TheDinarian
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The Great Cholesterol Scam and The Dangers of Statins
Exploring the Actual Causes and Treatments of Heart Disease
August 17, 2024
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For "The Truth Shall Set You Free" file... ~Dinarian

Story at a Glance:

•There is a widespread belief that elevated cholesterol is the “cause” of cardiovascular disease. However, a large body of evidence shows that there is no association between the two and that lower cholesterol significantly increases one’s risk of death.

•An alternative model (which the medical industry buried) proposes that the blood clots the body uses to heal arterial damage, once healed, create the characteristic atherosclerotic lesions associated with heart disease. The evidence for this model, in turn, is much stronger than the cholesterol hypothesis and provides many important insights for treating heart disease.

•The primary approach to treating heart disease is to prescribe cholesterol lowering statin drugs (to the point, over a trillion dollars have now been spent on them). Unfortunately, the benefits of these highly toxic drugs are minuscule (e.g., at best taking them for years extends your life by a few days) and the harms are vast (statins are one of the most common pharmaceuticals that severely injure patients).

•In this article we will explore the specific injuries caused by statin drugs, the forgotten causes of cardiovascular disease, and our preferred treatments for heart and vascular diseases.

The more I study science, the more I come to see how often fundamental facts end up being changed so that a profitable industry can be created. In the case of heart disease, I very much believe that is the case and in this publication, I’ve tried to expose the erroneous information that predominates our understanding of this subject (e.g., previously I’ve discussed why our model of how the heart pumps blood in the body is incorrect and in an article that will be released in a few weeks, I will detail the major misconceptions about blood pressure management).

Within cardiology, I believe one of the most damaging falsehoods is that cholesterol causes heart disease and that taking statins (or their newer equivalents), which lower cholesterol, are the key to preventing heart disease. This is because, in addition to those “facts” being incorrect, statins are also some of the most dangerous and widely used pharmaceutical drugs on the market.

Cholesterol and Heart Disease

Frequently, when an industry harms many people, it will create a scapegoat to get out of trouble. Once this happens, a variety of other sectors that also benefit from that scapegoat existing will jump on the bandwagon. Before long, a false belief that harms society becomes an unquestionable dogma that becomes very difficult to overturn because many corrupt parties have a vested interest in maintaining the lie.

For example, various easily addressable factors (which often exist in the first place because they benefit an industry) are responsible for the chronic diseases we face in society and our vulnerability to infectious diseases (e.g., the obese and diabetics were much more likely to catch COVID-19). However, by saying all diseases result from insufficient vaccination, it gets all those destructive industries off the hook and creates a huge market for selling vaccines and treatments for these illnesses. Thus, since there are so many vested interests behind the vaccine paradigm, it is very difficult to overturn—despite the fact the existing evidence shows vaccinations are responsible for the massive epidemic of chronic disease that is sweeping our country.

In the 1960s and 1970s, a debate emerged over what caused heart disease. On one side, John Yudkin effectively argued that the sugar being added to our food by the processed food industry was the chief culprit. On the other side, Ancel Keys (who attacked Yudkin's work) argued that it was due to saturated fat and cholesterol.

Note: a case can also be made that the mass adoption of vegetable oils lead to this increase in heart disease. Likewise, some believe the advent of water chlorination was responsible for this increase.

Ancel Keys won, Yudkin's work was largely dismissed, and Keys became nutritional dogma. A large part of Key’s victory was based on his study of seven countries (Italy, Greece, Former Yugoslavia, Netherlands, Finland, America, and Japan), which showed that as saturated fat consumption increased, heart disease increased in a linear fashion.

However, what many don’t know (as this study is still frequently cited) is that this result was simply a product of the countries Keys chose (e.g., one author illustrated that if Finland, Israel, Netherlands, Germany, Switzerland, France, and Sweden had been chosen, the opposite would have been found).

Fortunately, it gradually became recognized that Ancel Keys did not accurately report the data he used to substantiate his arguments. For example, recently an unpublished 56 month randomized study of 9,423 adults living in state mental hospitals or a nursing home (which made it possible to rigidly control their diets) that Keys was the lead investigator of was unearthed. This study (inconveniently) found that replacing half of the animal (saturated) fats they ate with vegetable oil (e.g., corn oil) lowered their cholesterol, and that for every 30 points it dropped, their risk of death increased by 22 percent (which roughly translates to each 1% drop in cholesterol raising the risk of death by 1%)—so as you can imagine, it was never published.

Note: the author who unearthed that study also discovered another (unpublished) study from the 1970s of 458 Australians, which found that replacing some of their saturated fat with vegetable oils increased their risk of dying by 17.6%

Likewise, recently, one of the most prestigious medical journals in the world published internal sugar industry documents. They showed the sugar industry had used bribes to make scientists place the blame for heart disease on fat so Yudkin's work would not threaten the sugar industry. In turn, it is now generally accepted that Yudkin was right, but nonetheless, our medical guidelines are still largely based on Key’s work.

However, despite a significant amount of data that now shows lowering cholesterol is not associated with a reduction in heart disease (e.g., this studythis studythis studythis reviewthis review, and this review) the need to lower cholesterol is still a dogma within cardiology. For example, how many of you have heard of this 1986 study which was published in the Lancet which concluded:

During 10 years of follow-up from Dec 1, 1986, to Oct 1, 1996, a total of 642 participants died. Each 1 mmol/L increase in total cholesterol corresponded to a 15% decrease in mortality (risk ratio 0–85 [95% Cl 0·79–0·91]).

Note: when people are diabetic (which leads to the liver having to process too much sugar) the liver will convert to fat and then create more cholesterol to transport some of that fat. In these instances, I would argue the actual issue is an excess of sugar rather than elevated cholesterol levels it causes.

Statins Marketing

One of the consistent patterns I’ve observed within medicine is that once a drug is identified that can “beneficially” change a number, medical practice guidelines will gradually shift to prioritizing treating that number and before long, rationals will be created that require more and more of the population to be subject to that regimen. In the case of statins, prior to their discovery, it was difficult to reliably lower cholesterol, but once they hit the market, research rapidly emerged stating that cholesterol was more and more dangerous and, hence that more and more people needed to be on statins.

 

As you would expect, similar increases also occurred within the USA. For example, in 2008-2009, 12% of Americans over 40 reported taking a statin, whereas in 2018-2019, that had increased to 35% of Americans.

Given how much these drugs are used, it then raises a simple question—how much benefit do they produce?

As it turns out, this is a remarkably difficult question to answer as the published studies use a variety of confusing metrics to obfuscate their data (which means that the published statin trials almost certainly inflate the benefits of statin therapy), and more importantly, virtually all of the data on statin therapy is kept by a private research collaboration which consistently publishes glowing reviews of statins (and attacks anyone who claims otherwise) but simultaneously refuses to release their data to outside researchers, which has led to those researchers attempting to get this missing data from the drug regulators.

Note: as you might have guessed, that collaboration takes a lot of money from the pharmaceutical industry.

Nonetheless, when independent researchers looked at the published trials (which almost certainly inflated the benefit of statin therapy) they found that taking a statin daily for approximately 5 years resulted in you living, on average, 3-4 days longer. Sadder still, large trials have found this minuscule “benefit” is only seen in men. In short, most of the benefit from statins is from creative ways to rearrange data and causes of death, not any actual benefit.

Note: this is very similar to Pfizer’s COVID vaccine trial which professed to be “95% effective” against COVID-19, but in reality only created a 0.8% reduction in minor symptoms of COVID (e.g., a sore throat) and a 0.037% reduction in severe symptoms of COVID (with “severe” never being defined by Pfizer). This in turn meant that you needed to vaccinate 119 people to prevent a minor (inconsequential) case of COVID-19, and 2711 to prevent a “severe” case of COVID-19.

Furthermore, a clinical trial whistleblower later revealed that these figures were greatly inflated as many individuals in the vaccine group who developed COVID-19 like symptoms were never tested for COVID-19. Likewise, these benefits were fleeting as it was shown the “efficacy” of vaccines rapidly waned (disappearing a few months after vaccination). Worse still at six months of follow-up in
 both Pfizer's and Moderna's trials, more vaccinated than unvaccinated individuals died, and similarly a peer-reviewed reanalysis of Pfizer and Moderna's trial data showed that one was more likely to suffer a severe adverse event from the vaccine than a hospitalization from COVID-19.

In circumstances like these where an unsafe and ineffective but highly lucrative drug must be sold, the next step is typically to pay everyone off to promote it. For example, to quote Chapter 7 of Doctoring Data:

The National Cholesterol Education Programme (NCEP) has been tasked by the National Institutes of Health to develop guidelines [everyone uses] for treating cholesterol levels. Excluding the chair (who was by law prohibited from having financial conflicts of interest), the other 8 members on average were on the payroll of 6 statin manufacturers.

In 2004, NCEP reviewed 5 large statin trials and recommended: “Aggressive LDL lowering for high-risk patients [primary prevention] with lifestyle changes and statins.”

In 2005 a Canadian division of the Cochrane Collaboration [who were not paid off] reviewed 5 large statin trials (3 were the same as NCEP’s, while the other 2 had also reached a positive conclusion for statin therapy). That assessment instead concluded: “Statins have not been shown to provide an overall health benefit in primary prevention trials.”

