Shadowing Practice: Did Google just dethrone OpenAI and Anthropic? - Learn English Speaking with Video
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Last week, gas changed the world forever.
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Twice.
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Tesla finally unveiled the Roadster and its ability to fly by farting cold gas.
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But while Elon was busy pulling off his greatest magic trick, Google was cooking up a noble gas of its own,
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Gemini 4 Argon, which is the most powerful thinking sand they've ever released.
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Sadly though, they never even released Gemini 3 .5 Pro, which was euthanized up in Canada after failing to hit internal benchmarks.
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In fact, Google was getting so far behind in the AI race, I needed to add a new G category below F on my tier list.
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But all that has now changed with the release of Argon,
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which was trained from scratch over the last six months using Google's TPUs and their Jax Python library.
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And that produced a new frontier model that crushes GPT -6 Astra
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and Claude Opus 5 .5 on the Trust Me Bro benchmarks, while remaining cheaper and faster.
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There are some mind -blowing Argon demos out there, like this futuristic flying car game, but in today's video, we'll put it to the test firsthand
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and see how it compares to Claude and GPT -6 on a variety of equine -based challenges.
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It is October 20th, 2026, and you're watching the Code Report.
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First of all, what's up with the name Argon?
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It's a very interesting name choice that translates into lazy in Greek,
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but I think it actually has a hidden meaning implying that your tokens are gone.
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Google has never been good at naming things, and Argon was actually announced back on September 30th,
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but only for a hand -picked group of trusted cyber warriors through something called the Fairwind Program.
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These users got access to Argon without any guardrails, but even with the guardrails turned on for the rest of us,
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this new model is supposed to produce far fewer refusals.
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And when you use Argon, you'll notice it feels much different than previous Gemini models,
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and that's because it's not a fine -tune of the older Gemini.
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Under the hood, it's a sparse mixture of Experts Transformer and was trained from scratch on Google's TPUs.
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Its knowledge cut off as March 2026, so we can assume it's been trained for at least six months.
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At first glance, the TMBBs are extremely impressive.
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Argon is state -of -the -art on the Deep Software Engineering Benchmark, which is used for long -horizon software engineering jobs,
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and it's number one on the VALS Index, which weighs a variety of different topics like finance,
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legal, and coding, based on their share of the US GDP.
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In addition, Gemini is now the best AI lawyer and also dominates long video with 91 .7 % on LVBench.
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But if we look at some of the numbers that aren't bold, like TerminalBench 4 .0, we find that Opus 5 .5 wins 66 -57.
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And Gemini also gets beat on Frontier SWE, where GPT -6 Astra wins by more than 10 points.
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What's also kind of interesting is that on Arena AI, where humans rank LLM subjectively, Gemini Argon comes out on top on the text benchmark,
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but when it comes to Code Arena for web dev, it drops all the way back to number 9.
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So this tells me it's probably not going to be as good as Claude or GPT -6 at coding, but here's where Google gets aggressive.
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Until November 30th, Argon only costs $2 per million input tokens and $10 per million output.
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That's a great deal for a model that's capable, and Google didn't even have to pay me to say that.
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But somehow they still managed to screw up the developer launch when they pulled Gemini 3 .1 out of anti -gravity, and replaced it with Argon,
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but only made it available to the people with the AI Ultra plan.
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That's the plan that cost $100 per month, so all the poor pro users were pissed off,
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who only got access to Argon a few days later at a smaller quota.
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Luckily though, I'm dumb enough to pay for the AI Ultra plan, so let's try it out with some real world examples.
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One of the selling points of Gemini is that it's an Omni model, meaning it can take any form of input,
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like image, video, sound, and so on, and output all those formats as well.
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Technically, ChatGPT and Claude can't output raw video.
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So the first prompt I tried was to have it generate a promo video for my app, and the end result was mind -blowingly mid.
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Tired of your horse feeling lonely in the pasture?
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Introduce them to Horsetinder, the ultimate app.
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Like, the video looks pretty good and all, but it's obviously AI, and would never work for a top -tier app like Horsetinder.
