Luyện nói tiếng Anh bằng Shadowing qua video: A $6.3 billion open-weight model just got embarrassed by the French...
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Yesterday, while you were being force -fed every iteration of that Rumpelstiltskin video, the Paris -based AI Lab Mistral released Mistral Large 4,
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a trillion -parameter open -weight model that they're claiming is the best of its kind, assuming you adopt the American ideology of just ignoring any technology coming out of China.
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It's natively multimodal, has a million -token context window, and claims to blah blah blah.
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The more interesting part of this release was the timing, because it landed in the middle of what many are calling the dumbest week in the history of the open weight race.
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It kicked off early in the week
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when Trump announced a super intelligence force whose job it is to keep America at the frontier of the AI race,
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with their first order of business claiming that AI is actually dumb and the real future is SI.
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Then Reflection AI, the $25 billion Nvidia -backed startup that calls itself America's Open Frontier Lab, will finally ship their first model after two years of fundraising.
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It's called Beam, it has 501 billion parameters, it was trained on 6 .3 billion dollars worth of GPUs, it benchmarked itself against an already outdated Chinese model,
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and 24 hours later it got out -benchmarked itself by Mistral.
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Then, just yesterday, Moonshot AI, the Chinese lab behind Kimi, closed its final funding round at a 50 billion dollar valuation and started prepping a Hong Kong IPO,
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which I'm told the superintelligence force has no interest investing in.
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In today's video, we'll look at what the French, the Americans, and the Chinese each ship this week.
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Find out why every government on earth suddenly wants an open model to call its own, and figure out which one you should actually use.
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It is October 7th, 2026, and you're watching the Code Report.
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If you're still not convinced about the benefits of open -weight models, last month a man in San Francisco learned them the hard way.
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When he had the authorities looking for him, after a joking conversation he had with Claude triggered a safety review, and just last week, a woman in Florida got arrested just days after telling Claude
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she was going to do something that's typically frowned upon in modern society.
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And even if you're not planning on committing a felony, with proprietary models, you're still sending every prompt to a data center, which is scanned by a classifier, but potentially read by a safety team,
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and then used in whatever way the lab wants, like training future models or kicking off the process to throw you in jail.
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And whole -ass countries are having the same problem too.
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Take a government that wants to use AI to find issues in their own power grid.
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Using a model like Mythos, which is designed for this exact purpose, would require handing a foreign company your private schematics and then hoping
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that they never get leaked or that someone on their payroll isn't a foreign spy, which Dario himself has said may already be the case, where if you were using an open -weight model,
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most of these problems go away, which is why nearly every country in the world is trying to build their own if they can, or find ones they can trust if they can't.
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Which brings us back to this week and the great model mid -off.
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The first announcement came from Reflection AI, which was started by two DeepMind researchers who raised $4 .6 billion, a billion with a B,
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on the premise that the best open model in the world should come from the West, and Llama 4 wasn't cutting it.
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Well, this week, we finally got to witness Manifest Destiny realized when they released Beam.
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It's got 501 billion parameters, and its base model was trained in just under four weeks on about 24 trillion tokens.
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After that, they spent four more weeks post -training it to get it better at writing code, using tools, and finishing long agent tasks.
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The result is a model that Reflection says keeps up with Zhipu AI's GLM 5 .2, while needing only a quarter of the hardware.
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But sadly, as of today, you still need an invite to use it, and the waits aren't actually out yet.
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Which means the best open American model is somehow still Gemma 4, which Google released under Apache 2 .0 back in April.
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Then the next day, Mistral showed up with something that sounds like you'd order it from McDonald's.
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It's a mixture of experts model, which means it has about a trillion parameters, but only 49 billion of them actually turn on for a given token.
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So the idea is
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that you get a trillion parameters worth of knowledge for roughly the cost of running a model 20 times smaller, which if that sounds familiar,
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it's probably because it's the same trick DeepSeq used last year to tank NVIDIA.