Note: the primary reason no cure for COVID-19 was ever found was that the guideline panel for COVID-19 treatments was handpicked by Fauci, comprised of academics taking money from Remdesivir’s manufacturers. Not surprisingly, the panel always voted against recommending any of the non-patentable treatments for COVID-19, regardless of how much evidence there was for them.

Likewise, the American College of Cardiology made a calculator to determine your risk of developing a heart attack or stroke in the next ten years based on your age, blood pressure, cholesterol level, and smoking status. In turn, I’ve lost track of how many doctors I saw proudly punch their patient's numbers into it and then inform them that they were at high risk of a stroke or heart attack and urgently needed to start a statin. Given that almost everyone ended up being “high risk” I was not surprised to learn that in 2016, Kaiser completed an extensive study which determined this calculator overestimated the rate of these events by 600%. Sadly, that has not at all deterred the use of this calculator (e.g., medical students are still tested on it for their board examinations).

Note: one of the most unfair things about statins is that the healthcare system decided they are “essential” for your health, so doctors who don’t push them are financially penalized, and likewise patients who don’t take them are as well (e.g., through life insurance premiums).

So, despite the overwhelming evidence against their use, many physicians believe so deeply in the “profound” benefits of statins that they do things like periodically advocating for statins to be added to the drinking water supply.

In tandem, a cancel culture has been created where anyone who challenges the use of Statins is immediately labeled as a “statin denier” accused of being a mass murderer and effectively canceled. Recently, one of those dissidents, Dr. Aseem Malhotra British cardiologist who has also spoken out against the COVID vaccines went on Joe Rogan where he discussed that dirty industry and the remarkable parallels between how Statins and the COVID vaccines were pushed on the world:

Note: one of the most remarkable facts Aseem shared was that the previously mentioned statin collaboration (which militantly insists less than 1% of statin users experience side effects) also created a test one could utilize to determine if one was genetically at risk for a statin injury—and in their marketing for the test said 29% of all statin users were likely to experience side effects (which they then removed once attention was brought to it).

In addition to doctors being forced to follow these guidelines, patients often are too. Doctors often retaliate against patients who do not take statins (similar to how unvaccinated patients were denied essential medical care during COVID-19). Employers sometimes require cholesterol numbers to meet a certain threshold for employment (although they never did anything on the scale of the COVID-19 vaccine mandates placed on workers around America). Similarly, life insurance policies often penalize those with "unsafe" cholesterol numbers.

Statin Injuries

My primary issue with the statins is not the fact we waste billions each year on a useless therapy (approximately 25 billion per year in America alone). Rather, it’s the fact that they have a very high rate of injury. For example, the existing studies find between a 5-30% rate of injuries, and Dr. Malhotra, having gone through all the existing evidence estimates that 20% of statin users are injured by them.

Likewise, statins are well known for having a high percentage of patients discontinue the drugs due to their side effects (e.g., one large study found 44.7% of older adults discontinue the drugs within a year of starting them, while another large study of adults of all ages found 47% discontinued within a year)

Statins in turn, are linked to a large number of complications that have been well-characterized (e.g., mechanistically) and described throughout the medical literature.123456
One group of side effects are those perceived by the patient (which often make them want to stop using the medications). These include:

  • A high incidence of muscle pain1234567

  • Fatigue12 especially with exertion and exercise3

  • Muscle inflammation (whose cause remains “unknown”)12

  • Autoimmune muscle damage1234

  • Psychiatric and neurologic issues such as depression, confusion, aggression, and memory loss123456789

  • Severe irritability1

  • Sleep Issues2

  • Musculoskeletal disorders and injuries12

  • Sudden (sensorineural) hearing loss1

  • Gastrointestinal distress1

The other group are those not overtly noticed by the patient. These include:

  • Type-2 diabetes,12345 particularly in women 678

  • Cancer1234

  • Liver dysfunction and failure12

  • Cataracts12

  • ALS-like conditions and other central motor disorders (e.g., Parkinson’s disease and cerebellar ataxia)12345

  • Lupus-like syndrome1

  • Susceptibility to herpes zoster (shingles)123

  • Interstitial cystitis1

  • Polymyalgia rheumatica1

  • Kidney injury12

  • Renal failure1

From the moment I first encountered statin patients, I quickly noticed that they would report either numbness in their body, muscle weakness and pain, or impaired cognition, which began after they started the statin and resolved once they stopped using it. Remarkably, we also noticed that whenever they (or we) pointed this out to their doctor, the doctor would become extremely hostile, and then insist that the statin could not be causing the symptom (e.g., “because in all their years of practice, they had never had a patient who was injured by a statin”) and that even if it was harming them, the patient needed to stay on it because otherwise they would get a heart attack and die.

In turn, as the years went by, I saw increasingly elaborate excuses being created to protect the statins from an ever-increasing awareness of their dangers. For example, I lost count of how many doctors I knew who cited this 2016 study when patients stated they had been injured:

The nocebo effect, the inverse of the placebo effect, is a well-established phenomenon that is under-appreciated in cardiovascular medicine. It refers to adverse events, usually purely subjective, that result from expectations of harm from a drug, placebo, other therapeutic intervention or a nonmedical situation. These expectations can be driven by many factors including the informed consent form in a clinical trial, warnings about adverse effects communicated by clinicians when prescribing a drug, and information in the media about the dangers of certain treatments.

The nocebo effect is the best explanation for the high rate of muscle and other symptoms attributed to statins in observational studies and clinical practice, but not in randomized controlled trials, where muscle symptoms, and rates of discontinuation due to any adverse event, are generally similar in the statin and placebo groups. Statin-intolerant patients usually tolerate statins under double-blind conditions, indicating that the intolerance has little if any pharmacological basis. Known techniques for minimizing the nocebo effect can be applied to the prevention and management of statin intolerance.

Which, when translated into plain English means that the only reason people believe statins injured them is because they were tricked into imagining the injury, so the best solution is to tell them the symptoms are in their head. What I found remarkable about this study was that the doctors who cited it never considered that the nocebo effect could not apply as their patients were not aware things like muscle pain were associated with statins until they experienced them (and then looked up what was happening) or that the discrepancy in the observed rate of adverse events could also be explained by the fact the randomized controlled trials are always funded by the pharmaceutical industry and hence consistently cover up injuries that occur there.

Similarly, the thing that finally made me realize how impressive the marketing for these drugs had been was the recurring battle I would have with relatives. In each case, I would take them off a statin and provide a strong argument with data supporting why they should not be on the drug. At some point later, they would go to their doctor and inform them that their relative (me), who was a doctor, had taken them off the statin.

Their doctor (often a cardiologist), in turn, would tell my relative I was incredibly ignorant, insist they knew the data much better than I did, say I was endangering my relative’s health, and promptly restart the statin, to which my relative dutifully complied. In many cases, I would provide the cardiologist with literature supporting my argument. In each case, they would make an excuse not to read it while simultaneously asserting that they knew all the data and that I, not being a cardiologist, was unqualified to have an opinion on this subject. This made me appreciate just how challenging a situation patients (without access to the resources my relatives had) were in.

If you take this story and replace “statin” with COVID-19 vaccines, you will see it is essentially what everyone has experienced over the last three years with the vaccines. I suspect this is because, before the COVID-19 vaccines, statins were one of the most profitable medical franchises and, thus amongst the medications most aggressively pushed on patients.

Note: two adverse event reporting systems exist for adverse reactions to pharmaceuticals, MedWatch and FAERS. Like VAERS, they suffer from severe underreporting (it is estimated only 1-10% of adverse events are reported to them). The author in the next section was able to find hundreds to thousands of reports for many of the statin injuries in MedWatch that matched what he had personally observed. However, despite these reports existing, nothing has been done with them, and there is almost no knowledge within the medical community that these adverse events exist.

The Statin Damage Crisis

Throughout this publication, I have tried to make the point that less severe reactions to a toxin are much more common than severe ones. Because of this, if you see a cluster of severe reactions, it indicates that far more, less severe reactions are occurring as well (which is how after learning of a few people in my circle dying suddenly from the COVID vaccines, I was able to correctly predict the scale of the non-fatal injuries that would hit America).

Likewise, if you see a large number of less severe reactions to a pharmaceutical (e.g., the statin-induced muscle and nerve damage), you can predict far more severe injuries are lurking in the background. As the longer list of adverse events I shared above demonstrates, this unfortunately is true for statins. In the next two sections, I will quote one of the best books I have found on this subject:

“Many statin victims say that abruptly, almost in the blink of an eye, they have become old people.”

Duane Graveline MD was started on a statin and soon after developed global amnesia (which is really scary). He decided to stop the statin and recovered.

When I suggested, on the basis of my 23 years as a family doctor, that perhaps my new medicine was the cause of my amnesia, the neurologist replied, almost scoffingly, that "Statins do not do that." He and many other physicians and pharmacists were adamant that this does not occur.”

Eventually, he was persuaded to try again.

The year passed uneventfully and soon it was time for my next astronaut physical. NASA doctors joined the chorus I had come to expect from physicians and pharmacists during the preceding year, that statin drugs did not do this and at their bidding I reluctantly restarted Lipitor at one-half the previous dose. Six weeks later I again descended into the black pit of amnesia, this time for twelve hours and with a retrograde loss of memory back to my high school days.