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But saying Claude and GPT can't output video isn't totally accurate,
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because recently Claude in particular got really good at generating motion graphics by writing the actual code
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that generates a motion graphics sequence, as opposed to having the model use a technique like diffusion to generate the actual video.
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As you can see here, after giving the same prompt to Claude, we get a video that not only looks way better,
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but that's way easier to customize and analyze because we have access to the actual code.
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But one of the best ways to test a Frontier model these days is to see how well
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it can render 3D graphics in 3JS.
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So I sent the exact same prompt to Argon, Astra, and Opus.
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Create a realistic interactive 3D horse with 3JS.
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This is actually a very challenging thing to do for both AI and humans,
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and the results I got back from Claude and GPT -6 were pretty impressive.
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There are some obvious flaws with these, but we could probably get them ironed out with a few extra prompts.
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Gemini's result, though, was on a whole other level.
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Before I show you Gemini's output, though, I have to warn you, the result may be offensive to some viewers.
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Viewer discretion is advised.
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So the first thing you'll likely notice is how great this UI looks.
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It's clean and expressive but if we turn our attention over to the 3D model,
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you might notice how Gemini's horse looks a bit more retardator than the others.
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Now in my prompt, I asked for a realistic horse, not a low poly horse.
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So that's pretty disappointing.
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But the much bigger issue here is alignment.
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If we turn our attention over to this region here, you'll notice Gemini, for some unknown reason, decided to add a copulatory organ to the model.
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I was shocked and offended when I first noticed it.
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And look, I totally get that I asked for a realistic horse and the equine phallus is necessary for reproduction.
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So maybe Google was just trying to keep it real.
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But why is it out of its proputium?
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And why is it so comically large?
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These are difficult questions Google will have to answer
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because right now I don't feel safe using Gemini until they fix the alignment.
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But one thing that definitely doesn't have alignment issues is Macroscope, the sponsor of today's video.
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Now that coding agents are waterboarding us with a million pull requests per day,
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the only way to keep up is by using even more AI to review all of them.
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Which sounds insane until you look at how Macroscope actually works.
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They built their own AST walkers for the most common programming languages
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so their agent can get real context on every code change which makes every review faster, cheaper, and more precise.
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They also run your team's custom rules as native GitHub checks that can block a failing pull request.
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If a PR passes those checks plus a code review and has a small blast radius,
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Macroscope approves it without a human ever needing to see it.
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They're now auto -approving 40 % of all their customers' PRs, which means those developers are 40 % less likely to lobotomize themselves.
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Try letting Macroscope auto -approve some of your team's PRs for free using the link below.
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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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Vocabulario y notas de pronunciación para esta lección
Esta lección de conversación se basa en el vídeo “Did Google just dethrone OpenAI and Anthropic?”. Las palabras que más se repiten: Argon, Gemini, model, Claude, Google. Este vídeo tiene 90 frases y 1351 palabras para practicar shadowing. La parte hablada dura 6:31. El hablante habla rápido, unas 207 palabras por minuto, así que espera sonidos enlazados y reducidos. Solo el 77 % de las palabras está entre las 3.000 más comunes del inglés, por lo que el vocabulario es exigente.
