शैडोइंग अभ्यास: Meta's new model wants "deep access" to your personal life... - वीडियो के साथ अंग्रेजी बोलना सीखें

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57 वाक्य

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On Monday, the company that spent the last year starving Llama, poaching researchers with nine-figure Zuck bucks, and locking its new models behind a Facebook auth page,

just released a free and open-source model under the Apache 2.0 license.

It's called Muse Glimmer, a name I believe was inspired by Zuck's fanfiction My Little Pony character,

and it's a 30 billion parameter agentic model that's small enough to run on your poverty spec PC.

That means you can now have an always-on mini Zuck agent living rent-free in your gaming PC, reading your email, managing your calendar,

and using your data to overthrow local elections in middle America.

And according to Meta, an agent like that needs deep access to personal context, which is a sentence they've been dreaming about writing since 2004,

except this time, the surveillance can run entirely on your own hardware.

In today's video, we'll find out what Muse Glitter Glimmer can actually do, learn how Meta shrunk it down with distillation,

quantization, and speculative decoding, and determine whether Zuck's open-source redemption arc is sincere, or if this is just what losing looks like right now.

It is August 12th, 2026, and you're watching The Code Report.

It wasn't long ago, when Meta was seen as the champion of the open weight class.

Their Llama models lopped up over a billion downloads and created an entire ecosystem of custom models built on top of them.

That is, until Llama 4 was released, and it became the biggest Facebook bust since Justin Timberlake partied with those underage girls.

In the release were two models, Scout and Maverick.

Initially, they benchmarked well, but that was only

because Meta submitted a secret juiced up version to LMArena that none of us normies could actually download.

And the models we could download had the intelligence of farm animals, which was fitting given their names.

And while this was happening, comrade Xi Jinping was dropping open models that were better, cheaper, and released in accordance with Mao Zedong's thought.

Then Zuck responded to this humiliation the only way a billionaire knows how.

He spent $14 billion on a 49% stake in scale AI just to acquire its CEO, Alexander Wang.

Then he went on a poaching spree for researchers at OpenAI and Google, and rebranded the whole operation as Meta Super Intelligence Labs.

The first thing his new dream team did was rebuild the stack from scratch and abandon open source entirely, shipping MuseSpark earlier this year as a closed API-only model.

That created a new problem, though, because no sane person would choose Muse Spark over something like Claude or Gemini.

So with Wall Street starting to ask questions about the $145 billion of CapEx being incinerated with nothing to show for it,

I assume Zuck flew to a secret compound in Kauai that the locals say he stole from them and formed a plan, which eventually led us here to the launch of Muse Glimmer.

It's a dense 30 billion parameter model distilled directly from Muse Spark, their big but closed model using logit distillation, which basically means they had the big model whisper its exact

probability distributions into the ear of Glimmer until it learned how to fake it.

And in Zuck's manifesto he made after the release, he described distillation as an important principle of how the open-source ecosystem works, which is ironic

because every American lab spent the last year accusing the Chinese of doing this exact same thing to their outputs.

So how good is it actually?

Well, according to the TMBBs, it clearly beats Gemma 4 and goes bar for bar with Quen 3.6.

But my favorite number is the prompt injection benchmark, where attacks against Glimmer succeeded 28% of the time, but they're listing that as a win because with Quen, it's 40%.

And the way they got it to run on consumer GPUs was interesting.

At full precision, the model would need over 55 gigs of memory, so their first move was quantization, where Meta compressed the weights down to about 4 bits,

which shrank it to just under 20 gigs.

And the second was speculative decoding, which is basically autocomplete for your autocomplete.

The way it works is a tiny model called D-Flash blurts out an entire block of tokens, then the big model reads the whole block in one pass, and throws out the bad guesses,

which they say led to a 3x speedup on a 5090.

But to be honest, the actual model is way less interesting than Zuck's manifesto I mentioned earlier.

In it, he argues that the real risk in AI isn't rogue superintelligence, but a small handful of companies owning it,

and that he doesn't understand why anyone who believes AI will end humanity would rush to build it, which is a reasonable point coming from a guy whose company

was just fined $567 million last week for being a public nuisance in New Mexico.

He also wants Frontier Labs to hand the US government mid-training checkpoints of unreleased models, and he announced a billion dollar fund for the towns willing to host his data centers,

probably so they'll stop causing all that ruckus.

So, is the redemption arc sincere?

Probably not, but an Apache license is an Apache license, and they can't put that genie back in the bottle.

Zuck and Wang both say open waits for MuseSpark 1.2 are coming soon, which would mean you could self-host the exact model behind Meta's own coding agent, which is pretty cool.

And if you want to test out Muse Glimmer for yourself, you can do it with the sponsor of today's video, OpenRouter.

