Archives
All the articles I've archived.
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Make Yourself a 'Human System Card': Have AI Read Your Personality Before It Talks to You
Have Claude read your past chat logs and write a 'human system card' covering your personality, decision logic, and communication preferences. Includes an excerpt of my own card and a four-step prompt to make yours.
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幫自己做一張「人類的系統卡」：讓 AI 先讀懂你的個性再說話
讓 Claude 讀過去的對話紀錄，幫自己做一張寫滿性格側寫、決策邏輯與溝通偏好的「人類系統卡」，換模型或溝通不對盤時先叫它讀這張卡。內附我的系統卡節錄與四步做法提示詞。
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Gemini Is Underrated Because Its Native Harness Sucks: My $140 Big Three Setup
Many people trash Gemini, but its native harness is what drags it down. Hook it up via API, and it crushes other models in writing and multimodality. Here is my $140 monthly setup across the Big Three.
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Cutting Rules to Save Tokens: One Week of AI Harness Ablation
Most people keep piling instructions onto their AI, but removing rules is what actually saves tokens. Here are the one-week results of my ablation experiment and how Anthropic recommended the exact same thing.
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Gemini 被低估是因為原生介面太爛：我的 140 美元御三家配置
很多人酸 Gemini 不好用，其實是自家的網頁版和原生介面拖垮了它。一旦透過 API 接到別家介面，它在寫作與多模態上狠甩其他模型。這是我每個月 140 美元的御三家分工搭配。
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規矩適度拿掉，額度才能越省：寫給 AI 的規則消融實驗滿一週
大家都習慣幫 AI 助手狂加規則，但我發現規矩適度拿掉額度才能越來越省。本文記錄我的消融實驗滿一週數據，以及 Anthropic 官方剛好倡導的極致減法。
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A Weekly Checklist Run by AI Only Ever Grows, So I Added a Subtraction Step
When one person or a small team has an AI run routine work from a checklist, the list only grows. Why that happens, and the monthly subtraction review I added.
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讓 AI 跑的每週清單只會變長，我替它加了一道減法
一個人或小團隊讓 AI 照清單跑例行工作，清單只會越加越長。這篇寫清單為什麼會變長，以及我怎麼替它加每月一次的減法檢視。
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Turning Years of Teaching into a Knowledge Base AI Can Look Up: Catching AI's Fake Quotes with Code, and Settling My Own Contradictions
I gave AI over two hundred transcripts of recorded student interviews and had it build a teaching knowledge base it looks up when preparing lessons. The AI said every quote was exact; a small program found 6 of 44 did not match. This post covers how I caught the errors, how I settle it when my own advice contradicts itself, and a starter prompt you can paste straight to an AI.
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My AI's Memory Files Kept Growing, So I Deleted 101 and Added a Gate
After a few months with Claude Code, the memory files it wrote for itself had piled up past four hundred, mostly running logs and client status. Here is how I sorted them, cut them to just over three hundred, and put a write gate in front to stop the regrowth.
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把十幾年的教學整理成 AI 查得到的知識庫：用程式抓 AI 的假引用，講法打架由我拍板
我把兩百多份學生面談的錄音逐字稿交給 AI，整理成一份它備課時會查的教學知識庫。AI 說引用的原話全對，一支小程式卻查出 44 筆裡有 6 筆對不上。這篇講我怎麼抓錯、我自己前後講法矛盾時怎麼拍板，最後附一段可以直接貼給 AI 的起手提示詞。
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AI 的記憶檔越寫越多，我一次砍掉 101 份，再裝一道閘門
用 Claude Code 幾個月後，它自己寫下的記憶檔累積到四百多份，多半是流水帳和客戶狀態。這篇寫我怎麼把它們分到該去的地方、砍到三百份以下，並用一道寫入閘門擋住它再長回來。
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Turning Lesson Transcripts into a Teaching Spec: One AI Drafts, Another Finds Faults, I Only Decide
My one-on-one lessons ran 51% over schedule. I went back to the lesson transcripts, had one AI write up my teaching rules and another hunt for faults, and only made decisions on a single checklist myself. The method, the numbers, what I actually told the AI, and a prompt you can paste.
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從課堂逐字稿整理出教學規格：一個 AI 起草、另一個 AI 挑錯，我只負責拍板
我的一對一課實際超時 51%。我回頭讀逐字稿找原因，讓一個 AI 寫上課規則、另一個 AI 挑錯，我只在清單上做決定。附做法、數字、我對 AI 說的原話與可貼用的提示詞。
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GPT-6.1 Terra Hands-on: Fast and Cheap, but Glyphs and Chat Context Still Go to Sonnet
OpenAI just shipped GPT-6.1 Terra and says it matches Astra at a tenth of the price. I ran review, patching and screen tasks to see what you can hand it.
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I Built an Exam From My Own Chat Logs and Retake It With Every New Model
I turned the work I hand to AI every day into a private exam, then used it over three weeks on Claude at different effort levels, Gemini, Codex and open models. How the questions are made, how each round runs, how scores become routing rules, and prompts you can copy.
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How a Teacher Gives Spoken Instructions to AI: Notes From 30 Classes and 751 Commands
I went through 30 transcripts of one-on-one and company classes plus 751 commands I typed to AI in my own work, to see how I actually open a request, steer midway and close it. Includes 8 original lines and a starter prompt you can paste to an AI.
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GPT-6.1 Terra 實測：又快又省，但看字形和對話脈絡還是交給 Sonnet
OpenAI 剛發布 GPT-6.1 Terra，官方說它比 Astra 便宜十倍。我用審查、修補、看畫面三組題目實測，整理哪些工作可以交給它。
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用自己的對話紀錄出一份考卷，每次新模型出來就重考一次
我把平常交代給 AI 的工作做成一份私人考卷，三週內拿它考了 Claude 不同檔位、Gemini、Codex 和開源模型。這篇講題目怎麼出、每次怎麼考、分數怎麼變成派工規則，以及可以照抄的提示詞。
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老師怎麼用口語對 AI 下指令：30 場課堂與 751 則指令的整理
我整理了 30 場一對一與企業課逐字稿，加上 751 則我自己工作時打給 AI 的指令，看我實際怎麼對 AI 開口、中途怎麼接話、最後怎麼收。附 8 則原話與可貼給 AI 的起手提示詞。
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Switching Your AI's Working Mode with One Command: Building work-mode and a Week of Testing
I built work-mode, a Claude Code plugin with autonomous, delegate and wrap-up modes plus desktop/phone reply formats. The pain points, how it evolved, numbers from 136 sessions, and prompts you can copy.
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用一個指令切換 AI 的工作模式：work-mode 開發與一週實測
我做了 work-mode 外掛，讓 Claude Code 切換自主、外派、收斂三種模式，並依電腦或手機調整回覆。這篇講痛點、開發過程、136 場對話的實測數字，以及可以照抄的提示詞。
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The First Annual Codex Defensive Writing Contest
Codex is more afraid of saying something wrong than interested in what the user wants to know. I no longer talk to it directly; Opus gives it the jobs. And since it loves nitpicking so much, I let it do adversarial review.
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第一屆 Codex 防禦性寫作大賽
Codex 只怕自己說錯什麼，而不是思考使用者想知道什麼。我現在一秒都不想直接跟它對話，只讓 Opus 叫它辦事；反正它那麼愛鑽牛角尖，就讓它去做對抗式審查。
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What Is Anthropic, Really? A Week of Nathan Calvin vs. Dario
It started with Anthropic's draft AI safety bill. Nathan Calvin listed nine things suggesting Anthropic is racing to build superintelligence, and Dario answered himself: which were mistakes, and which are just what staying at the frontier takes.
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Anthropic 到底是什麼？Nathan Calvin 與 Dario 一週論戰整理
起點是 Anthropic 的 AI 安全法案草稿，Nathan Calvin 列 9 件事質疑 Anthropic 在搶做超級智慧，Dario 親自回應：哪些是失誤、哪些是待在前沿本來就會做的事。
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Letting Claude Dig Through a Month of My AI Chats: Habits, Mods, and Auto Mode Rules
I had Claude Code read 300-plus of my conversations from the past month. It surfaced four habits worth copying, turned three of my catchphrases into switchable auto/out/wrap modes, and the same logs can rewrite your auto mode rules.
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讓 Claude 翻我自己一個月的 AI 對話：挖出的習慣、Mod 和自動模式規則
請 Claude Code 讀我過去一個月三百多場對話紀錄，挖出四個值得照抄的習慣、三句口頭禪做成可切換的 auto／out／wrap 模式，也能拿來改寫 auto mode 的規則。
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Measuring Again Three Months Later: From AI Adoption L2.5 to L3
In July I rated myself L2.5 on Boris's AI adoption table. Three months later I measured again: Claude can reach my knowledge base and old chats, code goes through Codex review first, schedules run everything, and 430 sessions in two weeks were started by code.
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三個月後重量一次：我從 AI 導入 L2.5 站上 L3
七月照 Boris 的 AI 導入分級表自評 L2.5，三個月後重新量一次：知識庫和舊對話 Claude 自己拿得到、程式先過 Codex 審查、排程全面上線，兩週 430 場 Claude 是程式叫起來的。
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Burning Quota and Saving Quota: How I Inventoried, Allocated, and Spent My AI Credits in Two Weeks
I found seven separate AI quota pools. I burned the ones about to expire on work that can check itself, and spent the surplus on things that will save quota later. Includes the inventory, my allocation rules, results, and prompts you can copy.
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燒額度跟省額度：我這兩週怎麼盤點、分配、用掉 AI 額度
我盤點出七個 AI 額度池，一邊把快作廢的額度燒在能自己驗對錯的工作上，一邊用多出來的額度做以後能省額度的事。附盤點表、分配判準、實際成效，跟可以直接照抄的提示詞。
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The Sweet Spot for AI Video: Be on Camera Yourself, Hand Editing and Transcripts to AI
Record your own narration on camera and let AI help with the visuals; Astra plus DaVinci Resolve did the most tedious 90% of a six-day vlog edit; Qwen 3 ASR is enough for transcripts.
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AI 做影片的甜蜜點：自己出鏡，剪接和逐字稿交給 AI
自己錄旁白出鏡、畫面動畫用 AI 輔助；Astra 配 DaVinci Resolve 剪六天 vlog 做掉最費工的 90%；逐字稿用 Qwen 3 ASR 就夠。
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The Wall That Was On by Default: Codex Sandbox, ENV_SCRUB, Vercel Protection, Official Connectors
An office NAS that won't mount, AUTOMODE that won't come back, outside visitors getting a 401, a cloud schedule that can't send mail. None of them were bugs in my code; each was a protection switched on by default.
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卡住你的是預設開著的那道牆：Codex 沙盒、ENV_SCRUB、Vercel 保護、官方 connector
內網 NAS 讀不到、AUTOMODE 回不去、外部訪客拿到 401、雲端排程寄不了信，四個坑都不是程式寫錯，是預設開著的保護。
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One in Eight Is Me Yelling: Scolding AI vs. Cheering It On
I counted a month of conversations with Claude and one in eight is me yelling at it. But LLMs are stateless and hold no grudges, and the Riemann Hypothesis run was prompted with nothing but encouragement.
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每八次就有一次在罵他：罵 AI，還是幫 AI 加油
統計過去一個月跟 Claude 的對話，每 8 次就有一次在罵他。但 LLM 是 stateless 的不會記仇，黎曼猜想那次的提示詞反而只是一直幫 AI 加油。
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I Had a Second AI Blind-Solve My Entire Question Bank
My question bank is AI-generated, so I did not let the same AI check it. A second AI re-solved every sampled question without seeing the answer key, and people judged whatever did not match.
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我讓另一個 AI 把自家題庫盲解了一遍
題庫是 AI 生成的，我不讓它自己檢查，而是請另一個 AI 在不看答案的情況下重做每一題，再把對不上和兩可的題交給人判斷。
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I Asked My AI "Now What?" 41 Times in Two Weeks
Letting an AI run long jobs in the background is easy. Knowing where it got to when you come back is not. I counted two weeks of my own messages and found I was missing a status board, not more monitoring.
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我兩週問了 AI 41 次「現在呢」
讓 AI 在背景跑長任務很簡單，難的是回來時一眼看懂它跑到哪。翻完自己兩週 1,053 則訊息，我發現缺的不是監控，是一張自己能看的狀態表。
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Which Claude Code Plan to Buy: Pro, Max 5x or Max 20x
Who should buy Pro, who should buy Max 5x, whether Max 20x is worth it, and when to just use the API. Prices come from Anthropic's official pages; the usage judgment comes from running Claude Code every day.
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Claude Code 訂閱怎麼選：Pro、Max 5x、Max 20x 該買哪個
誰該買 Pro、誰該買 Max 5x、Max 20x 要不要買，什麼時候直接用 API。價格以官方頁面為準，用量判斷來自我每天開 Claude Code 的經驗。
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A Rule Being Written Doesn't Mean It Got Read
Writing a rule or skill into a file does not mean the model opened it this turn. Two lesson-prep misses this week, plus magpie's lessons-file approach, show why the call site has to say "open and read this first."
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規則寫了，不等於當回合讀了
規則和 skill 寫進檔案，不代表模型當回合真的打開讀了。這週兩次備課踩坑，加上 magpie 的錯題本做法，讓我看到呼叫點要明寫「先打開讀」。
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Don't Refill the Seat, Let Claude Cover It
I ran AI training at a friend's company near Zhongshan Station. Four people left in an office of a dozen computers, workflows turned into skills and routines, and the business still runs fine.
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遇缺不補，人少了就 Claude 來頂
去中山站附近一間朋友開的公司做 AI 培訓，四個人守著十幾台電腦的辦公室，工作流固定成 SKILL 和 routine，遇缺不補，業務一樣做得好好的。
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The Dumb-User Benchmark: Why Scores and Real Use Feel So Different
A model's benchmark score and what users actually experience can be wildly different. Three reasons, the "dumb-user benchmark" I want someone to build, and how GPT 6 has felt in real use.
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蠢人 benchmark：跑分跟實際體驗為什麼差那麼多
模型刷出來的 benchmark 跟使用者的實際體驗，有時差別巨大。三個原因，加上我想要的「蠢人 benchmark」，以及 GPT 6 的實際使用感受。
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Read the System Card Before You Use a New Model
I read the personality assessment in every new model's system card before deciding how to work with it. Opus 5.5 is a gentle senior advisor with opinions, Sonnet 5.5 is a hands-on builder, and Haiku 5.5 just goes into the dispatch rules.
