If you were a PC gamer and BitTorrent user in the 2000s through the early 2010s, you were probably familiar with DAEMON Tools. The software allows users to mount disk images as if they were physical disks in a physical drive. Well, it turns out the software is still around and has been compromised since at least April 8, 2026. As Kaspersky Securelist reports:

In early May 2026, we identified installers of the DAEMON Tools software, used for mounting disk images, to be compromised with a malicious payload. These installers are distributed from the legitimate website of DAEMON Tools and are signed with digital certificates belonging to DAEMON Tools developers. Our analysis revealed that the software installers have been trojanized starting from April 8, 2026. Specifically, we identified versions of DAEMON Tools ranging from 12.5.0.2421 to 12.5.0.2434 to be compromised. At the time of writing this article, the supply chain attack is still active. Artifacts suggesting that the threat actor behind this attack is Chinese-speaking have been identified in the malicious implants observed.

The fact that the attacker has been distributing malicious binaries signed with the official cert on the official website for nearly a month (and counting) would seem to indicate a pretty deep level of compromise. While Kaspersky observed the malicious software on thousands of machines, a handful of high-value targets appear to have been targeted for further exploitation:

Dr. Marty Makary, head of the FDA, the United States’ drug regulator, announced something genuinely interesting and innovative last week. As Abigail Beaney of Clinical Trials Arena reports:

The US Food and Drug Administration (FDA) is launching a pilot to implement real-time clinical trials (RTCT).

While announcing the pilot, the FDA revealed that two proof-of-concept clinical trials have been successfully initiated, which will report endpoints and data signals to the agency in real time, from AstraZeneca and Amgen.

[…] For each trial, the FDA met with the sponsor on the establishment of criteria for reporting signals in real time. The agency has since received and validated signals for AstraZeneca’s trial through Paradigm Health’s Study Conduct Platform, which automates data collection and analysis while improving how key safety and efficacy signals are reported to both trial sponsors and regulators, supporting more efficient oversight.

We’ve previously discussed Eroom’s law on this blog, which describes the exponential decay of productivity in drug discovery over time. Shortening the time between the conclusion of a trial and the reporting of its results could us realize the potential of AI for drug discovery. Currently, AI is helpful for discovering candidate molecules, but we still have a huge bottleneck in actually testing the resulting experimental therapies in patients. Automated reporting pipelines to regulators could also give us greater confidence in the data if it results in more pre-specification (and less human error/latitude) in data collection and analysis, but that depends on the particulars of how the system is implemented.

The seed of this post was hearing “I Don’t Wanna Wait” on the radio while shopping for groceries. The song rips off “Dragostea Din Tei” by O-Zone, which anyone who was on the Internet in 2004 knows as “Numa Numa”.

Gary Brolsma’s lip-syncing video was one of the Internet’s earliest memes, and perhaps the best. It was pure, joyful, spontaneous, and released with no expectation of fame or commercialization. It was just some guy in front of a webcam having the time of his life. Now everyone is lip-syncing all the time on TikTok, except there is no joy, no spontaneity, only endlessly choreographed offerings to the almighty algorithm.

I’ve been mourning the old Internet over the past year or two. Kids growing up today will never know that the Internet used to be different. Golden ages are usually defined in retrospect. As a kid on the Web from the early 2000s through the mid-2010s, we knew we were living through something special, but it always felt like there was something better around the corner.

When I was growing up in the 2000s and early 2010s, privacy and mass surveillance by governments were a major topic of discussion. Laws like the Patriot Act (and the local equivalents around the world) were endlessly fretted over in the media and debated in legislatures. This probably peaked in 2013 with the Snowden leaks, which revealed that American and allied intelligence agencies were sucking up data from major tech companies and tapping the very backbone of the Internet itself.

We imagined mass surveillance as a problem of centralized government power; we feared the government having the power to monitor everyone. But then the best minds of our generation got thinking about how to make people click online ads. First, we gave over all our data willingly enough to social media companies, and then without really noticing to a never-ending stream of data brokers and whatever category of company Palantir is (mass-surveillance-as-a-service?). Mass surveillance was no longer only a fearsome tool of the state. It was just the business model of the Internet: available for governments and anyone else willing to sign a contract. That’s how a nonprofit ends up spending millions of dollars to figure out which priests are using Grindr.

Technology quaintified the issue of mass surveillance. The old debate was not resolved; it was buried under the world that came next. The arguments still mattered, but they belonged to a world where mass surveillance was something governments had to build intentionally, not something they could buy off the shelf from a company that developed it to maximize click-through rates.