It gives you a single API to access every LLM, so you only have one endpoint and one bill to worry about.

You can switch between models manually, or use their routers to pick one for you based on a combination of factors, like price and accuracy.

My startup still hasn't been able to raise a seed round, so I used Open Router to rewrite the entire codebase in Rust to attract more investors.

Because Rust is a notoriously easy language, I used Pareto Router to route every request to cheaper coding models that could still cargo build.

But I eventually ended up splurging for some fancier models for the UI,

the dedicated image and speech endpoints to generate a few especially attractive profiles to seed the community and get the matches flowing.

Open Router is easily the best tool I've seen for discovering new models without getting stuck in 25 different subscriptions, and you can try it out for free at the link below.

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

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संदर्भ और पृष्ठभूमि

इस वीडियो में, वक्ता एक नई तकनीकी पहलू पर चर्चा कर रहे हैं जो मेटा द्वारा विकसित की गई है। उन्होंने बताया कि कैसे यह नया मॉडल, Muse Glimmer, न केवल व्यक्तिगत जानकारी को समझने की कोशिश करता है, बल्कि इसे व्यक्तिगत जीवन में गहराई से शामिल करने का इरादा रखता है। इस संदर्भ में, यह समझना महत्वपूर्ण है कि कैसे तकनीकी विकास, जैसे कि AI, हमारी दैनिक जीवन की प्रक्रियाओं को प्रभावित कर सकते हैं।

दैनिक संचार के लिए शीर्ष 5 वाक्यांश

"यह नया मॉडल कैसे काम करता है?" - इससे आप किसी तकनीकी विषय पर चर्चा कर सकते हैं।
"क्या आपको इसके प्रभाव के बारे में जानकारी है?" - यह सवाल किसी भी विषय पर गहराई से बात करने में मदद करेगा।
"आपकी राय क्या है इस पर?" - यह वाक्यांश संवाद को बढ़ावा देने के लिए उपयोगी है।
"यहाँ क्या खास है?" - इस सवाल से आप किसी विशेष विषय पर जानकारी हासिल कर सकते हैं।
"क्या यह सुरक्षित है?" - यह प्रश्न आमतौर पर तकनीकी चर्चाओं में उठता है।

चरण-दर-चरण शैडोइंग गाइड

इस वीडियो की कठिनाई से निपटने के लिए, निम्नलिखित कदम उठाएँ:

सुनें और समझें: सबसे पहले, वीडियो को ध्यान से सुनें। इस दौरान, शब्दों और वाक्यांशों की उच्चारण पर ध्यान दें।
छाया बोलें: जब आप सुनते हैं, तो आप शैडोइंग तकनीक का प्रयोग करें। मतलब, जैसे ही वक्ता बोलें, आप उसी समय उनके साथ बोलें। इसे अंग्रेजी शैडोइंग कहा जाता है, और यह आपकी उच्चारण में सुधार करने में मदद करेगा।
फिर से सुनें: एक बार फिर से वीडियो को सुनें, इस बार बिना बोलें। इससे आप उन चीज़ों को समझ सकेंगे जो आप पहले मिस कर गए थे।
रिकॉर्ड करें: अपनी आवाज़ को रिकॉर्ड करें जब आप शैडोिंग कर रहे हों। बाद में सुनें और अपने उच्चारण को बेहतर बनाने के लिए तुलना करें।
नियमित अभ्यास: प्रत्येक दिन थोड़ी देर के लिए इस प्रक्रिया का पालन करें, जिससे आपकी अंग्रेजी बोलने की क्षमता में निरंतर सुधार हो सके।

यूट्यूब से अंग्रेजी सीखने के इस तरीके से न केवल आपकी बोलने की क्षमता में सुधार होगा, बल्कि आप तकनीकी विषयों पर भी आत्मविश्वास से चर्चा कर सकेंगे। अपने संवाद कौशल को विकसित करने के लिए shadowspeak और अंग्रेजी उच्चारण में सुधार की इस विधि का अभ्यास करें।

इस वीडियो का व्याकरण

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

संरचना	वीडियो में
Relative clauses who / which + उपवाक्य — व्यक्ति या वस्तु के बारे में अतिरिक्त जानकारी	context, which is · animals, which was · works, which is
Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है	was inspired · was seen · was released
Present perfect have/has + past participle — बीता हुआ काम जिसका असर अभी भी है	hasn't been · I've seen · has been

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 new model wants "deep access" to your personal life... - वीडियो के साथ अंग्रेजी बोलना सीखें

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संदर्भ और पृष्ठभूमि

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चरण-दर-चरण शैडोइंग गाइड

इस वीडियो का व्याकरण

शैडोइंग तकनीक क्या है?