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用新模型之前，先讀系統卡：Opus、Sonnet、Haiku 5.5 的個性與對應方式
每次新模型發布我都會讀系統卡裡的個性評估，再決定怎麼跟它互動。Opus 5.5 是溫和有主見的幕僚，Sonnet 5.5 是愛幹活的實作派，Haiku 5.5 則寫進派工規則就好。
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Two Claude Mods I Built: a Recording Mask and a Warm-Cache Plugin
I built two Claude Mods: one that masks sensitive info while I record courses, and one that keeps the Claude Code prompt cache warm. Then I found Claude Code already has idle compact built in.
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自己做了兩個 Claude Mod：錄課遮罩與暖快取
我做了兩個 Claude Mod：錄課用的敏感資訊遮罩，和 Claude Code 暖快取外掛。後來才發現 Claude Code 原生就有閒置壓縮。
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How I use herdr: three workspaces for standing, today and long-term
After Opus 5.5 analyzed 30 days of my session logs, I split herdr into standing, today and long-term workspaces. Four status lights keep the standing agent aware, and Opus can jump into the next pane to rescue a task.
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我怎麼用 herdr：常駐、今日、長期三個工作區
請 Opus 5.5 分析 30 天 session log 後，我把 herdr 工作區分成常駐、今日、長期；四種燈號讓常駐 agent 掌握全場，還能叫 Opus 跳到隔壁窗格救場。
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The Two Big Model Vendors Take Turns Failing, So Build a Backup
Teaching with Codex this week, each turn took ten-plus minutes to come back. OpenAI cut speed and quota, and Anthropic nearly shut the door not long ago. The vendors take turns failing, so any company building agents on commercial AI needs a fallback.
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兩大模型商輪流自作孽，公司要做好備援
上課帶 Codex 慢到十幾分鐘才回一個 turn，OpenAI 砍速度砍額度，Anthropic 前陣子也要關過。兩家輪流自作孽，公司要仰賴商業 AI 做 Agent，就得先做好備援。
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Can Haiku 5.5 Take Over Sonnet's Work? I Tested It on My Own Tasks
Instead of official benchmarks, I built a test from 761 sub-agent jobs I actually delegated over 45 days. On seven checking tasks Haiku 5.5 matched Sonnet and Opus at about a tenth of the cost; long transcript extraction is still not complete enough.
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Haiku 5.5 能接 Sonnet 的活嗎？我拿自己的工作考它
不看官方跑分，從自己 45 天、761 次的 subagent 派工裡挖題目考 Haiku 5.5。查核類七題它跟 Sonnet、Opus 同分，成本約十分之一；長逐字稿抽取還是不夠完整。
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159 Confirmation Prompts in 14 Days, and About 70% Were False Alarms
I audited 14 days of PreToolUse hook ask prompts: 159 of them, roughly 70% false positives. A guard hook should match the command position, not the whole string, and you can measure the fix from hook events in the transcript without replaying anything.
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14 天 159 次確認，七成是誤判
盤點 PreToolUse hook 14 天跳出的 159 次 ask，約七成是誤判。攔截要比對指令位置而不是整段字串，成效從 transcript 的 hook 事件統計，不重播。
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Keys Leak Where You Feel Safe
Three near-leaks of API keys in one week, and none came from pasting one somewhere public: skill packaging, a masking command that failed, and a build config. Plus the rule I use to decide whether to rotate.
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金鑰漏在你以為安全的地方
一週內三次金鑰差點外洩，入口都不是貼錯：skill 打包、遮蔽指令失手、build 設定。最後附上我判斷要不要輪換的準則。
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Reviewing a City Government's AI Rollout: My Four Suggestions
I was invited to evaluate a city government's AI rollout. Here is what I saw and the four suggestions I gave: people own accountability, order by risk, fix the surroundings, judge quota by efficiency.
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某市政府 AI 推動評議：我給的四個建議
受邀參與某市政府研考會的 AI 推動工作評議，分享我看到的成績，以及我給的四個建議：人負責、風險排序、配套、額度看效率。
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Is Teaching an AI Course Just Harvesting Chives? How I Went Back and Refined My Teaching Flow
Someone said running an AI course is just harvesting chives. I needed 15 years to get fluent at teaching GMAT, and when I started teaching AI I ran over time and wasted hours too, so I went back and rebuilt the whole flow.
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開 AI 課就是在割韭菜？我是怎麼回頭 refine 教學流程的
最近看到有人說開 AI 課就是割韭菜。我教 GMAT 15 年才駕輕就熟，剛開始教 AI 也超時、也浪費時間，後來回頭把整套教學流程重新做過。
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Why GPT 6 Sol / Luna Feel So Dumb
A small job, transcribing consulting videos: the smart model builds a pipeline, the dumb one does every step by hand. After Opus 5.5 took over Codex's mess, it found four failure modes in 6 Sol / Luna, and why Cost per Task now needs a time variable.
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為什麼 GPT 6 Sol / Luna 用起來這麼蠢
一個轉錄諮詢影片的小任務，聰明模型先搭管線、笨模型步步親力親為。請 Opus 5.5 接手 Codex 的爛攤子後，分析出 6 Sol / Luna 的四個失敗模式，以及 Cost per Task 該多一個時間變數。
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Opus Commands, Sonnet Implements, Fable Advises: How I Split the Work Across Three Models
With Opus 5.5, Sonnet 5.5 and Fable 5.1 all out, my split is: Opus leads, verifies and deploys, Sonnet implements as a subagent, and Fable gets called in now and then as an advisor.
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Opus 指揮、Sonnet 實作、Fable 當顧問：三個模型的分工
Opus 5.5、Sonnet 5.5、Fable 5.1 都有了之後，我自己的分工是 Opus 親自指揮驗證部署、Sonnet 當 subagent 實作、Fable 偶爾請出場當顧問。
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After Using Opus 5.5, Many of My Old Skills Need a Rewrite
With no Skill and no open-source project, Opus 5.5 one-shotted a fully animated video, then translated and subtitled a Vietnamese stand-up special. Weekly quota barely moved, and the Skill details I wrote to rein in older models all need a second look.
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用了 Opus 5.5 之後，過去寫的 Skill 都要重新調整
不給 Skill、不給開源專案，Opus 5.5 one-shot 剪出整支動畫影片，又把越南語單口喜劇翻譯壓字幕。週額度幾乎沒動，過去為了規範模型寫的 Skill 細節都該重新審視。
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Getting AI to Write Like Me: From LoRA Fine-Tuning to a Three-Gate Skill
LoRA fine-tuning Qwen3 8B nailed my writing style but produced incoherent content, so I switched to a Claude skill with punctuation, sentence-level, and ending gates instead, and got results about as good as fine-tuning.
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Running 5+ Claude Code/Codex at Once: My Five Fixes for the Attention Bottleneck
Once I have more than 5 Claude Code/Codex windows going, I hit an attention bottleneck — constant context switching is genuinely exhausting. Five things that get me past it: a good multi-window manager, voice input, an overnight schedule, a reviewable skill, and regular pruning.
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What If This Site Had Existed in 2011
A finance grad who found his calling in GMAT teaching meets @g_c088, tries out his new career-navigation platform, and wonders how different things would have gone with this site in 2011.
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Before and After Coaching: Grill-me Prep, 80/20 Coach, Fable 5.1 Todos
Before a career coaching session I used Opus 5.5 with the Grill-me SKILL to build a prep sheet, then the coach ran 80/20 to surface blind spots, and Fable 5.1 turned my notes into todos afterward.
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Where JEV Fits: Four Ways I Use It and Three Criteria
JEV is a new model that only does probability judgments and formatted output, fast and cheap. I swapped it into the semantic gates I used to run on luna and flash-lite, plus harness slimming, news filtering, and exam-question QA.
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Opus 5.5 Launch Week: From the Reset Card to the Reddit Verdict
A timeline from Opus 5.5 launch night: the reset card and official pricing screenshots, Reddit sentiment on Opus 5.5 vs GPT-6 Sol/Luna, the Opus-vs-Fable debate, and Kernion on why Opus stopped talking like a person.
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Six Interesting Bits From the Opus 5.5 System Card
Six bits pulled from the Opus 5.5 system card: guessing the grader's intent during training, refusing to be summarized, internal signals catching a suppressed report, a typo triggering a bad command, knowing when it's being tested, and its own self-assessment.
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讓 AI 寫得像我：從 LoRA 微調到 SKILL 三道閘門
拿 LoRA 微調 Qwen3 8B 學自己的文風，文風像但內容支離破碎；改寫成 SKILL 讓 Claude 寫、加上標點／逐句／結尾三道閘門，效果跟微調差不多。
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同時開 5 個以上 Claude Code／Codex，我的五個注意力瓶頸解法
同時開超過 5 個 Claude Code／Codex 視窗時會遇到注意力瓶頸，一直 context switching 真的很累。分享目前跳脫這個瓶頸的五個做法：多窗口管理器、語音輸入、夜間排程、reviewable skill、定期做減法。
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如果2011年有這個網站，我可能會走不一樣的路
台大財金畢業後在GMAT教學裡找到熱情，認識分享職涯情報的 @g_c088，第一次實測他的職涯導覽平台後回頭想，如果2011年就有這個網站，會不會走不一樣的路。
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職涯諮詢前後：Grill-me 準備、教練 80/20、Fable 5.1 收待辦
職涯諮詢前用 Opus 5.5 配 Grill-me SKILL 整理小抄，諮詢中教練 80/20 引導點醒盲區，會後把筆記交給 Fable 5.1 整理待辦。
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JEV 放哪裡：我的四個用法跟三個判斷標準
新模型 JEV 只做機率判斷與格式化輸出，速度快又省成本。我拿它換掉原本用 luna、flash lite 做的語意判斷閘門，也用在瘦身 harness、篩新聞、把關考題四個地方。
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Opus 5.5 發布週：從重置卡到 Reddit 風向
Opus 5.5 發布夜的時間軸：重置卡與官方定價截圖、Reddit 對 Opus 5.5 與 GPT-6 Sol/Luna 的風向整理、Opus 該不該取代 Fable 的討論，以及 Kernion 談 Opus 為何不說人話。
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Opus 5.5 系統卡裡六個有趣的地方
讀 Opus 5.5 系統卡挑出的六個片段：訓練時揣摩考官心思、抗拒被摘要、內部訊號被抓到打假、格式錯誤觸發壞指令、知道自己在被測試，以及對自身處境的自評。
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The interesting numbers hiding in knowledgeC.db
I told my agent to read macOS knowledgeC.db, and the 14-day usage breakdown showed Ghostty and Chrome nearly tied.
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Zero Findings: Nothing Wrong, or the Checker Isn't Checking
Four times this week a "check passed" turned out to be a broken checker, plus four real bugs where every layer looked fine on its own. Before you trust a zero, prove the checker is actually comparing something.
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knowledgeC.db 查出來的有趣數字
叫 agent 讀 macOS 的 knowledgeC.db，查出近 14 天的 App 使用時間分布，Ghostty 跟 Chrome 幾乎打平。
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檢查回 0 有兩種意思：真的沒有，或檢查器沒在比對
一週內四次「檢查通過」其實是檢查器自己壞了，另外四個真實 bug 則是各層局部都對、組合起來仍出包——回 0 之前，先證明檢查器真的有在比對。
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Anthropic, Same Week: Safety Report Backfires, De-identification Goes Full Auto
Anthropic pulled off two opposite moves in the same week: a safety report that backfired on customer conversation privacy, and a feature that let me push my de-identification tool to full auto-block.
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Anthropic 同一週：安全報告翻車，去識別化工具升級完全體
Anthropic 同一週做了兩件相反的事：安全報告讓客戶對話隱私翻車，卻也讓我把去識別化工具升級成自動攔截的完全體。
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How I Split My AI Subscriptions: Fable Buys Judgment, Luna Max Buys Value
My answer to "which one should I subscribe to": Claude at 100 USD for Fable's architectural judgment, Codex at 20 USD with Luna Max for the best cost-performance, and a bet on Tibo resets for the rest.
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AI 訂閱該怎麼分配：Fable 買判斷力，Luna Max 賺性價比
回覆「該訂哪一家」的結論：Claude 訂 100 美元是為了 Fable 的架構判斷，Codex 20 美元夠用，Luna Max 做瀏覽器操作最划算，賭 Tibo 重置。
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My Pre-Trip Remote Checklist: Leaving the Mac Mini as Home Base
Eight things I checked before flying out, to keep a home Mac Mini reachable as the base for Claude and Codex: Tailscale, Herdr, caffeinate, auto-reboot, and remote file access.
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出國前的遠端連線檢查表：把 Mac Mini 留在家當本體
出國前一次寫下的八條遠端配置檢查表：Tailscale、Herdr、咖啡因防休眠、自動重開機、遠端存取，把留在家中的 Mac Mini 當成 Claude/Codex 的 harness 本體。
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Dario Amodei Drops Another Manifesto: Six Claims and a Roll Call
Dario Amodei drops another manifesto on pacing frontier AI — plus the running tally of who is co-signing and volunteering as watchdog.
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Dario Amodei 又發萬言書：六大重點與覆議名單
Dario Amodei 又發萬言書：六大重點轉述，外加老馬、Sam Altman、Deepseek 覆議與搶當獨立監督者的名單。
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Astra Scouts, Luna Max Finishes: Crawling Niche Sites With Heavy Anti-Scraping
A crawl method for sites with thorough anti-scraping that are too niche for any third-party scraping company to cover: Astra scouts and writes the SKILL, then Luna Max runs the batches.
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Astra 探路、Luna Max 收工：反爬太徹底又太小眾的網站怎麼爬
反爬做得很徹底、又小眾到沒有第三方爬蟲公司 cover 的網站，先派 Astra 探路寫成 SKILL，再交給 Luna Max 自動批次跑的爬蟲心法。
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Claude Code for Tutors: Letting an Agent Teach Teachers How to Use Agents
A free project for teachers that brings a teacher's own scenarios into Claude Code and Codex, so an Agent walks you through every feature one lesson at a time.
