跟读练习: Meta’s Llama 4 is mindblowing… but did it cheat? - 通过视频学习英语口语

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Over the weekend, Meta unleashed the Llamaphore Herd,

上周末，Meta发布了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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音标练习掌握每个音

词汇学习建立你的词库

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FOR YOU

本课的词汇与口语要点

这段视频共有 43 个句子、808 个单词可供跟读。讲话部分时长为 3:43。说话人语速较快，每分钟约 217 个词，会出现较多连读和弱读。只有 79% 的单词属于英语最常用的 3,000 词，词汇难度较高。

视频中的重点词汇

视频中 15 个值得学习的单词，附发音和释义：

单词	发音	释义
vibe 名词	/vaɪb/	氛圍 /氛围
token 名词	/ˈtoʊkən/	代幣 /代币, 籌碼 /筹码
impressive 形容词	/ɪmˈpɹɛsɪv/	令人印象深刻
solve 动词	/sɒlv/	解決 /解决
tune 名词	/tjuːn/	曲調 /曲调, 曲子
memo 名词	/ˈmɛməʊ̯/	備忘錄 /备忘录, 便箋 /便笺
disappointed 形容词	/ˌdɪsəˈpɔɪnɪd/	敗興 /败兴, 失望
resource 名词	/ɹɪˈzɔːs/	資源 /资源
input 名词	/ˈɪnpʊt/	輸入 /输入
deny 动词	/dəˈnaɪ/	否認 /否认, 否定
requirement 名词	/ɹɪˈkwaɪɹmənt/	要求, 需求
complain 动词	/kəmˈpleɪn/	抱怨, 發牢騷 /发牢骚
usage 名词	/ˈjuːsɪd͡ʒ/	用法
interpretation 名词	/ɪnˌtɜːpɹəˈteɪʃən/	翻譯 /翻译
demonstrate 动词	/ˈdɛm.ənˌstɹeɪt/	论证

视频中出现的短语动词

单词	发音	释义
check out 动词		退房
come out 动词	/ˌkʌm ˈaʊt/	出, 出來 /出来
figure out 动词		弄清楚
try out 动词		試用 /试用

视频中的语法

说话人最常用的结构，并附上视频中的原话：

结构	视频中的用法
现在完成时 have/has + 过去分词 — 过去发生但与现在仍有关联的事	that's been · have been · has done
被动语态 be + 过去分词 — 强调发生了什么，而不是谁做的	is based · was leaked · being crushed
定语从句 who / which + 从句 — 补充说明人或事物	Shopify, which is · multimodal, which means

需要注意的发音

说话人用了 11 次缩略和弱读形式，例如 you're, can't, doesn't。请按听到的简短形式来说。

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

如何用这段视频练习

先完整听一遍视频，不要开口，记下不认识的单词。
先用 0.75 倍速逐句跟读，熟练之后再回到正常速度。
录下自己的声音并与原声对比，特别注意 vibe, token, impressive 这类单词。

什么是跟读法？

跟读法 (Shadowing) 是一种有科学依据的语言学习技巧，最初开发用于专业口译员的培训，并由多语言者Alexander Arguelles博士普及。这个方法简单而强大：您在听英语母语原声的同时立即大声重复——就像是一个延迟1-2秒紧跟说话者的影子。与被动听力或语法练习不同，跟读法强迫您的大脑和口腔肌肉同时处理并模仿真实的讲话模式。研究表明它能显着提高发音准确性，语调，节奏，连读，听力理解和口语流利度——使其成为雅思口语备考和真实英语交流最有效的方法之一。

影子跟读法: 阅读完整分步指南 →

跟读练习: Meta’s Llama 4 is mindblowing… but did it cheat? - 通过视频学习英语口语

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