शैडोइंग अभ्यास: Meta’s Llama 4 is mindblowing… but did it cheat? - वीडियो के साथ अंग्रेजी बोलना सीखें

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43 वाक्य

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Over the weekend, Meta unleashed the Llamaphore Herd,

its first open-weight, natively multimodal mixture of experts' family of large-language models with an unheard-of context window of 10 million tokens.

It's currently sitting on top of the LM Arena leaderboard and outclasses every other proprietary model except for Gemini 2.5 Pro.

That's incredibly impressive because on LM Arena, you can't game the benchmarks.

The ranking is based on thousands of head-to-head chats where a real human picks the better conversation.

Well, actually, Meta figured out a way to cheese the LM Arena

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.

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.

Llama 4 looks amazing on paper, but for some reason, it's not passing the vibe check.

And in today's video, we'll get to the bottom of it.

It is April 8th, 2025, and you're watching the Code Report.

I have some good news and I have some bad news.

The good news is that it looks like Llama 4 isn't going to take your job anytime soon.

But the bad news is that yesterday, an internal memo from the CEO of Shopify was leaked to the internet.

And it was a shocker for the AI-doubting boomers out there, because it detailed Shopify's AI-first strategy.

Before asking for more headcount and resources, teams must demonstrate why they cannot get the job done with AI.

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.

You're just not going to fit in with the Slopify culture.

But what he said is what literally every CEO in the world is thinking right now.

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.

The memo has bad optics for Shopify, which is also being crushed by the Trump tariffs right now.

But as a large language model, I appreciate the transparency.

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.

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,

but the craziest thing is that Scout has a 10 million token context window.

The only thing that comes remotely close is Gemini with 2 million tokens.

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.

The Scout is the smaller model and the medium-sized model Maverick only has a 1 million token context window.

And the Behemoth model is still actively training.

Generally speaking, people of the internet have been pretty disappointed with Llama 4's performance.

I'm a strong believer in vibes over benchmarks, but Llama has done so well on the benchmarks

that people have accused it of intentionally training on testing data for the benchmarks.

Meta has denied these salacious, outrageous, and preposterous accusations.

And despite being somewhat of a flop, let's not forget that these models are open.

Not truly open source, but free for most of us to use.

But if you want an AI agent that truly slaps, you need to check out Augment Code, the sponsor of today's video.

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.

Augment's context engine understands your team's entire codebase, allowing it to solve almost any task you throw at it.

Like migrations and testing, all with best-in-class code quality.

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,

allowing you to solve complex jobs without the need to clean up a bunch of slop.

Try out their developer plan for free, and you'll get access to all of Augment's features with unlimited usage.

This has been The Code Report, thanks for watching, and I will see you in the next one.

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इस पाठ की शब्दावली और बोलने के सुझाव

इस वीडियो में शैडोइंग के लिए 43 वाक्य और 808 शब्द हैं। बोला गया हिस्सा 3:43 लंबा है। वक्ता तेज़ बोलता है, लगभग 217 शब्द प्रति मिनट, इसलिए जुड़ी हुई और कमज़ोर ध्वनियाँ ज़्यादा मिलेंगी। केवल 79% शब्द अंग्रेज़ी के 3,000 सबसे आम शब्दों में से हैं, इसलिए शब्दावली कठिन है।

इस वीडियो की मुख्य शब्दावली

वीडियो के 15 सीखने लायक शब्द, उच्चारण और अर्थ के साथ:

शब्द	उच्चारण	अर्थ
benchmark संज्ञा	/ˈbɛn(t)ʃmɑːk/	मानक, मापदंड
vibe संज्ञा	/vaɪb/	माहौल
solve क्रिया	/sɒlv/	सुलझाना, हल करना
tune संज्ञा	/tjuːn/	धुन
rank संज्ञा	/ˈɹæŋk/	श्रेणीबद्ध
disappointed विशेषण	/ˌdɪsəˈpɔɪnɪd/	निराश
resource संज्ञा	/ɹɪˈzɔːs/	संसाधन
requirement संज्ञा	/ɹɪˈkwaɪɹmənt/	आवश्यकता, अनिवार्यता
complain क्रिया	/kəmˈpleɪn/	शिकायत करना
usage संज्ञा	/ˈjuːsɪd͡ʒ/	लौकिकता
interpretation संज्ञा	/ɪnˌtɜːpɹəˈteɪʃən/	निर्वचन, अर्थनिर्वचन
developer संज्ञा	/dɪˈvɛləpɚ/	विकासक
anytime क्रिया विशेषण		कभी भी
mixture संज्ञा	/ˈmɪkst͡ʃɚ/	मिलाना
migration संज्ञा	/maɪˈɡɹeɪʃ(ə)n/	प्रवास, माइग्रेशन

इस वीडियो का व्याकरण

वक्ता जिन संरचनाओं का सबसे अधिक प्रयोग करता है, वीडियो के असली शब्दों के साथ:

संरचना	वीडियो में
Present perfect have/has + past participle — बीता हुआ काम जिसका असर अभी भी है	that's been · have been · has done
Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है	is based · was leaked · being crushed
Relative clauses who / which + उपवाक्य — व्यक्ति या वस्तु के बारे में अतिरिक्त जानकारी	Shopify, which is · multimodal, which means

ध्यान देने योग्य उच्चारण

वक्ता 11 बार संक्षिप्त रूपों का प्रयोग करता है, जैसे you're, can't, doesn't। इन्हें वैसे ही छोटा बोलें जैसा आप सुनते हैं।

“sh” और “zh” की ध्वनियाँ: benchmark /ˈbɛn(t)ʃmɑːk/, interpretation /ɪnˌtɜːpɹəˈteɪʃən/, crush /kɹʌʃ/, migration /maɪˈɡɹeɪʃ(ə)n/, intentionally /ɪnˈtɛn(t)ʃənəli/
लंबे शब्द — बलाघात सही रखें: disappointed /ˌdɪsəˈpɔɪnɪd/, incredibly /ɪŋˈkɹɛdɪbli/, interpretation /ɪnˌtɜːpɹəˈteɪʃən/, developer /dɪˈvɛləpɚ/, unlimited /ʌnˈlɪm.ɪ.tɪd/

इस वीडियो से अभ्यास कैसे करें

पहले बिना बोले पूरा वीडियो एक बार सुनें और जो शब्द नहीं आते उन्हें लिख लें।
0.75× गति से शुरू करें, एक-एक वाक्य की शैडोइंग करें, और सहज लगने पर सामान्य गति पर लौटें।
अपनी आवाज़ रिकॉर्ड करें और मूल से तुलना करें, ख़ासकर benchmark, vibe, solve जैसे शब्दों पर ध्यान दें।

शैडोइंग तकनीक क्या है?

शैडोइंग (Shadowing) एक विज्ञान-समर्थित भाषा सीखने की तकनीक है जो मूल रूप से पेशेवर दुभाषिया प्रशिक्षण के लिए विकसित की गई थी। विधि सरल लेकिन शक्तिशाली है: आप मूल अंग्रेज़ी ऑडियो सुनते हैं और तुरंत इसे ज़ोर से दोहराते हैं — जैसे वक्ता की छाया 1-2 सेकंड की देरी से। शोध से पता चलता है कि यह उच्चारण सटीकता, स्वर, लय, जुड़ी हुई ध्वनियाँ, सुनने की समझ और बोलने की प्रवाहशीलता में काफ़ी सुधार करता है।

शैडोइंग तकनीक: पूरी चरण-दर-चरण गाइड पढ़ें →

शैडोइंग अभ्यास: Meta’s Llama 4 is mindblowing… but did it cheat? - वीडियो के साथ अंग्रेजी बोलना सीखें

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इस वीडियो का व्याकरण

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इस वीडियो से अभ्यास कैसे करें

शैडोइंग तकनीक क्या है?