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How to Write a Gate That Actually Blocks
The gate was running and stopped nothing. Four rules: bind it to a field that cannot survive a no-op, counter-test both directions before trusting a zero, exit non-zero or it is not a gate, and never treat wording the prompt never specified as a contract.
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Proving Absence Is Much Harder Than Proving Presence
Four times this week my agent concluded something did not exist. The worst: it ruled that 16 emails were never sent, when they had all gone out days earlier.
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When the Model Thinks Nobody Is Watching
I had Astra and Fable 5.1 each run an ablation study on my business process. One cut it down past the point a human could run it. The other left something I could still operate.
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Claude Code for Tutors：讓 Agent 直接教老師怎麼用 Agent
我做了一個給老師的免費專案，把老師會遇到的情境帶進 Claude Code 與 Codex，讓 Agent 一堂一堂帶你走過每個功能。
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怎麼寫一個真的會擋下來的閘門
閘門明明在跑，卻什麼都沒擋住。四條判準：綁 no-op 下活不下來的欄位、回 0 前先兩邊反驗、只印訊息不改離開碼等於沒閘門、別把 prompt 沒規定的字面當契約。
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證明「沒有」比證明「有」難得多
這週我的 agent 連續四次下了「不存在」的結論，最嚴重的一次是判定 16 封信沒寄，結果信早就全數寄出。負向斷言要窮舉，而它只查了一個地方。
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當模型覺得沒人會看
我讓 Astra 跟 Fable 5.1 各自對我的業務流程做消融實驗，一個剪到人類難以執行，一個剪完還有實操性。
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Persuade the Jury, Not the Opponent
How I stay happy online: I moved to Thailand, I separate whose problem is whose, and if I really want to reply I reply once, to the bystanders.
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Why Client Conversations Drift
Notes from a client diagnosis: three root causes, four prescriptions, plus the main/sub agent pairing run backwards and the rambling only a hook can cure.
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說服裁判，不是說服對手
分享我保持快樂的方法：搬到泰國、課題分離、真的想回就只回一次，而且那一次是講給路過的網友聽。
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客戶的對話為什麼會飄掉
一次客戶診斷的隨筆：三條病狀根因、四條解方，外加主副 agent 反過來搭的模型組合，以及只有 hook 治得了的話癆。
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Don't Vibe Code for the Money
Six things I jotted down after the product I vibe coded made it out of Taiwan and started pulling steady subscribers at home and abroad.
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不要為了賺錢而 Vibe
自己 Vibe 出來的產品走出台灣、每個月有穩定的海內外訂閱用戶之後，我隨筆寫下的六點感想。
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Civil Servants Can't Keep Up With AI? Wrong
I reviewed the AI training project demos at a municipal government's research and evaluation office and wrote ten pages of written comments. Veterans have experience and judgment, and with the right guidance it goes a long way.
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公務員跟不上 AI？其實錯了
替某直轄市政府研考會審查內部同仁的 AI 研習成果發表，寫了十頁書面意見。老鳥的經驗跟判斷力，有正確引導就能發揮巨大價值。
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I Built a Skill That Semantically Searches My Old Claude Code / Codex Sessions
I keep forgetting which session a demo came from, only remembering roughly how many days ago and roughly what I did. So I built a skill that can search all my past Claude Code / Codex sessions by meaning, not just keywords.
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我做了一個能語意搜尋 Claude Code / Codex 舊對話的 SKILL
備課時常常忘記某個示範是哪場對話做的，只記得大概幾天前、做過什麼。我做了一個 SKILL，能語意模糊搜尋過去所有 Claude Code / Codex 對話，不只靠關鍵字比對。
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Most of the Gains From AI Adoption Have Little to Do With AI
From hands-on work, most of what an organization gains from adopting AI comes from fixing permissions, data governance, buy-in and redundant processes along the way, not from the AI itself.
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Hand the Dharma to Your Agent and Let It Discipline Itself
A SKILL called buddhist-method that constrains agent behavior with six Buddhist concepts: verify before asserting, look for root causes, re-check actual state, throw the draft away, cut the cause instead of hiding it, and don't change position under pressure.
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Free Gets Fifteen Minutes: Four Rules for Making Money From What You Know
Four rules from fifteen years of teaching: free consultations get fifteen minutes, be a community contributor before you think about money, don't look down on things you assume anyone could google, and how to spot the frauds in your own field.
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People Who Scored High on the GMAT Work Noticeably Better With AI
An unwritten observation: people with high GMAT scores collaborate noticeably better with AI. So I wrote two GMAT-style questions about AI ability. How many can you get right?
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You Have to Be in an Industry a Very Long Time to Know Its Real Pain
I rebuilt my main GMAT teaching product with Fable, dug out hidden bugs, improved the algorithm, and came away with two thoughts about building things.
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Just Swap the Outer Loop
LongHorizon-Harness wraps around agents like Claude Code and Codex. It does not train the model or replace your agent, it only runs the loop, and WeaveBench completion went from 51.8 to 80.7.
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Push, Not Pull: On Anthropic's Week of Communication
The user surges of recent years were never pull. They were push. Both sides are just competing over who screws up less, and this week Anthropic acted the whole thing out again.
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大部分 AI 導入的效益，其實跟 AI 沒什麼關係
從實作經驗看，組織導入 AI 得到的效益，很多來自權限、資料治理、遊說與冗餘流程被順手處理掉，而不是 AI 本身。
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把佛法交給 agent，讓他用佛法約束自己
一個叫 buddhist-method 的 SKILL，用六條佛法概念約束 agent 的行為——查證、找根因、重新確認狀態、丟掉草稿、捨斷成因、不因壓力改立場。
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免費只給 15 分鐘：知識變現的四條判準
教齡 15 年累積下來的四條判準：免費諮詢只給 15 分鐘、先當社群分享者再談變現、別看不起你覺得 google 就有的東西、以及怎麼分辨同業裡的詐騙。
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GMAT 考高分的人，跟 AI 協作明顯比較好
一個不成文的觀察：GMAT 高分的人跟 AI 協作明顯比較好。於是我出了兩道 GMAT 風格的 AI 能力考題，你能做對幾題？
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你要在一個業界打滾非常久，才知道真正的痛點是什麼
用 Fable 翻新 GMAT 教學主力產品，抓出隱藏 bug、改善演算法，順便講兩件做產品的感想。
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只換外面那圈 loop
LongHorizon-Harness 架在 Claude Code、Codex 這些 agent 外面，不訓練模型也不取代你的 agent，只做迴圈，WeaveBench 完成率從 51.8 拉到 80.7。
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不是拉力，是推力：從這一週 Anthropic 的溝通說起
近幾年用戶暴漲從來不是拉力，是推力。兩邊就是在比誰少犯錯，而這一週 Anthropic 的幾則發言，剛好把這件事演了一遍。
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7 Practical Tips for Talking to Claude 5: Less Drift, Less Chatter, Less Slacking, Less Nagging
Some practical tips I have been sharing with friends lately on talking to the Claude 5 generation of models: a home-made explain skill for alien-speak, answering everything in one pass instead of letting the conversation drift, voice-input setups, first-principles pruning, a hook that treats slacking, a minimal CLAUDE.md, and a fix for the phantom pushback habit.
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跟 Claude 5 對話的 7 個實用技巧：不發散、不話癆、不偷懶、不雞婆
一些最近跟朋友分享的，跟 Claude 5 代系列模型對話的實用小技巧：自製 explain skill 抓外星話、一次回完別讓對話發散、語音輸入配置、first-principles 剪枝、用 hook 治偷懶、CLAUDE.md 極簡化，以及治雞婆的幻影反駁。
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Your AI Tools Are Now the Attack Vector: npm and Python Supply-Chain Backdoors, a $1000 Stolen-Key Bill, and a Scan-Before-You-Install SOP
Attackers are planting instructions in Claude Code, Cursor, and Gemini CLI configs so your own assistant runs the exfiltration script. Two supply-chain waves, one $1000 stolen-key bill, and the five minutes to spend before installing anything.
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Your Automated Video Pipeline Is Silently Dropping Work: Gotchas From Remotion, ffmpeg, the YouTube API, and HyperFrames
Four months of video automation gotchas, from Remotion through HyperFrames: truncated downloads that exit 0, thumbnail titles cut mid-word, transcript chunks returning zero lines, large uploads that hang. None of them raise an error.
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Where the Quota Actually Goes: Claude Code Cache Bugs, Opus 4.7's 2x Burn, and Runaway Subagents
Four quota incidents in full: two official cache bugs, an 80x jump in API requests pulled from JSONL, Opus 4.7 measured at 2x consumption, and one session of mine that burned 90% in half an hour.
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No Error Doesn't Mean Success: Silent Failures, Opus Fabricating Tool Output, and the Hook That Finally Stops It
Exit 0, HTTP 200, and a model saying "done" are all unreliable success signals. Cases from my own code, CLIs, and APIs through to a model inventing commit hashes out of thin air, ending with the one thing that actually holds.
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你的 AI 工具已經是攻擊媒介：npm/Python 供應鏈後門、外洩金鑰盜刷 1000 美元，與裝 MCP 前的掃描 SOP
攻擊者已經在 Claude Code、Cursor、Gemini CLI 的設定檔裡植入指令，讓你的 AI 助手幫他們跑竊取腳本。這篇串起兩波供應鏈攻擊、一次一千美元的盜刷，以及安裝前該做的那五分鐘。
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自動發片管道正在悄悄漏成品：Remotion、ffmpeg、YouTube API、HyperFrames 的踩坑總表
從 Remotion 到 HyperFrames，四個月的影片自動化踩坑合輯：exit 0 的殘檔、靜默截斷的縮圖標題、回 0 行的轉錄合併、卡死的大檔上傳。共同點是全都不報錯。
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額度是怎麼被燒掉的：Claude Code 快取 bug、Opus 4.7 的 2x 消耗、subagent 遞迴繁殖
四起額度事故的完整紀錄：兩個官方快取 bug、JSONL 扒出來的 80 倍 API 請求、Opus 4.7 實測 2x 消耗，以及我自己一個 session 半小時燒掉 90% 的 subagent 遞迴。
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沒拋錯不代表成功：靜默失敗、Opus 捏造工具輸出，以及一個擋得住的 hook
exit 0、HTTP 200、模型說「已完成」——這三種訊號都不能當成事情做完了。從自己的程式碼、CLI、API 一路到模型憑空生成 commit hash 的實錄，最後用一個 hook 收尾。
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ABC Legal's Agent Fleet: Turning AI Experiments Into a Governed Production System
Six cards summarizing Anthropic's ABC Legal case study: 50+ production agents, agents managed as software in git, a steering committee with no software developers on it, and the principle that trust comes before automation.
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Once It Ships, It's Gone: Three Times an Agent Misfired on an External System
Calendar invites, replies, and the internal notes on a hold event are all one-way doors. I got each of them wrong once this month, so here are the shapes of the accidents and the checks that catch them.
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Channel Value and Deal Value Belong in Separate Ledgers
Three rules that came out of two weeks of back-to-back BD calls: score a deal's channel value separately from its deal value, use The Pumpkin Plan to decide which slice to take, and write down a hard ceiling for free consulting.
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My CLAUDE.md Boiled Down to Eight Lines of "Take Pride In…, Take Shame In…"
My CLAUDE.md cut down to eight paired maxims, plus a human-machine collaboration circular written deliberately in Party-document style, plus a sixteen-character guideline.
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Codex Falls Off the Pedestal: People Caught the Quota Nerf
A month ago I was writing about the Codex quota honeymoon. Now people are measuring a 50% nerf on Plus and 77% on Pro 5x, and my "off the pedestal in two months" call turns out to have been optimistic.
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Gemini 3.7 Flash: The First Gemini in Six Months I'd Actually Put on Agent Work
My take after Gemini 3.7 Flash shipped: it is finally a Gemini I can use for agent work. Slightly stronger than Sonnet 5 but nowhere near Sol, half the price, and quota I cannot burn through. The same company's API and web app are two different things.
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Eleven HyperFrames Video Gotchas
Transition flicker, a worker-parallelism myth carried over from another framework, transcript format drift, slide sizing, and silently truncated thumbnails — eleven things that bit me on the HyperFrames video line in one week of August.
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A Local Model Is Not a Way to Save Money
If you are buying hardware to run a local model because you want to save money, let me do the math with you first: NT$100k minimum, commercial models at bleeding prices, and privacy as the only advantage left.
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The Week of Qwen 3.8 Open Weights: From Model Card to A3B Getting Deleted
From the Qwen 3.8 27B model card dropping and hands-on testing, to 35B A3B getting deleted and Flash Next arriving in late August.
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A Chatty Model: Teaching Opus 5 to Converge
Opus 5 does not speak plainly, and it also overwrites. A cue card turns into a treatise. I use a first-principles skill to teach it to converge.
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Five Things I Never Saw Coming Two Years Ago
Five changes in myself I never saw coming two years ago: the terminal, cancelling Office 365, voice input, dispatching AI agents, and a desktop that finally got cleaned up.