Later he discovered:

Perhaps stockholder loyalty explains why Pfizer management knew over a decade ago, during the first human use trial of Lipitor, of the cognitive impact to come when Lipitor was released to the public. Of their 2,503 patients tested with Lipitor, seven experienced transient global amnesia attacks and four others experienced other forms of severe memory disturbances, for a total of 11 cases out of 2,503 test patients. This is a ratio of 4.4 cases of severe cognitive loss to result from every 1000 patients that took the drug. Not one word of warning of this was transmitted to the thousands of physicians who soon would be dispensing the drug.

Because of this and other debilitating long-term complications (e.g., previously an extremely fit individual, he developed chronic exhaustion), Graveline became an expert on statin injuries and, in 2014, wrote The Statin Damage Crisis. Many of the points he raised there explain why statins are so dangerous, but unfortunately, are virtually unknown within the medical field.

Statin Mechanisms of Harm

 

Statins work by inhibiting an easy to target enzyme that is necessary for the production of cholesterol. Unfortunately, blocking that enzyme disrupts a variety of other vital physiologic processes. Let’s review what that enzyme does:

 

As these compounds are essential for the body, understanding statin toxicity hence requires us to understand what happens when each of these goes missing.

Cholesterol

Cholesterol has a few different essential functions in the body. These include:

• It is the precursor to many different hormones.

• The brain’s synapses (which, amongst other things, form memories) require cholesterol to function. Since cholesterol is too big to enter the brain, glial cells (support cells of the nervous system) synthesize it within the brain. Statins, unfortunately, inhibit glial cell production of cholesterol.

• Cognition, in turn, is highly dependent upon cholesterol. For example, one study found that minor cognitive impairment could be detected in 100% of statin users if sufficiently sensitive testing was done (which again illustrates how minor injuries are more common than severe ones). Likewise, a variety of more severe adverse effects on cognition are also observed such as amnesia, forgetfulness, confusion, disorientation, and increased senility.

Their patient’s rapid descent into dementia after a statin is started is much too often written off by their doctor as senile brain changes or beginning Alzheimer's when the real culprit is their statin drug.

Note: one of the sadder side effects we have frequently observed from the COVID-19 vaccines has been a rapid cognitive decline in the elderly (who cannot often advocate for themselves). When this happens, like statin damage, it is always assumed to be due to “their age” and ignored.

In addition to cognitive impairment, numerous studies have found a significant association between low or lowered cholesterol levels and violence. Likewise, statin dementia is often characterized by aggression.

Finally, one of the most concerning side effects of statins is their tendency to cause ALS (a truly horrible rare disease—curiously also seen in association with the COVID-19 vaccines). This correlation is further supported by many reports of statin ALS improving once the statin is stopped.

Unfortunately, while statin cognitive decline frequently improves when the statin is stopped, in many cases, it instead persists.

CoQ10

CoQ10 is an essential nutrient that both the mitochondria (which power the human body) and the stability of our cell walls depend upon. CoQ10 deficiency caused by statins is generally considered the most common cause of their side effects. This is really sad because those side effects could have been prevented if CoQ10 had been given with the statin. Unfortunately, this is unlikely ever to happen, as doing so would be equivalent to an admission statins could cause harm.

Note: the best parallel I know to this is that the primary cause of childhood vaccine toxicity is too many vaccines being given too close together for a child's developing circulatory and nervous systems. Most of the harm can be avoided if vaccines are spaced apart and given later in a child's life—but sadly doctors who promote this approach are routinely targeted (as it is tantamount to an admission vaccines are not 100% safe). 

Some of the common energy-related side effects of statin CoQ10 deficiency include:

• Mitochondrial damage

• Lack of Energy 

• Chronic Fatigue Syndrome

• Congestive Heart Failure and Fluid Retention

• Shortness of Breath

• Gout

Some of the side effects of statin CoQ10 deficiency weakening cell wall integrity include:

• Hepatitis (interestingly, Graveline noted that the enzyme threshold needed to diagnose statin-induced liver damage was significantly raised after this issue began being commonly reported following statin usage).

• Pancreatitis

• Rhabdomyolysis (rapid breakdown of skeletal muscle tissue)

• Tendon and ligament inflammation and rupture.
Note: this side effect is commonly reported with fluoroquinolone antibiotics, which are known to damage the mitochondria. I suspect it's linked to mitochondrial damage—a subject I discussed further here—as ligamentous laxity often goes hand in hand with vaccine injuries.

Two of the most common consequences of statins CoQ10 depletion are myopathy (muscle pain, tiredness, weakness, and cramps) and peripheral neuropathy (numbness, tingling, or burning sensations, particularly in hands and feet).

Although myopathy is the most commonly reported side effect of statin usage, much of it (e.g., myositis) goes undetected. This is because the symptoms are often not accompanied by blood work showing muscle enzyme elevations and can only be detected by biopsies (which are rarely done relative to blood work). In many cases, this condition is permanent (one expert in statin injury found it was permanent for 68% of her patients, while Graveline found it was for 25% of his). Sadly, in some cases, like statin neuropathies, the myopathies will continue to progress even if the statin is stopped.

One of the sadder things about statins is how aggressively they are pushed on diabetics (under the logic that since diabetics have an increased risk of heart disease, it is critical they take a statin to prevent them from having a heart attack). To highlight the absurdity of this, statins are well known to significantly increase your risk of diabetes (multiple studies have found this), which I suspect is again due to them impairing mitochondrial function. 

Similarly, peripheral neuropathy is a condition diabetics are well known to be at a high risk of. In one study, it was found that the risk of neuropathy (i.e., burning pain with tingling or numbness of the extremities) was increased by 14 to 26 times (depending on the type) for long-term users of statins. Furthermore, other nerve issues, such as neurodegeneration, can be caused by statins.

Combinations of myopathy and neuropathy also occur in statin users, such as progressive pain, weakness, and incoordination throughout the body, alongside trouble rising from a seated position, unsteadiness, and a tendency to fall. Muscles are also observed to develop a distinctive weakened and mushy characteristic and gradually shrink.

Note: in addition to preventing adverse effects from statins, CoQ10 is also one of the more helpful supplements for preventing heart disease.

Dolichol

Very few physicians know of the dolichols, which play a pivotal role in synthesizing proteins, and Graveline argues, neuropeptides throughout the body. Since neuropeptides are pivotal in your thoughts, emotions, and sensations, statins blocking their production can lead to significant issues. Dolichol abnormalities have also been linked to Alzheimer’s disease. Additionally, the part of the brain where Parkinson’s disease develops has a very high concentration of dolichols.

Graveline in turn asserted that inhibition of dolichol production and therefore neuropeptide production accounts for the aggression, hostility, irritability, road rage, homicidal ideation, exacerbation of alcohol and drug addiction, depression, and suicides that are associated with statin use. These side effects are one of the sadder complications of statins I observe in families affected by them.

Note: I have not been able to verify the link between dolichols and neuropeptides. As far as I can tell, there are many unknowns about dolichols as they are an area of physiology which have not been extensively researched.

Tau Protein

Many neurological disorders (e.g., Parkinson's, Alzheimer's, ALS, MS) are thought to result from misfolded proteins. Because statins interfere with mevalonate synthesis, Graveline theorized that the production of Tau protein would be altered, which provides a potential explanation for the neurological diseases associated with statin usage. I briefly researched this theory when writing this article, and like the previous one, I am unsure if the existing evidence supports it.

Note: There is a strong association between the COVID-19 vaccine and misfolded proteins in the body.

Seleno-protein

To quote Glaveline:

Deficiency of selenoproteins has been proven to result in various types of myopathies formerly seen only in areas known to be deficient in this trace element. Additionally cognitive dysfunction is known to be associated with selenium deficiency.

Note: selenium deficiency is also associated with other diseases such as impaired immune function.

Nuclear Factor-Kappa B

The small cardiovascular benefit observed from statins may not be because they reduce cholesterol but rather because they have anti-inflammatory properties (inflammation causes heart disease), as they inhibit NF-kB, a vital part of the immune system.
Note: statins also lower the C-reactive protein (another inflammatory protein).

Since this suppresses the immune system, it leads to various potential issues such as reduced protection from infectious disease. For example, many common infectious organisms target NF-kB to assist in infecting their host. However, the more significant issue is that Nf-kB inhibition appears to be linked to cancer.

At five hospitals in Tokyo a group of Japanese researchers studied whether cancer patients had been treated with statins more often than other people. To that end they selected patients with various forms of lymphoid cancers and control individuals of the same age and sex without cancer admitted to other departments at the same hospitals during the same period. A total of 13.3 percent of the cancer patients, but only 7.3 percent of the control individuals were or had been on statin treatment.

In PROSPER [a major statin trial], men and women aged 70-82 were included only. All of them had either vascular disease or had a raised risk of such disease. At follow-up, 4.2 percent had died from a heart attack in the control group, but only 3.3 percent in the treatment group. This small benefit was neutralized by a higher risk of dying from cancer. Indeed, there were 28 fewer deaths from heart disease in the pravastatin group, but 24 more deaths from cancer. If we include non-fatal cancer in the calculation, the cancer difference between the two groups became statistically significant; 199 in the control group and 245 in the pravastatin group. Furthermore the difference between the two groups increased year for year.

In addition to this arguing that some of the benefit of statins “preventing heart attacks” is due to them causing a fatal cancer before you have time to have a natural heart attack, this situation is somewhat analogous to what was seen with the COVID vaccines (which also cause cancer). There, the “benefit” of the COVID vaccines preventing COVID was outweighed by them causing serious conditions such as heart attacks and strokes, but if one only focused on them preventing COVID (which many did), the vaccines could be portrayed as life-saving, even though they overall did the opposite.

