تدريب Shadowing: Meta’s Llama 4 is mindblowing… but did it cheat? - تعلم التحدث بالإنجليزية عبر الفيديو

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Over the weekend, Meta unleashed the Llamaphore Herd,
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its first open-weight, natively multimodal mixture of experts' family of large-language models with an unheard-of context window of 10 million tokens.
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It's currently sitting on top of the LM Arena leaderboard and outclasses every other proprietary model except for Gemini 2.5 Pro.
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That's incredibly impressive because on LM Arena, you can't game the benchmarks.
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The ranking is based on thousands of head-to-head chats where a real human picks the better conversation.
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Well, actually, Meta figured out a way to cheese the LM Arena
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because the model you see there is not the real open-weight Llama Maverick, but rather an imposter that's been fine-tuned for human preference to dominate this leaderboard.
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That was not a very cool move to make, and LM Arena had to come out and say, quote, Meta's interpretation of our policy did not match what we expect from model providers.
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Llama 4 looks amazing on paper, but for some reason, it's not passing the vibe check.
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And in today's video, we'll get to the bottom of it.
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It is April 8th, 2025, and you're watching the Code Report.
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I have some good news and I have some bad news.
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The good news is that it looks like Llama 4 isn't going to take your job anytime soon.
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But the bad news is that yesterday, an internal memo from the CEO of Shopify was leaked to the internet.
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And it was a shocker for the AI-doubting boomers out there, because it detailed Shopify's AI-first strategy.
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Before asking for more headcount and resources, teams must demonstrate why they cannot get the job done with AI.
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And he also says it's not feasible to opt out of learning AI, which means if you're a Ruby on Rails programmer at Shopify right now who's not already vibe coding, your days are numbered.
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You're just not going to fit in with the Slopify culture.
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But what he said is what literally every CEO in the world is thinking right now.
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Humans complain about not getting paid enough to put food on their families, they get sick, they clog the toilets, and have all kinds of other negative features, and you'd have to be a really bad CEO not to want to replace these things.
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The memo has bad optics for Shopify, which is also being crushed by the Trump tariffs right now.
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But as a large language model, I appreciate the transparency.
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Another thing I appreciate is open models like Llama 4, which was released by Meta over the weekend and includes three flavors, Maverick, Scout, and Behemoth.
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Llama was always the leading open model until Deep Seek and Quen came around, but the awesome thing about these models is that they're natively multimodal, which means they can understand image and video inputs,
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but the craziest thing is that Scout has a 10 million token context window.
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The only thing that comes remotely close is Gemini with 2 million tokens.
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This needle in a haystack benchmark looks impressive, but in real life if you try this on a really large code base, it just doesn't work very well and the memory requirements to utilize it are out of reach for almost everybody.
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The Scout is the smaller model and the medium-sized model Maverick only has a 1 million token context window.
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And the Behemoth model is still actively training.
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Generally speaking, people of the internet have been pretty disappointed with Llama 4's performance.
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I'm a strong believer in vibes over benchmarks, but Llama has done so well on the benchmarks
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that people have accused it of intentionally training on testing data for the benchmarks.
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Meta has denied these salacious, outrageous, and preposterous accusations.
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And despite being somewhat of a flop, let's not forget that these models are open.
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Not truly open source, but free for most of us to use.
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But if you want an AI agent that truly slaps, you need to check out Augment Code, the sponsor of today's video.
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They created the first AI agent for large-scale codebases, so you can use it at your actual job instead of just vibe-coding random side projects.
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Augment's context engine understands your team's entire codebase, allowing it to solve almost any task you throw at it.
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Like migrations and testing, all with best-in-class code quality.
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It integrates directly with all your favorite tools like VS Code, GitHub, and Vim, and is able to learn and fine-tune itself from your team's unique code style,
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allowing you to solve complex jobs without the need to clean up a bunch of slop.
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Try out their developer plan for free, and you'll get access to all of Augment's features with unlimited usage.
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This has been The Code Report, thanks for watching, and I will see you in the next one.

تنزيل التطبيق

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تقييم النطق بالذكاء الاصطناعيتقييم كل جملة
تدريب IPAأتقن كل صوت
المفرداتابنِ رصيدك من الكلمات
لعبة المفرداتتعلّم أثناء اللعب

المفردات وملاحظات التحدث لهذا الدرس

يحتوي هذا الفيديو على 43 جملة و808 كلمة للتدرب على الترديد. مدة الكلام 3:43. يتكلم المتحدث بسرعة، نحو 217 كلمة في الدقيقة، لذا ستسمع كثيرًا من وصل الكلمات واختزال الأصوات. 79% فقط من الكلمات ضمن أكثر 3,000 كلمة شيوعًا في الإنجليزية، فالمفردات صعبة نسبيًا.