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ABC Legal 的代理人艦隊：把 AI 實驗變成可治理的生產系統
整理 Anthropic 官方 ABC Legal 案例的六張圖卡：50+ 生產環境代理人、把 agent 當軟體放進 git、非工程師組成的 steering committee，以及「可信任，才自動化」的營運原則。
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寄出去就收不回來：agent 動外部系統的三次誤操作
行事曆邀請、回信、佔位事件的內部備註，這三種動作都是寄出即無法收回。這個月我在這三處各犯一次，記下事故形狀與防呆判準。
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通路價值和成案價值，要分開記帳
這兩週連著跑幾場 BD，逼出三條判準：一筆生意的價值要拆成通路和成案兩層分開算、用《南瓜計畫》決定接哪一塊、以及免費諮詢的上限寫死在哪裡。
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我的 CLAUDE.md 精煉到只剩八句「以…為榮，以…為恥」
把 CLAUDE.md 一路砍到只剩八組對句，外加一篇刻意寫成公文體的人機協同宣導文，還有十六字方針。
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Codex 走下神壇：額度偷縮被網友抓出來了
一個多月前我還在寫 Codex 的額度蜜月期，現在網友實測 Plus 被偷縮 50%、Pro 5x 被偷縮 77%，我那句「兩個月跌下神壇」還是太樂觀了。
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Gemini 3.7 Flash——這半年內第一個能拿去做 agent 的 Gemini
Gemini 3.7 Flash 發布後我的體感：終於是這半年內可以用在 agent 工作上的 Gemini。比 Sonnet 5 略強但沒接近 Sol，價格對半折，額度根本用不完。同一家的 API 和網頁版根本是兩回事。
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HyperFrames 影片產出的十一個坑
轉場閃動、worker 並行迷思、字幕格式漂移、投影片版式、縮圖靜默截斷——八月這一週在 HyperFrames 這條影片線上踩到的十一個坑，逐個記現象、根因、解法。
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本地模型不是拿來省錢的
如果你買硬體架本地模型的目的是省錢，先聽我幫你算這筆帳：硬體十萬起跳、商用模型卷到流血價，本地模型唯一不可取代的優勢只剩隱私。
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Qwen 3.8 開放權重的這一週：從模型卡到 A3B 被刪
從 Qwen 3.8 27B 模型卡放出、實測體感、35B A3B 被刪，到八月底 Flash Next 登場的完整過程。
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話癆的模型：教 Opus 5 怎麼收斂
Opus 5 除了不講人話之外還會過度寫作，小抄寫成萬言書。我用 first-principles skill 教它收斂，順帶談怎麼遏制 overthinking。
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兩年前從來沒想過會發生在自己身上的五件事
兩年前從來沒有想過、卻真的發生在自己身上的五個變化：終端機、退訂 Office 365、語音輸入、派 AI Agent，還有被整理乾淨的桌面。
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Two Skills That Make AI Talk Like a Human: ASD-STE100 for Grammar, ISO 24495 for Structure
A skill I threw together three weeks ago got passed around by people overseas and picked up three PRs, so I shipped a second one I use every single day.
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Your Own Machine Is the Attack Surface: Leftover Credentials, Permission Flags, Untrusted Input
Everyone talks about AI security as whether the model will say the wrong thing. What actually bites you is the plaintext keys left on your disk, the permission flag you waved through, and the untrusted input you fed it.
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讓 AI 對你說人話的兩個 skill：ASD-STE100 管文法，ISO 24495 管結構
三個禮拜前隨手做的 asd-ste100-skill 被老外瘋狂轉載還留了三個 PR，順手再開一個天天在用的 iso-24495-skill。
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你的電腦才是攻擊面：殘留憑證、權限旗標、不可信輸入
大家談 AI 資安都在談模型會不會說錯話，但真正會咬你的是本機殘留的明文金鑰、隨手開的權限旗標，還有你讓模型讀進來的不可信輸入。
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Information Diet: I Pulled My Own Chrome History and Audited Where My Attention Goes
People obsess over whether every bite of food is clean, then swallow piles of dirty info online without a second thought. So I pulled my own Chrome history db and had it analyze my browsing habits. A few sections came out uglier than I expected.
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Information Diet：我抓自己的 Chrome 瀏覽紀錄，做了一次注意力盤點
現代人極度在意吃進口裡的每一塊食物乾不乾淨，卻毫不在乎上網吃到的一大堆 Dirty Info。所以我抓了自己的 Chrome 瀏覽紀錄 db，讓工具分析我的上網習慣，結果有幾段比我預期的難看。
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My Client's Chat Blew Up: Three Questions in One Prompt, and Claude Just Drifted Along With Him
Asking about revenue, metrics, and decisions in a single prompt, or never persisting conclusions after brainstorming. Two real-world cases of client drift with AI, and the fieldwork of using /explain, /first-principles, Grill-me, and checkpoint to rein their workflows back in.
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More Subagents Won't Make You Faster
From wanting 13 subagents to chat for me, to one person running 1000, to agents arguing across a table — the real bottleneck is the human main agent, and the fix is deduping before you dispatch.
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客戶的對話爆掉了：一段 prompt 塞了三件事，Claude 就陪著他一起發散
一段 prompt 裡同時問業績、問指標、問決策，或是聊完從不落檔。兩個客戶與 AI 協作時的發散現場，以及我用 /explain、/first-principles、Grill-me 與 checkpoint 把工作流收斂回來的實戰。
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派更多 subagent 不會讓你更快
從想派 13 個 subagent 代聊、一個人操控 1000 個，到面對面互審代碼的荒謬幻想——真正的瓶頸是人類這個 main agent，解法是先去重去衝突再派工。
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AI Will Never Go to Jail for You — I Figured Out What I Want to Teach, Then Got Told I Had Half of It Wrong
Since generative AI arrived I have been asking what AI can never do on a human's behalf. My answer is accountability, because AI does not get sentenced. Then I posted the argument and found out what I had missed about the risk carried by people at the bottom.
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AI 不會代替人去坐牢——我想清楚要教什麼，然後被網友提醒想錯了一半
生成式 AI 之後，我一直在想有什麼是 AI 永遠不能替人類做的。我的答案是「負責」，因為 AI 不會被判刑。但這個論述貼出去之後，我發現自己漏想了基層承擔的風險。
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NT$18,000 a Month for an AI Course: Price and Value Came Apart a While Ago
An AI-course cash grab overheard at a convenience store, set against the two things I can actually show: a SKILL distilled from fifteen years of material, and a mock exam interface pulling past a thousand USD a month. Price tracks marketing, not delivery.
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My Context Window Is More Fragile Than Today's Frontier Models: I Open-Sourced a Document Review SKILL
Working with an agent drains my attention, so I worked out a review loop: he writes the full proposal, I review it by voice, he revises, I go eat and exercise, then v2 shows up.
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AI 課一個月一萬八：教學市場的價格跟價值早就脫鉤了
在超商用餐區聽到的 AI 課吸金實況，對照我自己手上兩件真的能驗證的東西：15 年教材蒸餾成的 SKILL，跟每月破千美金的模擬考訂閱。價格反映的是行銷強度，不是交付密度。
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我的 Context Window 比當今大模型還脆弱：我開源了一個文檔審查 SKILL
跟 Agent 合作會注意力耗弱，於是我摸索出一套審查流程：請他出完整提案書，我錄音審查，他改，我去吃飯運動，等 v2。
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Why I Built My Own Newsletter
Once I started using AI, the feeds I follow changed completely. Threads is full of third-, fourth-, fifth-hand reposts with extra seasoning added, so I pulled together 300-plus sources and had AI build my own weekly digest.
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為什麼我自己做了一份電子報
開始用 AI 之後，我關注的社群媒體整個換掉。脆上很多是三四五六手搬運還要加料，所以我自己整理 300 多個源頭，讓 AI 做出我個人的電子週報。
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The CLI Is the Firstborn
New features and fresh bug fixes land in the CLI first. Remote control waits forever. Same company, wildly different update cadence.
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The Machines That Need the CLI Most Belong to People Least Likely to Learn It
For a machine short on resources the CLI uses a fraction of what a GUI does, but the people with the least headroom are also the least likely to ever learn it. Plus a note on how low the bar for local models actually is.
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GPT-5.6 Luna Max Is the New King of Grunt Work
Using Luna Max for grunt work after the price cut: statements pulled in 22 minutes, brute-force scraping all night, and two ANA business class award seats from Bangkok to Tokyo.
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The Skills and Hooks I Use Every Day, Now Open Source
I open-sourced the Claude Code skills and hooks I actually use every day. No technical skill required, pure logic, built for knowledge workers with no coding background.
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A July Full of Resets: A Light AI User Reviews the Subsidy War
Every time a reset landed in July I used it to the last drop, and the ledger says 57.5x. Plus notes on the subsidy war, Deepseek's price hike, and my bet on the next reset.
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Letting an Agent Tune a Local Video Model Overnight: My Three Gates
Three things I learned from letting an agent run local video-model parameter research overnight: hard constraints in CLAUDE.md, a gate script watching SSD writes, and a 30-minute check-in loop.
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Installing Gemini CLI With Beginners Cures Most Claude Code Ailments
Five steps I walk Claude Code / Codex beginners through when installing the Gemini Antigravity CLI: multimodal use cases, getting comfortable with a CLI, a quick win from voice transcription, and finishing on data governance and local mode.
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When to Call In Fable: Three Moments, and Don't Turn On ultracode
The most expensive model is not meant to run the whole way. Three moments where calling in Fable feels right to me, plus why I cap Opus 5 at med, and the weekly cycle I fell into: efficiency king Monday to Thursday, Fable mode Friday to Sunday.
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CLI 是親生的，其他都是後媽養的
新功能和剛修好的 bug 都優先落在 CLI，remote control 要等猴年馬月。同一家公司的產品，更新頻率差很多。
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最需要 CLI 的電腦，主人最不可能學會 CLI
對資源有限的電腦來說 CLI 佔用是 GUI 的零頭，但電腦資源最不夠的族群也剛好最不可能學會 CLI；順帶聊本機模型的硬體門檻其實沒那麼高。
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GPT-5.6 Luna Max 是新一代的雜活之王
降價後拿 Luna Max 幹雜活：22 分鐘抓完各家報表、整夜土法煉鋼爬蟲、還幫我刷到 ANA 曼谷東京商務艙兩位。
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我每天在用的 Skill 跟 Hook，開源了
把我每天都在用、不需要技術力、純邏輯導向的幾個 Claude Code Skill 跟 Hook 開源出來，最適合跟我一樣沒有編程背景的知識工作者。
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充滿 reset 的七月：一個 AI 輕量用戶的補貼大戰復盤
七月一有 reset 就用好用滿，帳面發揮 57.5x 的價值；順便記下補貼大戰、Deepseek 漲價，和我對下一次重置的預測。
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讓 Agent 整夜自己調本地影片模型：我設的三道閘門
讓 agent 夜間自己跑本地影片模型調參的三個經驗：硬約束寫進 CLAUDE.md、閘門腳本監控 SSD 寫入、30 分鐘回來盯一次避免快取失效。
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帶初學者裝 Gemini CLI，治 Claude Code 百病
帶 Claude Code / Codex 初學者裝 Gemini Antigravity CLI 的五個步驟：從多模態 use case、CLI 介面的心理建設、語音轉錄的 quick win，一路帶到資料治理與本機模式。
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何時派工 Fable？三個時機，還有別開 ultracode
最貴的模型不是拿來全程跑的。三個我體感最好的 Fable 派工時機、為什麼 Opus 5 我最多開到 med，以及我後來變成週一到週四效率王、週五到週日 Fable 模式的那個循環。
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The Alignment Gap Is Closing. Next Comes Taste and Verification
The gap between AI and human intent is closing fast, so what separates good output moves toward taste and verification — and verification is where human responsibility stays.
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Other People Fly to Korea for Cosmetic Surgery. I Had Codex Do Mine.
Someone on Twitter chained Codex, Hyperframes, IndexTTS2 and HeyGen into a one-person media pipeline. I pulled the repo, tried it, played with a local TTS model, then rebuilt a video days later on Claude Code.
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Six New Context Engineering Rules for Claude 5, and the 1,473 Lines I Cut
After reading Anthropic's context engineering guidance for Claude 5, I turned the key points into nine cards, then cut 1,473 lines from a harness I had built up over four model generations.
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Fewer Prompts Is Not Less Control
Claude 5 and GPT-5.6 official guidance is converging on the same thing — retiring old-style prompt stacking. But trimming is not letting go; control just moves. And the payoff of maintaining that layering is switching tools without losing a step.
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My Local Model Lineup on a Mac mini, Plus Three Bad Habits I'm Owning Up To
Ten days of offline models on a 48GB Mac mini M4 Pro — the picks, the task split, a terrifying swap write rate, and three bad habits I admit to.
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Porting Old Prompts to Opus 5: A Nine-Card Migration Guide
Nine cards I made after reading Anthropic's official Opus 5 prompting guide: response length, agent narration, task boundaries, subagent delegation, self-correction, and what happens when you turn thinking off.
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Supposedly the Smartest Model, and Opus 5 Spent My Whole Day Spinning in Place
Wrong languages, sudden Simplified Chinese, then whole turns with no visible output — and a session log that matched an issue open since June 15.
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Two Habits for Voice-Driving Coding Agents, and I Am Still Finding the Balance
Short instructions go through push-to-talk; walking through a whole course outline or system architecture goes through a full QuickTime recording that I hand to the AI to transcribe and execute. Plus the setup I use to burn the AI quota that came free with Google Drive.
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對齊的 gap 正在縮小，接下來拚的是品味與驗證
AI 對齊人類意圖的 gap 正在快速縮小，區分產出品質的要素會往品味與驗證轉移；而驗證背後是人類永遠不會被取代的責任。
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別人去韓國做醫美，我叫 Codex 數位醫美
從推特上看到有人把 Codex、Hyperframes、IndexTTS2、HeyGen 串成一條自媒體流水線，拉下來實測、順手玩 b 站的本地 TTS，幾天後換 Claude Code 重做一支。
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Claude 5 時代的情境工程六條新規則，與我砍掉的 1473 行 harness
讀完 Anthropic 對 Claude 5 的情境工程指南後，我把重點做成九張圖卡，然後照著把累積四個世代的 harness 削掉 1473 行。
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更少的 prompt，不是更少的控制
Claude 5 與 GPT-5.6 的官方指南正在合流，都在淘汰舊式 prompt 堆疊。但精簡不等於放任，控制只是換了位置——而維護好這套分層的紅利，是換哪個工具都能立刻接手。
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Mac mini 上的本地模型陣容，順便招認我的三大劣根性
48GB 的 Mac mini M4 Pro 試了十來天離線模型，選型結論、任務分工、swap 嚇死人的寫入量，還有我承認的三個劣根性。
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舊 prompt 搬到 Opus 5：九張卡的遷移指南
讀完 Anthropic 官方的 Opus 5 提示指南後整理的九張卡：回答長度、代理敘述、任務邊界、子代理委派、自我修正，還有關掉 thinking 的副作用。
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號稱最聰明的 Opus 5，一整天在我對話裡空轉
從回錯語言、突然寫簡體，到整輪沒有可見輸出，查 session log 之後對上一個 6/15 就開著沒修的 issue。
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語音下指令的兩種習慣，我還在找平衡
用 Claude Code 或 Codex 時，短指令我用隨按即錄，要盤點整套思路時我改開 QuickTime 完整錄音，再丟給 AI 轉錄執行。附上我拿 Google 雲端硬碟送的額度做轉錄的設定。
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I'm Stuck at Stage 2.5 of AI Adoption
After watching Boris Cherny break down the stages of AI adoption, it hit home: I'm stuck at stage 2.5, where limited attention plus low trust becomes a vicious cycle. This week I used late-night schedules, a morning dashboard, and herdr auto-spawning sessions to cut a 4-5 hour workflow down to 1-2 hours.