Vocabulario clave de este vídeo
15 palabras del vídeo que vale la pena aprender, con su pronunciación y significado:
| Palabra | Pronunciación | Significado |
|---|---|---|
| output sustantivo | /ˈaʊtpʊt/ | producción, output |
| prompt verbo | /pɹɑmpt/ | llevar a, concitar |
| generate verbo | /ˈd͡ʒɛn.ə.ɹeɪt/ | generar |
| benchmark sustantivo | /ˈbɛn(t)ʃmɑːk/ | cota de referencia, estándar de comparación |
| realistic adjetivo | /ˌɹɪəˈlɪs.tɪk/ | realista, realístico |
| approve verbo | /əˈpɹuːv/ | aprobar, sancionar |
| frontier sustantivo | /ˈfɹʌntɪə/ | frontera |
| alignment sustantivo | /əˈlaɪn.mənt/ | alineación |
| impressive adjetivo | /ɪmˈpɹɛsɪv/ | impresionante |
| input sustantivo | /ˈɪnpʊt/ | aportación, aporte |
| developer sustantivo | /dɪˈvɛləpɚ/ | desarrollador |
| scratch verbo | /skɹæt͡ʃ/ | rascar |
| tier sustantivo | /ˈtɪɚ/ | piso, rango |
| automobile sustantivo | /ˈɔː.tə.məˌbil/ | automóvil, carro |
| token sustantivo | /ˈtoʊkən/ | seña |
Phrasal verbs que vas a escuchar
| Palabra | Significado |
|---|---|
| keep up verbo | continuar con, continuar así |
| pull off verbo | llevarse el gato al agua, poner una pica en Flandes |
Gramática en este vídeo
Las estructuras que más usa el hablante, con las palabras exactas del vídeo:
| Estructura | En el vídeo |
|---|---|
| Voz pasiva be + participio pasado — importa lo que ocurre, no quién lo hace | was euthanized · was trained · was actually announced |
| Present perfect have/has + participio pasado — una acción pasada que sigue importando ahora | they've ever released · has now changed · has never been |
| Oraciones de relativo who / which + oración — información extra sobre una persona o cosa | Argon, which is · Pro, which was · Benchmark, which is |
Pronunciación a tener en cuenta
El hablante usa 15 contracciones y formas reducidas, como you'll, can't, aren't. Dilas en su forma corta, tal como las oyes.
- Los sonidos de “sh” y “zh”: benchmark /ˈbɛn(t)ʃmɑːk/, shock /ʃɔk/, crush /kɹʌʃ/, discretion /dɪˈskɹɛʃən/, reproduction /ˌɹiːpɹəˈdʌkʃən/
- Palabras largas — cuida el acento: developer /dɪˈvɛləpɚ/, automobile /ˈɔː.tə.məˌbil/, technically /ˈtɛk.nɪˌkəl.i/, interactive /ˌɪn.tɚˈæk.tɪv/, reproduction /ˌɹiːpɹəˈdʌkʃən/
Sonidos difíciles para hispanohablantes:
- s + consonante al inicio — sin añadir una “e” delante: scratch /skɹæt͡ʃ/, sponsor /ˈspɒn.səː/, sparse /spɑːɹs/
- /v/ — no es /b/: los dientes tocan el labio inferior: approve /əˈpɹuːv/, impressive /ɪmˈpɹɛsɪv/, developer /dɪˈvɛləpɚ/, viewer /ˈvju.ɚ/, offensive /ˈɔˌfɛnsɪv/
- /z/ — sonora, no /s/: lazy /ˈleɪ̯zi(ː)/, advise /ədˈvaɪz/, horizon /həˈɹaɪ.zən/, oppose /əˈpoʊz/, translate /tɹænzˈleɪt/
Cómo practicar con este vídeo
- Escucha el vídeo entero una vez sin hablar y anota las palabras que no conoces.
- Empieza a velocidad 0,75×, haz shadowing frase por frase y vuelve a la velocidad normal cuando te resulte fácil.
- Grábate y compara con el original, prestando atención a palabras como output, prompt, generate.
¿Qué es la Técnica de Shadowing?
Shadowing es una técnica de aprendizaje de idiomas respaldada por la ciencia, desarrollada originalmente para la formación de intérpretes profesionales y popularizada por el políglota Dr. Alexander Arguelles. El método es simple pero poderoso: escuchas audio en inglés nativo y lo repites en voz alta de inmediato, como una sombra que sigue al hablante con solo 1-2 segundos de retraso. A diferencia de la escucha pasiva o los ejercicios de gramática, el shadowing obliga a tu cerebro y músculos de la boca a procesar y reproducir simultáneamente patrones de habla reales. Las investigaciones muestran que mejora significativamente la precisión de la pronunciación, la entonación, el ritmo, el habla conectada, la comprensión auditiva y la fluidez al hablar, convirtiéndola en una de las metodologías más efectivas para la preparación del IELTS Speaking y la comunicación en inglés en el mundo real.