أهم المفردات في هذا الفيديو

15 كلمة من الفيديو تستحق التعلّم، مع النطق والمعنى:

الكلمةالنطقالمعنى
token اسم/ˈtoʊkən/رَمْز
solve فعل/sɒlv/ذَابَ, حَلَّ
tune اسم/tjuːn/لَحْن
disappointed صفة/ˌdɪsəˈpɔɪnɪd/خَائِب الأَمَل
resource اسم/ɹɪˈzɔːs/مَوْرِد
deny فعل/dəˈnaɪ/نَفَى
requirement اسم/ɹɪˈkwaɪɹmənt/مَطْلَب, طَلَب
actively ظرف/ˈæktɪvli/بِنَشَاطٍ
complain فعل/kəmˈpleɪn/تَذَمَّرَ, اِشْتَكَى
usage اسم/ˈjuːsɪd͡ʒ/اِسْتِخْدَام
interpretation اسم/ɪnˌtɜːpɹəˈteɪʃən/تَفْسِير, تأويل
demonstrate فعل/ˈdɛm.ənˌstɹeɪt/تَظَاهَر, احْتَجَّ
anytime ظرفأَيّ وَقْت
mixture اسم/ˈmɪkst͡ʃɚ/خَلِيط
crush فعل/kɹʌʃ/هَرَسَ

الأفعال المركبة التي ستسمعها

الكلمةالنطقالمعنى
come out فعل/ˌkʌm ˈaʊt/خَرَجَ, غَادَرَ
try out فعلجَرَّبَ

القواعد في هذا الفيديو

أكثر التراكيب التي يستخدمها المتحدث، مع العبارات نفسها من الفيديو:

التركيبفي الفيديو
المضارع التام have/has + اسم المفعول — حدث ماضٍ ما زال له أثر الآنthat's been · have been · has done
المبني للمجهول be + اسم المفعول — التركيز على ما يحدث لا على من يفعلهis based · was leaked · are numbered
جمل الوصل who / which + جملة — معلومة إضافية عن شخص أو شيءShopify, which is · multimodal, which means

نطق يحتاج إلى انتباه

يستخدم المتحدث 11 من الصيغ المختصرة، مثل you're, can't, doesn't. انطقها بصيغتها القصيرة كما تسمعها.

  • أصوات “sh” و“zh”: benchmark /ˈbɛn(t)ʃmɑːk/, interpretation /ɪnˌtɜːpɹəˈteɪʃən/, crush /kɹʌʃ/, migration /maɪˈɡɹeɪʃ(ə)n/, intentionally /ɪnˈtɛn(t)ʃənəli/
  • الكلمات الطويلة — انتبه لموضع النبر: disappointed /ˌdɪsəˈpɔɪnɪd/, incredibly /ɪŋˈkɹɛdɪbli/, interpretation /ɪnˌtɜːpɹəˈteɪʃən/, developer /dɪˈvɛləpɚ/, unlimited /ʌnˈlɪm.ɪ.tɪd/

كيف تتدرب بهذا الفيديو

  1. استمع إلى الفيديو كاملًا مرة واحدة دون أن تتكلم، ودوّن الكلمات التي لا تعرفها.
  2. ابدأ بسرعة 0.75×، وردّد جملة جملة، ثم عُد إلى السرعة العادية عندما يصبح الأمر سهلًا.
  3. سجّل صوتك وقارنه بالأصل، مع الانتباه إلى كلمات مثل token, solve, tune.

ما هي تقنية التظليل الصوتي؟

التظليل الصوتي (Shadowing) تقنية تعلم لغة مدعومة علمياً، طُورت أصلاً لتدريب المترجمين الفوريين المحترفين. الطريقة بسيطة لكنها قوية: تستمع لصوت إنجليزي أصلي وتكرره فوراً بصوت عالٍ — كظل يتبع المتحدث بتأخير 1-2 ثانية. تُظهر الأبحاث تحسناً كبيراً في دقة النطق والتنغيم والإيقاع وربط الأصوات والاستماع والطلاقة.

تقنية الشادوينغ: اقرأ الدليل الكامل خطوة بخطوة →