跟读练习: Did OpenAI actually build AGI? GPT-6 Astra first look - 通过视频学习英语口语

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

美国著名哲学家Smashmouth曾说过：“岁月不断涌来，从不停歇。”

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

在经历了仿佛置身于人工智能怪诞世界的漫长一周之后，我才真正深刻地理解了这句话的含义。

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.

首先，Anthropic 于周二发布了 Fable 和 Mythos 5.1，他们称这是全球在编程和知识工作领域最先进的模型。

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

随后在周三，Meta发布了Muse Spark 1.3，扎克伯格称这是一款前沿模型，其成本低到几乎可以忽略不计。

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

最终在周四，OpenAI发布了GPT-6 Astra，其总裁格雷格·布罗克曼称这就是AGI。

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

如果你是少数有幸获得访问权限的网红或企业客户之一。

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,

很遗憾，我这些年讲过太多关于奥特曼的同性恋梗，估计是没机会入选了，但在这期视频里，我会尽力解析这些新模型，看看那些被选中的人对Astra的评价如何。

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

还要好好查一查那个完全是巧合的时间点——就在它发布的那一刻，整个互联网刚好崩溃了。

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

今天是2026年9月4日，您正在收看《代码报告》。

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.

通常来说，九月的第一周总是格外安静，因为不管是身家丰厚的风投大佬还是囊中羞涩的项目经理，都忙着跑到内华达沙漠里挪用那些精神分裂式的亚文化。

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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为什么用这个视频练习口语？

看视频学英语是提升口语的高效方式，尤其是这类科技热点内容。视频中主持人用生动的口语化表达讨论AI圈动态，涵盖行业术语、幽默调侃和实时事件分析，非常适合雅思口语练习。其语速适中、逻辑清晰，能帮助学习者熟悉自然的英语思维和表达节奏，是英语影子跟读（shadowspeak）的优质素材。通过模仿主持人的语气、重音和停顿，可快速提升口语流畅度与语感。

语境中的语法与表达

虚拟语气："If you told them it was cheaper and open source" 体现与事实相反的假设，是雅思口语中表达观点的常用结构。
插入语："then found that the crash address pointed into a compiled vendor library they didn't have the source code for" 中的插入成分，展示英语句子的灵活构造，有助于提升口语复杂度。
习语活用："Occam's Razor opinion" 借用哲学概念表达简洁逻辑，类似表达能让口语更具深度，适合影子跟读时重点模仿。

常见发音陷阱

视频中部分词汇易读错，如 "Anthropic"（/ænˈθrɒpɪk/）的重音在第二音节，"embargoed"（/ɪmˈbɑːɡəʊd/）的元音发音需注意。此外，主持人快速连读 "double-digit percentage" 时，"double-digit" 与 "percentage" 间的爆破音失去，是影子跟读练习中需关注的细节。通过shadowing site反复练习这些难点，可有效纠正发音问题。

视频中的语法

说话人最常用的结构，并附上视频中的原话：

结构	视频中的用法
定语从句 who / which + 从句 — 补充说明人或事物	option, which makes · OSworld, which is · memorizing, which is
现在完成时 have/has + 过去分词 — 过去发生但与现在仍有关联的事	I've never understood · I've made · has been
被动语态 be + 过去分词 — 强调发生了什么，而不是谁做的	was released · are designed

什么是跟读法？

跟读法 (Shadowing) 是一种有科学依据的语言学习技巧，最初开发用于专业口译员的培训，并由多语言者Alexander Arguelles博士普及。这个方法简单而强大：您在听英语母语原声的同时立即大声重复——就像是一个延迟1-2秒紧跟说话者的影子。与被动听力或语法练习不同，跟读法强迫您的大脑和口腔肌肉同时处理并模仿真实的讲话模式。研究表明它能显着提高发音准确性，语调，节奏，连读，听力理解和口语流利度——使其成为雅思口语备考和真实英语交流最有效的方法之一。

影子跟读法: 阅读完整分步指南 →

跟读练习: Did OpenAI actually build AGI? GPT-6 Astra first look - 通过视频学习英语口语

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为什么用这个视频练习口语？

语境中的语法与表达

常见发音陷阱

视频中的语法

什么是跟读法？