Практика Shadowing: Did Google just dethrone OpenAI and Anthropic? - Изучайте разговорный английский по видео

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Last week, gas changed the world forever.

Twice.

Tesla finally unveiled the Roadster and its ability to fly by farting cold gas.

But while Elon was busy pulling off his greatest magic trick, Google was cooking up a noble gas of its own,

Gemini 4 Argon, which is the most powerful thinking sand they've ever released.

Sadly though, they never even released Gemini 3 .5 Pro, which was euthanized up in Canada after failing to hit internal benchmarks.

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.

But all that has now changed with the release of Argon,

which was trained from scratch over the last six months using Google's TPUs and their Jax Python library.

And that produced a new frontier model that crushes GPT -6 Astra

and Claude Opus 5 .5 on the Trust Me Bro benchmarks, while remaining cheaper and faster.

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

and see how it compares to Claude and GPT -6 on a variety of equine -based challenges.

It is October 20th, 2026, and you're watching the Code Report.

First of all, what's up with the name Argon?

It's a very interesting name choice that translates into lazy in Greek,

but I think it actually has a hidden meaning implying that your tokens are gone.

Google has never been good at naming things, and Argon was actually announced back on September 30th,

but only for a hand -picked group of trusted cyber warriors through something called the Fairwind Program.

These users got access to Argon without any guardrails, but even with the guardrails turned on for the rest of us,

this new model is supposed to produce far fewer refusals.

And when you use Argon, you'll notice it feels much different than previous Gemini models,

and that's because it's not a fine -tune of the older Gemini.

Under the hood, it's a sparse mixture of Experts Transformer and was trained from scratch on Google's TPUs.

Its knowledge cut off as March 2026, so we can assume it's been trained for at least six months.

At first glance, the TMBBs are extremely impressive.

Argon is state -of -the -art on the Deep Software Engineering Benchmark, which is used for long -horizon software engineering jobs,

and it's number one on the VALS Index, which weighs a variety of different topics like finance,

legal, and coding, based on their share of the US GDP.

In addition, Gemini is now the best AI lawyer and also dominates long video with 91 .7 % on LVBench.

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.

And Gemini also gets beat on Frontier SWE, where GPT -6 Astra wins by more than 10 points.

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,

but when it comes to Code Arena for web dev, it drops all the way back to number 9.

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.

Until November 30th, Argon only costs $2 per million input tokens and $10 per million output.

That's a great deal for a model that's capable, and Google didn't even have to pay me to say that.

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,

but only made it available to the people with the AI Ultra plan.

That's the plan that cost $100 per month, so all the poor pro users were pissed off,

who only got access to Argon a few days later at a smaller quota.

Luckily though, I'm dumb enough to pay for the AI Ultra plan, so let's try it out with some real world examples.

One of the selling points of Gemini is that it's an Omni model, meaning it can take any form of input,

like image, video, sound, and so on, and output all those formats as well.

Technically, ChatGPT and Claude can't output raw video.

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.

Tired of your horse feeling lonely in the pasture?

Introduce them to Horsetinder, the ultimate app.

Like, the video looks pretty good and all, but it's obviously AI, and would never work for a top -tier app like Horsetinder.

But saying Claude and GPT can't output video isn't totally accurate,

because recently Claude in particular got really good at generating motion graphics by writing the actual code

that generates a motion graphics sequence, as opposed to having the model use a technique like diffusion to generate the actual video.

As you can see here, after giving the same prompt to Claude, we get a video that not only looks way better,

but that's way easier to customize and analyze because we have access to the actual code.

But one of the best ways to test a Frontier model these days is to see how well

it can render 3D graphics in 3JS.

So I sent the exact same prompt to Argon, Astra, and Opus.

Create a realistic interactive 3D horse with 3JS.

This is actually a very challenging thing to do for both AI and humans,

and the results I got back from Claude and GPT -6 were pretty impressive.

There are some obvious flaws with these, but we could probably get them ironed out with a few extra prompts.

Gemini's result, though, was on a whole other level.

Before I show you Gemini's output, though, I have to warn you, the result may be offensive to some viewers.

Viewer discretion is advised.

So the first thing you'll likely notice is how great this UI looks.

It's clean and expressive but if we turn our attention over to the 3D model,

you might notice how Gemini's horse looks a bit more retardator than the others.

Now in my prompt, I asked for a realistic horse, not a low poly horse.

So that's pretty disappointing.

But the much bigger issue here is alignment.

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.

I was shocked and offended when I first noticed it.

And look, I totally get that I asked for a realistic horse and the equine phallus is necessary for reproduction.

So maybe Google was just trying to keep it real.

But why is it out of its proputium?

And why is it so comically large?

These are difficult questions Google will have to answer

because right now I don't feel safe using Gemini until they fix the alignment.

But one thing that definitely doesn't have alignment issues is Macroscope, the sponsor of today's video.

