シャドーイング練習: Meta’s Llama 4 is mindblowing… but did it cheat? - 動画で英語スピーキングを学ぶ

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Over the weekend, Meta unleashed the 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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このレッスンの語彙とスピーキングのポイント

この動画には、シャドーイング用の文が43文、単語が808語あります。音声の長さは3:43です。話す速さは速く、1分あたり約217語です。音のつながりや弱く発音される音が多くなります。英語の頻出3,000語に含まれる単語は79%だけなので、語彙は難しめです。

この動画の重要語彙

動画に出てくる覚えておきたい単語15語を、発音と意味つきで紹介します。

単語	発音	意味
benchmark 名詞	/ˈbɛn(t)ʃmɑːk/	水準点
vibe 名詞	/vaɪb/	氛囲気
token 名詞	/ˈtoʊkən/	トークン
impressive 形容詞	/ɪmˈpɹɛsɪv/	印象的な
solve 動詞	/sɒlv/	解決する
tune 名詞	/tjuːn/	曲, チューン
memo 名詞	/ˈmɛməʊ̯/	メモ
disappointed 形容詞	/ˌdɪsəˈpɔɪnɪd/	失望した
resource 名詞	/ɹɪˈzɔːs/	資源, 資産
deny 動詞	/dəˈnaɪ/	否む, 打ち消す
requirement 名詞	/ɹɪˈkwaɪɹmənt/	要求
complain 動詞	/kəmˈpleɪn/	ぼやく, ぶつぶつ言う
usage 名詞	/ˈjuːsɪd͡ʒ/	使い方
interpretation 名詞	/ɪnˌtɜːpɹəˈteɪʃən/	通訳
anytime 副詞		いつでも

動画に出てくる句動詞

単語	発音	意味
check out 動詞		試験する, 検査する
clean up 動詞		掃除する
come out 動詞	/ˌkʌm ˈaʊt/	出る
try out 動詞		試す, 試してみる

この動画の文法

話し手がよく使っている文型を、動画の実際の表現とともに紹介します。

文型	動画での表現
現在完了形 have/has + 過去分詞 — 過去の出来事が今も関係している	that's been · have been · has done
受動態 be + 過去分詞 — 誰がするかより、何が起きるかに焦点を当てる	is based · was leaked · being crushed
関係詞節 who / which + 節 — 人や物について情報を加える	Shopify, which is · multimodal, which means

注意したい発音

話し手はyou're, can't, doesn'tなど、短縮形や弱形を11回使っています。聞こえたとおりの短い形で発音しましょう。

「sh」と「zh」の音: benchmark /ˈbɛn(t)ʃmɑːk/, interpretation /ɪnˌtɜːpɹəˈteɪʃən/, crush /kɹʌʃ/, migration /maɪˈɡɹeɪʃ(ə)n/, intentionally /ɪnˈtɛn(t)ʃənəli/
長い単語（アクセントの位置に注意）: disappointed /ˌdɪsəˈpɔɪnɪd/, incredibly /ɪŋˈkɹɛdɪbli/, interpretation /ɪnˌtɜːpɹəˈteɪʃən/, developer /dɪˈvɛləpɚ/, unlimited /ʌnˈlɪm.ɪ.tɪd/

この動画での練習方法

まず声を出さずに動画を最後まで聞き、知らない単語をメモします。
まず0.75倍速で一文ずつシャドーイングし、慣れてきたら通常の速度に戻します。
自分の声を録音して元の音声と比べます。benchmark, vibe, tokenなどの単語に特に注意しましょう。

シャドーイングとは？英語上達に効果的な理由

シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。

シャドーイングのやり方: ステップ別の完全ガイドを読む →

シャドーイング練習: Meta’s Llama 4 is mindblowing… but did it cheat? - 動画で英語スピーキングを学ぶ

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