Shadowing-Übung: Did Google just dethrone OpenAI and Anthropic? - Englisch Sprechen Lernen mit 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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Wortschatz und Sprechhinweise zu dieser Lektion
Diese Sprechübung basiert auf dem Video „Did Google just dethrone OpenAI and Anthropic?“. Diese Wörter kommen am häufigsten vor: Argon, Gemini, model, Claude, Google. Dieses Video enthält 90 Sätze und 1351 Wörter zum Nachsprechen. Der gesprochene Teil dauert 6:31. Der Sprecher spricht schnell, etwa 207 Wörter pro Minute – rechne mit verbundenen und verkürzten Lauten. Nur 77 % der Wörter gehören zu den 3.000 häufigsten im Englischen, der Wortschatz ist also anspruchsvoll.
Wichtiger Wortschatz in diesem Video
15 lernenswerte Wörter aus dem Video, mit Aussprache und Bedeutung:
| Wort | Aussprache | Bedeutung |
|---|---|---|
| output Substantiv | /ˈaʊtpʊt/ | Output |
| prompt Verb | /pɹɑmpt/ | veranlassen, dazu bringen |
| generate Verb | /ˈd͡ʒɛn.ə.ɹeɪt/ | generieren |
| benchmark Substantiv | /ˈbɛn(t)ʃmɑːk/ | Maßstab, Höhenfestpunkt |
| graphics Substantiv | /ˈɡɹæfɪks/ | Grafik |
| realistic Adjektiv | /ˌɹɪəˈlɪs.tɪk/ | realistisch |
| approve Verb | /əˈpɹuːv/ | billigen, genehmigen |
| frontier Substantiv | /ˈfɹʌntɪə/ | Grenzland, Grenzgebiet |
| alignment Substantiv | /əˈlaɪn.mənt/ | Ausrichtung, Aufreihung |
| impressive Adjektiv | /ɪmˈpɹɛsɪv/ | beeindruckend, eindrucksvoll |
| developer Substantiv | /dɪˈvɛləpɚ/ | Entwickler, Entwicklerin |
| scratch Verb | /skɹæt͡ʃ/ | kratzen |
| tier Substantiv | /ˈtɪɚ/ | Schicht, Rang |
| automobile Substantiv | /ˈɔː.tə.məˌbil/ | Automobil, Auto |
| token Substantiv | /ˈtoʊkən/ | Zeichen, Symbol |
Phrasal Verbs, die du hören wirst
| Wort | Aussprache | Bedeutung |
|---|---|---|
| cut off Verb | /ˌkʌt ˈɔf/ [ˌkɐɾ ˈɔf] | abschneiden |
| keep up Verb | erhalten | |
| pull off Verb | abzupfen, wegziehen |
Grammatik in diesem Video
Die Strukturen, die der Sprecher am häufigsten verwendet, mit den genauen Worten aus dem Video:
| Struktur | Im Video |
|---|---|
| Passiv be + Partizip Perfekt – wichtig ist, was geschieht, nicht wer es tut | was euthanized · was trained · was actually announced |
| Present Perfect have/has + Partizip Perfekt – eine vergangene Handlung, die jetzt noch wichtig ist | they've ever released · has now changed · has never been |
| Relativsätze who / which + Satz – eine Zusatzinformation über eine Person oder Sache | Argon, which is · Pro, which was · Benchmark, which is |
Aussprache, auf die du achten solltest
Der Sprecher verwendet 15 Kurzformen und abgeschwächte Formen, zum Beispiel you'll, can't, aren't. Sprich sie in der kurzen Form, so wie du sie hörst.
- Die Laute „sh“ und „zh“: benchmark /ˈbɛn(t)ʃmɑːk/, shock /ʃɔk/, crush /kɹʌʃ/, discretion /dɪˈskɹɛʃən/, reproduction /ˌɹiːpɹəˈdʌkʃən/
- Lange Wörter – auf die Betonung achten: developer /dɪˈvɛləpɚ/, automobile /ˈɔː.tə.məˌbil/, technically /ˈtɛk.nɪˌkəl.i/, interactive /ˌɪn.tɚˈæk.tɪv/, reproduction /ˌɹiːpɹəˈdʌkʃən/
Laute, die Deutschsprachigen schwerfallen:
- /w/ — mit runden Lippen, nicht wie das deutsche „w“ (/v/): walker /ˈwɔkɚ/, sequence /ˈsiː.kwəns/, warrior /ˈwɔɹi.ɚ/, warn /woɹn/, weigh /weɪ/
- Stimmhafter Auslaut — /b/, /d/, /g/, /z/, /v/ am Wortende nicht verhärten: approve /əˈpɹuːv/, impressive /ɪmˈpɹɛsɪv/, offensive /ˈɔˌfɛnsɪv/, sand /sænd/, aggressive /əˈɡɹɛs.ɪv/
- /æ/ — offener als „ä“: graphics /ˈɡɹæfɪks/, scratch /skɹæt͡ʃ/, format /ˈfɔːɹ.mæt/, rank /ˈɹæŋk/, sand /sænd/
So übst du mit diesem Video
- Höre dir das ganze Video einmal an, ohne zu sprechen, und notiere die Wörter, die du nicht kennst.
- Beginne mit 0,75-facher Geschwindigkeit, sprich Satz für Satz nach und wechsle zur normalen Geschwindigkeit, sobald es leichtfällt.
- Nimm dich auf und vergleiche mit dem Original; achte dabei auf Wörter wie output, prompt, generate.
Was ist die Shadowing-Technik?
Shadowing ist eine wissenschaftlich fundierte Sprachlerntechnik, die ursprünglich für die professionelle Dolmetscherausbildung entwickelt und durch den Polyglotten Dr. Alexander Arguelles populär gemacht wurde. Die Methode ist einfach aber wirkungsvoll: Du hörst englisches Audio von Muttersprachlern und wiederholst es sofort laut — wie ein Schatten, der dem Sprecher mit nur 1–2 Sekunden Verzögerung folgt. Anders als passives Hören oder Grammatikübungen zwingt Shadowing dein Gehirn und deine Mundmuskulatur, gleichzeitig echte Sprachmuster zu verarbeiten und zu reproduzieren. Studien zeigen, dass es Aussprachegenauigkeit, Intonation, Rhythmus, verbundene Sprache, Hörverständnis und Sprechflüssigkeit signifikant verbessert — was es zu einer der effektivsten Methoden für die IELTS Speaking-Vorbereitung und reale englische Kommunikation macht.