Prática de Shadowing: China just dropped a new frontier model... and nobody can explain how - Aprenda a falar inglês com vídeo
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Last week, an anonymous mystery model named Kibitz showed up randomly on Chatbot Arena, performing at the frontier level.
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Kibitz is the Yiddish word for that guy standing behind you at a chess game, giving you tips on how to blunder your queen,
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which to be honest is the perfect analogy for a coding agent.
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But it was pretty obvious who was behind this mystery model from the beginning, because the first two letters of this word are a dead giveaway.
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Everybody knew it was Kimmy, then right on cue last Friday, Moonshot dropped Kimmy K3 .1,
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officially giving us a new 3 trillion parameter open weight model that will be fully downloadable on November 4th, or so they say.
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And just when you thought the AI bubble was popping and the Trust Me Bro benchmarks could not possibly get any better, Kimmy made them better.
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I can go lower.
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In today's video, we'll find out why Kimmy K3 .1's performance should be impossible, and put it to the test on the only benchmark that matters,
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building 3D horses with 3JS.
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It is October 26, 2026, and you're watching the Code Report.
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Earlier this year, Anthropic accused Moonshot AI, the company behind Kimi, alongside a bunch of other Chinese companies like DeepSeek and Minimax,
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of running large -scale distillation attacks against Claude by sending swarms of fake users to steal Claude's output and reasoning tokens,
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which could then be used to train these open models that they give to us for free.
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Now, I believe distillation is a fundamental human right
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because big AI companies have literally distilled the collective human brain by taking our internet weblogs, our githubs, our scientific papers,
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and they even destructively scanned our books without asking.
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Just google Project Panama.
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But OpenAI and Anthropic don't really see distillation as a human right like I do.
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To them, it's
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Grand Theft Autocomplete, and they've invested millions and millions of dollars into stopping it.
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And just a few weeks ago, Anthropic said it pretty much solved the distillation problem,
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with 90 to 99 % of distillation attacks from China now being blocked.
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And over at OpenAI, they've proved that their tech can even stop small -scale distilleries like PewDiePie.
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But if that's true, that means an open Chinese model with Kimi's performance should not be possible,
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because for years, Big AI has insinuated that the reason Chinese models get so good so fast is that they steal
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Big AI's output.
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But if we can trust this
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Trust Me Bro distillation data from Anthropic, that would mean that these
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Trust Me Bro benchmarks from Kimi should not be possible.
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Like Kimi K3 .1 is within one point of Opus 5 .5 on Terminal Bench 4 .0.
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That's pretty crazy, and no other open model is remotely close.
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And we might even be able to actually trust these numbers because artificial analysis ran it independently and got 67%,
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which is the highest score of any model they tested.
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But one thing to keep in mind is that the harness plays a big role here.
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Running different models with different harnesses will produce huge differences in benchmark results.
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In addition, Kimmy also wins on SWE Marathon, which is a test for multi -hour software requirements,
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or in other words, jobs that would take humans weeks of work to finish without AI assistance.
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That's all pretty impressive, but there is one big caveat when it comes to coding, and specifically cybersecurity.
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Moonshot deliberately ripped out a bunch of cybersecurity data from its training dataset,
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where the goal was to make the model even worse at hacking than pirate software.
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I'm a genius, dude.
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And it worked, because internally at Moonshot, an unfiltered checkpoint solved 90 .5 % of their illegal exploit tests,
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but the final release model only solves 81%, and that's a smart business move on their part, because just a few days ago, the US banned
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China's DeepSeq model from government use, including all federal civilian agencies,
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and they would just love a good reason to ban all Chinese models across the board, to hand OpenAI and Anthropic a huge win,
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and make all the insiders even more unbelievably rich than they already are.
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And that's why Moonshot really has no choice but to lobotomize its hacking capabilities.
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But despite that, Kimmy still performed on top of all publicly available models on the new cyber index at Artificial Analysis,
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although that's mostly because Claude and GPT -6 declined every prompt on safety grounds and just took the L.
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But nowadays, the only AI capability that I really care about is how well it can generate a 3D horse with 3JS.
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And to analyze the skill objectively and scientifically, I use a formal benchmark.
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It's a state -of -the -art eval I've developed after thousands of hours of research, which I call Thoroughbench.
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The way it works is I literally just go into the chat window
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and type create a realistic interactive 3D horse with 3JS and then press enter.
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From there, I rank them from 0 to 10 based on a proprietary loss function I keep in my brain.
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Using this technique, Google Argon achieved a 0 .69, GPT -6 Astra achieved a 5 .21, and Opus achieved a 6 .39.
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But now it's time to try Kimi K 3 .1.
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I tried to be cheap and use the free tier,
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but got denied, so now I'm paying 250 yuan per month to show you guys this demo.
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It was worth it though, because Kimi produced what is easily the most beautiful horse in this sample.
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Its coloring is spot on, it looks realistic, and most importantly, its copulatory organ is a reasonable size.
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I was blown away by its beauty at first glance, but then I pushed play on the animation and found a problem.
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The animations are absolutely terrible.
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If we look closely, you can see it achieves the animation by simply warping the original mesh
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which is a very unsophisticated and half -assed way to animate a 3D object.
