シャドーイング練習: Meta's new model wants "deep access" to your personal life... - 動画で英語スピーキングを学ぶ

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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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このビデオは、英語スピーキング練習において、速いスピーチの理解と自然なリズムの再現を目指す上で最適です。ネイティブスピーカーの早口の会話や、専門用語を含む複雑な文脈を追うことで、shadowspeak（シャドーイング）の技術を磨き、リアルタイムでのコミュニケーション能力を向上させることができます。

フレーズバンク

"poaching spree"（人材泥棒合戦）- 競合他社から優秀な人材を奪う様子を表す生きた表現。
"incinerated with nothing to show for it"（何も成果がないまま燃やされる）- 投資が無駄になることを誇張して表現。
"put that genie back in the bottle"（ジーニーを瓶に戻す）- 既に起こった出来事を元に戻せないことを比喩的に表す。
"bar for bar"（互角に戦う）- 勢いが拮抗している様子を表す口語表現。

弱点を直そう

このビデオは、shadow speakの練習に最適で、特に速いスピーチのリズムと弱読みされる単語の発音の問題を解決します。例えば、"and"や"the"などの機能語が早口の会話でどのように省略されるか、また、"parameter"や"distillation"などの長い単語のアクセントの位置を正確に捉えることが重要です。YouTubeで英語学習をする際は、ビデオをゆっくり再生して発音を模倣し、繰り返しシャドーイングを行うことで、自然なスピーキングスキルが身につきます。

この動画の文法

話し手がよく使っている文型を、動画の実際の表現とともに紹介します。

文型	動画での表現
関係詞節 who / which + 節 — 人や物について情報を加える	context, which is · animals, which was · works, which is
受動態 be + 過去分詞 — 誰がするかより、何が起きるかに焦点を当てる	was inspired · was seen · was released
現在完了形 have/has + 過去分詞 — 過去の出来事が今も関係している	hasn't been · I've seen · has been

シャドーイングとは？英語上達に効果的な理由

シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。

シャドーイングのやり方: ステップ別の完全ガイドを読む →

シャドーイング練習: Meta's new model wants "deep access" to your personal life... - 動画で英語スピーキングを学ぶ

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弱点を直そう

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シャドーイングとは？英語上達に効果的な理由