Posts
All the articles I've posted.
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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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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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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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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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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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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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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 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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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.