Pratica di Shadowing: Meta’s Llama 4 is mindblowing… but did it cheat? - Impara a parlare inglese con i video
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Over the weekend, Meta unleashed the Llamaphore Herd,
Nel fine settimana, Meta ha lanciato il 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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Vocabolario e note di pronuncia per questa lezione
Questo video contiene 43 frasi e 808 parole da ripetere con lo shadowing. Il parlato dura 3:43. Chi parla va veloce, circa 217 parole al minuto: aspettati suoni legati e ridotti. Solo il 79% delle parole rientra nelle 3.000 più comuni dell’inglese, quindi il lessico è impegnativo.
Vocaboli chiave di questo video
15 parole del video che vale la pena imparare, con pronuncia e significato:
| Parola | Pronuncia | Significato |
|---|---|---|
| benchmark sostantivo | /ˈbɛn(t)ʃmɑːk/ | riferimento, termine di paragone |
| vibe sostantivo | /vaɪb/ | atmosfera |
| token sostantivo | /ˈtoʊkən/ | simbolo, segno |
| impressive aggettivo | /ɪmˈpɹɛsɪv/ | impressionante, spettacolare |
| solve verbo | /sɒlv/ | risolvere |
| tune sostantivo | /tjuːn/ | melodia |
| rank sostantivo | /ˈɹæŋk/ | traversa |
| disappointed aggettivo | /ˌdɪsəˈpɔɪnɪd/ | deluso |
| resource sostantivo | /ɹɪˈzɔːs/ | risorsa |
| incredibly avverbio | /ɪŋˈkɹɛdɪbli/ | incredibilmente |
| input sostantivo | /ˈɪnpʊt/ | immissione |
| deny verbo | /dəˈnaɪ/ | negare |
| requirement sostantivo | /ɹɪˈkwaɪɹmənt/ | requisito |
| actively avverbio | /ˈæktɪvli/ | attivamente |
| complain verbo | /kəmˈpleɪn/ | lamentarsi, lagnarsi |
I phrasal verb che sentirai
| Parola | Pronuncia | Significato |
|---|---|---|
| check out verbo | investigare | |
| clean up verbo | ripulire | |
| come around verbo | /kʌ.məˈɹaʊnd/ | ripensarsi, tornare in sé |
| figure out verbo | scoprire, rendersi conto | |
| try out verbo | saggiare, collaudare |
La grammatica di questo video
Le strutture che chi parla usa di più, con le parole esatte del video:
| Struttura | Nel video |
|---|---|
| Present perfect have/has + participio passato — un’azione passata che conta ancora adesso | that's been · have been · has done |
| Forma passiva be + participio passato — conta ciò che accade, non chi lo fa | is based · was leaked · are numbered |
| Frasi relative who / which + frase — un’informazione in più su una persona o una cosa | Shopify, which is · multimodal, which means |
Pronuncia a cui fare attenzione
Chi parla usa 11 contrazioni e forme ridotte, come you're, can't, doesn't. Pronunciale nella forma breve, così come le senti.
- I suoni “sh” e “zh”: benchmark /ˈbɛn(t)ʃmɑːk/, interpretation /ɪnˌtɜːpɹəˈteɪʃən/, crush /kɹʌʃ/, migration /maɪˈɡɹeɪʃ(ə)n/, intentionally /ɪnˈtɛn(t)ʃənəli/
- Parole lunghe — attenzione all’accento: disappointed /ˌdɪsəˈpɔɪnɪd/, incredibly /ɪŋˈkɹɛdɪbli/, interpretation /ɪnˌtɜːpɹəˈteɪʃən/, developer /dɪˈvɛləpɚ/, unlimited /ʌnˈlɪm.ɪ.tɪd/
Come esercitarsi con questo video
- Ascolta tutto il video una volta senza parlare e annota le parole che non conosci.
- Inizia a velocità 0,75×, fai shadowing frase per frase e torna alla velocità normale quando diventa facile.
- Registrati e confronta con l’originale, facendo attenzione a parole come benchmark, vibe, token.
Cos'è la tecnica dello Shadowing?
Shadowing è una tecnica di apprendimento delle lingue supportata da studi scientifici, originariamente sviluppata per la formazione dei traduttori professionisti e resa popolare dal poliglotta Dr. Alexander Arguelles. Il metodo è semplice ma potente: ascolti un audio in inglese di madrelingua e lo ripeti immediatamente ad alta voce — come un'ombra che segue il parlante con un ritardo di solo 1–2 secondi. A differenza dell'ascolto passivo o degli esercizi di grammatica, lo shadowing costringe il tuo cervello e i muscoli della bocca a elaborare e riprodurre simultaneamente i modelli di discorso reale. La ricerca dimostra che migliora significativamente la precisione della pronuncia, l'intonazione, il ritmo, il discorso connesso, la comprensione dell'ascolto e la fluidità del parlato — rendendolo uno dei metodi più efficaci per la preparazione alla prova di speaking dell'IELTS e per la comunicazione reale in inglese.