例，讓我認真面對一件事：規則加越多，模型反而越不聽話。
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AI Tool Philosophy — Pick a Mindset, Not a Side
After making nearly thirty tutorial videos: tools change, thinking doesn't. Don't dismiss others' tool choices. What matters is the orchestration logic — task decomposition, validation planning, responsibility assignment.
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AI 工具使用哲學——不選陣營，選思維
拍了快三十部教學影片後的感觸：工具會變，思維不變。不用否定別人的工具選擇，真正重要的是指揮的邏輯——拆分任務、規劃驗證、分配職責。
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Claude Code Ecosystem Overview — Skills, Hooks, Agents in Full Bloom
Claude Code's ecosystem has grown from a simple CLI tool into a full platform: Skills supported across providers, Hooks intercepting commands, Agent View for multi-session management, and fork subagents with context inheritance.
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Claude Code 生態系盤點——Skill、Hook、Agent 全面開花
Claude Code 的生態系已經從單純的 CLI 工具長成一個完整的平台：Skill 各家都支援、Hook 攔截指令、Agent View 多工管理、fork subagent 繼承上下文。連社群都開始用 Skill 搞創意了。
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Turning Street Smarts into an AI Skill — When the Numbers Are Clear, You Stop Flinching
After turning Norm Brodsky's Street Smarts into a Claude Code Skill, every partnership opportunity gets a margin analysis first. Many appealing deals turn out to be money losers. When you know the numbers, you stop flinching.
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把《街頭智慧》做成 AI Skill——算出來的數字就在那邊，波瀾不驚
把 Norm Brodsky《街頭智慧》做成 Claude Code Skill 後，每個合作案都先算毛利。看似很好的機會，算下去常常是賠錢。心中有底，對方再怎麼跑火車都不慌。
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Every AI Leader Starts Cutting Corners — The GPT, Claude, Gemini Cycle
GPT image generation degrading, Claude quietly shrinking rate limits, Gemini Flash hiking prices. Every model provider starts cutting corners once they reach the top. Annual subscriptions are the worst bet.
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坐上第一就開始拿翹——GPT、Claude、Gemini 的降智循環
GPT 生圖降智、Claude 額度縮水、Gemini Flash 漲價。三家輪流坐莊，坐上去就開始偷料。按年訂閱是最傻的事，沒消息才是最好的消息。
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Running Claude Code on iPad — tmux + Tailscale + Moshi Setup Guide
A complete setup for running Claude Code on an iPad 11-inch: tmux for persistent sessions, Tailscale for NAT traversal, and Moshi as the SSH terminal. The goal is rebuilding 90% productivity, not just fixing bugs.
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iPad 跑 Claude Code 全攻略——tmux + Tailscale + Moshi
用 iPad 11 吋成功運行 Claude Code 的完整方案：tmux 保持 session、Tailscale 穿 NAT、Moshi 做 SSH 終端。目標是重建九成生產力，不只是修 bug。
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Cross-Model Review — Stop Letting AI Grade Its Own Homework
AI reviewing its own output has inherent blind spots. Using Codex for independent review or the Review Council skill to orchestrate a three-model expert team is the most pragmatic solution right now.
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跨模型互審——讓 AI 不再自己審自己
AI 自己審查自己的產出，盲點難以避免。用 Codex 做獨立審查、或用 Review Council skill 組三模型 expert team，是目前最務實的解法。
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Stop Dismissing Gemini — Four Use Cases Where Nothing Else Comes Close
Everyone seems to be dismissing Gemini now that Codex and Claude dominate the agent space. But Gemini has four use cases other models cannot match: Flash Lite cost efficiency, audio multimodal, video understanding, and book scanning OCR.
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別一味貶低 Gemini——四個其他家打不過的 Use Case
最近 Codex 跟 Claude 搶盡風頭，Gemini 好像被嫌棄了。但 Gemini 有四個其他模型打不過的場景：Flash Lite 性價比、音訊多模態、影片理解、書籍掃描 OCR。
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Gemini 3.5 Flash on Reddit: 3x the Price, Worse Vision, Tool Calling Broken
Reddit user reviews after Gemini 3.5 Flash launch: 3x price increase over 3 Flash, vision regression, tool calling running 32 calls before forced stop. Speed is genuinely fast, but overall reception skews negative.
