Prática de Shadowing: Meta’s Llama 4 is mindblowing… but did it cheat? - Aprenda a falar inglês com vídeo
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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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Vocabulário e dicas de fala para esta lição
Este vídeo tem 43 frases e 808 palavras para praticar shadowing. A fala dura 3:43. O falante fala rápido, cerca de 217 palavras por minuto, então espere sons ligados e reduzidos. Apenas 79% das palavras estão entre as 3.000 mais comuns do inglês, por isso o vocabulário é exigente.
Vocabulário principal deste vídeo
15 palavras do vídeo que vale a pena aprender, com pronúncia e significado:
| Palavra | Pronúncia | Significado |
|---|---|---|
| benchmark substantivo | /ˈbɛn(t)ʃmɑːk/ | padrão |
| vibe substantivo | /vaɪb/ | vibração, astral |
| token substantivo | /ˈtoʊkən/ | símbolo, sinal |
| impressive adjetivo | /ɪmˈpɹɛsɪv/ | impressionante |
| solve verbo | /sɒlv/ | resolver, solucionar |
| tune substantivo | /tjuːn/ | melodia |
| memo substantivo | /ˈmɛməʊ̯/ | nota |
| rank substantivo | /ˈɹæŋk/ | fileira, coluna |
| disappointed adjetivo | /ˌdɪsəˈpɔɪnɪd/ | decepcionado, desapontado |
| resource substantivo | /ɹɪˈzɔːs/ | recurso, expediente |
| incredibly advérbio | /ɪŋˈkɹɛdɪbli/ | incrivelmente |
| deny verbo | /dəˈnaɪ/ | negar |
| requirement substantivo | /ɹɪˈkwaɪɹmənt/ | exigência, requisito |
| actively advérbio | /ˈæktɪvli/ | ativamente |
| complain verbo | /kəmˈpleɪn/ | reclamar, queixar-se |
Phrasal verbs que você vai ouvir
| Palavra | Pronúncia | Significado |
|---|---|---|
| check out verbo | verificar, sacar só | |
| clean up verbo | dar uma limpeza, limpar | |
| come around verbo | /kʌ.məˈɹaʊnd/ | mudar de ideia |
| come out verbo | /ˌkʌm ˈaʊt/ | sair, partir |
| figure out verbo | descobrir, deduzir | |
| try out verbo | provar, experimentar |
Gramática neste vídeo
As estruturas que o falante mais usa, com as palavras exatas do vídeo:
| Estrutura | No vídeo |
|---|---|
| Present perfect have/has + particípio passado — uma ação passada que ainda importa agora | that's been · have been · has done |
| Voz passiva be + particípio passado — o foco está no que acontece, não em quem faz | is based · was leaked · are numbered |
| Orações relativas who / which + oração — informação extra sobre uma pessoa ou coisa | Shopify, which is · multimodal, which means |
Pronúncia para ficar de olho
O falante usa 11 contrações e formas reduzidas, como you're, can't, doesn't. Diga-as na forma curta, do jeito que você ouve.
- Os sons de “sh” e “zh”: benchmark /ˈbɛn(t)ʃmɑːk/, interpretation /ɪnˌtɜːpɹəˈteɪʃən/, crush /kɹʌʃ/, migration /maɪˈɡɹeɪʃ(ə)n/, intentionally /ɪnˈtɛn(t)ʃənəli/
- Palavras longas — acerte a sílaba tônica: disappointed /ˌdɪsəˈpɔɪnɪd/, incredibly /ɪŋˈkɹɛdɪbli/, interpretation /ɪnˌtɜːpɹəˈteɪʃən/, developer /dɪˈvɛləpɚ/, unlimited /ʌnˈlɪm.ɪ.tɪd/
Como praticar com este vídeo
- Ouça o vídeo inteiro uma vez sem falar e anote as palavras que você não conhece.
- Comece na velocidade 0,75×, faça shadowing frase por frase e volte à velocidade normal quando ficar fácil.
- Grave a sua voz e compare com o original, prestando atenção a palavras como benchmark, vibe, token.
O que é a Técnica de Shadowing?
Shadowing é uma técnica de aprendizado de idiomas com base científica, originalmente desenvolvida para o treinamento de intérpretes profissionais. O método é simples, mas poderoso: você ouve áudio em inglês nativo e repete imediatamente em voz alta — como uma sombra seguindo o falante com 1-2 segundos de atraso. Pesquisas mostram melhora significativa na precisão da pronúncia, entonação, ritmo, sons conectados, compreensão auditiva e fluência na fala.