Практика Shadowing: Meta’s Llama 4 is mindblowing… but did it cheat? - Изучайте разговорный английский по видео
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43 предложений
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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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Лексика и советы по произношению к этому уроку
В этом видео 43 предложений и 808 слов для шедоуинга. Речь длится 3:43. Говорящий говорит быстро, около 217 слов в минуту, поэтому звуки часто сливаются и сокращаются. Только 79% слов входят в 3000 самых частых слов английского языка, поэтому лексика сложная.
Ключевая лексика этого видео
15 слов из видео, которые стоит выучить, с произношением и значением:
| Слово | Произношение | Значение |
|---|---|---|
| benchmark существительное | /ˈbɛn(t)ʃmɑːk/ | крите́рий, мери́ло |
| vibe существительное | /vaɪb/ | атмосфе́ра, а́ура |
| token существительное | /ˈtoʊkən/ | знак, си́мвол |
| impressive прилагательное | /ɪmˈpɹɛsɪv/ | впечатля́ющий, внуши́тельный |
| solve глагол | /sɒlv/ | реша́ть, реши́ть |
| tune существительное | /tjuːn/ | мело́дия, моти́в |
| memo существительное | /ˈmɛməʊ̯/ | запи́ска, па́мятка |
| rank существительное | /ˈɹæŋk/ | ряд, шере́нга |
| disappointed прилагательное | /ˌdɪsəˈpɔɪnɪd/ | разочаро́ванный |
| resource существительное | /ɹɪˈzɔːs/ | ресу́рс, запа́с |
| incredibly наречие | /ɪŋˈkɹɛdɪbli/ | невероя́тно |
| input существительное | /ˈɪnpʊt/ | вклад |
| deny глагол | /dəˈnaɪ/ | отрица́ть, отверга́ть |
| requirement существительное | /ɹɪˈkwaɪɹmənt/ | необходи́мость, тре́бование |
| actively наречие | /ˈæktɪvli/ | акти́вно |
Фразовые глаголы, которые вы услышите
| Слово | Произношение | Значение |
|---|---|---|
| check out глагол | проверя́ть, прове́рить | |
| clean up глагол | убира́ться, прибираться | |
| come around глагол | /kʌ.məˈɹaʊnd/ | переду́мать |
| come out глагол | /ˌkʌm ˈaʊt/ | выходи́ть, вы́йти |
| figure out глагол | разбира́ться, разобра́ться | |
| try out глагол | про́бовать, попро́бовать |
Грамматика в этом видео
Конструкции, которые говорящий использует чаще всего, с точными словами из видео:
| Конструкция | В видео |
|---|---|
| Present Perfect have/has + причастие прошедшего времени — прошлое действие, важное сейчас | that's been · have been · has done |
| Пассивный залог be + причастие прошедшего времени — важно, что происходит, а не кто это делает | is based · was leaked · are numbered |
| Придаточные определительные who / which + предложение — уточнение о человеке или предмете | Shopify, which is · multimodal, which means |
Произношение, на которое стоит обратить внимание
Говорящий использует 11 сокращённых и редуцированных форм, например you're, can't, doesn't. Произносите их коротко, так, как слышите.
- Звуки «sh» и «zh»: benchmark /ˈbɛn(t)ʃmɑːk/, interpretation /ɪnˌtɜːpɹəˈteɪʃən/, crush /kɹʌʃ/, migration /maɪˈɡɹeɪʃ(ə)n/, intentionally /ɪnˈtɛn(t)ʃənəli/
- Длинные слова — следите за ударением: disappointed /ˌdɪsəˈpɔɪnɪd/, incredibly /ɪŋˈkɹɛdɪbli/, interpretation /ɪnˌtɜːpɹəˈteɪʃən/, developer /dɪˈvɛləpɚ/, unlimited /ʌnˈlɪm.ɪ.tɪd/
Как заниматься с этим видео
- Прослушайте всё видео один раз молча и выпишите незнакомые слова.
- Начните со скорости 0,75×, повторяйте предложение за предложением и вернитесь к обычной скорости, когда станет легко.
- Запишите себя и сравните с оригиналом, обращая внимание на такие слова, как benchmark, vibe, token.
Что такое техника Shadowing?
Shadowing — это научно обоснованная техника изучения языка, изначально разработанная для подготовки профессиональных переводчиков и популяризированная полиглотом доктором Александром Аргуэльесом. Метод прост, но эффективен: вы слушаете аудио на английском от носителей языка и немедленно повторяете вслух — как тень, следующая за говорящим с задержкой в 1–2 секунды. В отличие от пассивного прослушивания или грамматических упражнений, Shadowing заставляет мозг и мышцы рта одновременно обрабатывать и воспроизводить реальные речевые паттерны. Исследования показывают, что это значительно улучшает точность произношения, интонацию, ритм, связную речь, понимание на слух и беглость речи — что делает его одним из самых эффективных методов для подготовки к IELTS Speaking и реального общения на английском.