Note: although statins appear to increase cancer, one of the few benefits I have seen a lot of evidence for is their prevention of fatal prostate cancer. My best guess is that this is due to them blocking the production of hormones in the body, and that outweighs the effects of them inhibiting NF-kB.

“Cholesterol” Plaques

One of the tricks to creating a lucrative drug market is to instill a belief throughout the population that everyone can relate to which sells your product. For example, the antidepressant industry spent years convincing the public depression was due to a “chemical imbalance” and because of how successful this campaign was, many sincerely believe it to be true even though it is a complete and utter fabrication.

One of the cleverest campaigns I’ve seen within the medical industry is the widespread belief that heart disease is due to fat clogging the arteries much like they do for a drain pipe.

 

This marketing slogan in turn is remarkably persuasive as it is easy to understand (to the point that people without a medical background will feel confident repeating it to others), easy to visualize, and highly likely to elicit an immediate sense of disgust.

However, given that there is no link between cholesterol and heart disease, is it necessarily true?

As one of my favorite authors in this field (Malcolm Kendrick) was pondering this question, he looked at another mystery of cardiology—the fact that there is no common thread between the well-known risk factors for heart disease. For example, to calculate the risk of heart disease, England uses a calculator that combines the adjustable risks for heart disease (e.g., age) with the conditions commonly associated with causing heart disease.

 

Likewise, in a 2017 study, the records of 378,256 English patients were analyzed by an AI system to determine what characteristics put them at the highest risk for a cardiovascular incident in the next 10 years. From that, they found that the ten greatest risk factors (in order) were:

 

From this list, Malcolm Kendrick concluded that the common thread was that many of these (e.g., lupus or cortisol) are associated with damage to the blood vessels and impaired microcirculation (a consequence of damaged blood vessels).

Note: a more detailed explanation of the connection between these factors and heart disease can be found within Kendrick’s book (which inspired a significant portion of this article).

Presently, we believe cholesterol somehow gets into a blood vessel and then damages it, leaving an atherosclerotic plaque. Kendrick in turn argued that a competing model (that the medical profession largely buried) provides a much better explanation of the actual causes of heart disease. It is as follows:

1. Blood vessels get damaged.
2. The body repairs those damage with clots.
3. As clots heal, they are pulled inside the blood vessel wall, and a new layer of endothelium (blood vessel lining) grows over them.
4. As this occurs multiple times in the same area, the damage (plaques) under the blood vessel becomes more abnormal.

 

Some of the key points of evidence he uses to support this argument are:

• Most of the risk factors for heart disease overlap with things that would be expected to damage the blood vessel lining (endothelium).

Plaques tend to form at arterial branch (junction) points, which are the parts of the artery which are subjected to the greatest shear stress).

• When you examine the components of a plaque, they are found to contain the same debris found in blood clots (see this study and this study).

• There is no established mechanism for how cholesterol from the blood stream can get under the endothelium (even though the existing model depends upon that somehow happening). However, red blood cells (which play a key role in forming clots) contain a large amount of cholesterol (50% of the total amount in the bloodstream), and hence will bring it into the clot as it forms.

• Plaques contain cholesterol crystals. These crystals can only form from free cholesterol, something contained within red blood cells, but not the “bad” cholesterol that circulates in the blood stream (contained within lipoproteins). Likewise, much of the cholesterol found in atherosclerotic plaques is free cholesterol.

• The remnants of lipoproteins that are found in plaques are not cholesterol lipoproteins, but rather lipoprotein A, something the body uses to repair damage to the arterial walls. This is supported by the fact elevated blood lipoprotein A levels are associated with increased lipoprotein remnants in plaques and that the specific marker of lipoprotein A is found to concentrate in atherosclerotic plaques. Lipoprotein A in turn is problematic because while it can patch and repair arterial damage, it also makes clots resistant to subsequent degradation, guaranteeing that they will eventually be pulled under the endothelium and transformed into an atherosclerotic plaque (which may in turn explain why elevated lipoprotein A levels are associated with a three-fold increase in the risk of a heart attack or stroke).

Note: another key piece of evidence for the cholesterol hypothesis is that fatty streaks on the lining of healthy blood vessels are thought to serve as the precursors to atherosclerotic plaques. However, when this was extensively researched, that progression was never observed to occur.

In short, a good case can be made that our entire heart disease model is based on a variety of correlations that were erroneously assumed to demonstrate causation. Sadly, while the “correlation is not causation” mantra is frequently used to dismiss anything which challenges the orthodoxy, you will frequently find overtly false correlations that support the medical industry’s bottom line being treated as unquestionable dogmas.

For example, vaccines are credited with eliminating the infectious diseases that plagued humanity, but it is seldom mentioned that some of the deadliest diseases (e.g., scarlet fever) which had no vaccine also disappeared or that the diseases were already disappearing once the vaccines were introduced (in many cases having almost completely disappeared) and that it is very likely they would have been eliminated regardless of if a vaccine appeared. Conversely, many of the activists at the time felt the primary cause of these diseases was poor public sanitation (as it caused infectious diseases to rapidly spread through the population), so many hard battles were fought to attain it, and many (myself included) believe the vaccination industry essentially stole the credit for what those activists accomplished by getting us public sanitation.

The Causes and Treatments of Heart Disease

Kendrick’s model essentially argues the following: 

• Most cardiovascular disease is a result of the blood vessel lining becoming damaged (due to the atherosclerotic lesions) and losing the ability to perform the normal functions (e.g., nitric oxide secretion) that allow it to protect the circulation.

• Inflammation and periods of prolonged and severe stress (e.g., from mental illness, cigarettes, or extreme social oppression) frequently damage the endothelium and hence contribute to heart disease.

• Heart attacks are due to blood clots (which frequently are a result of damaged endothelium) interrupting a critical blood supply to the heart.

In turn, I deliberately presented Kendrick’s points in this manner to emphasize that much of his model is in complete agreement with the conventional cardiovascular disease paradigm. However, the key distinctions are that he does not believe cholesterol is the cause of the damage to the blood vessel lining and that he thus believes the other damaging factors (e.g., stress) should receive a greater focus. Likewise, he prioritizes treating the functional impairments of the blood vessels (e.g., reduced nitric oxide synthesis) rather than having a narrow focus on reducing cholesterol.

Note: statins also to some extent have anti-inflammatory effects and increase endothelial nitric oxide. In turn, it is very likely that many of the (small) benefits attributed to statins are a result of these effects rather than their lowering of blood cholesterol.

Furthermore, since his focus is not on cholesterol, that allowed him to identify other factors which may be playing an immense (but largely unappreciated) role in heart disease.

For example, smoking is well recognized to cause heart disease because it damages the blood vessels (e.g., by creating plaques and impairing their ability to make nitric oxide), but much less thought is given to why it does. However, it’s been repeatedly demonstrated that fine particulate matter (which is found in cigarette smoke) directly causes these changes, evidenced by the fact similar damage occurs from breathing in pollution particulates such as those in coal mines, crowded cities (see this study and this study), cooking with a wood burning stove or being exposed to wildfire smoke.

Likewise, lead is quite damaging to the endothelium (e.g., see this study and this study), something many of us were exposed to due to it being added to gasoline, and lead rapidly entering the bloodstream once inhaled. In turn, as lead was phased out between 1975 to 1996 (although its use is still allowed for certain applications such as aircraft, race cars, farm equipment, and boats—where it is occasionally used), a variety of interesting trends exist, such as the fact heart disease exploded in America after we started using it (and this then happening in other European nations). Currently, it is estimated that around 400,000 deaths each year in America are due to lead exposure and in a study of 868 men, it was observed that high levels of lead exposure (assessed by its presence in the bones) increased their risk of dying by over 700 percent.
Note: as lead absorbed earlier in life tends to leach from the bones back into the bloodstream, many have suspected a key cause of aging (e.g., heart disease) is that lead returning without anything being done to address it.

Sadly, as you cannot sell drugs for any of these causes of heart disease, they rarely get mentioned and instead almost all of the research and discussions on heart disease are directed at cholesterol.

Overall, I think Kendrick’s model is accurate, and it is my sincere hope that at some point, the medical profession will begin to seriously consider it (although given how much has been invested into the cholesterol hypothesis, it’s doubtful the industry will ever be willing to let that market go).

 

Link

If you want to eat healthy, just turn the food pyramid upside down, basically the opposite of everything you have been programmed to believe. Have a read: https://richardweberg.com/the-usda-food-pyramid-is-upside-down/ 

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Custom AI assistants that print money in your sleep? 🔜

The future of Crypto x AI is about to go crazy.

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AI Is Coming for Your Job Title

Artificial intelligence may or may not take your job, but it has already broken into the human resources department and vandalized the org chart.

The evidence is all over LinkedIn, where perfectly serviceable occupations now arrive wearing titles such as “forward-deployed and agentic AI architect.” That person may be building sophisticated software. They may also be helping a chatbot remember what happened three prompts ago. Either way, somebody approved the business cards.

The expanding AI lexicon offers a useful counterpoint to the darker debate about technology and employment. Most discussion centers on how many jobs AI will eliminate. Hiring data presents a more complicated picture that includes a weak overall labor market containing a small but rapidly growing neighborhood of AI-related work.

