쉐도잉 연습: Did OpenAI actually build AGI? GPT-6 Astra first look - 영상으로 영어 말하기 배우기

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The famous American philosopher Smashmouth once said, The years start coming and they don't stop coming,

and I've never understood those words more deeply than I did this week after what felt like years in AI Bizarro land.

First, on Tuesday, Anthropic released Fable and Mythos 5.1, which they're calling the world's most advanced models for coding and knowledge work.

Then on Wednesday, Meta released Muse Spark 1.3, which Zuck says is a frontier model that's almost too cheap to meter.

Then finally, on Thursday, OpenAI announced GPT-6 Astra, which President Greg Brockman says is AGI

if you're one of the handful of lucky influencers or enterprise customers who got access to it.

Unfortunately, I think I've made too many gay scam Altman jokes over the years to make the cut, but in this video, I'll do my best to break down the new models, see how the anointed are liking Astra,

and look into the totally coincidental timing where the internet broke right as it was released.

It is September 4th, 2026, and you're watching The Code Report.

Normally, the first week of September is a quiet one, because all the rich VCs and poor project managers are busy appropriating schizophrenic culture in the Nevada desert.

Because you did so well, you get to hear a magic tune on my ocarina.

But apparently, AGI waits for no one, despite how dead your ego might be.

On Tuesday, Anthropic got things rolling with the release of Fable and Mythos 5.1.

Although they're the same model, Mythos is the one you can't use while Fable is the one you can.

It just changes the subject when you ask about Anthrax.

The TMBBs were fine, but the more interesting bits were in the case studies.

A Backstreet Boys-inspired hedge fund named Millennium had a piece of code

that for the last five years would crash about once in a million runs, and regardless of how much Adderall they took, they couldn't figure it out.

So recently, they gave it to Fable 5.1, which took the memory snapshot the program leaves behind when it crashes,

then found that the crash address pointed into a compiled vendor library they didn't have the source code for.

They disassemble that library back into raw assembly and traced the crash to a bug in the vendor's code.

And speaking of drugs, Mythos 5.1 is also pretty good at designing new ones.

The first step in making most modern medicines is designing a protein that sticks to a specific target in your body.

Typically AI gets this right about 10% of the time, but Mythos 5.1 was able to increase that number to about 50%.

And for those of you who have the planet-flavored version of autism, it was able to train a neural network on 30-year-old NASA

radar data to build a new elevation map for a part of Venus.

Then on Wednesday, Meta dropped Muse Spark 1.3, which was the fourth release in five months from Meta Super Intelligence Labs.

And what do you know, these TMBBs were also pretty good.

But the more impressive part is in the price.

The standard endpoint is $1.25 in and $4.25 out.

But if you're the give up essential liberty to purchase a little temporary safety type, there's a contributor tier at 10 cents in

and 20 cents out as long as you agree to let Meta train on everything you send it.

Alexander Wang says a double-digit percentage of developers are choosing that option, which makes sense

because a double-digit percentage of developers would also choose to eat the mystery beef from Argentina

if you told them it was cheaper and open source.

Which brings us to yesterday morning when right before the Astra released, ChatGPT, Claude, Grok, and Cursor all happened to go down at the same time.

The Occam's Razor opinion is that it was most likely an Azure issue since they reported an outage around the same time,

but the more entertaining theory is that Astra's first act as a public model was to literally kill its competition.

But once the lights came back on, the launch itself was somehow even messier.

OpenAI put up their launch page, and shortly after, Reuters, CNBC, and The Verge published their embargoed stories.

Then for reasons no one really knows, OpenAI took down their announcement

and the tech influencers got started on the modern status game of flexing how long they've secretly had access to Astra 4, and to be honest, I would have done the same thing

if it wasn't for slippery scam Altman and his grubby little gang of misfits.

Anyway, about 90 minutes later, when the post was back up, it turned out the model wasn't actually available to anyone publicly yet, and the rollout to Plus and Pro users would happen in the coming days.

By the evening, Sam had posted an apology for the messy rollout, and when a Pro subscriber asked Sam if he should stay up and wait for it,

Sam told him something we've all heard after making a desperate late-night plea, go to bed.

Sam also told CNBC the model went through a formal review with the Trump administration before release, so even the government got to try AGI before we did.

As far as Astra itself, it was pre-trained on more than 100,000 GPUs at the Stargate site in Texas,

and it's the first model from OpenAI where previous models did a significant chunk of the supervising during training.

The main pitch today seems to be computer use, where Astra can fill out forms, crunch spreadsheets, and operate engineering tools like KiCat and Blender.

On OSworld, which is a benchmark that drops a model into a real desktop

and makes it do office work with a mouse and keyboard, just like a human meatbag does until it begs for Xanax, it scored 73% while taking about 40 minutes for each task,

compared to Sol's 65% at 75 minutes.

And of course, the agents on the Trust Me Bro benchmarks are the best to ever do it.

It got 100% on ExploitBench and 65% on TerminalBenchScience, which beats the numbers Anthropic was bragging about on Tuesday.

It also got 99% on ARC-AGI3, which is the benchmark that's supposed to prove your model actually generalizes instead of memorizing, which is kind of the definition of AGI,

depending on who you ask.

And according to OpenAI, Astra is the first model to hit the critical cyber threshold in its preparedness framework, which is just a fancy way of saying it can find

and exploit zero days on its own without a human telling it what to do.

Pricing is the same as Fable 5.1 at $10 per million input tokens and $50 out.

