Pratica di Shadowing: Gemini 4 Argon explained in 5min.. - Impara a parlare inglese con i video

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It's been about seven months since Google announced a new flagship model, Gemini 4 Argon.

Even during Google I .O back in May, I expected Google to drop Gemini 3 .5 Pro.

But Google has been releasing a lot of flash models instead,

and a lot of them basically struggle to get market adoption given a variety of other flagship models

and cheaper alternatives from Chinese open models.

And Google finally made a flagship model with the codename Argon, and the preliminary benchmarks show a pretty strong number as you can see.

The first thing you might have noticed here is coding benchmarks, more specifically, scoring 77 .9 % on DeepSuite,

which would put Gemini 4 Argon above every other model we currently have.

But it's always good to have some healthy skepticism here.

Recently, EPOC found issues on DeepSuite Benchmark, finding 23 tasks that are flawed.

And looking back to when the benchmark was actually released back in May, the entire 113 tasks and their solutions are made public,

which means labs that have access to these could easily study them during post -training and benchmarks their models around public benchmarks.

And when we look at other benchmarks that Google reported here, a large majority of them are public.

So the further we go out in time, labs that release their models later have a slight advantage to potentially include them during post -training.

And of course, as users, all of this makes it really difficult to track real progress in AI since

so many public benchmarks like DeepSuite can be contaminated pretty fast.

Not only that, benchmarks get saturated a lot faster now than before, which forces benchmarks to be refreshed more frequently.

So consumers now carry a huge burden keeping track of real scientific progress in AI.

But regardless of the case, achieving this kind of leap in benchmark is a huge improvement from Google.

Now, when we take a step back and look at how Google is competing with other labs, a lot of people still primarily use clock code,

codecs, or open harnesses when it comes to coding.

And despite being a strong model, Google still hasn't really solved making their harness anti -gravity a truly competitive option for developers.

For a recent report, Codex has more than doubled the amount of weekly active users at 5 million compared to 2 .4 million for anti -gravity.

And on face value, Gemini 4 Argon currently undercuts both Anthropic

and OpenAI when it comes to the cost of intelligence until the temporary discount ends and the price doubles again.

But the cost benefit that we find here doesn't actually translate in Pareto Frontier, as you can see.

Argon falls below the Pareto Frontier, showing you that Anthropic and OpenAI still leads ahead in both the harness and model adoption.

Now, one angle we should think about, though, is the upcoming IPO for Anthropic and possibly OpenAI.

While OpenAI and Anthropic have a huge incentive to subsidize the model through subscription pretty heavily to grow their users,

Google already sits on top of one.

And given that Google is already a publicly traded company,

they want to be more careful about how to allocate their existing compute towards actually creating value within their ecosystem,

which could help increase their perceived revenue.

Even looking at how Argon is being rolled out in segments, the model is set to be made available to ultra -subscription users

and paid API users where there's already a huge revenue being

captured instead of fighting the subscription rates in channeling their compute towards that.

On a recent Google's Q2 earning call, they said that their API is processing 22 billion tokens per minute,

which is 6 billion more than the previous quarter.

That's more than 11 quadrillion tokens per year in just API channel alone.

And when you start factoring in other channels that Google owns in its distribution,

we really get a closer picture into what Google really means when they said that they're supply constrained.

And eventually, OpenAI and Anthropic will have to solve this scaling problem while maintaining healthy margins at the same time,

not just subsidizing subscription to capture more users.

So looking at Gemini for Argon, Google is demonstrating their capability in keeping pace at the frontier

while they also try to figure out how to capture value at scale.

One interesting note on Argon is how Google extended their output tokens from 64 ,000 to 1 million tokens.

This might not sound like a big deal, but it's definitely a huge move from Google

because maintaining coherence as autoregressive models generate longer and longer tokens get increasingly difficult the longer the output tokens get.

Scientists like Yen Le Koon made criticism on autoregressive models for this very reason,

where autoregressively generating one token after another can compound errors over long trajectory, let alone 1 million from Argon.

If you can imagine each token that's 99 % reliable, chaining 100 independent steps together would leave about 37 % chance of every step being correct.

Now, of course, that's just in theory, assuming that every token error is independent.

But the fact

that Google has enough confidence to extend their commercial product to

1 million output tokens really opens up door for longer trajectory tasks like code migration

and long -running research tasks that could benefit from a model that can generate more.

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Vocabolario e note di pronuncia per questa lezione

Questa lezione di conversazione si basa sul video “Gemini 4 Argon explained in 5min.”. Le parole che ritornano più spesso: Google, model, benchmark, token, Argon. Questo video contiene 56 frasi e 877 parole da ripetere con lo shadowing. Il parlato dura 4:56. Chi parla ha un ritmo naturale di circa 178 parole al minuto, vicino a una conversazione quotidiana. Solo il 77% delle parole rientra nelle 3.000 più comuni dell’inglese, quindi il lessico è impegnativo.