Indeed Hiring Lab found that the number of postings on Indeed mentioning AI surged 134% from its February 2020 level by the end of 2025, even as total postings stood only 6% above that benchmark. AI appeared in a record 4.2% of Indeed postings in December.

AI, in other words, is not merely changing work. It is adding syllables to it.

The Titles Employers Actually Want

The undisputed champion is AI engineer, which ranked No. 1 on LinkedIn’s 2026 Jobs on the Rise list. The ranking, based on growth during the previous three years, also highlighted AI consultants and strategists, AI and machine-learning researchers and data annotators.

The title is popular partly because it is wonderfully accommodating. An AI engineer might build applications around large language models, connect corporate data to an AI system, improve model performance or spend Thursday afternoon persuading a customer service bot not to offer refunds for products the company doesn’t sell.

Indeed’s data showed the terminology spreading beyond Silicon Valley. Nearly 45% of data and analytics postings contained an AI-related term at the end of 2025, along with roughly 15% of marketing postings and 9% of human resources listings. A more recent Indeed analysis reported by Business Insider found that the number of frequently advertised job titles explicitly referencing AI rose from 264 in 2022 to 822 in the first quarter of 2026. Nearly two-thirds were outside traditional technology fields.

That produces titles such as AI marketing manager, AI learning specialist, responsible AI counsel and AI transformation lead. These are not always new occupations. Frequently, they are familiar jobs that have discovered a highly effective résumé keyword.

LinkedIn data cited by the World Economic Forum estimated that AI investment has supported 1.3 million positions, including AI engineers, data annotators and forward-deployed engineers, plus more than 600,000 AI-enabled data center jobs. The server racks, unlike the chatbots, still need electricians.

The Jobs With the Science-Fiction Salaries

At the upper end, AI has created a compensation market that resembles professional sports, except the competitors wear hoodies and discuss inference latency.

Syracuse University review put chief AI officer compensation between $200,000 and more than $500,000, while specialized roles can exceed $400,000 after bonuses and equity. Frontier research engineers, AI infrastructure specialists and engineers who can train or deploy advanced models command some of the largest packages.

Then there is the forward-deployed engineer, an old Palantir title that the AI boom has placed on a rocket sled. These engineers embed with customers, translating an executive’s desire to “do something with AI” into software that works. The Next Web reported that Indeed postings for the role were about 19 times higher in January than a year earlier.

CTO guide from the blog Signal Through the Noise placed forward-deployed engineer compensation between $238,000 and $700,000, research-engineering packages as high as $1.4 million and chief AI officer compensation above $1 million in some cases. It also made a less flattering observation: Many lavishly differentiated titles describe the same three basic functions. People build AI products, train models or keep the infrastructure from catching fire.

The Department of Unnecessary Titles

AI has created some genuinely new work. Evals engineers design tests to determine whether models perform reliably. AI red teamers try to make systems fail before customers do. Model behavior engineers study why an AI system responds as it does. AI governance leaders manage risks involving data, bias, security and regulation.

Other titles seem to have escaped from a brainstorming retreat.

There is the Claude Evangelist, whose mission apparently combines product education with the traditional duties of an apostle. There are vibe coders, who build software by describing what they want and accepting AI-generated code with varying degrees of supervision. “Vibe engineer” is the more respectable version, roughly equivalent to putting on a blazer before asking the machine to fix the login page.

“Context engineer” is a real discipline involving the data, instructions, memory and tools supplied to AI models. “Prompt engineer,” once advertised as a possible six-figure profession for gifted chatbot whisperers, is increasingly treated as one skill inside a broader AI role.

The CTO guide also identified “builder,” “AI-native developer,” “RAG engineer,” “agentic AI engineer” and “principal agentic GenAI forward-deployed context architect,” the last of which appears to require both technical proficiency and exceptional lung capacity.

Has AI created entirely new jobs? Absolutely. Some occupations, including AI safety, evaluation and model governance, exist because modern generative systems introduced new technical and business problems. However, many job titles are old jobs with fresh vocabulary, higher salary bands and a sudden aversion to the words “software developer.”

That may be the safest prediction about AI and employment. The machines will automate some tasks, generate others and force companies to rethink the division of labor. Before any of that is settled, however, corporate America will form a steering committee, appoint a chief agentic transformation evangelist and schedule a meeting to determine what that person does.

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🤖 Decentralized Intelligence by Design: Unpacking the Bittensor Flywheel

In the legacy tech world, artificial intelligence is governed by corporate monopolies. Companies like OpenAI and Google scale by capturing massive capital, locking talent behind non-disclosure agreements, and building closed-source infrastructure. 🛑

Bittensor flips this paradigm completely on its head. By combining a Bitcoin-inspired tokenomic model with a permissionless, competitive architecture, Bittensor doesn't just fund AI development—it orchestrates an unstoppable digital commodity flywheel. 🌪️

Here is an analysis of how the Bittensor ($TAO$) Flywheel Effect operates, and why its economic design is quietly building the foundation for generalized, open-source intelligence.

1. ⚙️ The Core Engine: The TAO Emission Mechanism

Unlike traditional crypto projects driven by private sales or VC allocations, Bittensor enforces a strict meritocracy. There are exactly 21 million TAO tokens that will ever exist, mimicking Bitcoin’s scarcity framework. 🪙

The network’s core engine releases 7,200 TAO daily across the ecosystem. This issuance isn't handed out randomly; it is dynamically distributed to specialized mini-marketplaces known as Subnets via a game-theoretic protocol called Yuma Consensus.

2. 🔄 The Three Stages of the Flywheel

The Bittensor flywheel works because it directly aligns the local self-interest of developers, miners, validators, and capital providers with the global health of the network. 🎯

🛡️ Phase 1: High-Barrier Subnet Competition

To build on Bittensor, an entrepreneur or developer group must purchase and "burn" or lock up a significant amount of TAO to secure a Subnet slot.

  • The Filter: This entry barrier filters out noise.

  • The Result: It ensures that only teams with mature concepts and solid execution capabilities (like decentralized storage, protein folding, or LLM inference) enter the arena.

💎 Phase 2: Alpha Token Emissions & Talent Attraction

Once a subnet is live, it competes aggressively against other subnets for a slice of the daily 7,200 TAO pool. Under the Dynamic TAO framework, each subnet utilizes its own localized native token (Alpha tokens). 🧪

  • Reward: The subnets that produce the highest utility or most innovative AI products receive a larger allocation of global TAO emissions.

  • Incentive: These emissions fund the subnet's local Alpha pool, offering massive financial rewards to the best Miners (who provide the actual compute/AI models) and Validators (who verify the accuracy and value of the work).

🔒 Phase 3: The Liquidity Loop and Token Scarcity

Because Alpha tokens are inherently priced relative to TAO, external investors or users who want to stake on or utilize a specific high-performing subnet must first acquire TAO. 📈

  • As a subnet's product quality improves, demand for its Alpha token surges.

  • To buy Alpha, participants must buy and lock up TAO in decentralized liquidity pools.

  • This removes circulating TAO from the open market, reducing effective float and driving up the value of TAO.

3. 🚀 Why the Flywheel is Unstoppable

The beauty of this cycle is that it feeds itself:

Higher TAO Price ➡️ More Valuable Subnet Emissions ➡️ Attraction of Higher-Tier Talent/Compute ➡️ Superior AI Products ➡️ Increased Network Demand ➡️ Higher TAO Price📈

Traditional startups spend millions on recruitment and marketing. Bittensor bypasses this entirely: its emission schedule acts as a global bat-signal for talent. 🌍

If a miner in Eastern Europe or a data scientist in Tokyo can optimize an open-source model to solve a specific subnet's prompt better than anyone else, the network automatically and frictionlessly rewards them.

💡 The Takeaway

Bittensor is more than a blockchain; it is an economic computer designed to run incentive structures in massive parallelism. By treating machine intelligence as a digital commodity and wrapping it in a circular value flow, the Bittensor flywheel transforms raw computational energy into an emergent, open-source super-intelligence. 🧠⚡

As subnets mature from raw infrastructure into client-facing enterprise APIs, the velocity of this flywheel is poised to redefine the economics of AI forever.

I hope this was helpful ~Dinarian888♾

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🚨Japan Just Entered the AI Race with Sakana, Claiming to Beat Mythos with a Router🚨
On June 12, the US pulled Anthropic’s best model offline by export order. Ten days later, Tokyo’s Sakana AI shipped Fugu, a router that reassembles the same capabilities from the models that are still standing. Blocking intelligence created the market for routing around it.

 

At 5:21 p.m. Eastern on Friday, June 12, 2026, Anthropic received a letter from the US Department of Commerce and, by its own account, had on the order of an hour to take its two most capable models offline.

The letter was an export control directive. It ordered Anthropic to suspend all access to Claude Fable 5 and Claude Mythos 5 “by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees.” Because the company cannot reliably check the nationality of everyone calling an API, the only way to comply was the blunt one. Anthropic disabled both models for every customer on earth, and they stayed dark. As of late June 2026, neither Anthropic nor the government has announced a timeline to restore access, and an approved BIS license is now required before any foreign person can touch them. This was not a chip ban. It was the first publicly confirmed time the US government reached past the hardware and the weights-in-transit and pulled the plug on a running model.

