GamingChairModel

Fable 5 spawning a herd of Codex 5.6 Sol to write metal shaders.

Fable is Anthropic's current flagship LLM model (Mythos) with safeguards/restrictions intended to prevent it from writing malware. Version 5 is the latest, released in June, briefly banned by the US Government, and then made available again on July 1.

Codex is OpenAI's coding-oriented interface for interacting with OpenAI's models. ChatGPT Sol is the most powerful flagship model, and version 5.4 released on July 9.

Metal is Apple's programming interface for Apple's GPUs, and is common to iPhone/iPad/Mac.

Shaders are program functions that set up tasks for a GPU to process visual output, like those that calculate how light interacts with colorful objects of varying reflectivity, or how a house should look when viewed through some fog, etc.

The original post describes what is now a relatively common workflow: tell Anthropic's most powerful model to manage some cheaper models to do specific tasks and put the output together into something that can be used. As the post shows, it doesn't always work. And when it fails, it can do so in a very expensive way.

But it isn't encoding knowledge, it's encoding word correlations.

I'm saying that humans do this a lot, too. Qualitatively, it's different, in that this particular batch of frontier LLMs will get things wrong in ways that most human brains wouldn't, but as a category of error it's not unique to LLMs.

I know a ton of facts that I learned only through reading, and have no actual firsthand knowledge/experience or ability to test it: Jupiter is larger than Saturn, the atmosphere during the Carboniferous period was high in oxygen, cigarettes cause cancer, Thomas Jefferson owned slaves, the capital of Norway is Oslo. At best, I can cross reference other sources and see that things are consistent with each other. Is my belief in those facts "knowledge," or is it merely recognizing from my training data that those particular words can validly be presented in that order?

If you ask average people on the street whether FAT32 is a good filesystem for a 64GB removable drive, most of them won't know, but there are a handful of bullshitters who might confidently parrot back things they can Google but not understand. That's part of the human condition, too.

I'm by no means an AI booster/enthusiast. I suspect LLMs/transformers are actually a dead end, and expect the upcoming crash to be economically and financially devastating to the tech and financial sectors. But I also have a pretty dim view of human intelligence, too, and see way too many parallels in LLMs as bullshit artists to humans as bullshit artists, too.

It modifies the prompt, aka the input, not the output. It is smuggling 3 bits of secret user/session data in a wrapper that doesn't look like it contains that data. As the article explains:

So the marker becomes part of the system context sent to the model.

This is a normal timestamp on a prompt:

Today's date is 2026-07-11.

But if your system timezone is a Chinese mainland timezone, it looks like:

Today's date is 2026/07/11.

Then, if your base URL includes a keyword like "deepseek," it silently replaces the apostrophe from a ' to a ʼ:

Todayʼs date is 2026-07-11.

Or if the base URL has one of the domains on the list, like any .cn domain, it replaces the apostrophe with another apostrophe character:

Today’s date is 2026-07-11.

And if it has both a URL and a keyword on the watchlist, the prompt context includes:

Todayʹs date is 2026-07-11

That's 3 bits of information: does this system have a mainland Chinese time zone, does the base URL contain a known keyword (associated with Chinese AI competitors) or a known domain (associated with mainland China or its major tech companies). And it sneaks it on by without making it obvious.

That's steganography.

up to 150kW at full load.

That's the last generation. They're moving from Blackwell to Rubin chips now, and the 72-GPU Rubin servers use up to 230 kW.

The typical residential connection in the U.S. has a 24 to 48 kW electrical connection. A block of houses might not have enough power infrastructure to power just one of these server racks.

It's that they are trying to use statistics to encode entire thought processes into hidden variables from conversation snippets. They want to use statistics to go from many individual interactions to a large model, and then use that model to predict individual interactions again.

Has it been shown that the human brain doesn't model the world in a similar way, though? A huge portion of human knowledge is both stored and transmitted in the form of language. Lots of human knowledge also follows the garbage in, garbage out theory, where you can have entire areas of knowledge that aren't actually true but might be internally consistent, at least within certain scopes: conspiracy theories, belief in the supernatural, entire academic disciplines built on a religion or theology that not everyone believes, etc. Or even world building in fiction, the words on a page can be enough to convey ideas such that it "tricks" human brains into filling in the gaps so that they internally see a rich, fleshed out world that is entirely fictional and where specific details might not find strong direct support in the underlying text.

it has no concept of correctness

But statistical weight on what is more or less likely to be correct still makes a difference to objective quality of the outputs. If the model weights are trained on the reality that high quality university texts describe something and reflect some sort of underlying model of what is described using language, then can't the model itself learn as much as a human could from those words on a page?

All models are wrong, but some can be useful. And different models have different quality in different domains. So although I don't believe LLMs will overtake the hump of getting ahead of human knowledge, I also don't believe that any given LLM can be evaluated on quality, and that Facebook's LLMs are significantly behind other LLMs we see.

And that maybe a huge part of it is its internal process of preparing the model to evaluate the quality of its inputs, such that the output it produces can also score high on quality.

Yeah, but if they spread the cost across many customers, the cost per customer is going to be much smaller, even if it doesn't last as long before needing a replacement.

If it costs $100,000 to build a fiber line to a single home for 30 years (360 months) that house will need to pay $278/month for 30 years to break even. Throw in interest rates/inflation, and it'll be more.

But if a satellite that costs $1.5 million to build and launch into orbit can serve even 200 customers for 5 years, that's only $125/month per customer.

As it stands right now, Starlink serves something like 12 million customers on 10,000 satellites. So that's an average of 1200 customers served by each satellite, which is what makes $50/month service feasible as a business.

That just means the high up front costs of either trenching fiber or launching satellites need to serve a lot of people to recover that cost. That means the last mile for rural residents tends not to be cost effective for fiber, because there aren't enough connections served by any given segment.

But making it so any given satellite can serve lots of people in its footprint at any given moment might make it cost effective to serve rural residents.

One common strategy is to run fiber to a specific central location and run point to point microwave antennas to the individual houses/buildings served. That way the fiber itself can carry the traffic of hundreds of users, and each house just needs to have an antenna with line of sight to the place where the fiber is terminated. Rural WISPs have been doing this from before Starlink.

Publication date April 2, based on reporting from Bloomberg on April 1. Anyone have an update 3 months later? I want to see what happened to the remaining half.

Intellectual property is as real as any other property is: it's all made up by people, but it has the force of social/economic/governmental institutions behind it so it can affect your life.

It's just that legal concepts tend to attract discussions by people who can't tell the difference between a descriptive or positive argument (how things are) versus a normative argument (how things should be).

CNBC ran this article earlier this week, too:

Employers who laid off workers citing AI are already starting to regret it

If CNBC of all places is running stories like this, that represents a huge vibe shift.

It's like responding to your employee losing an arm, ripped off by your tiger, and saying "I'm never going to financially recover from this."