Pratica di Shadowing: China just dropped a new frontier model... and nobody can explain how - Impara a parlare inglese con i video
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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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Vocabolario e note di pronuncia per questa lezione
Questa lezione di conversazione si basa sul video “China just dropped a new frontier model... and nobody can explain how”. Le parole che ritornano più spesso: model, horse, distillation, Kimmy, Anthropic. Questo video contiene 82 frasi e 1258 parole da ripetere con lo shadowing. Il parlato dura 6:21. Chi parla va veloce, circa 198 parole al minuto: aspettati suoni legati e ridotti. Solo il 76% 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 |
| animation sostantivo | /ˌæn.ɪˈmeɪ.ʃən/ | ravvivamento, animazione |
| bone sostantivo | /boʊn/ | osso |
| solve verbo | /sɒlv/ | risolvere |
| impressive aggettivo | /ɪmˈpɹɛsɪv/ | impressionante, spettacolare |
| mystery sostantivo | /ˈmɪs.t(ə.)ɹi/ | mistero, arcano |
| output sostantivo | /ˈaʊtpʊt/ | risultato, produzione |
| steal verbo | /stiːl/ | rubare, derubare |
| artificial aggettivo | /ˌɑː.tɪˈfɪʃ.əl/ | artificiale, artificioso |
| realistic aggettivo | /ˌɹɪəˈlɪs.tɪk/ | realistico |
| capability sostantivo | /ˌkeɪ.pəˈbɪl.ɪ.ti/ | capacità, possibilità |
| independently avverbio | /ˌɪndɪˈpɛndəntli/ | indipendentemente |
| frontier sostantivo | /ˈfɹʌntɪə/ | confine, frontiera |
| mesh sostantivo | /mɛʃ/ | maglia, rete |
| harness verbo | /ˈhɑɹ.nəs/ | imbracare, imbragare |
I phrasal verb che sentirai
| Parola | Significato |
|---|---|
| blow away verbo | spazzare via |
| check out verbo | investigare |
| find out verbo | scoprire |
| stand behind verbo | mettere la faccia |
La grammatica di questo video
Le strutture che chi parla usa di più, con le parole esatte del video:
| Struttura | Nel video |
|---|---|
| Present perfect have/has + participio passato — un’azione passata che conta ancora adesso | they've invested · they've proved · has insinuated |
| Forma passiva be + participio passato — conta ciò che accade, non chi lo fa | being blocked · was blown · are animated |
Pronuncia a cui fare attenzione
Chi parla usa 12 contrazioni e forme ridotte, come I'm, I've, it'll. Pronunciale nella forma breve, così come le senti.
- I suoni “sh” e “zh”: benchmark /ˈbɛn(t)ʃmɑːk/, animation /ˌæn.ɪˈmeɪ.ʃən/, artificial /ˌɑː.tɪˈfɪʃ.əl/, mesh /mɛʃ/, evaluation /ɪˌvæljuˈeɪʃn̩/
- Parole lunghe — attenzione all’accento: animation /ˌæn.ɪˈmeɪ.ʃən/, artificial /ˌɑː.tɪˈfɪʃ.əl/, capability /ˌkeɪ.pəˈbɪl.ɪ.ti/, independently /ˌɪndɪˈpɛndəntli/, fundamental /ˌfʌn.dəˈmɛn.təl/
I suoni difficili per chi parla italiano:
- Consonante finale — senza aggiungere una vocale dopo: benchmark /ˈbɛn(t)ʃmɑːk/, solve /sɒlv/, impressive /ɪmˈpɹɛsɪv/, output /ˈaʊtpʊt/, realistic /ˌɹɪəˈlɪs.tɪk/
- /æ/ — più aperta della “e”: animation /ˌæn.ɪˈmeɪ.ʃən/, rank /ˈɹæŋk/, gather /ˈɡæðɚ/, evaluation /ɪˌvæljuˈeɪʃn̩/, scan /skæn/
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, animation, bone.
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.