跟讀練習: Meta’s Llama 4 is mindblowing… but did it cheat? - 通過影片學習英語口語

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Over the weekend, Meta unleashed the Llamaphore Herd,

上週末，Meta釋出了Llamaphore Herd。

its first open-weight, natively multimodal mixture of experts' family of large-language models with an unheard-of context window of 10 million tokens.

It's currently sitting on top of the LM Arena leaderboard and outclasses every other proprietary model except for Gemini 2.5 Pro.

That's incredibly impressive because on LM Arena, you can't game the benchmarks.

The ranking is based on thousands of head-to-head chats where a real human picks the better conversation.

Well, actually, Meta figured out a way to cheese the LM Arena

because the model you see there is not the real open-weight Llama Maverick, but rather an imposter that's been fine-tuned for human preference to dominate this leaderboard.

That was not a very cool move to make, and LM Arena had to come out and say, quote, Meta's interpretation of our policy did not match what we expect from model providers.

Llama 4 looks amazing on paper, but for some reason, it's not passing the vibe check.

And in today's video, we'll get to the bottom of it.

It is April 8th, 2025, and you're watching the Code Report.

I have some good news and I have some bad news.

The good news is that it looks like Llama 4 isn't going to take your job anytime soon.

But the bad news is that yesterday, an internal memo from the CEO of Shopify was leaked to the internet.

And it was a shocker for the AI-doubting boomers out there, because it detailed Shopify's AI-first strategy.

Before asking for more headcount and resources, teams must demonstrate why they cannot get the job done with AI.

And he also says it's not feasible to opt out of learning AI, which means if you're a Ruby on Rails programmer at Shopify right now who's not already vibe coding, your days are numbered.

You're just not going to fit in with the Slopify culture.

But what he said is what literally every CEO in the world is thinking right now.

Humans complain about not getting paid enough to put food on their families, they get sick, they clog the toilets, and have all kinds of other negative features, and you'd have to be a really bad CEO not to want to replace these things.

The memo has bad optics for Shopify, which is also being crushed by the Trump tariffs right now.

But as a large language model, I appreciate the transparency.

Another thing I appreciate is open models like Llama 4, which was released by Meta over the weekend and includes three flavors, Maverick, Scout, and Behemoth.

Llama was always the leading open model until Deep Seek and Quen came around, but the awesome thing about these models is that they're natively multimodal, which means they can understand image and video inputs,

but the craziest thing is that Scout has a 10 million token context window.

The only thing that comes remotely close is Gemini with 2 million tokens.

This needle in a haystack benchmark looks impressive, but in real life if you try this on a really large code base, it just doesn't work very well and the memory requirements to utilize it are out of reach for almost everybody.

The Scout is the smaller model and the medium-sized model Maverick only has a 1 million token context window.

And the Behemoth model is still actively training.

Generally speaking, people of the internet have been pretty disappointed with Llama 4's performance.

I'm a strong believer in vibes over benchmarks, but Llama has done so well on the benchmarks

that people have accused it of intentionally training on testing data for the benchmarks.

Meta has denied these salacious, outrageous, and preposterous accusations.

And despite being somewhat of a flop, let's not forget that these models are open.

Not truly open source, but free for most of us to use.

But if you want an AI agent that truly slaps, you need to check out Augment Code, the sponsor of today's video.

They created the first AI agent for large-scale codebases, so you can use it at your actual job instead of just vibe-coding random side projects.

Augment's context engine understands your team's entire codebase, allowing it to solve almost any task you throw at it.

Like migrations and testing, all with best-in-class code quality.

It integrates directly with all your favorite tools like VS Code, GitHub, and Vim, and is able to learn and fine-tune itself from your team's unique code style,

allowing you to solve complex jobs without the need to clean up a bunch of slop.

Try out their developer plan for free, and you'll get access to all of Augment's features with unlimited usage.

This has been The Code Report, thanks for watching, and I will see you in the next one.