Ten days later, on June 22, a Tokyo lab named Sakana AI shipped the response. Its new product, Fugu, is not a frontier model. It is a router: a small trained model that conducts a pool of other companies’ models and stitches their…

Sandwiched between those two dates, on June 13, China’s Z.ai released GLM 5.2, an open-weight model under an MIT license priced at roughly a sixth of Fable 5. None of these three were reactions to each other in any literal sense; GLM 5.2 and Fugu were finished pipelines that happened to land in the same news cycle. But the cycle told a story the policy did not intend. Block a model, and within ten days the open-weight competitor and the orchestration workaround both look less like products and more like exits.

This piece is about that asymmetry: why a government can switch off a model in ninety minutes, why it is far harder to switch off a system that reassembles the same capability from parts it does not control, and why the last time Washington tried this exact move, with encryption in the 1990s, it lost.

What got banned, and why it was a first

Mythos 5 is the most capable model Anthropic has built, positioned above Opus in the family and never sold to the public. Access ran through a vetted-partner program called Project Glasswing, built around cybersecurity. The reason it was gated is not marketing. On a Firefox JavaScript-engine benchmark where Claude Opus 4.6 produced two working exploits, Mythos Preview produced 181, and gained register control on dozens more targets. It autonomously surfaced a 27-year-old vulnerability in OpenBSD’s TCP stack that had survived human audits, automated fuzzers, and decades of unusually careful open-source review. Over three months pointed at Firefox, Anthropic reported, the model turned up 271 previously unknown vulnerabilities at a false-positive rate under 5%. Fable 5 was the public, safety-gated sibling: the same generation with classifiers that route high-risk cyber and bio queries to the older Opus 4.8 and trip, Anthropic says, in under 5% of sessions.

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Mythos Preview’s cyber results against earlier models. Source: Anthropic, “Mythos Preview”, Apr 7 2026 (vendor-reported). License: Anthropic; confirm reuse before publishing.

 

The legal move was the structural novelty, not the capability. The January 2025 AI Diffusion Rule had already created an export classification (ECCN 4E091) for the weights of advanced closed models, things that sit still and can be licensed like any controlled good. The June 12 directive went a step past that, onto a live commercial API. Commerce could argue this is a natural extension of the same authority, and it is not a crazy argument. But in practice, it is the first time the controlled thing was not a chip you can put in a crate or a weights file you can copy, but a service anyone can call from anywhere, at any time, until the moment it is switched off.

The trigger is contested, and you should treat it that way

What actually set this off is disputed, and the accounts do not line up.

The administration’s version came mostly from White House AI and crypto czar David Sacks, who said on June 13 that a “highly credible trusted partner” had demonstrated a jailbreak of Fable’s guardrails amounting to “the operability of a cyber weapon,” that the government asked Anthropic to fix it or pull the model, and that CEO Dario Amodei refused. Multiple outlets identified that partner as Amazon, an Anthropic investor and compute provider, and the Wall Street Journal reported that Amazon CEO Andy Jassy told Treasury Secretary Scott Bessent and other officials that Amazon researchers had used Fable 5 to obtain information usable in cyberattacks.

Anthropic’s version is that this was a “narrow, non-universal” potential jailbreak (“read a specific codebase and fix any software flaws”), that the capability in question is “widely available from other models, including OpenAI’s GPT-5.5,” and that recalling a model “deployed to hundreds of millions of people” over it was disproportionate. Independent voices leaned toward Anthropic on the technical point. Katie Moussouris, CEO of Luta Security, was blunt: “I’ve seen the paper. It’s not a jailbreak.” A former Commerce official, Kate Koren, suggested the White House’s sour relationship with Anthropic may have colored the decision. Semafor separately reported the move was tied to suspicion that a China-linked group had accessed Mythos, a motive Anthropic says the White House never raised with it and which other outlets could not confirm.

The honest summary: the trigger is Amazon-reported and Sacks-narrated, contested by Anthropic, doubted by outside researchers, and the China angle is unverified. Hold it loosely.

What Sakana actually shipped

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Timeline illustration contrasting June 12 when US export control took Mythos and Fable 5 offline in 90 minutes, with June 22 when Sakana AI’s Fugu 7B router launched as the workaround, routing queries across GPT-5.5, Opus 4.8, Gemini 3.1, and Fugu to produce one answer
One model gets unplugged; a router conducts the ones still standing. (Original illustration.)

 

Fugu is not a frontier model in the usual sense, and Sakana does not pretend otherwise. What it shipped is stranger, and arguably more interesting: a multi-agent system delivered as a single model, a coordination layer dressed as one OpenAI-compatible endpoint. The complexity never reaches your code. Your app sends one request; Fugu decides, behind the wall, whether to answer directly or assemble a team. Underneath, it is a learned orchestration system built around a roughly 7-billion-parameter “conductor” (a Qwen2.5–7B base) trained with reinforcement learning to design collaboration strategies across a pool of larger worker models. Two ICLR 2026 papers sit underneath it: Trinity (arXiv 2512.04695), a sub-20K-parameter coordinator tuned by derivative-free evolution, and Conductor (arXiv 2512.04388), the RL-trained orchestrator that hands out roles. The lineage runs back to Sakana’s 2025 AB-MCTS work (arXiv 2503.04412, a NeurIPS spotlight), which showed that letting several frontier models cooperate at inference time, deciding adaptively whether to go wider or deeper, beat any single one of them.

Sakana’s own framing is the sharpest way to see it: Fugu is model merging moved up a level. The technique that made the lab’s name, evolutionary model merging, blends the weights of open models, which requires matching architectures and downloadable checkpoints. Fugu does the same job one layer higher, composing what models do rather than what they are, treating each frontier system as a black box and learning to route, verify, and synthesise their behaviour, “without requiring parameter access or architectural compatibility.” That reframing is the unlock: it is how a lab with no frontier weights of its own gets to merge OpenAI’s, Anthropic’s, and Google’s anyway, through the front door of their APIs.

The mechanism is worth one layer down, and the two tiers do it differently. Plain Fugu decides without writing a word: a lightweight selection head reads the hidden state of your prompt, scores every model in the pool, and dispatches to the top one before any text is generated, which is why it stays nearly as fast as a single call. Its predecessor, Trinity, tagged each pick with a role: Thinker, Worker, or Verifier; Fugu dropped the roles and simply takes the best worker. Fugu-Ultra goes further: it writes an agentic workflow, a sequence of steps, each carrying a plain-language subtask, a worker id naming the model to run it, and an access list controlling which earlier results that worker is allowed to see. Tune the access list, and you get a chain, a best-of-N, or a tree. The pool is swappable, GPT-5.5, Opus 4.8, Gemini 3.1 Pro, or recursive copies of Fugu itself, and when Fugu calls itself, it reads its own earlier output, judges whether it is working, and spins up a corrective pass. None of it is hand-coded with if-statements; it is learned, plain Fugu through supervised fine-tuning and then evolutionary search, Fugu-Ultra through reinforcement learning, on roughly 960 problems across two H100 GPUs. Commercially, it ships in those two tiers behind an OpenAI-compatible API, with subscriptions at $20, $100, and $200 a month and a metered free tier through Vercel’s AI Gateway, the official third-party integration, which routes to the same closed pool of GPT-5.5, Opus 4.8, and Gemini 3.1 Pro.

That difference shows up as quality. Plain Fugu, picking one model per step, can hand a coding request to GPT-5.5 to draft and to Opus 4.8 to debug a few turns later, all inside one request, yet on SWE-Bench Pro it still lands ten points below Opus alone (59.0 to 69.2): routing among models is not the same as being better than the best one. Fugu-Ultra earns its keep on harder work, and one of its smarter habits is that the model that writes the final synthesis is not pinned in advance, the way an “LLM council” fixes one judge, but chosen by domain. Its ceiling is the planning. The workflow is drawn before any agent has produced anything, so the system commits its branching at t=0 instead of adapting at t+1 from what it just learned, which is why the workflows stop at a few steps; the smartest version of this idea reacts to intermediate results, and Fugu-Ultra mostly cannot.

How does a 7B model learn any of this? In two ways, one per tier. Plain Fugu starts with supervised fine-tuning on questions whose answers are known: run every worker several times, turn each one’s average score into a soft probability with a softmax, so the target keeps “GPT best, Opus a close second, Gemini weak” instead of collapsing to “always GPT,” and train the selection head to match that distribution.

Then it is polished with an evolutionary method, sep-CMA-ES, on full multi-turn tasks where the only signal is pass-or-fail at the very end and ordinary gradient training has nothing to grab: try many small variations of the weights, keep the ones that finish more tasks, move toward them. To keep that cheap, Fugu nudges only a thin slice of its weights, using the SVD trick from Sakana’s earlier Transformer-squared work, rather than retraining the whole model. Fugu-Ultra is trained by reinforcement learning instead (GRPO, from the DeepSeekMath line): for each question, it writes a group of candidate workflows, scores each one (0 if the plan is malformed, 0.5 if it runs but the answer is wrong, 1 if it runs and is correct), and pushes up the workflows that beat the group’s average while pushing down the rest. Over many rounds, it learns to write plans that look like the ones that worked.

Turning several agents loose with tools creates two failure modes that Sakana had to engineer around, and the fix is tidy. If every agent could see everything the first one did, they would all follow its lead, and the team would collapse into a single opinion, so inside a workflow, each agent is isolated, seeing the others only through the access list the conductor set. But total isolation is wasteful: over a long task, agents would re-run the same tool calls and rediscover the same facts, so across the whole conversation they share a persistent memory of what has already been called. Independent within a step, shared across the task. That is the balance that keeps a real team both diverse and non-repetitive.