Vocaboli chiave di questo video

15 parole del video che vale la pena imparare, con pronuncia e significato:

Parola	Pronuncia	Significato
benchmark sostantivo	/ˈbɛn(t)ʃmɑːk/	riferimento, termine di paragone
token sostantivo	/ˈtoʊkən/	simbolo, segno
subscription sostantivo	/səbˈskɹɪpʃən/	abbonamento
output sostantivo	/ˈaʊtpʊt/	risultato, produzione
capture verbo	/ˈkæp.(t)ʃɚ/	catturare, riprendere
generate verbo	/ˈd͡ʒɛn.ə.ɹeɪt/	generare
frontier sostantivo	/ˈfɹʌntɪə/	confine, frontiera
flagship sostantivo	/ˈflæɡʃɪp/	nave ammiraglia, ammiraglia
harness verbo	/ˈhɑɹ.nəs/	imbracare, imbragare
solve verbo	/sɒlv/	risolvere
gravity sostantivo	/ˈɡɹævɪti/	gravità
adoption sostantivo	/əˈdɑp.ʃən/	adozione
trajectory sostantivo	/tɹəˈd͡ʒɛktəɹi/	traiettoria
earn verbo	/ɝn/	guadagnare
primarily avverbio	/ˈpɹaɪ.mə.ɹə.li/	principalmente, fondamentalmente

I phrasal verb che sentirai

Parola	Significato
figure out verbo	scoprire, rendersi conto
open up verbo	aprire

La grammatica di questo video

Le strutture che chi parla usa di più, con le parole esatte del video:

Struttura	Nel video
Forma passiva be + participio passato — conta ciò che accade, non chi lo fa	are flawed · was actually released · can be contaminated
Present perfect have/has + participio passato — un’azione passata che conta ancora adesso	It's been · hasn't really solved

Pronuncia a cui fare attenzione

Chi parla usa 3 contrazioni e forme ridotte, come doesn't, hasn't, they're. Pronunciale nella forma breve, così come le senti.

I suoni “sh” e “zh”: benchmark /ˈbɛn(t)ʃmɑːk/, subscription /səbˈskɹɪpʃən/, capture /ˈkæp.(t)ʃɚ/, flagship /ˈflæɡʃɪp/, adoption /əˈdɑp.ʃən/
Parole lunghe — attenzione all’accento: trajectory /tɹəˈd͡ʒɛktəɹi/, primarily /ˈpɹaɪ.mə.ɹə.li/, temporary /ˈtɛmp(əˌɹ)ɛɹi/, potentially /pəˈtɛnʃ(ə)li/, developer /dɪˈvɛləpɚ/

I suoni difficili per chi parla italiano:

/h/ — va soffiata, non è muta: harness /ˈhɑɹ.nəs/, coherence /koʊˈhɪɹ.ən(t)s/
Consonante finale — senza aggiungere una vocale dopo: benchmark /ˈbɛn(t)ʃmɑːk/, output /ˈaʊtpʊt/, lab /læːb/, generate /ˈd͡ʒɛn.ə.ɹeɪt/, flagship /ˈflæɡʃɪp/
/æ/ — più aperta della “e”: capture /ˈkæp.(t)ʃɚ/, lab /læːb/, flagship /ˈflæɡʃɪp/, gravity /ˈɡɹævɪti/, flash /flæʃ/

Come esercitarsi con questo video

Ascolta tutto il video una volta senza parlare e annota le parole che non conosci.
Inizia a velocità 0,75×, fai shadowing frase per frase e torna alla velocità normale quando diventa facile.
Registrati e confronta con l’originale, facendo attenzione a parole come benchmark, token, subscription.

Cos'è la tecnica dello Shadowing?

Shadowing è una tecnica di apprendimento delle lingue supportata da studi scientifici, originariamente sviluppata per la formazione dei traduttori professionisti e resa popolare dal poliglotta Dr. Alexander Arguelles. Il metodo è semplice ma potente: ascolti un audio in inglese di madrelingua e lo ripeti immediatamente ad alta voce — come un'ombra che segue il parlante con un ritardo di solo 1–2 secondi. A differenza dell'ascolto passivo o degli esercizi di grammatica, lo shadowing costringe il tuo cervello e i muscoli della bocca a elaborare e riprodurre simultaneamente i modelli di discorso reale. La ricerca dimostra che migliora significativamente la precisione della pronuncia, l'intonazione, il ritmo, il discorso connesso, la comprensione dell'ascolto e la fluidità del parlato — rendendolo uno dei metodi più efficaci per la preparazione alla prova di speaking dell'IELTS e per la comunicazione reale in inglese.

Tecnica dello shadowing: leggi la guida completa passo dopo passo →

Pratica di Shadowing: Gemini 4 Argon explained in 5min.. - Impara a parlare inglese con i video

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