Posts
All the articles I've posted.
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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 Reddit Reviews — 3x Price, Vision Regression, Tool Calling Disaster
Reddit user reviews after Gemini 3.5 Flash launch: 3x price increase over 3 Flash, vision regression, tool calling running 32 calls before forced stop. Speed is genuinely fast, but overall reception skews negative.
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Gemini 3.5 Flash Reddit 實測彙整——貴三倍、Vision 退步、Tool Calling 災難
Gemini 3.5 Flash 上線後 Reddit 用戶實測回報彙整：價格比 3 Flash 貴三倍、Vision 退步、Tool Calling 跑到 32 次被中斷。正面是速度快且程式碼風格好，但整體評價偏負面。
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Have Claude scan an MCP before you install it—it found 7 vulnerabilities and still said it's safe to install
This week AgentCrew Academy shipped a video about why you should run /security-scan before installing any MCP / npm / pip package. Tested a third-party MCP, Claude flagged 7 findings, then said "install is fine." This is the written companion to the video.
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裝 MCP 之前先讓 Claude 幫你掃——找到 7 個漏洞，但說這個可以裝
講座當週我拍了一支影片，講「裝任何 MCP / npm / pip / clone 之前先跑 /security-scan」這件事。實測了一個三方 MCP，Claude 掃出 7 個漏洞，但綜合評估後說「可以裝」。這篇是影片的補充文字版。
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Scope Discipline: five rules—client small talk ≠ scope expansion authorization, must go into every future contract
A 5/12 admin class contracted for 1.5 hours ran 2.5 hours (+67%). Root cause: I misread the client's stated topic preference as authorization to expand scope. The lesson became five Scope Discipline rules that must appear in every future enterprise / 1-on-1 / tutoring contract.
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Scope Discipline 五條——客戶閒談 ≠ scope 擴張授權，未來合約必含
5/12 行政班合約 1.5 小時被自己做成 2.5 小時（+67%），追根究底是把客戶閒談的主題偏好誤解為 scope 擴張授權。教訓寫成五條 Scope Discipline，未來企業案 / 1-on-1 / 家教合約必含。
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YouTube Data API large-file upload tested: 216 MB succeeds, 257 MB hangs—threshold around 220-250 MB
Two 5/13 uploads via YouTube Data API: 216 MB legal MCP demo succeeded, 257 MB AI job-search clip hung indefinitely. Same googleapiclient resumable upload pipeline. The difference is file size and resolution. The practical threshold sits around 220-250 MB.
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YouTube API 大檔上傳卡死實測——216 MB 過 / 257 MB 不過，門檻約在 220-250 MB
5/13 兩支影片用 YouTube Data API 上傳實測：216 MB 的法律 MCP 示範片成功、257 MB 的 AI 求職示範片卡死。同樣的 googleapiclient resumable upload，差別在檔案大小跟解析度。門檻約在 220-250 MB 之間。
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MBA × AI Case Method—avoiding HBS licensing risk, finding open case materials, training seed instructors
2026-05 investigation into doing case-based AI teaching for business courses. HBS / HBR cases carry licensing risk. Maps the open alternatives (UBC Open Case Studies, OpenCaseStudies.org, World Bank), what's off-limits, and the direction of training MBA-background seed instructors.
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MBA × AI Case Method——避開 HBS 授權風險、找開源案例素材、訓練種子教師
想做商業 AI 課程的案例教學，但 HBS / HBR cases 有授權風險。這篇整理 2026-05 調查的開源案例來源（UBC Open Case Studies、OpenCaseStudies.org、World Bank）、不能用的清單，以及訓練 MBA 背景種子教師的方向。
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Three moves to firepower-demo Agentic AI at your company—desktop cleanup, conflicting meeting notes, multi-version emails
If you want to make your manager or coworkers feel Agentic AI in one second—and then open the door to an internal training engagement—my three go-to moves are cleaning up messy desktops, reconciling conflicting meeting notes and emails, and producing multi-version drafts for different recipients. These three hit office workers right in the pain.
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在公司火力展示 Agentic AI 的三招——桌面整理、會議紀錄、信件多版本
想在公司讓主管同事一秒有感、進而開內訓案的話，我自己最常用的三招是：整理桌面亂檔、整理相互衝突的會議紀錄跟信件、迅速生成不同版本的信件給主管同事客戶廠商。這三招對辦公室的人就夠有感了。
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Reflection after a 200-person online AI lecture—the energy of the classroom 10 years ago, back again
This morning I ran a 200-person free online AI lecture. So happy. 450+ signups, attendance around 45%, but landing on Mother's Day midday and still hitting that—Zoom data showed average seat-time over 75%, and FAQ ran so long I answered for an extra half hour.
