Shadowing Practice: Meta’s Llama 4 is mindblowing… but did it cheat? - Learn English Speaking with Video
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Over the weekend, Meta unleashed the Llamaphore Herd,
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its first open-weight, natively multimodal mixture of experts' family of large-language models with an unheard-of context window of 10 million tokens.
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It's currently sitting on top of the LM Arena leaderboard and outclasses every other proprietary model except for Gemini 2.5 Pro.
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That's incredibly impressive because on LM Arena, you can't game the benchmarks.
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The ranking is based on thousands of head-to-head chats where a real human picks the better conversation.
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Well, actually, Meta figured out a way to cheese the LM Arena
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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.
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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.
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Llama 4 looks amazing on paper, but for some reason, it's not passing the vibe check.
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And in today's video, we'll get to the bottom of it.
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It is April 8th, 2025, and you're watching the Code Report.
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I have some good news and I have some bad news.
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The good news is that it looks like Llama 4 isn't going to take your job anytime soon.
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But the bad news is that yesterday, an internal memo from the CEO of Shopify was leaked to the internet.
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And it was a shocker for the AI-doubting boomers out there, because it detailed Shopify's AI-first strategy.
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Before asking for more headcount and resources, teams must demonstrate why they cannot get the job done with AI.
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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.
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You're just not going to fit in with the Slopify culture.
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But what he said is what literally every CEO in the world is thinking right now.
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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.
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The memo has bad optics for Shopify, which is also being crushed by the Trump tariffs right now.
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But as a large language model, I appreciate the transparency.
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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.
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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,
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but the craziest thing is that Scout has a 10 million token context window.
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The only thing that comes remotely close is Gemini with 2 million tokens.
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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.
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The Scout is the smaller model and the medium-sized model Maverick only has a 1 million token context window.
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And the Behemoth model is still actively training.
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Generally speaking, people of the internet have been pretty disappointed with Llama 4's performance.
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I'm a strong believer in vibes over benchmarks, but Llama has done so well on the benchmarks
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that people have accused it of intentionally training on testing data for the benchmarks.
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Meta has denied these salacious, outrageous, and preposterous accusations.
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And despite being somewhat of a flop, let's not forget that these models are open.
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Not truly open source, but free for most of us to use.
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But if you want an AI agent that truly slaps, you need to check out Augment Code, the sponsor of today's video.
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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.
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Augment's context engine understands your team's entire codebase, allowing it to solve almost any task you throw at it.
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Like migrations and testing, all with best-in-class code quality.
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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,
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allowing you to solve complex jobs without the need to clean up a bunch of slop.
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Try out their developer plan for free, and you'll get access to all of Augment's features with unlimited usage.
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This has been The Code Report, thanks for watching, and I will see you in the next one.
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Vocabulary and speaking notes for this lesson
This video has 43 sentences and 808 words to shadow. The speech runs for 3:43. The speaker talks fast, about 217 words per minute, so expect linked and reduced sounds. Only 79% of the words are among the 3,000 most common in English, so the vocabulary is demanding.
Key vocabulary in this video
15 words from the video worth learning, with pronunciation and meaning:
| Word | Pronunciation | Meaning |
|---|---|---|
| benchmark noun | /ˈbɛn(t)ʃmɑːk/ | A standard by which something is evaluated or measured. |
| vibe noun | /vaɪb/ | An atmosphere or aura felt to belong to a person, place or thing. |
| token noun | /ˈtoʊkən/ | Something serving as an expression of something else. |
| impressive adjective | /ɪmˈpɹɛsɪv/ | Making, or tending to make, a positive impression; having power to impress. |
| solve verb | /sɒlv/ | To find an answer or solution to a problem or question; to work out. |
| tune noun | /tjuːn/ | A melody. |
| memo noun | /ˈmɛməʊ̯/ | A short note; a memorandum. |
| rank noun | /ˈɹæŋk/ | A row of people or things organized in a grid pattern, often soldiers. |
| disappointed adjective | /ˌdɪsəˈpɔɪnɪd/ | Defeated of expectations or hope; experiencing disappointment; let down. |
| resource noun | /ɹɪˈzɔːs/ | Something that one uses to achieve an objective, e.g. raw materials or personnel. |
| incredibly adverb | /ɪŋˈkɹɛdɪbli/ | In an incredible manner; not to be believed. |
| input noun | /ˈɪnpʊt/ | The act or process of putting in; infusion. |
| deny verb | /dəˈnaɪ/ | To disallow or reject. |
| toilet verb | /ˈtɔɪ.lət/ | To assist another (a child, etc.) in using a toilet. |
| requirement noun | /ɹɪˈkwaɪɹmənt/ | A necessity or prerequisite; something required or obligatory. Its adpositions are generally of in relation to who or what has given it, on in relation to whom… |
Phrasal verbs you will hear
| Word | Pronunciation | Meaning |
|---|---|---|
| check out verb | To record the departure or withdrawal of someone or something (such as guests, employees, books, etc.). | |
| clean up verb | To make an area or a thing clean; to pick up a mess; to tidy. | |
| come around verb | /kʌ.məˈɹaʊnd/ | To change one's mind, especially to begin to agree or appreciate what one was reluctant to accept at first. |
| come out verb | /ˌkʌm ˈaʊt/ | To be discovered; to be revealed. |
| figure out verb | To come to understand; to discover or find a solution; to deduce. | |
| try out verb | To test (something) to see how it works or whether it is suitable. |
Grammar in this video
The structures the speaker uses most, with the exact words from the video:
| Structure | In the video |
|---|---|
| Present perfect have/has + past participle — a past action that still matters now | that's been · have been · has done |
| Passive voice be + past participle — the focus is on what happens, not who does it | is based · was leaked · are numbered |
| Relative clauses who / which + clause — extra information about a person or thing | Shopify, which is · multimodal, which means |
Pronunciation to watch
The speaker uses 11 contractions and reduced forms, such as you're, can't, doesn't. Say them the short way, as you hear them.
- The “sh” and “zh” sounds: benchmark /ˈbɛn(t)ʃmɑːk/, interpretation /ɪnˌtɜːpɹəˈteɪʃən/, crush /kɹʌʃ/, migration /maɪˈɡɹeɪʃ(ə)n/, intentionally /ɪnˈtɛn(t)ʃənəli/
- Long words — get the stress right: disappointed /ˌdɪsəˈpɔɪnɪd/, incredibly /ɪŋˈkɹɛdɪbli/, interpretation /ɪnˌtɜːpɹəˈteɪʃən/, developer /dɪˈvɛləpɚ/, unlimited /ʌnˈlɪm.ɪ.tɪd/
How to practise with this video
- Listen to the whole video once without speaking and note the words you do not know.
- Start at 0.75× speed, shadow it sentence by sentence, then go back to normal speed once it feels easy.
- Record yourself and compare with the original, paying attention to words like benchmark, vibe, token.
What is the Shadowing Technique?
Shadowing is a science-backed language learning technique originally developed for professional interpreter training and popularized by polyglot Dr. Alexander Arguelles. The method is simple but powerful: you listen to native English audio and immediately repeat it out loud — like a shadow following the speaker with just a 1–2 second delay. Unlike passive listening or grammar drills, shadowing forces your brain and mouth muscles to simultaneously process and reproduce real speech patterns. Research shows it significantly improves pronunciation accuracy, intonation, rhythm, connected speech, listening comprehension, and speaking fluency — making it one of the most effective methods for IELTS Speaking preparation and real-world English communication.