But what do the anointed humans think about it?

The early access reviews are positive in the way early access reviews typically are from people who don't publicly slander Sham Altman,

but the demos, specifically the ones that require spatial awareness, are impressive.

Sharif Shamim had it recreate the Palace of Fine Arts in San Francisco, and it did so almost perfectly in Blender.

And Thomas Ricard from OpenAI posted a walkthrough of the demo house from the launch post, which Astra first modeled in Blender and then turned into a fully walkable Unreal 5 scene.

But the most interesting was Matt Schumers, who asked Astra to build a world in Unreal Engine and fill it with a dozen Astra-powered agents.

A day later, he heard voices coming from his living room

and walked out to a bunch of agents talking about what

I can only assume was a plan to create a dating app for horses.

With all that said, when artificial analysis ran Astra through their independent intelligence index,

it scored a 61, which is exactly the same as GPT 5.6 SOL, and 5 points behind Fable 5.1.

Something doesn't add up here, but one thing that does add up is CodeRabbit, the sponsor of today's video.

They got tired of seeing me make new videos about new exploits hitting the timeline every week,

so they just launched CodeRabbit Security to provide continuous code security that's powered by actual reasoning instead of brittle regex rules.

Their security agents are designed to think like an attacker in order to hunt for real vulnerabilities across your entire codebase.

Then it prioritizes actual risks based on reachability, exploitability, and blast radius.

And it explains the problem to you in plain English and suggests a fix you can approve and merge from the diff.

CodeRabbit Security reviews every PR before merge, and you can also schedule deep scans across your full codebase to protect your backdoor 24-7.

Get 10 free code scans to try it out for free today 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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영어 학습에 유용한 비디오 배경 이해하기

이 비디오는 AI 업계의 최신 동향을 재미있게 분석한 내용으로, Anthropic, Meta, OpenAI의 새로운 모델 출시 소식과 그 과정에서의 이슈들을 다룹니다. 빠른 속도로 진행되는 대화와 다양한 전문 용어가 등장하지만, 일상 대화에서 자주 사용되는 표현들도 많이 포함되어 있어 영어 듣기와 말하기 연습에 좋습니다. 특히 "shadowspeak"이나 "shadow speak"와 같은 쉐도잉 기술을 활용하면, 자연스러운 발음과 리듬을 익힐 수 있습니다.

일상 대화에 쓸 수 있는 인기 표현 5가지

"wait for no one" - "누구를 기다리지 않는다"는 뜻으로, "AGI waits for no one"처럼 중요한 일이 시간을 기다리지 않을 때 사용합니다.
"make the cut" - "선발되다" 또는 "합격하다"는 의미로, "I didn't make the cut"처럼 선택에 포함되지 못했을 때 씁니다.
"Occam's Razor" - "오컴의 면도날"로, 가장 간단한 설명이 가장 가능성이 높다는 뜻입니다. 논쟁에서 간단한 해석을 제안할 때 사용합니다.
"flexing" - "과시하다"는 뜻으로, "flexing how long they've had access"처럼 자신의 경험을 과시할 때 사용합니다.
"go down" - "다운되다" 또는 "작동하지 않다"는 의미로, "ChatGPT went down"처럼 서비스가 중단되었을 때 씁니다.

비디오 쉐도잉 연습 가이드: 단계별 방법

이 비디오는 속도가 빠르고 전문 용어가 많아 처음에는 어려울 수 있지만, "영어 쉐도잉" 기술을 활용하면 효과적으로 연습할 수 있습니다. 다음 단계를 따라해 보세요:

1회 차: 단순 듣기 - 전체 내용을 한 번 듣고 전반적인 흐름을 파악합니다. 어려운 부분은 표시해 두세요.
2회 차: 구절별 반복 - 10~15초 단위로 구절을 나누고, 화자의 발음과 리듬을 따라 말합니다. "shadowing site"에서 제공하는 도구를 사용하면 더 쉽습니다.
3회 차: 의미 파악 - 각 구절의 의미를 이해하면서 말합니다. 모르는 단어는 즉시 찾아보고, 문장 구조를 분석합니다.
4회 차: 완전 모방 - 화자의 음량, 강세, 억양까지 모방합니다. "shadow speech" 기술을 사용해 자연스러운 발음을 연습합니다.

매일 10~15분씩 연습하면, 빠른 속도의 대화도 자연스럽게 따라할 수 있게 됩니다. 작은 진전부터 시작해 지속적으로 연습하면 분명히 실력이 향상될 것입니다!

이 영상의 문법

화자가 가장 많이 쓰는 문형을 영상 속 실제 표현과 함께 정리했습니다.

문형	영상 속 표현
관계절 who / which + 절 — 사람이나 사물에 대한 추가 정보	option, which makes · OSworld, which is · memorizing, which is
현재완료 have/has + 과거분사 — 과거의 일이 지금도 관련이 있을 때	I've never understood · I've made · has been
수동태 be + 과거분사 — 누가 하는지보다 무슨 일이 일어나는지에 초점	was released · are designed

쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법

쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.

섀도잉 방법: 단계별 전체 가이드 읽기 →

쉐도잉 연습: Did OpenAI actually build AGI? GPT-6 Astra first look - 영상으로 영어 말하기 배우기

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영어 학습에 유용한 비디오 배경 이해하기

일상 대화에 쓸 수 있는 인기 표현 5가지

비디오 쉐도잉 연습 가이드: 단계별 방법

이 영상의 문법

쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법