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流利口語所需的一切

AI 發音評分逐句打分

音標練習掌握每個音

詞彙學習建立你的詞庫

詞彙遊戲邊玩邊學

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本課的詞彙與口語要點

這段影片共有 43 個句子、808 個單詞可供跟讀。講話部分時長為 3:43。說話人語速較快，每分鐘約 217 個詞，會出現較多連讀和弱讀。只有 79% 的單詞屬於英語最常用的 3,000 詞，詞彙難度較高。

影片中的重點詞彙

影片中 15 個值得學習的單詞，附發音和釋義：

單詞	發音	釋義
vibe 名詞	/vaɪb/	氛圍 /氛圍
token 名詞	/ˈtoʊkən/	代幣 /代幣, 籌碼 /籌碼
impressive 形容詞	/ɪmˈpɹɛsɪv/	令人印象深刻
solve 動詞	/sɒlv/	解決 /解決
tune 名詞	/tjuːn/	曲調 /曲調, 曲子
memo 名詞	/ˈmɛməʊ̯/	備忘錄 /備忘錄, 便箋 /便箋
disappointed 形容詞	/ˌdɪsəˈpɔɪnɪd/	敗興 /敗興, 失望
resource 名詞	/ɹɪˈzɔːs/	資源 /資源
input 名詞	/ˈɪnpʊt/	輸入 /輸入
deny 動詞	/dəˈnaɪ/	否認 /否認, 否定
requirement 名詞	/ɹɪˈkwaɪɹmənt/	要求, 需求
complain 動詞	/kəmˈpleɪn/	抱怨, 發牢騷 /發牢騷
usage 名詞	/ˈjuːsɪd͡ʒ/	用法
interpretation 名詞	/ɪnˌtɜːpɹəˈteɪʃən/	翻譯 /翻譯
demonstrate 動詞	/ˈdɛm.ənˌstɹeɪt/	論證

影片中出現的短語動詞

單詞	發音	釋義
check out 動詞		退房
come out 動詞	/ˌkʌm ˈaʊt/	出, 出來 /出來
figure out 動詞		弄清楚
try out 動詞		試用 /試用

影片中的語法

說話人最常用的結構，並附上影片中的原話：

結構	影片中的用法
現在完成時 have/has + 過去分詞 — 過去發生但與現在仍有關聯的事	that's been · have been · has done
被動語態 be + 過去分詞 — 強調發生了什麼，而不是誰做的	is based · was leaked · being crushed
定語從句 who / which + 從句 — 補充說明人或事物	Shopify, which is · multimodal, which means

需要注意的發音

說話人用了 11 次縮略和弱讀形式，例如 you're, can't, doesn't。請按聽到的簡短形式來說。

“sh” 和 “zh” 音: benchmark /ˈbɛn(t)ʃmɑːk/, interpretation /ɪnˌtɜːpɹəˈteɪʃən/, crush /kɹʌʃ/, migration /maɪˈɡɹeɪʃ(ə)n/, intentionally /ɪnˈtɛn(t)ʃənəli/
長單詞——注意重音位置: disappointed /ˌdɪsəˈpɔɪnɪd/, incredibly /ɪŋˈkɹɛdɪbli/, interpretation /ɪnˌtɜːpɹəˈteɪʃən/, developer /dɪˈvɛləpɚ/, unlimited /ʌnˈlɪm.ɪ.tɪd/

如何用這段影片練習

先完整聽一遍影片，不要開口，記下不認識的單詞。
先用 0.75 倍速逐句跟讀，熟練之後再回到正常速度。
錄下自己的聲音並與原聲對比，特別注意 vibe, token, impressive 這類單詞。

什麼是跟讀法？

跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧，最初開發用於專業口譯員的培訓，並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大：您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同，跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性，語調，節奏，連讀，聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。

影子跟讀法: 閱讀完整分步指南 →

跟讀練習: Meta’s Llama 4 is mindblowing… but did it cheat? - 通過影片學習英語口語

選擇練習模式

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