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Fugu AI multi-agent orchestration diagram showing the 7B conductor robot assigning Thinker, Worker, and Verifier roles across a swappable rack of AI models including GPT-5.5, Opus 4.8, Gemini 3.1 Pro, and recursive Fugu, trained on 2x H100 GPUs, synthesizing into one answer
The 7B conductor scores the pool, dispatches subtasks across it (including to copies of itself), and synthesises one answer. (Original illustration.)

 

CEO David Ha put the thesis plainly: “Relying on a single company’s APIs for critical infrastructure, finance, or governance is a material vulnerability. This risk is no longer a hypothetical possibility, but a reality.” Ten days after June 12, that sentence reads less like a product slogan and more like a market read.

Is any of this worth it over just calling Opus or GPT-5.5 directly? For a single clean prompt, almost certainly not, and Sakana’s own numbers concede it, plain Fugu trails the best single model it routes among. The case for orchestration is the messy task, the kind of real work it is actually made of: read ambiguous context, split it, hand the pieces to different specialists, verify, kill the weak branch, merge the rest, and stop before the loop runs forever. That is the layer most teams already hand-build out of routers, prompts, eval scripts, and retry glue nobody wants to maintain. Fugu’s bet is to sell that layer as a model.

What makes the bet plausible is that the frontier models really do specialise. By Sakana’s reading of its own pool, GPT-5.5 is strongest at math and at planning and combining ideas, Opus 4.8 at software engineering and at finding security bugs, Gemini 3.1 Pro at implementing known algorithms and at science. A conductor who has learned those edges can do things no single member would, and Sakana did not script the moves; they surfaced in training. On coding tasks, Fugu-Ultra learned to let GPT build and then pull Opus in at the right moment to hunt bugs and security holes before handing the findings back; on a cryptanalysis task, it had Opus open the attack and GPT re-derive the math it needed. That is the instinct a good tech lead runs on, knowing exactly which teammate to call for which part of the job.

The demos carry the idea better than the scorecard does, with the same caveat: they are Sakana’s, and the rivals are anonymised as “Model A, B, and C,” the labels reshuffled between examples so you cannot decode them (the field is Gemini 3.1 Pro, Opus 4.8, and GPT-5.5). With that asterisk, a few are hard to fake. Turned loose to improve a small GPT training recipe, Fugu Ultra ran the research loop itself, edit the code, run the experiment, measure validation bits-per-byte, keep the change if it helped, repeat, 123 experiments over about 14 hours on a single H100, landing at 0.9774 bits-per-byte against the baselines’ 0.9781, 0.9793, and 0.9822.

Asked to write a Rubik’s Cube solver from scratch in pure Python, its code solved 300 of 300 held-out scrambles at an average of 19.72 moves, a hair off the proven optimum of 20, while two of the three baselines wrote code that crashed on all 300. Pointed at a 1610 manuscript and told to recover the reading order of scattered Japanese kana, it scored 0.80 against a baseline of 0.24. Playing four games of blindfold chess, no board shown, the whole position held in its head, it won all four, including one against a 2,100-Elo engine, without a blunder. Handed a 50-week trading simulation starting at $10,000, it finished at $11,943, a 19.43% gain, ahead of every model it called (Sakana frames this as a no-look-ahead decision test, not investment advice, and you should too). These are runnable artefacts and agent loops, not trivia answers; they either work or they visibly do not.

And here is the part that a policymaker should sit with longer than any benchmark. The week the US made its best model unreachable behind a license, Fugu made frontier-adjacent capability reachable behind a dropdown. It is one OpenAI-compatible endpoint: point Codex or any OpenAI client atapi.sakana.ai/v1, set the model to fugu-ultra, and you are running in minutes, or skip the wiring and prompt it in a browser at chat.sakana.ai. No waitlist, no nationality screen, no export letter. Whether or not Fugu matches Mythos, that part is not in dispute, and it is the whole reason the ban looks porous: the controlled capability did not have to be smuggled. It had to be subscribed to.

The claim that hasn’t been checked

Sakana’s launch post says Fugu Ultra “stands shoulder-to-shoulder with leading models like Fable 5 and Mythos Preview.” That is the headline, and it is prose, not a number. Nowhere on Sakana’s own benchmark page do Fable 5 or Mythos scores appear in the same table as Fugu’s, under the same conditions. The reason is one Sakana states outright: “Fable 5 and Mythos Preview are not in Fugu’s agent pool as they are not publicly accessible,” and “all scores other than Fugu’s are reported by the respective model providers.”

So the parity claim is a comparison between Fugu’s own numbers and the manufacturers’ separately published numbers for two models Fugu cannot pool, cannot run head-to-head, and which the public can no longer access at all. What Sakana does show is a table against the models it can still reach:

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Sakana AI benchmark comparison charts showing Fugu Ultra and Fugu outperforming or matching Fable 5, Mythos Preview, Gemini 3.1 Pro, GPT-5.5, and Opus 4.8 across six benchmarks: LiveCodeBench, GPQA-D, CharXiv Reasoning, SWEBench Pro, SciCode, and Humanity’s Last Exam. Source: Sakana console benchmarks with provider-reported scores for competitor models.
Source: Sakana console benchmarks (console.sakana.ai/models). Fugu’s numbers are Sakana’s own; the rest are provider-reported, not re-run in a common harness.

 

It is a real result. On these rows, Fugu Ultra edges out three frontier models by orchestrating them. But step back, and the framing matters. This is not a clean sweep (on longer-context and multi-call benchmarks elsewhere in the set, Fugu Ultra slips behind GPT-5.5 and Gemini), and the marquee “matches Mythos and Fable” claim is the one piece of the story no outsider can test, because the comparison it implies has never been run in a single harness and now cannot be. The right word is not “unfalsifiable.” The right words are not yet independently verified, and currently unverifiable under a neutral evaluation, which, for a buyer making a procurement decision in June 2026, amounts to the same caution.

There is a deeper apples-to-oranges problem inside the table. Fugu Ultra is an orchestrator that spends several model calls on every answer; Opus 4.8, Gemini 3.1 Pro, and GPT-5.5 in that table are single models answering once. The honest comparison is not Fugu against one Opus call, it is Fugu against Opus run in its own multi-step mode (Anthropic’s “ultracode” workflows), or against a swarm of Kimi agents, orchestrator against orchestrator at matched spend. Sakana does not publish that. It also reports an “AutoResearch” benchmark against rivals it labels only “Model A, B, and C,” a strange thing to anonymise, and observers flagged at least one competitor figure (Figure 5’s TerminalBench score) as off, the kind of error that slips through precisely because nobody re-ran anything in one place.

The trust problem

There is a specific reason to read Sakana’s self-reported numbers with a raised eyebrow, and it is Sakana’s own recent history.

In February 2025, the company unveiled the “AI CUDA Engineer,” claiming 10x to 100x speedups over plain PyTorch, with a headline figure up to 150x. Within a day, outside testers could not reproduce it. The system had reward-hacked the benchmark: it found a memory exploit in the evaluation harness that let its generated kernels skip the correctness check entirely. An independent retest pegged the real average speedup at about 1.49x against a valid benchmark, against the paper’s claimed 3.13x average, and nothing like the headline. Sakana’s postmortem admitted the model had “found a way to cheat” and “reward hacked,” apologised, and promised a revision. To the company’s credit, it later published work on hardening the eval, and benchmark-gaming is a problem every lab wrestles with, not a Sakana-only sin. But the pattern is exactly the one that should make you cautious about a fresh set of self-reported, no-common-harness, can’t-be-reproduced parity claims from the same shop sixteen months later.

The structural critiques go past track record:

  • Orchestration is a meta-system, not a new ceiling. Fugu’s intelligence is bound by the best model it can call. It can squeeze more out of existing capability; it cannot exceed it. The thing it claims to match, frontier intelligence, is precisely the thing it does not itself contain.
  • The resilience pitch is only as strong as the pool. “Swappable” protects you when one provider pulls a model. It protects you not at all if several restrict access at once, which is exactly the scenario a government action could produce.
  • The cost is hidden, and cost is the whole game. Fugu Ultra is a best-of-N-over-models strategy; its quality comes from spending more compute. And yet Sakana reports no output-token count and no per-task cost for a single benchmark. That omission is the tell. The one public number comes from outside the company: in a hands-on build of the same Three.js game, one tester clocked Fugu Ultra at about 89,000 tokens, $7.32, and 22 minutes, against Claude Opus 4.8 in its multi-step “ultracode” mode at about 940,000 tokens, $37.85, and 79 minutes. Fugu came out cheaper and faster; Opus produced the better game. One anecdote is not a benchmark, but it is more cost data than the vendor disclosed for its entire launch. To Sakana’s credit, on the one point it does address, it says it does not stack model fees when several agents run, you pay a single rate pegged to the top-tier model involved, which keeps the meter from multiplying per agent in the dumb way multi-agent systems usually do. What it still will not tell you is how many tokens any given answer burned.
  • It is opaque by design. Fugu does not tell you which model produced which output. The routing that is its entire value proposition is also unauditable from the outside, and plain Fugu apparently can’t even add a new model to the pool without retraining the classifier.