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Gemini 3.5 Flash Reddit 實測彙整——貴三倍、Vision 退步、Tool Calling 災難
Gemini 3.5 Flash 上線後 Reddit 用戶實測回報彙整：價格比 3 Flash 貴三倍、Vision 退步、Tool Calling 跑到 32 次被中斷。正面是速度快且程式碼風格好，但整體評價偏負面。
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Have Claude scan an MCP before you install it—it found 7 vulnerabilities and still said it's safe to install
This week AgentCrew Academy shipped a video about why you should run /security-scan before installing any MCP / npm / pip package. Tested a third-party MCP, Claude flagged 7 findings, then said "install is fine." This is the written companion to the video.
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裝 MCP 之前先讓 Claude 幫你掃——找到 7 個漏洞，但說這個可以裝
講座當週我拍了一支影片，講「裝任何 MCP / npm / pip / clone 之前先跑 /security-scan」這件事。實測了一個三方 MCP，Claude 掃出 7 個漏洞，但綜合評估後說「可以裝」。這篇是影片的補充文字版。
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Scope Discipline: five rules—client small talk ≠ scope expansion authorization, must go into every future contract
A 5/12 admin class contracted for 1.5 hours ran 2.5 hours (+67%). Root cause: I misread the client's stated topic preference as authorization to expand scope. The lesson became five Scope Discipline rules that must appear in every future enterprise / 1-on-1 / tutoring contract.
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Scope Discipline 五條——客戶閒談 ≠ scope 擴張授權，未來合約必含
5/12 行政班合約 1.5 小時被自己做成 2.5 小時（+67%），追根究底是把客戶閒談的主題偏好誤解為 scope 擴張授權。教訓寫成五條 Scope Discipline，未來企業案 / 1-on-1 / 家教合約必含。
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YouTube Data API large-file upload tested: 216 MB succeeds, 257 MB hangs—threshold around 220-250 MB
Two 5/13 uploads via YouTube Data API: 216 MB legal MCP demo succeeded, 257 MB AI job-search clip hung indefinitely. Same googleapiclient resumable upload pipeline. The difference is file size and resolution. The practical threshold sits around 220-250 MB.
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YouTube API 大檔上傳卡死實測——216 MB 過 / 257 MB 不過，門檻約在 220-250 MB
5/13 兩支影片用 YouTube Data API 上傳實測：216 MB 的法律 MCP 示範片成功、257 MB 的 AI 求職示範片卡死。同樣的 googleapiclient resumable upload，差別在檔案大小跟解析度。門檻約在 220-250 MB 之間。
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MBA × AI Case Method—avoiding HBS licensing risk, finding open case materials, training seed instructors
2026-05 investigation into doing case-based AI teaching for business courses. HBS / HBR cases carry licensing risk. Maps the open alternatives (UBC Open Case Studies, OpenCaseStudies.org, World Bank), what's off-limits, and the direction of training MBA-background seed instructors.
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MBA × AI Case Method——避開 HBS 授權風險、找開源案例素材、訓練種子教師
想做商業 AI 課程的案例教學，但 HBS / HBR cases 有授權風險。這篇整理 2026-05 調查的開源案例來源（UBC Open Case Studies、OpenCaseStudies.org、World Bank）、不能用的清單，以及訓練 MBA 背景種子教師的方向。
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Three moves to firepower-demo Agentic AI at your company—desktop cleanup, conflicting meeting notes, multi-version emails
If you want to make your manager or coworkers feel Agentic AI in one second—and then open the door to an internal training engagement—my three go-to moves are cleaning up messy desktops, reconciling conflicting meeting notes and emails, and producing multi-version drafts for different recipients. These three hit office workers right in the pain.
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在公司火力展示 Agentic AI 的三招——桌面整理、會議紀錄、信件多版本
想在公司讓主管同事一秒有感、進而開內訓案的話，我自己最常用的三招是：整理桌面亂檔、整理相互衝突的會議紀錄跟信件、迅速生成不同版本的信件給主管同事客戶廠商。這三招對辦公室的人就夠有感了。
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Reflection after a 200-person online AI lecture—the energy of the classroom 10 years ago, back again
This morning I ran a 200-person free online AI lecture. So happy. 450+ signups, attendance around 45%, but landing on Mother's Day midday and still hitting that—Zoom data showed average seat-time over 75%, and FAQ ran so long I answered for an extra half hour.
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200 人線上 AI 講座結束後的感言——10 年前實體教室那種時光，又回來了
今天早上辦了一場 200 人的免費 AI 線上講座，好開心。報名人數 450+，出席率大概 45%，但是撞到母親節中午還能有這種成績，Zoom 數據拉下來發現留座時間平均超過 75%，FAQ 踴躍到多回答了半小時問題。
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Why Obsidian is something special to me, and why I only made this video now
I'd been wanting to make this video for a long time, but kept putting it off. Obsidian's flexibility is so high that it grows into a different shape in every person's hands. The payoff isn't immediate either—it's only after the vault has grown for a while that you realize you can't go back.
