शैडोइंग अभ्यास: Did Google just dethrone OpenAI and Anthropic? - वीडियो के साथ अंग्रेजी बोलना सीखें

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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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इस पाठ की शब्दावली और बोलने के सुझाव

यह स्पीकिंग पाठ वीडियो “Did Google just dethrone OpenAI and Anthropic?” पर आधारित है। सबसे ज़्यादा दोहराए गए शब्द ये हैं: Argon, Gemini, model, Claude, Google। इस वीडियो में शैडोइंग के लिए 90 वाक्य और 1351 शब्द हैं। बोला गया हिस्सा 6:31 लंबा है। वक्ता तेज़ बोलता है, लगभग 207 शब्द प्रति मिनट, इसलिए जुड़ी हुई और कमज़ोर ध्वनियाँ ज़्यादा मिलेंगी। केवल 77% शब्द अंग्रेज़ी के 3,000 सबसे आम शब्दों में से हैं, इसलिए शब्दावली कठिन है।

इस वीडियो की मुख्य शब्दावली

वीडियो के 15 सीखने लायक शब्द, उच्चारण और अर्थ के साथ:

शब्द	उच्चारण	अर्थ
generate क्रिया	/ˈd͡ʒɛn.ə.ɹeɪt/	उपजाना
benchmark संज्ञा	/ˈbɛn(t)ʃmɑːk/	मानक, मापदंड
graphics संज्ञा	/ˈɡɹæfɪks/	चित्रालेख, ग्राफ़िक्स
realistic विशेषण	/ˌɹɪəˈlɪs.tɪk/	वास्तविक
approve क्रिया	/əˈpɹuːv/	अनुमोदन करना
frontier संज्ञा	/ˈfɹʌntɪə/	सीमा, सरहद
developer संज्ञा	/dɪˈvɛləpɚ/	विकासक
scratch क्रिया	/skɹæt͡ʃ/	नोचना
automobile संज्ञा	/ˈɔː.tə.məˌbil/	मोटरवाहन, गाड़ी
viewer संज्ञा	/ˈvju.ɚ/	दर्शक, ईक्षक
topic संज्ञा	/ˈtɑpɪk/	प्रसंग, विषय
trick संज्ञा	/ˈtɹɪk/	चाल
technique संज्ञा	/tɛkˈniːk/	तकनीक
offensive विशेषण	/ˈɔˌfɛnsɪv/	अपकारी
shock संज्ञा	/ʃɔk/	झटका

इस वीडियो का व्याकरण

वक्ता जिन संरचनाओं का सबसे अधिक प्रयोग करता है, वीडियो के असली शब्दों के साथ:

संरचना	वीडियो में
Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है	was euthanized · was trained · was actually announced
Present perfect have/has + past participle — बीता हुआ काम जिसका असर अभी भी है	they've ever released · has now changed · has never been
Relative clauses who / which + उपवाक्य — व्यक्ति या वस्तु के बारे में अतिरिक्त जानकारी	Argon, which is · Pro, which was · Benchmark, which is

ध्यान देने योग्य उच्चारण

वक्ता 15 बार संक्षिप्त रूपों का प्रयोग करता है, जैसे you'll, can't, aren't। इन्हें वैसे ही छोटा बोलें जैसा आप सुनते हैं।

“sh” और “zh” की ध्वनियाँ: benchmark /ˈbɛn(t)ʃmɑːk/, shock /ʃɔk/, crush /kɹʌʃ/, discretion /dɪˈskɹɛʃən/, reproduction /ˌɹiːpɹəˈdʌkʃən/
लंबे शब्द — बलाघात सही रखें: developer /dɪˈvɛləpɚ/, automobile /ˈɔː.tə.məˌbil/, technically /ˈtɛk.nɪˌkəl.i/, interactive /ˌɪn.tɚˈæk.tɪv/, reproduction /ˌɹiːpɹəˈdʌkʃən/

हिंदी भाषियों के लिए मुश्किल ध्वनियाँ:

/v/ और /w/ का फ़र्क़ — /v/ में ऊपर के दाँत निचले होंठ को छूते हैं, /w/ में होंठ गोल होते हैं: approve /əˈpɹuːv/, impressive /ɪmˈpɹɛsɪv/, developer /dɪˈvɛləpɚ/, viewer /ˈvju.ɚ/, offensive /ˈɔˌfɛnsɪv/
/z/ — इसे “ज” (/dʒ/) न बनाएँ: lazy /ˈleɪ̯zi(ː)/, advise /ədˈvaɪz/, horizon /həˈɹaɪ.zən/, oppose /əˈpoʊz/, translate /tɹænzˈleɪt/
शब्द की शुरुआत में s + व्यंजन — पहले “इ” न जोड़ें: scratch /skɹæt͡ʃ/, sponsor /ˈspɒn.səː/, sparse /spɑːɹs/

इस वीडियो से अभ्यास कैसे करें

पहले बिना बोले पूरा वीडियो एक बार सुनें और जो शब्द नहीं आते उन्हें लिख लें।
0.75× गति से शुरू करें, एक-एक वाक्य की शैडोइंग करें, और सहज लगने पर सामान्य गति पर लौटें।
अपनी आवाज़ रिकॉर्ड करें और मूल से तुलना करें, ख़ासकर generate, benchmark, graphics जैसे शब्दों पर ध्यान दें।

शैडोइंग तकनीक क्या है?

शैडोइंग (Shadowing) एक विज्ञान-समर्थित भाषा सीखने की तकनीक है जो मूल रूप से पेशेवर दुभाषिया प्रशिक्षण के लिए विकसित की गई थी। विधि सरल लेकिन शक्तिशाली है: आप मूल अंग्रेज़ी ऑडियो सुनते हैं और तुरंत इसे ज़ोर से दोहराते हैं — जैसे वक्ता की छाया 1-2 सेकंड की देरी से। शोध से पता चलता है कि यह उच्चारण सटीकता, स्वर, लय, जुड़ी हुई ध्वनियाँ, सुनने की समझ और बोलने की प्रवाहशीलता में काफ़ी सुधार करता है।

शैडोइंग तकनीक: पूरी चरण-दर-चरण गाइड पढ़ें →

शैडोइंग अभ्यास: Did Google just dethrone OpenAI and Anthropic? - वीडियो के साथ अंग्रेजी बोलना सीखें

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इस पाठ की शब्दावली और बोलने के सुझाव

इस वीडियो की मुख्य शब्दावली

इस वीडियो का व्याकरण

ध्यान देने योग्य उच्चारण

इस वीडियो से अभ्यास कैसे करें

शैडोइंग तकनीक क्या है?