fiat_lux 🆕 🏠

Buy, buy, buy — may not always play, but definitely buy. We know this habit is a bit odd.

不太奇怪，西方人也这样。谢谢你的故事。很有意思啊！我希望你以后会多分享一些。

Fair. In my case I wish someone had not overlooked the systemic inflammation (from a different condition that has been recently correlated with OA, somewhat unexpectedly) and the malmechanics I was experiencing, so that I might have avoided some of the further issues, but, so it goes.

I manage to shift some of the chronic pain, but sadly society really likes to build worlds that have only one blessed way of doing certain things, which makes it impossible to shift more consistently. So I will have to mostly content myself with smugly sore.

Given you appear to be a doctor though, I do have one favor to ask. If you ever get a flexible kid with crepitus come through your doors, maybe add a CRP test to their blood work, just on the off-chance and even if only for the chain of evidence.

So when people told me as a kid not to worry about my crepitus, they were in fact completely wrong? If so, I’m feeling vindicated but also sore, but that’s better than only sore.

Peter dinklage doesn’t have a Twitter account. This is someone who just wants to keep using people with dwarfism as the butt of their jokes.

I’m not sure I understand. Peripheral hallucinations are a recognized type of hallucination, and hallucinations can involve anything from shadows or flashes of light through to full vivid imagery. It only requires perceiving anything that isn’t really there and but it feels real. The patterns or colors you see when you close your eyes are considered hallucinations too.

We had slightly different readings.

As he was writing he became aware that he was being watched, and a figure slowly emerged to his left. It was indistinct and on the periphery of his vision but it moved as V.T. would expect a person to. The apparition was grey and made no sound… V.T. was unable to see any detail and finally built up the courage to turn and face the thing. As he turned the apparition faded and disappeared.

He experienced a visual disturbance in his periphery manifesting as the false perception of a person. Even without it being interpreted as a person, that’s a textbook mild hallucination.

Once V.T. knew this he calculated the frequency of the standing sound wave … 18.97Hz … plus or minus 10%

Table IV on page 212 of this book shows frequencies causing disturbance to the eyes and vision to be within the band 12 to 27 Hz.

Most interestingly, a NASA technical report mentions a resonant frequency for the eye as 18 Hz (NASA Technical Report 19770013810).

He cited two sources inline with ranges narrower than 8-40Hz which indicate that vision can be affected at the same frequencies he measured in the lab. He even noted that everyone would have slightly different resonant frequencies.

No, it’s not a full research paper, but it is the citation you requested.

I believe the poster is referring to The Ghost in the Machine, Published in the Journal of the Society for Psychical Research, Vol.62, No 851 April 1998 (pdf)

Wrong end of the spectrum, unfortunately. You’ll have better luck with your local ham radio club.

1. If you know you’re alone at home and then hear voices, that might be one way. There are ways to distinguish the presence of people beyond sight.

2. Blindness is much more than total blindness, which only describes a minority of blind people. There are different definitions, but the World Health Organization puts the definition as less than 3/60 or a visual field of less than 10 degrees in the better-seeing eye. That basically means that if you need to be more than 20 times closer to an object to be able to see the same level of detail, or you have almost no peripheral vision, you qualify.

Anyone want to trade 20 routers for shortwave radios? I hope nobody was planning on tuning in to Voice of America tonight.

“While cavitation occurs at low frequencies and destroys both viruses and tissues through the collapse of gas bubbles, acoustic resonance operates at high frequencies of 3–20 MHz,”

Brb, making a blanket out of 802.11ah routers.

If I saw this, I would not assume that Gawker, WikiLeaks, or FOX Weather would be included in a blocklist called “No-QAnon”. The list itself might not be smuggled, but it’s not accurately representing itself either. If it has simply evolved over time, then it needs to be renamed or split into separate blocklists.

Additionally, if someone installing this changes their mind or realizes that what they received is not what they expected, then requiring them to either directly modify the database or click “remove” over 3000 times is arguably a dark pattern.

Yeah, even there. A page loading is one thing, but browser features are somewhat independent of the content. There’s also a good chance this is being used as a hook for other Google products like Drive or Docs (which are basically websites under the hood) to allow offline file management, creation, etc.

It’s a bad choice, but it wouldn’t be the first bad choice Google has made.