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Obsidian 為什麼是個特別的存在——我為什麼到現在才拍這部影片
這部影片我想做很久了，但是卻一直沒做，直到今天。Obsidian 的自由度太高，所以在每個人的手上會長成不同的樣子。他的成效又不是立刻就能看到的，而是越長越大你才會發現離不開它。
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Seven gotchas from running Resend and Gmail email workflows—HTML templates, scheduled_at, Cloudflare, open-rate alignment
Two weeks of heavy use across Resend batch sends and Gmail draft creation for course notifications, lecture thank-you emails, and BD outreach. Over 1,400 emails sent. Seven lessons worth writing down.
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Resend 跟 Gmail 寄信工作流踩坑七連——HTML 模板、scheduled_at、Cloudflare、開信率對齊
這兩週密集用 Resend 批次寄信跟 Gmail draft 兩條路徑做課程通知、講座感謝信、BD outreach。整理七個踩坑：HTML inline style、scheduled_at 不生效、Cloudflare 1010、Click tracking、bad-recipients、Gmail thread 漏看、客製 vs 通用開信率對齊。
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Covey's time management matrix, now with Claude Code—how each quadrant shifts
Covey's time management matrix from The 7 Habits of Highly Effective People splits time on two axes: important × urgent. Drop Claude Code into the picture and each quadrant's time allocation shifts. The long-term compounding from AI isn't in Q1, it's in Q2.
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Covey 七個習慣的時間矩陣放上 Claude Code 之後——每一格的時間分配怎麼變
Covey《高效能人士的七個習慣》的時間矩陣是兩軸切四象限，重要 × 緊急。把 Claude Code 放進去之後，每一格的時間分配會被重新洗牌。Q1/Q3 被 AI 吃掉，多出來的時間搬到 Q2 才是 AI 協作的長期複利。
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Two MCP servers for Taiwan public data—what mcp-taiwan-legal-db and Twinkle Hub are each good for
Two MCP servers wrapping Taiwan's public data sources—one focused on court judgments and statutes, the other aggregating 52,960 government open datasets plus 37 local utility tools. They turn out to be complementary; I now run both.
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台灣公開資料兩個 MCP 評測——mcp-taiwan-legal-db 跟 Twinkle Hub 各適合什麼場景
兩個串接台灣公開資料的 MCP server——一個專精法律判決跟法規查詢、一個聚合 5.3 萬筆政府開放資料 + 37 個在地工具。實測下來各有強項，課程跟顧問場景的搭配方式不一樣。
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What I only learned after taking on enterprise AI training—prep time, the client liaison, instructor flexibility, and how to pick a consultant
Four things I learned after running a handful of enterprise AI training sessions. Prep time is 3-5x the session itself. The client liaison decides everything. The instructor has to handle wildly different skill levels. And if you're looking to hire a trainer, the signals are like picking a pop star.
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親自接 AI 企業培訓之後才知道的事——準備時間、甲方對接、講師應變、選顧問訊號
接了幾場企業 AI 培訓之後整理出來的四件事：準備時間是上課時間的 3-5 倍以上、甲方對接者決定一切、講師要能應付各種程度跟提問、想找講師的話訊號跟追星很像。
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How to tune Opus 4.7's effort, and why I still keep non-coding tasks on Sonnet 4.6 medium
Opus 4.7's default adaptive effort is a disaster—it turns into a lazy emperor. You have to crank it up to high or xhigh to avoid that, but token consumption goes nuts. After testing, Sonnet 4.6 medium is the sweet spot for non-coding work.
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Opus 4.7 的 effort 怎麼調，跟為什麼我非編程任務還是停在 Sonnet 4.6 medium
Opus 4.7 預設的 adaptive effort 是場災難，會變成偷懶摸魚皇帝。要開到 high 或 xhigh 才能避免，但 token 就開始狂燒。實測下來，非編程任務停在 Sonnet 4.6 medium 反而是最好的選擇。
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Claude Code Desktop vs CLI right now—why I think beginners should just start with the desktop app
Two or three weeks ago Anthropic overhauled the desktop app. Over 90% of the features now match the CLI. If you're a beginner who's scared of the terminal, starting with the desktop app is fine.
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Claude Code 桌面版跟終端機現在差在哪——我為什麼覺得初學者直接從桌面版開始就好
兩三週前 Anthropic 把桌面 App 大翻新，90% 以上功能跟終端機同步。初學者對終端機有恐懼，現在從桌面版開始完全沒問題。