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On the TMBBs, it barely beats GLM 5 .3 at long coding jobs, it beats Beam by 18 points and lands in the top five for finding security bugs,
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which is the one Mistral actually cares about
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because the power grid scenario from earlier is the thing they're actually trying to sell.
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The catch is the numbers really are, trust me bros, because they were provided by Mistral
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and they actually haven't finished training yet and the weights don't come out until later this month under their own custom license.
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Which brings us to China, which already has the thing both of them are trying to build with Kimi K3.
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It's a 2 .8 trillion parameter open model that Moonshot released back in July,
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which became so popular they had to stop taking new subscribers two days later because they ran out of GPUs.
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Since then, Microsoft, Amazon, and Google have all been negotiating to host it.
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Anthropic accused them of running 300 ,000 requests through fake accounts to copy Claude, and the company went from a $4 billion valuation last November
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to $50 billion yesterday with China's own state AI fund involved in the round.
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So which one should you actually use though?
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If all you've got is a gaming PC, go with Gemma 4 and get your money up.
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If you have your own data center and no compliance department, go with Kimi K3.
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Or if you have a data center and a compliance department, then go with Mr. Large 4.
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And if you're a patriot, Beam's weights are coming later this month.
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But no matter which model you choose, they'll need to get on the internet, which they can do with Kernel, the sponsor of today's video.
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They provide open source browser infrastructure that lets your agents act on the internet blazingly fast.
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But none of that actually matters if your agent keeps getting blocked by bot checks, which is why they just launched Config Registry.
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Send it the URL you want to access from either their dashboard or the API, and it'll give you back the browser and proxy settings that have worked for that site.
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Right now, my agent keeps getting stopped by a bot check, but when I spin up a browser with the config Kernel recommends, it's able to access the page without any issues.
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Kernel builds these configs by continuously testing real browser and proxy setups against the live site, and each one comes with a success rate, sample size, and last verified date.
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Use the code Fireship at the link below to get $30 in free credits, which covers your first month on the hobbyist plan this has been the code report thanks for watching
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and i will see you in the next one
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Từ vựng và ghi chú luyện nói cho bài học này
Bài luyện nói này dựa trên video “A $6.3 billion open-weight model just got embarrassed by the French..”. Những từ được nhắc lại nhiều nhất trong bài: model, billion, Mistral, parameter, weight. Video này có 63 câu và 1255 từ để luyện shadowing. Phần lời nói dài 6:09. Người nói nói nhanh, khoảng 204 từ mỗi phút, nên sẽ có nhiều chỗ nối âm và nuốt âm. 82% số từ nằm trong 3.000 từ tiếng Anh thông dụng nhất; phần còn lại nên xem trước khi luyện.
Từ vựng quan trọng trong video
15 từ đáng học trong video, kèm phiên âm và nghĩa:
| Từ | Phiên âm | Nghĩa |
|---|---|---|
| grid danh từ | /ɡɹɪd/ | lưới, mạng |
| ignore động từ | /ɪɡˈnɔː/ | lơ, lờ |
| infrastructure danh từ | /ˈɪnfɹəˌstɹʌkt͡ʃə/ | cơ sở hạ tầng |
| custom danh từ | /ˈkʌstəm/ | tập quán, phong tục |
| spin động từ | /spɪn/ | quay |
| announcement danh từ | /əˈnaʊns.mənt/ | thông báo |
| hardware danh từ | /ˈhɑɹdˌwɛɹ/ | cương liệu, phần cứng |
| invite động từ | /ɪnˈvaɪt/ | mời |
| announce động từ | /əˈnaʊn(t)s/ | thông báo |
| convince động từ | /kənˈvɪns/ | thuyết phục |
| trigger danh từ | /ˈtɹɪɡɚ/ | cò |
| bug danh từ | /bʌɡ/ | bọ |
| adopt động từ | /əˈdɑpt/ | nhận làm con nuôi |
| scenario danh từ | /sɪˈnɛəɹioʊ/ | trường hợp, kịch bản |
| mixture danh từ | /ˈmɪkst͡ʃɚ/ | hỗn hợp |
Ngữ pháp trong video
Những cấu trúc người nói dùng nhiều nhất, kèm đúng cụm từ trong video:
| Cấu trúc | Trong video |
|---|---|
| Câu bị động be + quá khứ phân từ — nhấn vào việc xảy ra, không phải người làm | was trained · is scanned · is designed |
| Mệnh đề quan hệ who / which + mệnh đề — thêm thông tin về người hoặc vật | center, which is · Mythos, which is · away, which is |
| Thì hiện tại hoàn thành have/has + quá khứ phân từ — việc đã xảy ra nhưng còn liên quan đến hiện tại | has said · haven't finished · have worked |
Phát âm cần chú ý
Người nói dùng 19 dạng rút gọn, ví dụ you're, they're, aren't. Hãy nói theo dạng ngắn đúng như bạn nghe.