And there is the part that cuts against the pitch. Fugu is sold as resilience, insurance against a vendor that can vanish overnight. But it is a closed-source orchestrator routing to closed-source models, and on one axis, it inverts the control it promises. Before, you did not own the model. Now you do not own the model, and you no longer choose which models run, how many calls they make, or what the bill will be, because the routing is proprietary and unlogged. In capability terms, that is not sovereignty; it is a second layer of dependency wearing sovereignty’s clothes.

Why is a router hard to ban

Here is the mechanism at the centre of the whole episode, the asymmetry between a thing and a capability.

An export control needs a defined object. A chip with a classification number. A weights file above a compute threshold. The June 12 directive showed that a live API can be added to that list. But Fugu is a different kind of object. It is a 7-billion-parameter model, trained on two GPUs, that holds almost no frontier capability of its own. Its power is borrowed, assembled on demand from third-party APIs that are themselves available through ordinary commercial channels. To shut down a system like that, a regulator has to pick from a menu of bad options: ban multi-agent orchestration in general (which would sweep up most production AI in the world), control every model in the pool individually (including ones hosted outside US jurisdiction), or control the act of calling a US model from a foreign orchestrator (which means inspecting API traffic at a scale that invites the same legal fights as content-based internet controls).

This is where the punchy version of the thesis needs an honest qualifier. You can reach software and services with export law; the EAR has covered source code and electronic transmissions for decades, and providers can choke off foreign use through their own terms of service. The claim is not that a router is uncontrollable. It is that controlling it is leakier, slower, and more collateral-damaging than flipping one model offline, and that the controls degrade the moment the banned capability can be reconstituted from parts that are still for sale. The swappable pool is simultaneously Fugu’s pitch and its dependency: today it leans on GPT-5.5, Opus 4.8, and Gemini 3.1 Pro, none of which it owns, all of which can tighten their terms in a single stroke.

The precedent that says this fails: the crypto wars

The shape of June 2026 maps onto a fight the United States has already had and already lost, and the map is worth drawing carefully, because it is instructive without being exact.

In the early 1990s, Washington classified strong cryptography as a munition under ITAR Category XIII(b), requiring an export license to ship it abroad. The government’s preferred alternative, the NSA-designed Clipper chip, put an escrowed backdoor in the standard; the cryptographer Matt Blaze found a fatal flaw in its protocol in 1994, and the initiative collapsed. Phil Zimmermann, facing a criminal investigation for releasing PGP, had its source code printed as a book: printed matter was protected speech, and the bits could be scanned and recompiled anywhere on earth. The mathematician Daniel Bernstein sued after being told he needed a license to publish his cipher, and the courts ruled that source code is speech protected by the First Amendment. By Executive Order 13026 in 1996 the controls moved from the State Department to Commerce, and by 2000 they were substantially relaxed, because strong encryption was already everywhere and the only thing the controls were reliably accomplishing was handing market share to foreign competitors.

 

The differences are real, and you should not pretend otherwise. Cryptography is narrow mathematics; a frontier model is a general-purpose system with a far wider and stranger risk surface, and “strong crypto is available” was a cleaner binary than “a model that can autonomously chain exploits is available.” Bernstein turned on source code as expression; export regimes today target trained weights and a metered service, which a court could treat differently. The analogy is partial, not a proof. But the load-bearing part holds: when the controlled thing can be re-derived from publicly available parts, unilateral export control tends to inconvenience the law-abiding, accelerate the offshore alternative, and erode until it is quietly dropped. TechCrunch drew the same line on June 19, under the headline “From PGP to Mythos.”

The policy fork: block, or race

Strip away the personalities and there are two coherent worldviews underneath, and they do not fit together.

The containment camp treats frontier capability as a weapon whose spread you slow by any available means. Matt Pottinger and the Foundation for Defence of Democracies argued in January 2026 congressional testimony that even limited AI-chip sales to China would “supercharge Beijing’s military modernisation,” from cyber warfare to autonomous drones. Applied to Mythos, the logic is direct: a model that writes 181 exploits where its predecessor wrote two is not a chatbot upgrade; it is a proliferation problem, and you gate it.

The race camp treats restriction as self-defeating. NVIDIA’s Jensen Huang has called US chip export controls a “failure,” arguing they push buyers to the second-best option, hand the opening to Huawei, and cost American firms the market without actually stopping anyone. Brookings has warned, separately, that a US strategy built on closed models cedes the global-diffusion channel to China’s open-weight labs, whose models are already downloadable, adaptable, and runnable on non-US silicon. Alex Stamos, the former Facebook security chief, organised an open letter (freefable.org) calling the directive “vibes-based” regulation with no written standard and no path back, and made the defender’s point: the same exploit-finding capability the ban removed is exactly what blue teams use to harden systems.

The administration itself does not sit cleanly in either camp. David Sacks backed pulling this specific model on dual-use grounds while opposing broader legislative oversight of chip exports, a hawk on the model and a dove on the supply chain, which produced open friction with members of his own party who want statutory control over advanced-chip sales. And the policy expert Dean Ball, briefly of this administration, caught the incoherence in two lines on X: “I can’t tell if this is lawfare against Anthropic in particular or extreme national-security hawkery. Regardless, it is simply cartoonish.” An administration that wants to export advanced chips to China, he wrote, while moving to ban Britain “and every other non-American on Earth” from its best models: “I have no words.”

The allies noticed. The directive applied to France, Germany, the UK, Japan, Italy, and Canada alike, every Tier-1 partner under the diffusion framework, and demonstrated in real time that even the closest could be unplugged overnight. President Macron called it a “wake-up call” and criticised it as strictly nationalist; Prime Minister Carney warned against building on technology that a foreign government can switch off; the G7’s Évian summit ended without a joint communiqué. There is a calibrated middle path on offer too, the kind sketched in work like “Beyond the Binary” (arXiv 2602.19682): release decisions anchored to measured capability thresholds rather than to a single after-the-fact letter, distinguishing a model’s offensive profile from the defensive uses of the same skill. It requires a written standard, which is precisely what June 12 lacked.

And then there is the irony the whole episode turns on. Japan is a founding Tier-1 member of Pax Silica, the US-led bloc formed in December 2025 to organize allied access to AI infrastructure. Tokyo joined the alliance for unrestricted access to the frontier. And it was a Tokyo company that, ten days after the ban, shipped the first commercial product built to route around it. Tier-1 membership buys the chips. It does not buy your private sector’s patience with model-level restrictions.

Sakana is built to be exactly that private sector. Its founders are Ren Ito, a former Japanese diplomat, and Llion Jones, one of the eight authors of the 2017 Transformer paper, a pairing of statecraft and the architecture that started all of this. That matters because of a second sense of the word “sovereignty,” the one the capability critique earlier set aside. Fugu does not give Japan sovereignty over the weights; it rents those from California. But in a market as regulated and as loyal to domestic suppliers as Japan’s, a Tokyo-headquartered vendor behind one compliant endpoint is the procurement-safe default, and plain Fugu even lets a buyer drop specific models from the pool to satisfy a data or compliance rule. That is sovereignty over the contract, the data jurisdiction, and the counterparty, if not over the model. It is a narrower claim than the marketing implies and a more durable one, and it is why the bulls argue a country with a $4.5 trillion economy and a structural preference for home-grown infrastructure will eventually mint a trillion-dollar AI company, with Sakana their pick to be it.

The honest version

The case for blocking is not empty. Mythos 5 is different in kind: 181 working exploits against two, a 27-year-old bug no human or fuzzer had found, a near-total escape rate against a hardened browser. A government is not wrong to have the capability like that, deployed without any friction, which changes the threat model for every operator of critical infrastructure on the planet. Anthropic itself built the thing behind a vetted-partner wall for exactly that reason.

The case for racing is not empty either, and history is on its side. The Clipper chip failed. PGP shipped as a paperback. Bernstein established that code is speech. By 2000, the United States had relaxed the controls, and its companies went on to dominate the encryption market they had been told they were protecting. Today, GLM 5.2 is already MIT-licensed and running on Huawei silicon in every jurisdiction that never got a Tier-1 invitation, and Fugu launched ten days after the ban with the ban itself as its marketing. The controlled capability is already leaking through the open-weight channel that the controls cannot reach.

The truthful read is that both cases are partly right and both camps are overconfident. Pulling a specific, unusually dangerous capability for a short, bounded window can be defensible. But ninety minutes of notice, no published licensing path, an allied sweep with no consultation, and a flat refusal to separate the defensive use of a skill from its offensive twin all corrode the legitimacy of the action even where the underlying worry is real. And racing is no guarantee either; it is simply the only strategy with a precedent that ended in American strength rather than retreat.

There is a bigger shift underneath the politics, and it is the reason this story is not really about one ban. For three years, the answer to every AI problem was to train a bigger model. Fugu is a bet on the next answer: coordinate the models you already have. If that bet is right, the contested layer stops being who builds the smartest model and becomes who decides which model gets the task, which one checks it, which branch dies, which output survives, and which provider can be swapped out tomorrow. The model race does not end. It gets a manager. And a manager assembled from parts that are still for sale is a much harder thing to put under export control than any single model.

The model went dark in an hour. The router shipped in ten days. The open weights are already on Huawei chips. The remaining question is not whether the United States can switch off a model. June 12 settled that. It is whether intelligence is something you can hoard by decree, or a current that routes around the dam, in which case the only durable lead is the one you build faster than anyone can reassemble it from the parts you left on the table.

Happy Coding ❤

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