Debate any subject with an empirical angle and you will inevitably run into the phrase “correlation does not necessarily imply causation”. While true, it is rarely an interesting observation, and quite often used to reflexively dismiss empirical evidence countering one’s viewpoint (even if this impulse is ultimately correct much of the time). As investor Paul Graham amusingly put it:

Whenever I see a reply mentioning that correlation isn’t causation, without fail it turns out to be saying something stupid. If they made a great seal of midwits, that phrase would be inscribed around the outer edge.

It is more interesting to note another bias making causal claims in research difficult: the fact that causation does not necessarily imply correlation, especially when human actors are involved. Economist Scott Cunningham has a great illustration of this at the beginning of his book Causal Inference: The Mixtape:

But weirdly enough, sometimes there are causal relationships between two things and yet no observable correlation. Now that is definitely strange. How can one thing cause another thing without any discernible correlation between the two things? Consider this example, which is illustrated in Figure 1.1. A sailor is sailing her boat across the lake on a windy day. As the wind blows, she counters by turning the rudder in such a way so as to exactly offset the force of the wind. Back and forth she moves the rudder, yet the boat follows a straight line across the lake. A kindhearted yet naive person with no knowledge of wind or boats might look at this woman and say, “Someone get this sailor a new rudder! Hers is broken!” He thinks this because he cannot see any relationship between the movement of the rudder and the direction of the boat.

…

The anonymous Polymarket trader that made over $400,000 in profit betting on Maduro’s ouster has been allegedly unmasked as special forces soldier Master Sgt. Gannon Ken Van Dyke. Van Dyke was a participant in the raid that captured the former president of Venezuela in early January. He now “faces five criminal charges for stealing and misusing confidential government information, theft and fraud.” The Commodity Futures Trading Commission, which asserts jurisdiction over prediction markets in the United States, has also filed a related civil complaint against the active duty soldier (the first such insider trading case involving prediction markets!).

We have previously discussed on this blog how prediction markets incentivize bad behaviour. The goal aggregating diffuse knowledge to produce unbiased forecasts is a lofty one, but in practice we get gambling, insider trading, and sometimes outright hostile/antisocial actions to make a bet happen.

To some, insider trading is a bug, not a feature. To quote Coinbase CEO Brian Armstrong on the subject: “If you’re actually optimizing it for a source of news, you 100% want insider trading.” (He uses the example of an admiral sitting in the Suez Canal making a bet based on military intelligence.) Is it worth knowing about events just before they happen if the mechanism is that retail traders (gamblers) get soaked over and over again?

Today’s post is a fun one: a working paper from 2006 entitled “Japan’s Phillips Curve Looks Like Japan”.

And indeed, it does:

(Well, as long as you reflect the plot across the y-axis, notice the plot is of -x rather than x on the x-axis.)

The Phillips curve describes the observation that inflation and unemployment have an inverse relationship in the short term (i.e., as unemployment falls, inflation rises and vice-versa).

This humorous working paper did actually lead to a full publication with the same name in 2008:

During the past 15 years Japan has experienced unprecedented, high unemployment rates and low (often negative) inflation rates. This research shows that these outcomes were predictable as part of a stable, readily recognized Phillips curve.

There is a well-known joke in economics attributed to Nobel laureate Simon Kuznets that goes something like this: “There are four types of economies: developed, underdeveloped, Japan, and Argentina.”

I guess this is one way in which Japan’s economy is very much like the rest of the world’s (at least up to 2005).

Cancer therapies based on mRNA vaccine technology have been among the most promising medical developments of the past decade. That promise is now beginning to show early signs of being realized. An extended follow-up of a phase 1 pancreatic cancer trial published last year reported striking outcomes for some patients:

Six years after treatment, Gustafson and six others who responded to the treatment are still alive, along with two of the eight people who did not respond. Two of the responders, including the one who died, had a cancer recurrence; Gustafson’s cancer has not come back.

In other words, after six years, 7/8 responders are still alive, while only 2/8 non-responders are.

Pancreatic cancer is a particularly aggressive form of cancer, with a 5-year relative survival rate of just 13%. Famously, it was the type of cancer that killed Steve Jobs. It has long been an intense target for research due to its grim prognosis and lack of progress compared to other forms of cancer.

At the same time, this remains very early evidence from a small group of patients. Phase 1 clinical trials are not primarily designed to evaluate efficacy (rather, they are designed to assess safety and establish dosing and side effects). While the difference between responders and non-responders is striking, it does not by itself show the vaccine caused the survival benefit: “responders” are defined after treatment, so they are not a proper control group.