- Âm “sh” và “zh”: valuation /ˌvæ.ljuːˈeɪ.ʃən/, benchmark /ˈbɛn(t)ʃmɑːk/, potentially /pəˈtɛnʃ(ə)li/, negotiate /nəˈɡəʊ.ʃi.eɪt/, dashboard /ˈdæʃˌboɹd/
- Từ dài — đặt trọng âm cho đúng: parameter /pəˈɹæm.ə.tɚ/, valuation /ˌvæ.ljuːˈeɪ.ʃən/, infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, potentially /pəˈtɛnʃ(ə)li/, ideology /aɪ.diˈɑ.lə.d͡ʒi/
Những âm người Việt thường gặp khó:
- Cụm phụ âm cuối — đọc đủ từng âm, đừng nuốt âm cuối: compliance /kəmˈplaɪ.əns/, invest /ɪnˈvɛst/, announcement /əˈnaʊns.mənt/, announce /əˈnaʊn(t)s/, convince /kənˈvɪns/
- Âm /tʃ/, /dʒ/ và /ʒ/ — tiếng Việt không có, đừng đọc thành “ch” hay “gi”: infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, mixture /ˈmɪkst͡ʃɚ/, joking /ˈd͡ʒoʊkɪŋ/, ideology /aɪ.diˈɑ.lə.d͡ʒi/, researcher /ˈɹiˌsɝ.t͡ʃɚ/
- Âm /æ/ — mở miệng rộng hơn âm “e”: parameter /pəˈɹæm.ə.tɚ/, valuation /ˌvæ.ljuːˈeɪ.ʃən/, sadly /ˈsædli/, scan /skæn/, anthropic /ænˈθɹɒp.ɪk/
Cách luyện với video này
- Nghe hết video một lần, chưa cần nói, và ghi lại những từ bạn chưa biết.
- Bắt đầu ở tốc độ 0,75×, nói đuổi từng câu, rồi quay lại tốc độ bình thường khi đã quen.
- Ghi âm giọng mình rồi so với bản gốc, chú ý các từ như grid, ignore, infrastructure.
Phương Pháp Shadowing Là Gì?
Shadowing là kỹ thuật học ngôn ngữ có cơ sở khoa học, ban đầu được phát triển cho chương trình đào tạo phiên dịch viên chuyên nghiệp và được phổ biến rộng rãi bởi nhà đa ngôn ngữ học Dr. Alexander Arguelles. Nguyên lý cốt lõi đơn giản nhưng cực kỳ hiệu quả: bạn nghe tiếng Anh của người bản xứ và lặp lại to ngay lập tức — như một "cái bóng" (shadow) đuổi theo người nói với độ trễ chỉ 1–2 giây. Khác với luyện ngữ pháp hay học từ vựng bị động, Shadowing buộc não bộ và cơ miệng phải đồng thời xử lý và tái tạo ngôn ngữ thực tế. Các nghiên cứu khoa học xác nhận phương pháp này cải thiện đáng kể phát âm, ngữ điệu, nhịp điệu, nối âm, kỹ năng nghe và độ lưu loát khi nói — đặc biệt hiệu quả cho người luyện IELTS Speaking và muốn giao tiếp tiếng Anh tự nhiên như người bản ngữ.