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If we compare that to Claude, the horse itself may not look as beautiful,
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but it actually built a mesh with 95 ,000 vertices with each shape tied to one of 35 bones, and all those bones are animated independently.
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Even Gemini Argon used bones, but for some reason it added an extra one.
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Skeletal animation is more difficult, but it's why the horse doesn't look like it's being torn apart by a black hole when it walks.
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So the final score for KimiK 3 .1 on Thoroughbench is a 4 .20, which is pretty dang impressive, but still behind the other Frontier models.
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But if you really want to build an amazing 3D horse or user interface, you need to check out Mobbin, the sponsor of today's video.
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I've been using Mobbin for over five years,
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and its MCP server now lets your coding agent study its library of 600 ,000 real UI screens before it designs anything.
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So instead of asking for a login page and getting the same purple gradient vibe slop, you start by telling ClaudeCode,
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Cursor, or Codex to study how the best apps built that complete user flow.
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It'll use Mobbin to gather references and build a visual report of the patterns they all share.
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Then it'll recreate that flow and adapt it to your app.
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Every result points back to the original screen, so you can check the source, and after you connect Mobbin,
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everything your agent designs will feel like it has actual taste.
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But try Mobbin for free today and get a 20 % discount at 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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Vocabulário e dicas de fala para esta lição
Esta aula de conversação usa o vídeo “China just dropped a new frontier model... and nobody can explain how”. As palavras que mais se repetem: model, horse, distillation, Kimmy, Anthropic. Este vídeo tem 82 frases e 1258 palavras para praticar shadowing. A fala dura 6:21. O falante fala rápido, cerca de 198 palavras por minuto, então espere sons ligados e reduzidos. Apenas 76% 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 |
| animation substantivo | /ˌæn.ɪˈmeɪ.ʃən/ | animação |
| bone substantivo | /boʊn/ | osso |
| solve verbo | /sɒlv/ | resolver, solucionar |
| impressive adjetivo | /ɪmˈpɹɛsɪv/ | impressionante |
| mystery substantivo | /ˈmɪs.t(ə.)ɹi/ | mistério |
| output substantivo | /ˈaʊtpʊt/ | produção |
| steal verbo | /stiːl/ | roubar, furtar |
| artificial adjetivo | /ˌɑː.tɪˈfɪʃ.əl/ | artificial |
| realistic adjetivo | /ˌɹɪəˈlɪs.tɪk/ | realístico |
| capability substantivo | /ˌkeɪ.pəˈbɪl.ɪ.ti/ | capacidade |
| independently advérbio | /ˌɪndɪˈpɛndəntli/ | independentemente |
| frontier substantivo | /ˈfɹʌntɪə/ | fronteira |
| mesh substantivo | /mɛʃ/ | malha |
| harness verbo | /ˈhɑɹ.nəs/ | arrear, atrelar |
Phrasal verbs que você vai ouvir
| Palavra | Significado |
|---|---|
| check out verbo | verificar, sacar só |
| find out verbo | descobrir |
| rip out verbo | arrancar |
Gramática neste vídeo
As estruturas que o falante mais usa, com as palavras exatas do vídeo:
| Estrutura | No vídeo |
|---|---|
| Present perfect have/has + particípio passado — uma ação passada que ainda importa agora | have literally distilled · they've invested · they've proved |
| Voz passiva be + particípio passado — o foco está no que acontece, não em quem faz | being blocked · was blown · are animated |
Pronúncia para ficar de olho
O falante usa 12 contrações e formas reduzidas, como I'm, I've, it'll. Diga-as na forma curta, do jeito que você ouve.
- Os sons de “sh” e “zh”: benchmark /ˈbɛn(t)ʃmɑːk/, animation /ˌæn.ɪˈmeɪ.ʃən/, artificial /ˌɑː.tɪˈfɪʃ.əl/, mesh /mɛʃ/, evaluation /ɪˌvæljuˈeɪʃn̩/
- Palavras longas — acerte a sílaba tônica: animation /ˌæn.ɪˈmeɪ.ʃən/, artificial /ˌɑː.tɪˈfɪʃ.əl/, capability /ˌkeɪ.pəˈbɪl.ɪ.ti/, independently /ˌɪndɪˈpɛndəntli/, fundamental /ˌfʌn.dəˈmɛn.təl/
Sons difíceis para falantes de português:
- Consoante final — sem acrescentar um “i” depois: benchmark /ˈbɛn(t)ʃmɑːk/, output /ˈaʊtpʊt/, realistic /ˌɹɪəˈlɪs.tɪk/, technique /tɛkˈniːk/, alongside /əˌlɔŋˈsaɪd/
- /l/ final — a língua toca o céu da boca, não vira “u”: steal /stiːl/, artificial /ˌɑː.tɪˈfɪʃ.əl/, formal /ˈfɔɹməl/, skill /skɪl/, fundamental /ˌfʌn.dəˈmɛn.təl/
- s + consoante no início — sem um “i” antes: steal /stiːl/, skill /skɪl/, scan /skæn/, sponsor /ˈspɒn.səː/, skeletal /ˈskɛl.ə.təl/
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, animation, bone.
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.