Trauma responses are hard. I think it’s great you’re actively working on it and are conscious of your own biases, that’s huge. Good luck!

They need their features to work offline too probably.

Very fair, on both counts.

Your pizzas always look fabulous, but I really want to introduce your wife to some better olives. If you ever get the chance to pick up some kalamata or ligurian olives, be sure to try them out, but you’ll probably want to reduce the quantity you add, because they have a lot of flavor.

Black olives are one of the food victims of industrial farming. It’s difficult to find the ones that are actually black from natural ripening instead of processing to look ripe, but they taste very different.

Link is to a shit pdf on a proton drive. It’s a basic description of the Google auction house. The prices they list are largely driven by the bids advertisers place, but that’s not to say Google doesn’t charge a bigger minimum for different demographic segments, they very much do. As does Facebook etc.

For example, one reason that parents are worth less is because of the products they listed. Diapers cost less than business lawyers, so the margins are much slimmer, so advertisers aren’t going to bid as much for an ad placement.

It does miss one thing that is, in my opinion, one of the more revolting aspects of their auction house. As a bidder your dollar is worth less than a big company’s dollar, even as little as one tenth. You could bid a million dollars on an ad space that Apple only bid $100001 on and you’d lose. That gap is dynamically calculated (at least in part) based on comparative search rankings.

Here’s the text without their ad at the end:

The Price of Free Google

What the Ad Industry Pays to Target Americans

A Proton Mail analysis of 54,216 advertiser-defined profiles across the U.S.

The price of your attention

Every user has a price

Every Google search triggers an invisible, real-time auction where advertisers bid for access to your attention. These bids are calculated in milliseconds based on how likely you are to spend. This is how the system decides what you are worth to advertisers.

Proton analyzed 54,216 advertiser-defined profiles across 251 U.S. cities using real ad-market pricing.

● Highest-value user: $17,929/year
● Lowest-value user: $31/year

That’s a 577x difference. This disparity is not an anomaly — it is the business model.

“Google doesn’t just build a profile from the information you knowingly provide. If you sign up for services, click ads, or ignore others, that creates signals the system can use to infer much more than you realize. It can start with age or interests, then expand into assumptions about income, family status, political leanings, or religion.
When the system isn’t sure, it tests those assumptions by serving different ads, links, or recommendations and watching how you respond. It doesn’t just tracking who you are. It’s constantly learning, so it can price access to you more precisely.”
— Eamonn Maguire, Director of Engineering, Machine Learning & AI

Who the system values most — and least These two profiles illustrate how the same system assigns radically different value.

$17,929/year
● 35–44, male
● Bozeman, MT
● Not a parent
● Desktop, heavy user

High-intent, high-margin services:
● business lawyer
● home renovation
● golf courses

$31/year
● 18–24, male
● Fort Smith, AR
● Parent
● Android, casual user

Price-sensitive, lower-margin searches:
● cheap diapers
● family apartments
● toddler clothes

Same system. Same country. 577x difference.

Value is not distributed equally
The gap between the average and the median shows that a small number of high-value users disproportionately influence the system.

The top 10% of users generate 43% of total value.

● Average value: $1,605/year
● Median value: $760/year

Most users are worth far less than the system’s top performers.

How your value is calculated

Your value is constantly recalculated

Your value is not fixed. It is continuously recalculated based on signals that predict the likelihood of a commercially valuable action.

These signals include:
● What you search
● When you search
● What device you use
● Who you are inferred to be

High-intent searches — such as legal services, insurance, or financial products — command significantly higher prices than general browsing or informational queries. Your value can change from one moment to the next depending on what you do. In this system, behavior matters more than time spent

The signals behind the price

Your device changes your value

Device usage has a measurable impact on how users are valued.
● Desktop: $2,894/year
● iPhone: $1,338/year
● Android: $585/year

Desktop users are worth nearly 5x more than Android users — even when everything else is the same.

These differences reflect observed behavior — including conversion rates and commercial intent — not the cost of the device itself. Your device becomes a proxy for purchasing behavior.

Parents are systematically valued less

Parental status affects how users are priced within the system.

Non-parents are worth ~17% more on average.

The gap increases during peak earning years:
● 25–34: +24%
● 35–44: +34.5%

Having children reduces your perceived commercial value.

Same age — same location — same device. Different value.

Value peaks in midlife

User value is highest between the ages of 25 and 44.