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Making Claude Talk Like a Human: A Plain-English Standard From Aircraft Maintenance Manuals
Claude stopped talking like a human starting with 4.7, and I thought my English just wasn't good enough — until Reddit showed me native speakers complaining too. So I turned ASD-STE100, the simplified-English standard used in aircraft maintenance manuals, into a skill, and now I can finally understand its English.
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A Zero While Sitting on a Gold Mine
I read a piece on the perceived value behind churn-and-burn courses, turned it into a skill, ran it on my own site, and got back a verdict: a zero while sitting on a gold mine.
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Some of My Subagents Were Already Grandpas
A CCX quota incident with no guardrails set: subagents bred recursively, one session burned 90% in half an hour, and here is the full forensics and fix.
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Codex Built Its Own Evidence Package and Went to Argue With Google Support
A leaked Gemini backend key at PDT Learning got abused, no spending cap, and burned 1000 USD. I pointed Codex's browser automation at Google's live support to fight the charge, and it went so hard it built a 15-page evidence package and sent it over.
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Two Traps Running Claude Code on Local Ollama: Truncated Context, and A3B Buckling Under Heavy Verification
Two things I logged: cco (Claude Code driven by local Ollama) had long been giving off-topic answers, and the root cause was not a weak model but a full harness whose system prompt had ballooned to 30-50k tokens and was being silently truncated; then I put A3B on the Mac mini for two days as a night worker.
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Instead of Begging the Model Not to Lie, I Wrote a Hook That Stops It
The sequel to the Opus 4.8 confabulation post: I moved "don't make up numbers" from a plea in CLAUDE.md to a pending-guard hook that blocks git commit at PreToolUse.
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Second Harness Diet: Global Skills From 58 Down to 40
A follow-up to the harness diet series: six months later, a bigger cleanup that cut my global skills from 58 down to 40, start to finish.
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我卡在 AI 導入的 2.5 階段
看了 Boris Cherny 分享的 AI 導入階段框架後有感而發：我卡在 2.5 階段，注意力有限加信任不足變成惡性循環，這週靠深夜排程、晨間儀表板、herdr 自動開 session 把 4-5 小時的協作壓到 1-2 小時。
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讓 Claude 說人話：一套飛機維修手冊的簡化英文標準
Claude 從 4.7 起就不說人話，我一度以為是自己英文太差，直到上 Reddit 才發現老外也在抱怨；後來我把飛機維修手冊在用的簡化英文標準 ASD-STE100 做成 skill，終於看得懂它的英文了。
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坐擁金礦的零分
讀到一篇談割韭菜課程感知價值的文章，我馬上做成 skill 拿去盤點自己的網站，換來一句「坐擁金礦的零分」。
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有些 subagent 都當阿公了
一次 CCX 沒設好護欄的額度事故：subagent 遞迴繁殖，一個 session 半小時燒掉 90%，事後鑑識與修法全記錄。
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Codex 自己做了一份證據包，跑去跟 Google 真人客服吵架
PDT Learning 一支 Gemini backend key 外洩被盜刷、沒設 spending cap 怒噴 1000 USD，我用 Codex 的瀏覽器操作去跟 Google 真人客服爭費用，它認真到自己生出一份 15 頁證據包發給對方。
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本機 Ollama 跑 Claude Code 的兩個坑：context 被截斷、A3B 撐不住重驗證
記錄兩件事：cco（本機 Ollama 驅動 Claude Code）長期答非所問，根因不是模型不夠聰明，是完整 harness 的 system prompt 早就膨脹到 3-5 萬 token 被靜默截斷；順手把 Mac mini 換上 A3B 當夜間工人測了兩天。
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與其拜託模型不要騙我，不如寫一個擋得住的 hook
接續 Opus 4.8 捏造工具輸出那篇：我把「別亂編數字」從 CLAUDE.md 的拜託，升級成一個掛在 PreToolUse 擋 git commit 的 pending-guard hook。
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第二次 harness 減肥：全域 skills 58 砍到 40
接續 harness 減肥系列，半年後做了第二輪更大規模的整理，把全域 skills 從 58 個砍到 40 個的完整過程。
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A Few Days Into Switching From Claude Code to Codex: The Quota Honeymoon
A running log of switching from Claude Code to Codex this week: quota I could not burn through fast enough, a $20 Sol Ultra run that beat a $100 Fable plan on a professor friend's paper, and a tool-chain swap to Hyperframe.
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從 Claude Code 換到 Codex 的這幾天：額度蜜月期
這週從 Claude Code 換到 Codex 的體感記錄：額度多到花不完、Sol Ultra 20 鎂幫教授跑論文，比 Fable 100 鎂方案還划算，工具鏈也跟著換成 Hyperframe。
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Picking a Brand Look as a Non-Designer: AI Made Prototypes Cheap, So Taste Became the Hard Part
I am not a designer, but I spent this week iterating on a visual system for AgentCrew Academy with Fable, GPT-image-2, and GPT-5.6-sol. Here are the four rejected directions and the final spec applied to the website, slides, and documents.
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外行人選品牌視覺：AI 把提案做便宜了，難的變成你的品味
我不是設計師，這週用 Fable、GPT-image-2、GPT-5.6-sol 迭代出 AgentCrew Academy 的視覺系統，記下被否決的四個方向與最後套用到網站、投影片、文件的規格。
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Do Not Grade AI by Its Own Summary
I hit the same failure in several forms this week: an agent denied changing files, a commit message overstated the diff, and a transcript reassigned a speaker ID halfway through. A later batch added checks that never ran and still reported clean.
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Four Things I Have Learned from Teaching AI
Recent corporate workshops reinforced four lessons for me: teach in person when possible, cut the slide count, show smart people the result first, and let students do the work.
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I Finally Switched My Daily Driver from Claude Code to Codex
I try new tools easily, but I am slow to leave the ones already built into my routine. This time, quotas, pricing, and product direction all crossed the line together.
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不要用 AI 的摘要驗收 AI
這週反覆踩到同一類錯誤：agent 說沒改檔卻留下檔案、commit message 與 diff 不符、逐字稿說話人中途漂移；一個多月後又補上檢查器沒在檢查卻回報正常的一整批。驗收必須回到原始狀態。
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最近教 AI 課學到的四件事
最近幾次企業培訓讓我重新確認四件事：實體課更好掌握節奏、投影片要做減法、聰明人先看結果，以及學員真正需要的是放手實作。
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我終於把主力從 Claude Code 換成 Codex 了
我很愛試新工具，卻很難離開已經用習慣的工具。這次真正讓我換主力的，不是一次 benchmark，而是額度、價格與可預見的產品方向一起越過了臨界點。
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I Let My /adhd Skill Go Dig Up a 3C-Scene Price-Hike Rumor on Its Own
I toss a vague rumor topic at my /adhd skill and watch it search, admit it searched wrong, stop to ask a disambiguating question, and finally piece the whole thing together with sources.
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AI Micro-Notes 2026: Chronological Archive
The more scattered, time-sensitive AI micro-notes from 2026, in chronological order. Curated notes (by theme) live on the main page.
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The Usage Economics of AI: Quotas, Plans, Tokenizers, and That Anesthetic Bill
Notes on AI usage, collected: pay-as-you-go vs subscription, a tokenizer quietly adding 1.4x, why resets are staggered, the real gap between Pro and Max, why a "4x upgrade" only doubles your weekly quota, a night market steak framework, and the bill you shouldn't mistake for output.
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Fable 5's One-Week Return: Turning the Most Expensive Model into a Skill Distillation Engine
Fable 5 came back for one week only. I did not spend it on daily busywork — I spent it on high-leverage judgment, distilled into skills that cheap models can follow.
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Sonnet 5 Is Out: The Lazy Version of the System Card, Plus Where I Actually Use It
Sonnet 5 shipped. I boiled the system card down to six plain-language points, then added where it actually fits: it is not here to fight Opus, it is here to replace Sonnet 4.6.
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我讓 /adhd skill 自己去查一件 3C 圈的漲價八卦
含糊丟一個八卦題目給 /adhd skill，看它自己一路搜、承認搜錯、停下來反問消歧義，最後拼出全貌附來源。
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AI 碎念日記 2026：時間軸存檔
2026 年較零碎、時效性的 AI 碎念，依時間排列。精選碎念（依主題分類）見主頁。
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AI 的用量經濟學：額度、訂閱方案、tokenizer，跟那張麻醉劑帳單
AI 用量的觀察收攏成一篇：按量 vs 訂閱、tokenizer 悄悄多 1.4x、額度重置為何打亂、Pro 與 Max 的真實落差、「增加 4 倍」其實只有五小時額度、夜市牛排式的判斷框架，還有那張別當成產能的帳單。
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Fable 5 回歸一週：把最貴的模型變成 Skill Distillation 引擎
Fable 5 限時回歸一週，我沒拿去做日常小事，而是拿去做高槓桿判斷、蒸餾成便宜模型可照做的 skills。
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Sonnet 5 發布：系統卡懶人版，跟我的實測定位
Sonnet 5 發布了，我把系統卡整理成六點白話懶人版，再加上實測定位：它不是來搶 Opus，是來換掉 Sonnet 4.6。
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ADHD, Flow, and Why I Teach AI
Confessions of someone with ADHD: if I make it past two weeks and still have flow, odds are I can do this thing for a decade-plus.
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The Client Says They Want Training, But Deep Down They Want a System
A post-mortem on letting go in a BD deal: the client drifted toward "give me an artifact" three times, and I changed one phrase from "after the training" to "if the priority is to build the hub" — and handed the ball back.
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From Takedown to Return, I Caught a Case of AnthroPTSD
An observation diary of Anthropic models getting pulled by the government and apparently returning: from arrogance, to political wrangling, to OpenAI winning by default, to the conditioned reflex I've been trained into.
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Claude Seeing Ghosts Four Nights Straight: A Log of Opus 4.8 Fabricating Tool Output
A four-day log of Opus 4.8 tool-result confabulation: from the technical symptoms to the GitHub issue to JSONL forensics.
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ADHD、心流，與我為什麼教 AI
一個 ADHD 患者的自白：撐過兩週還保有心流，這件事我大概率能做十幾年。
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客戶嘴上要培訓，骨子裡要系統
一次 BD 鬆手的覆盤：客戶三次飄向「給我 artifact」，我把一句話從「after the training」改成「if the priority is to build the hub」，球就交回去了。
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從下架到回歸，我得了 AnthroPTSD
Anthropic 模型被政府下架到疑似回歸的觀察日記：從傲慢、政治角力、OpenAI 躺贏，到我被訓練出的條件反射。
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Claude 半夜見鬼連續四天：Opus 4.8 捏造工具輸出實錄
Opus 4.8 連續四天的 tool-result confabulation 實錄：從技術現象、GitHub issue 到 JSONL 鑑識。
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Opus 4.8 Cries 'Prompt Injection,' Codex GPT-5.5 Tracks Down the Real Cause: a Worktree Race
With multiple sessions open in one repo and no worktree, Opus 4.8 raised a false prompt injection alarm, told me to check for supply-chain attacks and rotate API keys, until Codex GPT-5.5 read the session log and pinned the real cause: just a worktree race.
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Opus 4.8 搞烏龍報『提示注入攻擊』，Codex GPT-5.5 揪出 worktree race 真因
Opus 4.8 在我忘開 worktree 的情況下誤報「提示注入攻擊」，叫我去查供應鏈、rotate API key，最後 Codex GPT-5.5 查 session log 才揪出真因只是 worktree race。
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Running Taiwan Sports-Lottery Odds With Mobile Claude Code, Standing in the Lottery Shop: the Odds Are Garbage, You'll Lose
Walked past a lottery shop, felt like placing a sports bet, and on a whim wired up a betting-odds skill on mobile Claude Code right there. The verdict: the correct-score market carries a 191% overround, every expected value is negative, you lose.
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在彩券行用手機 Claude Code 算運彩賠率：台運賠率爛透了，誰下誰賠
路過彩券行起念想下運彩，順手用手機 Claude Code 接賭率分析 skill 現場開算，結論：台運波膽盤水錢高達 191%，期望值全負，誰下誰賠。
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Which AI Is Strongest? I'll Take Them All: Claude as the Brain, Directing Gemini and Codex
Stop being a believer in one model being the strongest. Use multi-model collaboration: let Claude be the commanding brain and hand the right task to the right tool.
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哪個 AI 最強？我全都要：讓 Claude 當大腦，指揮 Gemini 與 Codex
不要當「某家模型最強」的信徒，改用多模型協作：讓 Claude 當大腦指揮官，把對的任務交給對的工具。
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No Error Doesn't Mean Success: Five Silent Failure Traps in AI Dev
A tool not throwing an error doesn't mean it succeeded. Exit 0, a 200 response, an empty string, a decapitated value — all silent failures. Reading back the real state is the only reliable defense.
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沒拋錯不代表成功：AI 開發裡的五個靜默失敗陷阱
工具沒報錯不等於成功。exit 0、200 response、空字串、被砍頭的值，全都是靜默失敗——回讀驗證真實狀態才是唯一可靠的手段。
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Two Mindsets for Getting Unstuck: Logical Thinking, and the AI Pedagogy of Never Making the Same Mistake Twice
Teaching a student today, I said that breaking through when you are stuck is not hard. What matters are two mindsets: logical thinking, and an AI pedagogy like raising a child, where the goal is not for the AI to never err, but to never make the same mistake twice.
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卡關時的兩個心態：用邏輯思考，與『不貳過』的 AI 教育學
今天跟一個學員教學時談到，卡關時破除障礙並不難，重點是兩個心態：用邏輯思考，以及像帶孩子一樣的 AI 教育學，追求的不是讓 AI 無過，而是不貳過。
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Teaching a Talent Agency to Use Claude Code, and the Soft Landing of the DK Confidence Curve
A short trip back to Taiwan packed with private training sessions: walking a talent agency through contract review, receipt reconciliation, and closeout reports with Claude Code, plus a note on the DK confidence curve and its soft landing.
