Prática de Shadowing: Gemini 4 Argon explained in 5min.. - Aprenda a falar inglês com vídeo

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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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Vocabulário e dicas de fala para esta lição

Esta aula de conversação usa o vídeo “Gemini 4 Argon explained in 5min.”. As palavras que mais se repetem: Google, model, benchmark, token, Argon. Este vídeo tem 56 frases e 877 palavras para praticar shadowing. A fala dura 4:56. O falante fala em ritmo natural, cerca de 178 palavras por minuto, próximo de uma conversa do dia a dia. Apenas 77% das palavras estão entre as 3.000 mais comuns do inglês, por isso o vocabulário é exigente.

Vocabulário principal deste vídeo

15 palavras do vídeo que vale a pena aprender, com pronúncia e significado:

Palavra	Pronúncia	Significado
benchmark substantivo	/ˈbɛn(t)ʃmɑːk/	padrão
token substantivo	/ˈtoʊkən/	símbolo, sinal
subscription substantivo	/səbˈskɹɪpʃən/	subscrição, assinatura
output substantivo	/ˈaʊtpʊt/	produção
capture verbo	/ˈkæp.(t)ʃɚ/	capturar
generate verbo	/ˈd͡ʒɛn.ə.ɹeɪt/	gerar
frontier substantivo	/ˈfɹʌntɪə/	fronteira
flagship substantivo	/ˈflæɡʃɪp/	navio-almirante, capitânia
harness verbo	/ˈhɑɹ.nəs/	arrear, atrelar
solve verbo	/sɒlv/	resolver, solucionar
extend verbo	/ɛkˈstɛnd/	estender, ampliar
gravity substantivo	/ˈɡɹævɪti/	gravidade
adoption substantivo	/əˈdɑp.ʃən/	adoção
trajectory substantivo	/tɹəˈd͡ʒɛktəɹi/	trajetória, trajectória
earn verbo	/ɝn/	receber

Phrasal verbs que você vai ouvir

Palavra	Significado
figure out verbo	descobrir, deduzir

Gramática neste vídeo

As estruturas que o falante mais usa, com as palavras exatas do vídeo:

Estrutura	No vídeo
Voz passiva be + particípio passado — o foco está no que acontece, não em quem faz	are flawed · was actually released · can be contaminated
Present perfect have/has + particípio passado — uma ação passada que ainda importa agora	It's been · hasn't really solved

Pronúncia para ficar de olho

O falante usa 3 contrações e formas reduzidas, como doesn't, hasn't, they're. Diga-as na forma curta, do jeito que você ouve.

Os sons de “sh” e “zh”: benchmark /ˈbɛn(t)ʃmɑːk/, subscription /səbˈskɹɪpʃən/, capture /ˈkæp.(t)ʃɚ/, flagship /ˈflæɡʃɪp/, adoption /əˈdɑp.ʃən/
Palavras longas — acerte a sílaba tônica: trajectory /tɹəˈd͡ʒɛktəɹi/, primarily /ˈpɹaɪ.mə.ɹə.li/, temporary /ˈtɛmp(əˌɹ)ɛɹi/, potentially /pəˈtɛnʃ(ə)li/, developer /dɪˈvɛləpɚ/

Sons difíceis para falantes de português:

Consoante final — sem acrescentar um “i” depois: benchmark /ˈbɛn(t)ʃmɑːk/, output /ˈaʊtpʊt/, lab /læːb/, generate /ˈd͡ʒɛn.ə.ɹeɪt/, flagship /ˈflæɡʃɪp/
/h/ — um sopro suave, diferente do “r”: harness /ˈhɑɹ.nəs/, coherence /koʊˈhɪɹ.ən(t)s/
s + consoante no início — sem um “i” antes: slight /slaɪt/, skepticism /ˈskɛp.tɪˌsɪ.zəm/

Como praticar com este vídeo

Ouça o vídeo inteiro uma vez sem falar e anote as palavras que você não conhece.
Comece na velocidade 0,75×, faça shadowing frase por frase e volte à velocidade normal quando ficar fácil.
Grave a sua voz e compare com o original, prestando atenção a palavras como benchmark, token, subscription.

O que é a Técnica de Shadowing?

Shadowing é uma técnica de aprendizado de idiomas com base científica, originalmente desenvolvida para o treinamento de intérpretes profissionais. O método é simples, mas poderoso: você ouve áudio em inglês nativo e repete imediatamente em voz alta — como uma sombra seguindo o falante com 1-2 segundos de atraso. Pesquisas mostram melhora significativa na precisão da pronúncia, entonação, ritmo, sons conectados, compreensão auditiva e fluência na fala.

Técnica de shadowing: leia o guia completo passo a passo →

Prática de Shadowing: Gemini 4 Argon explained in 5min.. - Aprenda a falar inglês com vídeo

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