跟讀練習: Did Google just dethrone OpenAI and Anthropic? - 通過影片學習英語口語

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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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China just dropped a new frontier model... and nobody can explain how

FOR YOU

本課的詞彙與口語要點

這節口語課以影片“Did Google just dethrone OpenAI and Anthropic?”為素材。影片中反覆出現的詞有：Argon, Gemini, model, Claude, Google。這段影片共有 90 個句子、1351 個單詞可供跟讀。講話部分時長為 6:31。說話人語速較快，每分鐘約 207 個詞，會出現較多連讀和弱讀。只有 77% 的單詞屬於英語最常用的 3,000 詞，詞彙難度較高。

影片中的重點詞彙

影片中 15 個值得學習的單詞，附發音和釋義：

單詞	發音	釋義
output 名詞	/ˈaʊtpʊt/	輸出 /輸出
graphics 名詞	/ˈɡɹæfɪks/	圖學 /圖學
realistic 形容詞	/ˌɹɪəˈlɪs.tɪk/	現實 /現實, 栩栩如生
approve 動詞	/əˈpɹuːv/	批准, 贊成 /贊成
frontier 名詞	/ˈfɹʌntɪə/	邊界 /邊界, 邊疆 /邊疆
alignment 名詞	/əˈlaɪn.mənt/	對齊 /對齊
impressive 形容詞	/ɪmˈpɹɛsɪv/	令人印象深刻
input 名詞	/ˈɪnpʊt/	輸入 /輸入
scratch 動詞	/skɹæt͡ʃ/	抓, 劃
tier 名詞	/ˈtɪɚ/	層 /層, 排
automobile 名詞	/ˈɔː.tə.məˌbil/	汽車 /汽車, 車 /車
token 名詞	/ˈtoʊkən/	代幣 /代幣, 籌碼 /籌碼
viewer 名詞	/ˈvju.ɚ/	觀眾 /觀眾, 看客
topic 名詞	/ˈtɑpɪk/	主題 /主題, 話題 /話題
trick 名詞	/ˈtɹɪk/	詭計 /詭計, 騙術 /騙術

影片中出現的短語動詞

單詞	發音	釋義
cut off 動詞	/ˌkʌt ˈɔf/ [ˌkɐɾ ˈɔf]	切下, 割下

影片中的語法

說話人最常用的結構，並附上影片中的原話：

結構	影片中的用法
被動語態 be + 過去分詞 — 強調發生了什麼，而不是誰做的	was euthanized · was trained · was actually announced
現在完成時 have/has + 過去分詞 — 過去發生但與現在仍有關聯的事	they've ever released · has now changed · has never been
定語從句 who / which + 從句 — 補充說明人或事物	Argon, which is · Pro, which was · Benchmark, which is

需要注意的發音

說話人用了 15 次縮略和弱讀形式，例如 you'll, can't, aren't。請按聽到的簡短形式來說。

“sh” 和 “zh” 音: benchmark /ˈbɛn(t)ʃmɑːk/, shock /ʃɔk/, crush /kɹʌʃ/, discretion /dɪˈskɹɛʃən/, reproduction /ˌɹiːpɹəˈdʌkʃən/
長單詞——注意重音位置: developer /dɪˈvɛləpɚ/, automobile /ˈɔː.tə.məˌbil/, technically /ˈtɛk.nɪˌkəl.i/, interactive /ˌɪn.tɚˈæk.tɪv/, reproduction /ˌɹiːpɹəˈdʌkʃən/

中文母語者容易讀錯的音：

/v/ — 不要讀成 /w/，上齒輕觸下唇: approve /əˈpɹuːv/, impressive /ɪmˈpɹɛsɪv/, developer /dɪˈvɛləpɚ/, viewer /ˈvju.ɚ/, offensive /ˈɔˌfɛnsɪv/
詞尾子音 — 要讀清楚，後面不要加母音: output /ˈaʊtpʊt/, prompt /pɹɑmpt/, generate /ˈd͡ʒɛn.ə.ɹeɪt/, benchmark /ˈbɛn(t)ʃmɑːk/, graphics /ˈɡɹæfɪks/
/r/ 和 /l/ 的區別: realistic /ˌɹɪəˈlɪs.tɪk/, translate /tɹænzˈleɪt/, refusal /ɹɪˈfjuːzl̩/, guardrail /ˈɡɑɹdˌɹeɪl/

如何用這段影片練習

先完整聽一遍影片，不要開口，記下不認識的單詞。
先用 0.75 倍速逐句跟讀，熟練之後再回到正常速度。
錄下自己的聲音並與原聲對比，特別注意 output, graphics, realistic 這類單詞。

什麼是跟讀法？

跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧，最初開發用於專業口譯員的培訓，並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大：您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同，跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性，語調，節奏，連讀，聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。

影子跟讀法: 閱讀完整分步指南 →

跟讀練習: Did Google just dethrone OpenAI and Anthropic? - 通過影片學習英語口語

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