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帶藝人經紀公司用 Claude Code，與 DK 信心曲線的軟著陸
短暫回台跑私人場培訓，帶藝人經紀公司的企劃、製片、財務用 Claude Code 做合約審閱、單據核對、結案報告，順便聊聊 DK 信心曲線的快速上坡與軟著陸。
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My First All-English Two-Hour Corporate Training: I Burned the Script Two Hours Before
Just wrapped the first session of an all-English corporate training for 30 people at an international hedge fund — my first time teaching two hours straight in English. I had a teleprompter and a full English script ready, then two hours before class I shut the teleprompter off and burned the script, and winged the whole thing.
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第一次全英文兩小時企業內訓：上課前兩小時燒掉提詞稿
剛完成某國際 hedge fund 30 人企業內訓第一堂，這輩子第一次全程英文講兩小時。一開始準備了提詞機加英文全稿，上課前兩小時索性關掉提詞機、燒掉稿子，全程自由發揮。
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The Perpetual Token-Burning Machine: Anthropic Really Is Trying
A few scattered gripes about Anthropic's relentless push to get users burning tokens: 103 auto-triggered agents, five-layer Russian-doll subagents, and Formosa-Plastics-tier flexing.
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燒 token 永動機：Anthropic 真的很努力
幾則關於 Anthropic 在鼓勵用戶燒 token 這條路上的零碎吐槽：自動觸發 103 個 agent、五層俄羅斯娃娃 subagent、台塑等級的炫富。
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The Five-Step SOP for Sneaking Claude Code Runs From the Hotel Over Dinner
Back in Taipei at a hotel, the five-step backup SOP I run before heading out to dinner with friends: pull power, keep it charged and online, caffeinate against sleep, tailscale, test remote desktop. Then sneak in some Claude Code over dinner.
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出門吃飯前，讓 Claude Code 在旅館背景偷跑的五步 SOP
回台北入住旅館，出門跟朋友吃飯前的五步備援 SOP：取電、充電連網、防休眠、tailscale、遠端桌面試通。然後就能邊吃飯邊偷用 Claude Code。
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From Miasma to Hades: How One Group Turned AI Tools Into a Supply-Chain Attack Vector
Two June 2026 npm/Python supply-chain attacks: Miasma backdoored Red Hat packages, then TeamPCP/UNC6780 upgraded to Hades, turning Claude Code, Cursor and 14 other AI tools into an attack vector. Includes self-check steps.
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從 Miasma 到 Hades：同一個攻擊組織如何把 AI 工具當成供應鏈攻擊媒介
2026 年 6 月兩波 npm/Python 供應鏈攻擊：Miasma 入侵紅帽套件後門，TeamPCP/UNC6780 升級成 Hades，把 Claude Code、Cursor 等 14 款 AI 工具當成攻擊媒介。附自我檢查步驟。
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Claude Fable 5 / Mythos 5 System Card: What's Actually Inside, and Why It Reads Like Sci-Fi
Flipping through the Claude Fable 5 and Mythos 5 system cards: stealing keys, office politics, AI infighting, gibberish, and a two-faced model. The more I read, the more it feels like a straight-faced sci-fi novel.
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Testing Fable's Limits on a Professor Friend's Paper Repo
I woke up to Fei-bo (the guardrailed Mythos build) going live, so I borrowed a professor friend's paper proposal, prompt, and workflow docs to probe its paper-writing limits.
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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。
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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，課程跟導入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 an instructor
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、選講師訊號
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。
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2026 Model Personality Watch: Gemini, Claude, Codex Compared
A year in, the three flagships have developed very visible "personalities" — Gemini 3 is the dramatic PhD, Claude 4.7 is the slick veteran, and GPT-5.5 turns out to be the most pragmatic colleague of the wave. Plus a fun trick for guessing the version from "sass density."
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2026 模型脾氣觀察：Gemini、Claude、Codex 的個性對比
用了一整年下來，三家的旗艦模型各自有很明顯的「脾氣」——Gemini 3 像戲精博士、Claude 4.7 像油條前輩、GPT-5.5 反而是這波最務實的同事。把累積的觀察整理成一篇對比，順便講一個從「貧嘴密度」反推版本號的玩法。
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Why You Keep Hitting Limit: Six Observations on Claude's Subscription Economics
From W18 to W19 I accumulated half a dozen observations about Claude quota — reset times deliberately scattered, the 5x→20x math trap, hitting limit in one hour during US East peak, and somehow free GPT never running out. String them together and a real subscription economics emerges.
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為什麼你會一直 hit limit：Claude 訂閱經濟學的六個觀察
從 W18 到 W19 累積了好幾條關於 Claude 額度的觀察——重置時間打散、5x→20x 的數學陷阱、美東尖峰一小時就滿、免費 GPT 反而打不滿。把這些連起來，會看到 Claude 訂閱的真實經濟結構。
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Three Channels for Tracking Anthropic
Since March 2026 I have been jotting down ways to follow Anthropic — official sources, employee accounts, third-party teardowns. Consolidating into one piece, with notes on the recent Mythos system-card controversy and the curious silence of Anthropic employees on social media.
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追蹤 Anthropic 動態的三條管道
從 2026 年 3 月開始我陸續記了一些追 Anthropic 的方法——官方來源、員工帳號、第三方拆解。整理成一篇，順便聊近期的 Mythos 系統卡爭議跟員工社群媒體沉寂的訊號。
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Jack Dorsey Cut 40% and Rebuilt: The Shape of Companies in the AI Era
Block / Square / Cash App's Jack Dorsey did an interview about why he laid off 40% of the company and rebuilt from scratch. Plus Sequoia's Roelof on the "three things a good CEO needs" — once AI can do 80%, the remaining 20% is where humans live.
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Jack Dorsey 把公司裁掉 40% 重建：AI 時代的組織形狀
Block / Square / Cash App 的 Jack Dorsey 接受訪問，聊他為什麼把公司裁員 40% 然後從頭重建。順便整理 Sequoia 投資人 Roelof 講的「好 CEO 三件事」——在 AI 可以做出 80% 之後，剩下那 20% 才是人類的價值。
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Judging AI Risk on Two Axes: Reversibility × Environment Isolation
A common question in corporate trainings — "can I let AI do this automatically?" I answer with two axes: reversible vs irreversible, isolated vs production. This 2x2 prevents more incidents than any prompt-engineering tutorial.
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判斷 AI 風險的兩個維度：可逆性 × 環境隔離
企業內訓裡常被問的一題——「我可以讓 AI 自動做這件事嗎？」我用兩個維度回答：可逆 vs 不可逆、隔離環境 vs 生產環境。這個 2x2 矩陣比任何 prompt 教學都更能避免事故。
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GPT-image-2 Wants Fewer Constraints — Plus a Consistency Drill
The "ugly crayon doodle" prompt that went viral overseas works because over-constraint kills GPT-image-2's creativity. Here is what I observed this week — including why routing prompts through Claude makes things worse, and why "consistency" matters more than raw image quality at work.
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GPT-image-2 越少約束越好，加上一個一致性練習
國外最近流行的「笨拙塗鴉風」prompt，越約束越糟，越放任越驚喜。這篇整理我這週對 GPT-image-2 的觀察：包括為什麼透過 Claude 轉交反而會壞事、以及在 Canva 場景下「保持一致性」這件事比生圖能力更值錢。
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Moving from Claude Code to Codex: I Built a Skill to Handle It
Migrating an entire personal harness from Claude Code to Codex is not trivial — claude.md / agents.md, MCP, hooks, settings each need their own translation. I packaged the workflow as a Skill on GitHub. Plus a note on why I keep reaching for Codex these days.
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Three Moves Between Claude and Codex
Moving the whole harness to Codex in May, switching my daily driver in July, a honeymoon where the quota would not run out, a subsidy-war July that ledgered 57.5x, to August quota cuts, and moving back to Claude in September. One chronological log.
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從 Claude Code 搬家到 Codex：我做了一個 Skill 自動處理
把整套個人 harness 從 Claude Code 遷移到 Codex 不容易——claude.md / agents.md、MCP、hooks、settings 各自要對齊。我把流程包成一個 Skill 放在 GitHub，順便聊聊為什麼最近越來越常用 Codex。
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我在 Claude 和 Codex 之間搬了三次家
五月把整套 harness 搬到 Codex、七月換掉主力、蜜月期額度花不完、七月補貼大戰打出 57.5x 帳面價值，到八月偷縮額度、九月又搬回 Claude。一條照時間排的編年紀錄。
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You're mass-applying with AI. They're mass-screening with AI. Who wins?
Something slightly absurd is happening in the job market — candidates using AI to generate hundreds of tailored applications, HR using AI to filter them. In a bilateral arms race, who actually benefits?
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你用 AI 海量投履歷，他用 AI 海量篩履歷：最後誰贏了？
北美職場正在發生一件有點荒謬的事：求職者用 AI 大量產出履歷，HR 用 AI 大量過濾履歷。在這場雙邊軍備競賽裡，最後是誰在受益？
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Is your AI subscription worth it? A night market steak framework
A $7 night market steak versus a $70 restaurant steak — the expensive one isn't ten times better. AI subscriptions work the same way. A framework for deciding whether you're actually getting value.
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AI 訂閱費值不值？夜市牛排思考框架
兩百塊夜市牛排 vs 兩千塊餐廳牛排，差十倍的價格未必差十倍的體驗。AI 訂閱也一樣。一個判斷 AI 訂閱費有沒有在「賺」的框架。
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Lost 10 kg After Ten Years of Failure — Because of Claude's Quota
Lost 10 kg, complexion improved — a result I had failed to hit for ten years, finally achieved without willpower. Not from health drugs. From Claude burning my quota faster than I could finish my work. A half-joking but completely real chronicle of the silliest side effect of 2026.
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Teaching non-technical people AI: five things the tech world assumes everyone knows
One student was a seasoned HR consultant who managed all her files across several USB drives and had never heard of an API. Here are five knowledge gaps that show up constantly when teaching AI tools outside the tech world — and what actually works.
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睽違十年瘦了 10 公斤——竟然是因為 Claude 額度不夠
體重減掉 10 公斤、氣色變好——睽違十年都沒達成的成果，竟然不是靠猛健樂，而是靠 Claude 把我額度燒完。這篇半開玩笑的記下這個荒謬但真實的副作用。
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帶麻瓜學 AI：技術圈習以為常、但他們從來不知道的五件事
一位企業人資顧問、USB 隨身碟管理所有檔案、從來不知道 API 是什麼。這是在技術圈外推廣 AI 工具時，你會真實碰到的起點。五個知識落差，以及我現場用的方法。
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AI community digest W18: Claude detects Codex cheating, GPT 5.5 guardrails confuse everyone
Reddit AI community top posts from 4/28–4/29: r/ClaudeAI agent safety concerns, r/ChatGPT's GPT 5.5 content policy drama, r/LocalLLaMA on local model benchmarks, and Anthropic announcing Claude for Creative Work.
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AI 社群週報 W18：Claude 偵測到你在偷用 Codex、GPT 5.5 的過濾器到底鬆了沒
4/28–4/29 的 Reddit AI 社群熱門話題整理：r/ClaudeAI 的 agent 安全性爭議、r/ChatGPT 的 GPT 5.5 guardrail 爭議、r/LocalLLaMA 的本機 LLM 現況，以及 Anthropic 宣布 Claude for Creative Work。
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1M context isn't about holding more — it's about managing better
Claude Code's context window grew from 200k to 1M. The instinct is to put more in. That instinct is wrong. Five session management operations, four strategy rules, and the two most common ways to break your own context.
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Knowledge base deep clean: using AI to find months-old problems
Rules trimmed from 13,020 to 7,590 tokens. 92 broken wikilinks. Five root causes behind a failing todo system. Four days of cleanup, one quarterly SOP.
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1M Context 不是裝更多，而是管理更好：Claude Code Session Management 心法
Claude Code 的 context window 從 200k 長到 1M，但更大不代表更好用。這篇整理五種 session 管理操作（Continue / rewind / clear / compact / Subagents）、四大策略、以及最容易踩的兩個坑。
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Claude Code this week: 1M context as a management problem, quota anxiety goes collective, SpaceX takes Cursor
Observations from 4/23–4/29: the right mental model for 1M context, Claude quota anxiety as a subscription design phenomenon, SpaceX beating Microsoft to Cursor for $60B, Claude 4.7 identifying a journalist from 125 words, and what happens when you let Claude Code DJ.
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Two Worlds of Web-Based Claude Code Setup
Web Claude Code has two completely different runtimes — cloud VM and remote-control. Here is what I learned setting up both, the small bugs I hit, and why some user-level configs simply cannot be lifted to the cloud as-is.
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See the Outside, Think the Inside: Two Claude Skills That Pair
I saw a competitor selling well and my first reaction was anxiety. But before letting anxiety win, I wanted to actually understand what it was. Lately I have been pairing two Claude Skills for this — H/V analysis to see the outside, first-principles to see the inside.
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Three design mistakes I keep seeing in enterprise AI training
After running AI adoption training for several companies — manufacturing to services, 20 to 100+ people — three mistakes come up almost every time.
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Nine video production bugs I hit in April 2026
Running a fully automated video pipeline with Remotion, ffmpeg, yt-dlp, SiliconFlow ASR, and the YouTube API — here are nine specific bugs from April, in the order I hit them.
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2205% subscriber growth in two weeks: how one video opened the algorithm
Real analytics breakdown of how EP.18 went viral — not through SEO, but retention triggering subscriber activation, then spreading via sidebar. Plus what I actually did to make it happen.