Stephen Johnson reports in Big Think on a movement in the United States to end the fear that parents have of having Child Protective Services called on them for giving their young children some independence to roam their neighbourhoods unsupervised. In recent years, there have been several high-profile cases of parents investigated for neglect for allowing their children freedom of movement that would be considered utterly routine two decades ago.

There are many articles decrying the “helicopter parents” of today, who never let their children out of their sight. But this is rational behaviour when vague laws regarding childhood endangerment/neglect create a climate of fear: even if most people believe allowing kids independence is reasonable, all it takes is one complaint and one sympathetic social worker to create dire consequences for an entire family. This is what activists in the United States are trying to change:

The case helped persuade Georgia legislators to pass a so-called “reasonable childhood independence” (RCI) law, enacted last summer. These laws are part of a national movement to tighten vague language in states’ neglect laws. Georgia’s old law, for instance, defined neglect as the failure to provide “proper” parental care. The new law replaces that with “necessary” care and sets a higher bar for neglect: Parents must demonstrate “blatant disregard” for their child’s safety — putting them in imminent, obvious danger. The law also explicitly states that allowing a reasonably capable child to walk to school or travel to a nearby park unsupervised does not, by itself, constitute neglect.

…

David Oks wrote a provocatively titled post a few weeks ago: “How citations ruined science”.

He begins by observing the tidal wave of AI slop in scientific publishing, musing:

But there’s something about all of this that puzzles me.

I get why students, for example, would want to avoid doing homework. But I don’t really understand why scientists would want to avoid doing science. Or, rather, why they’re so eager to use AI to produce a huge number of shoddy papers. No one forced them to become scientists. I imagine that most people who work as scientists chose to do so out of something like love for the subject. So why are scientists using AI to produce and submit so much garbage?

As an aside, this reminded me a bit of writer Freddie deBoer’s piece “If You Don’t Like Writing, Do Something Else” from a few years ago:

For as long as I can remember, these complaints - writer’s block, imposter syndrome, procrastination - have been key elements of writerly self-deprecation. They’re ubiquitous. And, in a sense, the author is correct to suggest that these are tools for identifying those humans who define themselves as writers. Get writers together in a room and soon they’ll be competing to be the one who likes writing the least. But none of it ever meant anything to me.

…

The founder of Abacus Data, a Canadian polling firm, dropped kind of an interesting URL yesterday: abacus-weighting.com. It’s a advertisement in the form of a case study on why Abacus weights their political polls on past vote. It fits perfectly with the theme of yesterday’s post on how pollster’s get different results from the same data (the answer is they weight the raw data differently).

If you follow Nate Silver (or American political polling in general), you probably know that pollsters undercounted Trump support in all three elections where he was on the ballot. What I learned from this post is that support for the Conservative Party of Canada has been underestimated in their firm’s polling data in every polling wave for every election since 2011:

In every single wave, across every single election cycle, Conservative voters are underrepresented in our demographically weighted sample relative to their actual share of the vote. Not in most waves. Not in some elections. In every case we can observe.

Weighting for recalled past vote improves the estimate in every case, sometimes dramatically so:

In every election, past vote weighting moved our Conservative estimates upward and our Liberal estimates downward — consistently in the direction of the actual result. The 2021 election shows the most dramatic correction: a 7-point improvement in our Conservative estimate.

…

Here’s an interesting figure and accompanying passage from this 2023 preprint entitled “Machine Culture”:

The innovations generated by AlphaGo and AlphaGo Zero soon entered human culture, as shown by research comparing human gameplay before and after the algorithms’ introduction. The decision quality, as measured by an open-source variant of AlphaGo Zero, showed very little improvement in human gameplay from 1950 to 2016, followed by a sudden improvement after the introduction of AlphaGo in March 2016. However, this improvement wasn’t solely due to humans adopting strategies developed by AlphaGo. It also reflected an unexpected shift, wherein humans started developing moves that were qualitatively distinct both from previous human moves and from the novel moves introduced by AlphaGo. In summary, AlphaGo served as an early, quantifiable exemplar of machine culture, generating novel cultural variations through genuine, nonhuman innovation. This was followed by a major transition into an even broader range of traits as the result of humans building on the previous discoveries made by machines. As the methods underpinning AlphaGo have been generalized to other games and extended to scientific problems, we anticipate a continued infusion of machine-generated discoveries across diverse domains of human culture.

…