Now that coding agents are waterboarding us with a million pull requests per day,

the only way to keep up is by using even more AI to review all of them.

Which sounds insane until you look at how Macroscope actually works.

They built their own AST walkers for the most common programming languages

so their agent can get real context on every code change which makes every review faster, cheaper, and more precise.

They also run your team's custom rules as native GitHub checks that can block a failing pull request.

If a PR passes those checks plus a code review and has a small blast radius,

Macroscope approves it without a human ever needing to see it.

They're now auto -approving 40 % of all their customers' PRs, which means those developers are 40 % less likely to lobotomize themselves.

Try letting Macroscope auto -approve some of your team's PRs for free using the link below.

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

Этот урок разговорной практики построен на видео «Did Google just dethrone OpenAI and Anthropic?». Чаще всего повторяются слова: Argon, Gemini, model, Claude, Google. В этом видео 90 предложений и 1351 слов для шедоуинга. Речь длится 6:31. Говорящий говорит быстро, около 207 слов в минуту, поэтому звуки часто сливаются и сокращаются. Только 77% 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
output существительное	/ˈaʊtpʊt/	произво́дство, проду́кция
prompt глагол	/pɹɑmpt/	побужда́ть, внуша́ть
generate глагол	/ˈd͡ʒɛn.ə.ɹeɪt/	порожда́ть, производи́ть
benchmark существительное	/ˈbɛn(t)ʃmɑːk/	крите́рий, мери́ло
graphics существительное	/ˈɡɹæfɪks/	проекти́рование, черче́ние
realistic прилагательное	/ˌɹɪəˈlɪs.tɪk/	реалисти́чный
approve глагол	/əˈpɹuːv/	утвержда́ть, утверди́ть
frontier существительное	/ˈfɹʌntɪə/	грани́ца, рубе́ж
alignment существительное	/əˈlaɪn.mənt/	выра́внивание
impressive прилагательное	/ɪmˈpɹɛsɪv/	впечатля́ющий, внуши́тельный
input существительное	/ˈɪnpʊt/	вклад
developer существительное	/dɪˈvɛləpɚ/	разрабо́тчик, разрабо́тчица
scratch глагол	/skɹæt͡ʃ/	цара́пать, поцара́пать
tier существительное	/ˈtɪɚ/	я́рус, слой
automobile существительное	/ˈɔː.tə.məˌbil/	автомоби́ль, автомаши́на

Фразовые глаголы, которые вы услышите

Слово	Произношение	Значение
cut off глагол	/ˌkʌt ˈɔf/ [ˌkɐɾ ˈɔf]	отреза́ть, отре́зать
keep up глагол		сохранять, поддерживать
pull off глагол		оттащить, стянуть

Грамматика в этом видео

Конструкции, которые говорящий использует чаще всего, с точными словами из видео:

Конструкция	В видео
Пассивный залог be + причастие прошедшего времени — важно, что происходит, а не кто это делает	was euthanized · was trained · was actually announced
Present Perfect have/has + причастие прошедшего времени — прошлое действие, важное сейчас	they've ever released · has now changed · has never been
Придаточные определительные who / which + предложение — уточнение о человеке или предмете	Argon, which is · Pro, which was · Benchmark, which is

Произношение, на которое стоит обратить внимание

Говорящий использует 15 сокращённых и редуцированных форм, например you'll, can't, aren't. Произносите их коротко, так, как слышите.

Звуки «sh» и «zh»: benchmark /ˈbɛn(t)ʃmɑːk/, shock /ʃɔk/, crush /kɹʌʃ/, discretion /dɪˈskɹɛʃən/, reproduction /ˌɹiːpɹəˈdʌkʃən/
Длинные слова — следите за ударением: developer /dɪˈvɛləpɚ/, automobile /ˈɔː.tə.məˌbil/, technically /ˈtɛk.nɪˌkəl.i/, interactive /ˌɪn.tɚˈæk.tɪv/, reproduction /ˌɹiːpɹəˈdʌkʃən/

Звуки, трудные для русскоязычных:

/w/ — губы округлены, это не /v/: walker /ˈwɔkɚ/, sequence /ˈsiː.kwəns/, warrior /ˈwɔɹi.ɚ/, warn /woɹn/, weigh /weɪ/
/h/ — лёгкий выдох, а не русское «х»: horizon /həˈɹaɪ.zən/, firsthand /ˈfɜːstˌhænd/
Звонкие согласные в конце слова — не оглушайте их: approve /əˈpɹuːv/, impressive /ɪmˈpɹɛsɪv/, offensive /ˈɔˌfɛnsɪv/, sand /sænd/, aggressive /əˈɡɹɛs.ɪv/

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 output, prompt, generate.

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.

Техника шедоуинга: читать полное пошаговое руководство →

Практика Shadowing: Did Google just dethrone OpenAI and Anthropic? - Изучайте разговорный английский по видео

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