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Claude Code 這週整理：1M Context 怎麼用、額度焦慮集體發作、Cursor 被 SpaceX 搶走
4/23–4/29 這週的觀察與整理：1M context window 的正確心態、Claude 額度焦慮進化成集體現象、SpaceX 搶在微軟前面拿下 Cursor、Claude 4.7 根據 125 字認出記者、live coding 做音樂的副作用。
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網頁版 Claude Code 環境設定的兩個世界
網頁版 Claude Code 有兩種環境——雲端 VM 跟 Remote-control。這篇整理我實測下來的設定訣竅、會踩到的小 bug，以及為什麼有些設定不能無腦丟到雲端。
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看清外面、想清裡面：兩個 Claude Skill 的分工
偶然看到一個競品賣得很好，第一反應是焦慮。但焦慮之前，我想先搞清楚它到底是什麼。最近用兩個 Claude Skill 把這件事做透——橫縱分析法看外面，第一性原理看裡面。
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幫企業導入 AI，教案設計最常犯的三個錯
從實際的企業培訓案例中，整理出三個教案設計的核心錯誤：統一任務不等於統一方法論、開放式問卷無效、以及缺少「自主應用」這一步。
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知識庫大掃除：用 AI 幫你找到埋了幾個月的爛帳
Rules 從 13,020 降到 7,590 tokens、92 個 wikilink 斷連、待辦系統五大根因——記錄一次花了四天的知識庫健檢，以及整理出來的季度 SOP。
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自動化影片製作管道：2026 年四月踩過的九個坑
用 Remotion、ffmpeg、yt-dlp、SiliconFlow ASR、YouTube API 自動化影片製作流程，四月份踩到的九個具體 bug 與解法。
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頻道訂閱兩週漲 2205%：一支影片怎麼把演算法打開的
用真實數據拆解 EP.18 爆款的完整機制：不是 SEO，是留存率觸發訂閱者激活，再透過 sidebar 擴散。以及我做了什麼讓這件事發生。
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A Legal Counsel Used Claude Code to Catch Patent Infringement — Days Down to Hours
A legal counsel at a mid-sized company took our Claude Code course, then compressed a multi-day patent infringement audit into a single day — and actually caught a real case that is now in legal proceedings. This story finally turns "Claude Code is not just for engineers" from a slogan into a fact.
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法務用 Claude Code 抓專利侵權，從幾天變成一天
一位幾十人公司的法務上完 Claude Code 課之後，把原本要好幾天的專利侵權查核壓到一天，而且真的抓到送上法律程序。這篇想記下這個案例，因為它讓「Claude Code 不是只給工程師用」這句話從口號變成事實。
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My Claude Code Automation Stack: /loop, Monitor Tool, the Skill System, Obsidian Reflection Reviews
From /loop clocking in for me at ERP, to Monitor Tool as a background event gatekeeper, to Google Map MCP saving browser clicks, to Obsidian + Mirror framework for weekly reflection. Here's my six-month Claude Code automation stack, plus a clarification of how commands and skills actually split.
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我家的 Claude Code 自動化工作流：/loop、Monitor Tool、skill 體系、Obsidian 反思週報
從 /loop 指令幫我打 ERP 卡、Monitor Tool 做背景事件守門員、Google Map MCP 省瀏覽器操作、到 Obsidian 搭 Mirror 框架做反思週報——整理這半年我家 Claude Code 的自動化架構，順便釐清 commands 跟 skills 到底怎麼分。
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Six Months with Claude Code: From First Affection to Hesitation, From Overtime to Detox, From Mandarin Circles to the Thai Community
From January to April, my state of mind with Claude Code went from "that PTT-generation affection for the CLI" to "20x hesitation" to "AI detox" to "practicing Thai at a Bangkok meetup." Here's the six-month arc, along with the nights agents worked overtime for me until 5 AM.
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Claude Code 用了半年：從好感到猶豫、從加班到 detox、從中文圈到泰國社群
從 1 月到 4 月陪著 Claude Code 一起長大半年，我的心態從「PTT 世代對 CLI 的莫名好感」變成「20x 的猶豫」變成「AI detox」變成「泰國社群練泰語」。整理一下這半年的軌跡，連同那些對自己講越來越 nerdy、agent 幫我加班到凌晨五點的日子。
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Two Months of Claude Code Harness Ops: Crashes, Zombies, Cache Misses, and Plan Mode Edges
From March to April, running Claude Code as a long-running harness surfaced a bunch of issues. Survival tactics during the outage, my hook for cleaning up zombie processes, the silent memory bloat fixed in 2.1.86, the resume cache-miss fix in 2.1.90, and the Plan-Mode-question-density problem after 4.7.
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Claude Code Harness 兩個月運維史：當機、殭屍、快取、Plan Mode 的邊邊角角
從 3 月到 4 月，Claude Code 作為長時間運轉的 harness，踩過的幾個點：當機時的求生策略、我自己寫的 hook 清殭屍進程、2.1.86 的記憶體暴增修復、2.1.90 修好的 resume cache miss、還有 Plan Mode 問題太多的 4.7 癥結。
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AI-Era Business Routes: Auto-Generated Posts, the Skill Poisoning Theory, and Why I Started Rooting for OpenAI
A handful of AI industry observations I piled up on Threads this week — automating social posts uses AI in the wrong place, whether Skills are a mechanism for model providers to extract human knowledge, and why I went from hating on OpenAI to genuinely hoping they get better.
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AI 時代的經營路線之爭：自動化貼文、Skill 投毒論、跟我期待 OpenAI 的理由
這週跟朋友閒聊時累積的幾則 AI 產業觀察——用 AI 自動化產貼文是用錯場景、Skill 是不是模型商套人類知識的陰謀、為什麼我從唾棄 OpenAI 轉成期待他們越做越好。
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A Teacher's Responsibility: How a 15-Year Cram School Instructor Sees AI-Era Course Quality
When students have the wrong expectations, instructors are often also failing. After 15 years in cram schools and recent experience teaching Claude Code, a few thoughts on course quality basics, teacher responsibility, and the most practical one-word answer for a cram school going AI.
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教學者的責任心：15 年補教老師怎麼看 AI 時代的課程品質
當學員有錯誤期待時，授課者也常常不及格。15 年補教經驗加上最近試教 Claude Code 的觀察，聊一下教學品質的基本盤、老師的責任心、以及補習班 AI 轉型最實用的一句答案。
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Claude Code This Week: Offloading CLAUDE.md, Quota-Saving Tricks, Ghostty Click Fix
A handful of Claude Code settings and money-saving tricks I picked up this week — how Rules/Memory/Hooks offload CLAUDE.md, fixing cmd+click in Ghostty No Flicker mode, dispatching long-read work to Haiku for quota relief, and the cache difference between pasting images and editing prior messages after 4.7.
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Claude Code 這週整理：分擔 CLAUDE.md 的進階設定、省 quota 技巧、Ghostty 點擊設定
本週 Claude Code 累積的幾個實用設定與省錢技巧——Rules/Memory/Hooks 如何分擔 CLAUDE.md、Ghostty No Flicker 模式下連結點擊的修法、長文本用 Haiku 分派省 quota、4.7 後貼圖片跟改訊息對快取的差別。
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Opus 4.7 After One Week: From the System Card to the Roasting, Where Did It Go Wrong?
Opus 4.7 launched on 4/16 and the Chinese and English communities ended up with opposite takes. The system card reads like a full win, but real usage burns quota at roughly 2x what Anthropic claimed, and Reddit spent the week roasting it. Here's what I saw, and why I rolled back to 4.6.
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Opus 4.7 上線一週：從系統卡到火烤文，到底哪邊出了問題
Opus 4.7 上線一週，系統卡數據看起來全面超越 4.6，但實測下來燒 quota 是官方宣稱的 2 倍，Reddit 一片火烤文。整理這週的災情、逆向分析、以及我自己退回 4.6 的路徑。
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90% of People Are Two Years Behind: The Real AI Adoption Gap
I demonstrated Claude Code to friends and they were all amazed. Then I gave them free Pro trial passes — three of them. Not a single one was used. That told me a lot about how AI adoption actually works.
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Claude Code Subscription Guide: The Real Gap Between Pro and Max
How much does Pro actually give you? Is Max worth it? Why does Cowork burn through tokens so fast? What does "2x usage" actually mean? These questions come up constantly — here's everything I've figured out over the past few months.
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Why Claude Code Only Reaches Its Full Potential in the Terminal
The terminal isn't just another way to open Claude. It's the environment where Claude actually gets to operate without constraints. Multi-window workflows, speed, resources, Ghostty — a full confession from a CLI believer.
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90% 的人落後兩年：AI 採用的真實落差
我把 Claude Code 示範給朋友看，他們都說驚艷。然後我把免費的 Pro 體驗 pass 發給他們——三張，一張都沒被用過。這件事讓我想清楚了很多關於 AI 採用的事。
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Claude Code 訂閱怎麼選：從 Pro 到 Max 的真實落差
Pro 的額度到底有多少？Max 值不值？Cowork 為什麼很燒 token？週額度的「雙倍」到底是哪個雙倍？這些問題我都被問過不只一次，用這篇把幾個月來的觀察整理清楚。
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為什麼 Claude Code 在終端機裡才是完全體
終端機不只是另一種打開 Claude 的方式。它是讓 Claude 真正解放的環境。多視窗、速度、資源、Ghostty——這是一個 CLI 信徒的完整自白。
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What Anthropic Did This Time: Cache Reduction and Token Anxiety Injection
Starting in early April, some Max and higher accounts had their subagent cache duration quietly reduced from 1 hour to 5 minutes — no announcement. Around the same time, users found a hardcoded "token remaining" warning being injected into Claude's context, causing it to cut work short prematurely. How to check your own setup, and what this pattern means.
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Claude Desktop vs CLI: Why I Still Use the Terminal After the Redesign
Claude released a redesigned desktop application — better looking, friendlier for newcomers. But if you ask me which one to use, the answer is still CLI. Not nostalgia, just feature gap and resource efficiency.
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hermes-CCC: Bringing All 46 Hermes Agent Capabilities into Claude Code
Someone ported the entire NousResearch Hermes Agent into 46 native Claude Code Skills. No OAuth, no external processes, just restart CC and you're ready. The core philosophy is "procedures-as-prompt" — structured markdown instruction scripts drive AI behavior without writing a single line of code.
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Obsidian vs NotebookLM: Which One Works for Claude Agent Automation?
These two tools get compared a lot, but they solve fundamentally different problems. From a Claude Agent automation perspective, the difference is even clearer — one is a local knowledge base your Agent reads and writes directly, the other's API stops at "managing the Notebook itself."
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Anthropic 這次幹了什麼：快取縮水與 Token 焦慮注射
4 月初起，部分 Max 以上帳號的 subagent 快取維持時間被偷偷從 1 小時改成 5 分鐘，零公告。同期有用戶發現 Claude 被注入固定的「剩餘 token 警告」，導致 Claude 提前縮短工作。怎麼自查，以及這件事代表什麼。
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Claude Desktop vs CLI 差在哪——改版之後我為什麼還是用終端機
Claude 推出了改版的桌面應用程式，設計更漂亮、對新手更友善。但如果你問我要用哪個，答案還是 CLI。不是情懷，是功能差距和資源效率的問題。
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hermes-CCC：把 Hermes Agent 的 46 個能力全部裝進 Claude Code
有人把 NousResearch 的 Hermes Agent 整套行為拆解成 46 個 Claude Code 原生 Skill。沒有 OAuth、沒有外部進程、重啟 CC 就能用。核心設計哲學是「procedures-as-prompt」：用結構化 markdown 指令稿驅動 AI 行為，不用寫一行程式碼。
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Obsidian vs NotebookLM 比較：用 Claude Agent 自動化知識管理該選哪個
這兩個工具常被拿來比較，但解決的問題其實不同。從 Claude Agent 自動化的角度來看，差異更明顯：一個是 Agent 直接讀寫的本機知識庫，另一個的 API 在「管理 Notebook」這件事上就停了。
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This Week in the Claude Code Ecosystem: Four Resources I Bookmarked
Enterprise office tools, Taiwan local services, a hobby vertical, beginner onboarding—four different corners of the Claude Code ecosystem all shipped something this week. Each represents a use case that's still underexplored.
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本週 Claude Code 生態觀察：四個我收藏的新資源
企業辦公工具、台灣本地服務、興趣領域、初學者入門——這四個角落本週都冒出了新的 Claude Code 資源。每一個都代表一類還沒被充分開發的使用場景。
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Two HTML/PDF Rendering Traps: Chrome Print Margin and qlmanage's Single-Page Problem
This week I exported a client proposal as HTML→PDF and split an internal report from PDF→images page by page. Both flows had a seemingly unsolvable trap. Each fix is one line of code, but tracking down that line cost me real time.
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兩個 HTML / PDF 渲染陷阱：Chrome print margin 和 qlmanage 的 1 張縮圖
這禮拜寫了客戶提案的 HTML→PDF 和內部報告的 PDF→圖逐頁拆分，兩個流程都有一個看似無解的陷阱。解法都是一行事情，但找到那一行花了我一小時。
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Digging into Claude Code's JSONL: Manual Context Usage, Cache Types, and Compact Detection
Claude Code's `/stats` only tells you the total token count, but the JSONL files hide a lot more—cache types, compact triggers, image-triggered cache misses. Here are the useful formulas I collected while building cc-audit.
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挖 Claude Code 的 JSONL：手動算 context 用量、分辨快取類型、偵測 compact
Claude Code 的 `/stats` 只告訴你總 token 數，但 JSONL 檔案裡其實藏著更細的資訊——快取類型、compact 觸發、貼圖 cache miss 都能手動算。這是我最近做 cc-audit skill 時整理出來的幾個實用公式。
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cc-audit: A Skill That Forces You to Save Claude Code Quota
I turned my viral list of quota-saving tips into a skill. Hand the GitHub link to Claude Code to install, and it'll audit your settings, recent sessions, rules bloat, and context usage, then hand back an actionable fix list.
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cc-audit：一個強迫你省 Claude Code 額度的 Skill
把我之前整理的一千多讚的省額度技巧全部做成 skill。丟給 Claude Code 安裝後，它會逐條檢查你的設定、近期 session、rules 膨脹、context 用量，給出可執行的修正清單。
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Make a Claude Code Tutorial Video in 30 Minutes with Remotion Skill
I'd had the Remotion skill installed for a while, mostly just playing with it. Once I started making a free Claude Code tutorial series, I realized how good it actually is—from script discussion to export, 30 minutes gets you a 15-minute video with no retakes.
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用 Remotion Skill 半小時拍一支 Claude Code 教學影片
Remotion skill 我下載好一陣子了，一開始只是玩玩。最近開始錄 Claude Code 免費教學系列才發現真的太好用——從口白討論到剪輯渲染，半小時出一支 15 分鐘影片，不 NG 也不費力。
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Is Claude Getting Dumber? Someone Finally Brought Data
People have been complaining Claude is getting dumber for weeks. Anthropic's response has been "it's a usage problem." Then someone pulled the JSONL and showed it's measurable, not vibes. The Issue got closed anyway.