This period corresponds with:
● Major financial decisions
● High-value purchases
● Career-related services

As users age, overall value declines — but does not disappear. For users 65+, approximately 75% of value is concentrated in:

● Health
● Real estate
● Financial planning

The system adapts by narrowing focus rather than reducing targeting.

Gender is not a primary driver of value

Gender has a measurable but limited impact on how users are priced within the ad ecosystem.

Average values across genders are broadly similar — with differences in the single digits.

Differences in value are driven primarily by how advertisers price categories of demand — not by gender alone. Higher-value industries — such as finance, legal services, and B2B technology — tend to influence outcomes more strongly than identity itself.

As a result, gender can affect value indirectly, but it is not a consistent or defining factor.

Where you live affects what you’re worth

Local economies shape how much advertisers are willing to pay for access to users.

Location alone can dramatically change what you’re worth.

Highest-value markets include:

1. Edmond, OK
2. Bozeman, MT
3. Naperville, IL
4. Santa Fe, NM
5. Durham, NC

Lowest-value markets include:
247. Greensboro, NC
248. Gulfport, MS
249. Fort Smith, AR
250. Lowell, MA
251. West Valley City, UT

More usage means more value

Frequency of use acts as a multiplier on user value.

● Heavy users: $3,611/year
● Average users: $843/year
● Casual users: $362/year

Heavy users generate nearly 10x more value than casual users. More usage doesn’t just increase your value — it multiplies it.

This creates strong incentives to maximize engagement.

We can see that it’s solved by the fact that AI models continue to get better despite an increasing amount of AI-generated data being present in the world that training data is being drawn from.

Even if it logically followed that model improvement means model collapse is a solved problem, which it absolutely doesn’t, even the premise that models are improving to a significant degree is up for debate.

Massive Multitask Language Understanding (MMLU) benchmark vs time 07-2023 to 01-2026

A lot of people really want to believe that AI is going to just “go away” somehow, and this notion of model collapse is a convenient way to support that belief

Model collapse may for some people be an argument used to support a hope that AI will go away, but the reality of that hope does not alter the validity of the model collapse problem.

You can tell it’s not a solved problem because researchers are still trying to quantify the risk and severity of collapse - as you can see even just from the abstracts in the links I provided.

Some choice excerpts from the abstracts, for those who don’t want to click the links:

Our results show that even the smallest fraction of synthetic data (e.g., as little as 1% of the total training dataset) can still lead to model collapse

…we establish … that collapse can be avoided even as the fraction of real data vanishes. On the other hand, we prove that some assumptions … are indeed necessary: Without them, model collapse can occur arbitrarily quickly, even when the original data is still present in the training set.

It can’t only be from data from previous generations, even if the initial demonstration used that, because that would mean a single piece of human-generated text is sufficient to avoid collapse.

The loss of data from generation to generation is one way model collapse can occur, but it’s only one way. The actual issues that cause collapse are replication of errors and increasing data homogeneity. In a world where an unknown quantity of new data is AI generated, it is not possible to ensure only a certain quantity is used as future training data.

Additionally, as new human generated content is based on the information provided by AI, even if not used intentionally in the construction of the text itself, the error replication and data diversity issues cross over from being only an AI-generated content problem to an all content problem. You can see examples of this happening now in the media where a journalist relies on AI output to fact check, and then the article with the error gets republished by other media outlets.

Real AI training methods may stave off some model collapse, if we ignore existing issues around the cultural homogeneity of training data from across all time periods, or assume the models are sufficiently weighted to mitigate those issues, but it’s by no means settled that collapse is a non-problem.

You’ve mentioned using data mixing to prevent collapse, but some of the research suggests that even iterative mixing isn’t sufficient dependent on the quantities of real vs synthetic data. Strong Model Collapse (2024), Dohmatob, Feng, Subramonian, Kempe goes into that, and since then there’s been When Models Don’t Collapse: On the Consistency of Iterative MLE (2025) Barzilai, Shamir which presents one theoretical case where collapse won’t occur provided some assumptions hold, but the math is beyond me. They also note multiple situations where near-instant collapse can occur.

How much data poisoning might affect any of that is not at all clear, it would need to be in sufficient quantity for whatever model to have an effect, but it certainly wouldn’t help. The recent Bixonimania scandal suggests it’s feasible.