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200 人線上 AI 講座結束後的感言——10 年前實體教室那種時光，又回來了
今天早上辦了一場 200 人的免費 AI 線上講座，好開心。報名人數 450+，出席率大概 45%，但是撞到母親節中午還能有這種成績，Zoom 數據拉下來發現留座時間平均超過 75%，FAQ 踴躍到多回答了半小時問題。
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Why Obsidian is something special to me, and why I only made this video now
I'd been wanting to make this video for a long time, but kept putting it off. Obsidian's flexibility is so high that it grows into a different shape in every person's hands. The payoff isn't immediate either—it's only after the vault has grown for a while that you realize you can't go back.
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Obsidian 為什麼是個特別的存在——我為什麼到現在才拍這部影片
這部影片我想做很久了，但是卻一直沒做，直到今天。Obsidian 的自由度太高，所以在每個人的手上會長成不同的樣子。他的成效又不是立刻就能看到的，而是越長越大你才會發現離不開它。
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Seven gotchas from running Resend and Gmail email workflows—HTML templates, scheduled_at, Cloudflare, open-rate alignment
Two weeks of heavy use across Resend batch sends and Gmail draft creation for course notifications, lecture thank-you emails, and BD outreach. Over 1,400 emails sent. Seven lessons worth writing down.
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Resend 跟 Gmail 寄信工作流踩坑七連——HTML 模板、scheduled_at、Cloudflare、開信率對齊
這兩週密集用 Resend 批次寄信跟 Gmail draft 兩條路徑做課程通知、講座感謝信、BD outreach。整理七個踩坑：HTML inline style、scheduled_at 不生效、Cloudflare 1010、Click tracking、bad-recipients、Gmail thread 漏看、客製 vs 通用開信率對齊。
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Covey's time management matrix, now with Claude Code—how each quadrant shifts
Covey's time management matrix from The 7 Habits of Highly Effective People splits time on two axes: important × urgent. Drop Claude Code into the picture and each quadrant's time allocation shifts. The long-term compounding from AI isn't in Q1, it's in Q2.
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Covey 七個習慣的時間矩陣放上 Claude Code 之後——每一格的時間分配怎麼變
Covey《高效能人士的七個習慣》的時間矩陣是兩軸切四象限，重要 × 緊急。把 Claude Code 放進去之後，每一格的時間分配會被重新洗牌。Q1/Q3 被 AI 吃掉，多出來的時間搬到 Q2 才是 AI 協作的長期複利。
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Two MCP servers for Taiwan public data—what mcp-taiwan-legal-db and Twinkle Hub are each good for
Two MCP servers wrapping Taiwan's public data sources—one focused on court judgments and statutes, the other aggregating 52,960 government open datasets plus 37 local utility tools. They turn out to be complementary; I now run both.
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台灣公開資料兩個 MCP 評測——mcp-taiwan-legal-db 跟 Twinkle Hub 各適合什麼場景
兩個串接台灣公開資料的 MCP server——一個專精法律判決跟法規查詢、一個聚合 5.3 萬筆政府開放資料 + 37 個在地工具。實測下來各有強項，課程跟顧問場景的搭配方式不一樣。
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What I only learned after taking on enterprise AI training—prep time, the client liaison, instructor flexibility, and how to pick a consultant
Four things I learned after running a handful of enterprise AI training sessions. Prep time is 3-5x the session itself. The client liaison decides everything. The instructor has to handle wildly different skill levels. And if you're looking to hire a trainer, the signals are like picking a pop star.
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親自接 AI 企業培訓之後才知道的事——準備時間、甲方對接、講師應變、選顧問訊號
接了幾場企業 AI 培訓之後整理出來的四件事：準備時間是上課時間的 3-5 倍以上、甲方對接者決定一切、講師要能應付各種程度跟提問、想找講師的話訊號跟追星很像。
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How to tune Opus 4.7's effort, and why I still keep non-coding tasks on Sonnet 4.6 medium
Opus 4.7's default adaptive effort is a disaster—it turns into a lazy emperor. You have to crank it up to high or xhigh to avoid that, but token consumption goes nuts. After testing, Sonnet 4.6 medium is the sweet spot for non-coding work.
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Opus 4.7 的 effort 怎麼調，跟為什麼我非編程任務還是停在 Sonnet 4.6 medium
Opus 4.7 預設的 adaptive effort 是場災難，會變成偷懶摸魚皇帝。要開到 high 或 xhigh 才能避免，但 token 就開始狂燒。實測下來，非編程任務停在 Sonnet 4.6 medium 反而是最好的選擇。
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Claude Code Desktop vs CLI right now—why I think beginners should just start with the desktop app
Two or three weeks ago Anthropic overhauled the desktop app. Over 90% of the features now match the CLI. If you're a beginner who's scared of the terminal, starting with the desktop app is fine.
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Claude Code 桌面版跟終端機現在差在哪——我為什麼覺得初學者直接從桌面版開始就好
兩三週前 Anthropic 把桌面 App 大翻新，90% 以上功能跟終端機同步。初學者對終端機有恐懼，現在從桌面版開始完全沒問題。
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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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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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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.