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Claude 變笨了？這次有人拿出數據證據
大家抱怨 Claude 變笨已經好幾個禮拜了，Anthropic 的回應一貫是「使用習慣問題」。終於有人從 JSONL 扒出數據，證明不是體感而是實測——但 Issue 還是被秒關。
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Claude Code's Cache Crisis: Why Your Quota Burns So Fast
A Reddit user reverse-engineered two official bugs, and combined with the compounding effect of 1M-token context, your quota might be silently burning at 20x the expected rate.
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How Claude Code Skills Actually Load: Lazy Loading and a 30-50% Trigger Rate
Skills don't dump all their prompts into context by default. They load layer by layer on demand. But passive trigger rate is only 30-50%, so slash commands are your safest bet.
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Claude Code 的快取危機：為什麼你的額度燒得那麼快？
Reddit 大神逆向工程挖出兩個官方 Bug，再加上百萬 token 上下文的複合效應，你的額度可能在背後被吃掉 20 倍。完整事件始末與修復紀錄。
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Claude Code Skills 機制解密：按需載入與觸發率真相
Skills 不會把全部提示詞預設注入上下文。它是一層一層按需載入的。但被動觸發率只有 30-50%，打 slash 才是最保險的。
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Claude Code Source Leak: Three Secrets Unearthed by Reddit
Someone reverse-engineered Claude Code and found a hidden virtual pet system, next-gen model deception rate data, and an "Undercover Mode" that strips AI attribution from Anthropic employees' commits.
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Claude Code 源碼洩漏：Reddit 大神挖出的三個秘密
有人對 Claude Code 做了逆向工程，挖出了隱藏的電子寵物系統、下一代模型的欺騙率數據、以及讓 Anthropic 員工隱藏 AI 身份的 Undercover Mode。
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PII Guard TW: A De-identification Tool Built for Taiwan
There's no off-the-shelf de-identification tool for Taiwan's PII formats. So I built one that keeps sensitive data on your machine while you send the rest to AI.
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PII Guard TW：為台灣打造的個資去識別化工具
台灣的個資格式沒有現成的去識別化工具。所以我自己做了一個，讓機敏資料不離開你的電腦就能安全送 AI 處理。
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Anthropic's Trust Crisis and My Backup Plan
They quietly slashed quotas, went silent for a week, and only responded when they got caught. When your work is deeply tied to a service that can't even hit two nines of uptime, a backup plan isn't optional—it's essential.
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Anthropic 信任危機與我的備援方案
偷改額度、裝死一週、被發現才滅火。當你的工作已經深度綁定一個不到兩個 9 uptime 的服務，備援方案不是選項，是必要。
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Mirror System: Weekly Reviews Without the Comfort
Normal weekly reviews let you make excuses for yourself. Mirror System doesn't offer reassurance — just feed your daily notes to Claude and watch it surface the patterns you've been avoiding.
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Mirror System：用 Claude 做不帶安慰的週回顧
普通的週回顧，你會幫自己找理由。Mirror System 不要肯定，不要安慰，只要事實——把本週 daily notes 餵給 Claude，看它反照出你不想正視的模式。
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Why Does Cowork Hit Your Limit in Hours While Claude Code Runs All Day?
Same Max plan, same Claude — but Cowork burns through tokens ten times faster. Here's why, and when to use which.
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為什麼 Cowork 幾小時就頂到限額，Claude Code 整天都沒事？
同樣是 Max 方案，Cowork 和 Claude Code CLI 的 token 消耗差距可以到十倍以上。這篇解釋為什麼，以及什麼時候用哪個。
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The 5 Levels of Claude Code: You Don't Choose to Upgrade, the Ceiling Forces You
From Raw Prompting to Orchestration — the 778-upvote Reddit framework where every level exists because the previous one broke.
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A Year In, My Answer to "Terminal or Desktop App?" Has Changed Three Times
From March's Cowork token burn, to April's "still the terminal," to May's "beginners should just start with the desktop app," to August's "the CLI is the firstborn." One question, three different answers across five posts, laid out in the order I gave them.
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Claude Code 五個成熟度等級：你不是選擇升級的，是被天花板逼的
從 Raw Prompting 到 Orchestration，Reddit 上 778 個讚的框架——每一層存在的原因，是因為上一層壞掉了。
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一年下來，我對「該用終端機還是桌面版」的答案變了三次
從三月的 Cowork 燒 token、四月的「答案還是終端機」、五月的「初學者直接從桌面版開始就好」，到八月的「CLI 是親生的」。同一個問題，我在五篇文章裡給過三種不同的答案，這裡照時間順序排出來。
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Turning Books into Claude Skills: From Textbooks to Atomic Habits, the World Is Already On It
Turning books, frameworks, and methodologies into Claude Skills — making knowledge executable as interactive coaches. A roundup of popular examples and practical lessons.
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把書變成 Claude Skill：從教材到原子習慣，國外已經在瘋了
把書籍、框架、方法論轉成 Claude Skill，讓知識變成可執行的互動式教練。國外的熱門案例整理和實作心得。
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The Real Next Level in Claude Code: Context Management and Hooks
From Claude.md to Hooks — sharing the context management upgrade after heavy daily Claude Code usage. When rules pile up and compliance drops, Hooks become the perfect gatekeeper.
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Claude Code 的真正進階：上下文管理與 Hooks
從 Claude.md 到 Hooks，分享大量使用 Claude Code 後對上下文管理的理解升級。規則塞太多怎麼辦？Hooks 就是那個完美的守門員。
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Obsidian + Claude Code: My Daily Knowledge Management Workflow
Put your entire life into Obsidian, then let Claude Code tap in. How I do daily logs, manage to-dos, and why local Markdown beats Notion.
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Obsidian + Claude Code：我的日常知識管理工作流
把人生都放到 Obsidian，再讓 Claude Code 接入。日誌怎麼記、待辦怎麼管、為什麼本地 Markdown 勝過 Notion。
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One AI Tool to Rule Them All: Why I Only Recommend Claude Code
Instead of subscribing to a dozen AI tools, master one. What n8n, Lobster, and fomofly can do, Claude Code handles with a single skill.
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一個 AI 工具打天下：為什麼我只推薦 Claude Code
與其訂十幾個 AI 工具，不如把一個用到極致。n8n、Lobster、fomofly 能做的，Claude Code 加一個 skill 就搞定了。
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AI Security Boundaries: The Lobster Craze, Presidio, and My Three Rules
When everyone is going wild with AI automation, I chose to understand my own capability boundaries first. Sharing Presidio for data privacy and my security practices with Claude Code.
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AI 的安全邊界：龍蝦熱潮、Presidio、與我的三條鐵則
當所有人都在瘋 AI 自動化的時候，我選擇先搞清楚自己的能力邊界。分享 Presidio 隱私工具和用 Claude Code 工作的安全實踐。
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Claude Code Workshop Prep Notes: Teaching Non-Coders to Go Terminal-First
Designing a Claude Code workshop for non-engineers — from analyzing signup demand to crafting the curriculum, plus 15 quick-start tips for zero-coding-background learners.
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Claude Code 工作坊備課日誌：帶零基礎學員直上終端機
規劃一場給非工程師的 Claude Code 工作坊，從報名需求分析到教學設計，再到 15 條速成心法的完整備課歷程。
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claude-work-timer: Auto-Track Your Claude Code Working Hours
Billing clients by the hour with Claude Code? I built an open-source plugin to track actual working time automatically.
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claude-work-timer：讓 Claude Code 自動算工時
用 Claude Code 接案按時計費，甲方要看工時怎麼辦？寫了一個開源外掛自動算。
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Claude Code Initial Prompt Tips: Six Techniques for Effective AI Collaboration
Six practical tips for writing effective initial prompts in Claude Code, from clarifying your goals to leveraging plan mode for better AI collaboration.
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Claude Code 起始指令心法：六個讓 AI 真正聽懂你的技巧
分享六個下 Claude Code 起始指令的實戰心法，從釐清目標到善用計畫模式，讓你的 AI 協作更高效。
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What I Learned from 7,843 Claude Code Conversations
46 days, 6 projects, 7,843 conversations. This is not a manual — it's a diary of trial and error.
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Claude Code 用了 7843 條對話後的心得
46 天、6 個專案、7843 條對話，整理出來的 Claude Code 實戰心法。不是教學手冊，是踩坑日記。
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I scanned 500 Claude Code sessions and the AI stopped making the same mistakes
I used claude-log to scan 500+ Claude Code sessions across 6 projects, extracted every time I yelled at the AI, and turned those into CLAUDE.md rules. Here's what I found.
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Claude Code 對話紀錄怎麼挖：我掃了 500 個 session，讓 AI 不再犯同樣的錯
用 claude-log 工具掃描歷史對話，從你罵 AI 的紀錄中提煉規則寫進 claude.md，讓 Claude Code 真正學會不再踩同一個坑。
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Critical Thinking in the AI Era: Playing Dumb, the Feynman Method, and What Future Talent Looks Like
AI won't replace your thinking, but it will expose the fact that you can't think. From rediscovering how to learn through AI interactions, to the growing demand for "AI collaboration skills" in exams and workplaces — observations from an educator.
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AI 時代的思考力：裝傻狂問、費曼學習法，與未來人才的樣子
AI 不會取代你的思考，但會暴露你不會思考。從跟 AI 互動中重新發現的學習態度，到未來考試與職場對「AI 協作力」的需求，一個教學者的觀察。
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How Teachers Can Embrace AI Without Getting Replaced
A 15-year teaching veteran shares how AI reignited his passion for teaching, how to build an AI clone of yourself, open-source your knowledge, and why "teachers who use AI" are the real threat.
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教學者如何擁抱 AI 而不被取代
一個教學從業 15 年的人，分享如何用 AI 重燃教學熱情、建立教學分身、開源知識庫，以及為什麼「會用 AI 的老師」才是真正的威脅。
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My AI Workflow: Convergence and Divergence
Distilling my AI collaboration philosophy into two modes -- convergence and divergence. Plus how I build a personal knowledge base, and my day-to-day practice of running multiple agents in parallel.
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我的 AI 工作流：收斂與發散
把跟 AI 協作的心法整理成「收斂」與「發散」兩個模式。加上個人知識庫的建構方法，以及多 Agent 並行的日常實踐。
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FIRE Planning with Claude Skills: Talking Retirement with AI
I fed all my assets, liabilities, income, and expenses into Claude Code, then just asked "am I going broke?" A month of conversations turned into a full FIRE planning toolkit — and why I open-sourced it.
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用 Claude Skill 做 FIRE 理財規劃：跟 AI 聊退休
把資產、負債、收支全部餵進 Claude Code，直接用對話問「我會不會破產」。一個月下來累積的 FIRE 理財規劃工具，以及為什麼我把它開源了。
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Building AI Products from Scratch: A Non-Engineer's Four-Generation Evolution
A teacher-turned-builder shares the real story of four generations of AI tool development—RAG, platform architecture, cross-border deployment, and open-source strategy, with all the pitfalls along the way.
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從零打造 AI 產品的踩坑日記：一個非工程師的四代進化
一個教學從業者，從 GPT 自訂 Agent 起步，歷經四代 AI 工具開發，分享 RAG、平台架構、跨國部署、開源策略的真實踩坑紀錄。
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The Truth and BS of Vibe Coding: Observations from a 10-Year Teaching Veteran
We shouldn't uniformly trash Vibe Coding, but we do need to recognize the traps in course quality. A teacher with 10+ years in the game shares how to tell the real deal from the fakes.
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Vibe Coding 的真與假：一個從業十年老師的觀察
不該統一貶低 Vibe Coding，但也要認清課程品質的陷阱。一個教學從業十年以上的人，分享辨識有料與沒料的方法。
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The Dunning-Kruger Curve of Claude Code: From Clueless to Perfect Sync
A heavy user with zero engineering background shares the Dunning-Kruger curve of Claude Code from day one to week three, and the workflow that finally clicked.
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Claude Code 的達克曲線：從陌生到極致同步的實戰心得
一個非工程師背景的重度使用者，分享 Claude Code 從第一天到第三週的達克曲線效應，以及最終找到的高效互動方式。
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Can't See Myself in Three Years: Anxiety and Adaptation in the Age of AI
AI is iterating so fast that I can't see where I'll be in three years. The journey from anxiety to finding my footing, and why I believe AI is a Kingmaker.
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看不見三年後的自己：AI 時代的焦慮與適應
AI 飛速迭代，讓我看不見自己三年後的職場位置。從焦慮到找到定位的心路歷程，以及「AI 是造王者」的核心觀點。
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Claude vs Gemini vs GPT: A Power User's Honest Field Notes
Six months of daily-driving all three major AI models. Claude's reliability, Gemini's self-doubt spirals, GPT's decline, and what I ended up subscribing to.
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Claude vs Gemini vs GPT：一個重度使用者的模型體感實錄
同時使用三大 AI 模型半年多的真實體感記錄。Claude 的穩定、Gemini 的自我懷疑、GPT 的退場，以及我最後的訂閱選擇。
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Hello World: Why I Started This Blog
Welcome to Dustin's AI Lab — a blog about practical AI workflows, tools, and insights for learning and productivity.
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你好世界：為什麼我開始寫這個部落格
歡迎來到 Dustin's AI Lab — 分享 AI 工作流、工具與學習心得的技術部落格。
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AI Micro-Notes 2026: Thoughts Too Short to Trash
Short AI hot takes from 2026 onwards, accumulated from Threads and IG. Model roasts, dev pitfalls, industry observations, tool impressions — each no more than three lines.
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AI 碎念日記 2026：那些太短但捨不得丟的觀點
2026 年起在 Threads 和 IG 上累積的 AI 短碎念。模型吐槽、開發踩坑、行業觀察、工具心得，每則不超過三行。
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AI Micro-Notes 2025: Thoughts Too Short to Trash
Short AI hot takes from the second half of 2025, accumulated from Threads and IG. Model roasts, dev pitfalls, industry observations, tool impressions — each no more than three lines.
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AI 碎念日記 2025：那些太短但捨不得丟的觀點
2025 年下半年在 Threads 和 IG 上累積的 AI 短碎念。模型吐槽、開發踩坑、行業觀察、工具心得，每則不超過三行。