Always in the middle of something.

叫 AI 數 strawberry 有幾個 r,它曾經很有自信地答錯。新模型現在大多答對了,但它當初為什麼會錯——用一個積木的比喻聊聊,順便講為什麼那個原因到現在還沒真的消失。

AI's most dangerous trait isn't that it's wrong sometimes. It's that its tone when wrong is identical to its tone when right. Here's my plain-language take on why, including why it won't just say 'I don't know'.

Not a benchmark or a verdict on which AI is best — just the small habits I picked up from keeping ChatGPT, Claude, and Gemini all open: route by task, give context first, don't expect one perfect answer, and verify the confident-sounding stuff.

AI 最會唬人的地方，不是它會錯，是它錯的時候那個口氣跟講對的時候完全一樣。用『它一直在猜下一個最順的字』這個角度，白話聊聊為什麼篤定不等於知道。

沒什麼大道理，就是同時用 ChatGPT、Gemini、Claude 一陣子之後，自己順手摸出來的幾個小習慣。不同事丟不同家、先講清楚再問、別期待一次到位這類的。

GSM8k 99%、MMLU 90 出頭、HLE 在 2026 年中已進入 40 分檔。每出一份『更難的 benchmark』看起來都在解決問題，但結構性的事沒變：我們從來沒在驗證模型學會了什麼，只是在量它有沒有看過。

GSM8k at 99%, MMLU at the 88-94% noise band, HLE already in the mid-40s by mid-2026. Each round of harder benchmarks looks like progress, but the field never solved the underlying problem: we measure correlation with a test distribution and call it capability.

A relaxed take on Python list comprehensions: translate them back into the equivalent for-loop, and check what's actually true about variable leaking and speed on Python 3.14.

用比較白話的方式聊 Python 列表推導式：把它翻回普通的 for 迴圈來看，順便用 Python 3.14 實測一下變數外洩跟效能到底是怎樣。

Your first Claude Code skill won't look like the polished examples in tutorials. It'll look like a prompt you've typed three times in a row, saved into a four-line markdown file. This post walks that minimum shape, shows the three things that break, and compares it to a real seventeen-line production-grade skill from the framework I use daily.