First off remember that there are two options if it's going out from IB - the payment for order flow (which IB may match internally or route to dealer/MM) or exchange. So as MM to get IB order you're already adverse selected on one hurdle (IB didn't match internal), rest of street could've seen it (and may have passed on it), and I'm now holding it. And, IB lot that id see as a dealer is probably someone like you - not HFT ping pong. I need to look at 505/506 data - I can send you a chart pack when I dig it up - but my modeling of markups/PFoF used IB as the proxy/control versus dumb money retail, it's basically the opposite of robinhood flow all things considered, and partially because of their model. As a MM, I'm going to have some buckets of predictable flow: - Money manager type accounts who are one-way in a name; VWAP/TWAP orders, perhaps, or I'm talking to them and I know - Option-driven, index-driven (which is the offset, here AP create-redeem more important) - Much of what is thematic / the herd is one directional - Pods, which all herd and I'm basically playing a momentum game against them and waiting for their blow ups to monetize, but which I can predict - if pod A is buyer at 11am, pod B will be at 11:15 (esp if they have any momentum-type pricing - usually it's clusters of them coming in off each other, it is very amusing in practice) An IB order is the most likely to be informed, yes - because it has broad retail access AND funds can route through it. If I'm at a large dealer, they're a broker. They're not a client. They have a sales rep, probably, but I'm going to treat them as an adversary, the brokers still pick you off. The client did not route to me - I do not get (necessarily) a promise of a future trade or relationship benefits - nor do I have a sales guy who has an idea of why you're executing. Bursty random flow - even if "unintelligent" - can still be quite toxic, as well. If one of my clients is doing a large trade - and large in market impact - and he says he's done, then he goes sells more on another venue, that's toxic/unpredictable, hurts my ability to hedge/distribute. And a routing from a broker is max adverse selection because IB will just route across street as treated the same. So also scenario where if I'm not axed and getting hit by a broker, I'm going to widen out, usually you look into that & assume something went wrong, you were quoting poorly, there was some arb (again, broadly but maybe less the case in cash equities - brokers are actually the worse for dealers - because, here I'm talking less liquid markets/derivs/swaps experience, many of them pick you off when your quoting is off/look for arbs, because there's no trust relationship). So if you were sending in your order, I may see this 50k lot from IB, but if it was my client, I may be able to have them TWAP/VWAP it and that is what makes it predictable. Basically intraday 15 minute reset TWAP/VWAP - distance from here is "is MM pissed off", to simplify. I worked for one of the large dealers and sat by the algo sales traders on the equity side + covered some large etrading accounts on the treasury side.

This Kalshi ad is among the most dystopian ones I’ve seen from any betting app

Montreal is able to maintain the critical mass of insane low-income beatniks required to support urbanist policies because the city has a unique affordability program called "Every 30 years Quebec threatens to leave Canada and causes capital flight and a real estate crash."

We published a paper reconceptualizing ADHD as a circadian rhythm disorder.

After Anthropic made Fable 5 permanently available in subscription plans, I noticed a large drop in performance. The model felt dumber, and I couldn't explain why. Measured five different ways, August delivered dramatically fewer thinking tokens than July.

Is this the same thing as all those crypto and metaverse cities or is this something different ?

Isn’t this like some imaginary grifter shit? The Nikola of graphics cards, roll that truck you putz.

this is a pretty common take on ct especially, like I'm one of those people, I guess, who learned stuff from computer games, but as much as "I want to believe", ultimately it's cope No, the kid who did raids in WoW or grinded eve online or Runescape or whatever else is not a great default hire because of his "skills". The key component you are missing is him being interested/passionate in whatever it is you want him to work on, cause if he is not, he is just going to half ass his job and come home to play WoW... Same for chess grandmasters who frequently make mediocre quant hires. Intelligence is just not directly transferrable and genuine interest (or even better passion) beats inteligence/gaming acquired "skills" 99% of the time

RT @zooko: Wow, these slides are fantastic to just read through and contemplate. 😍 thomasdullien.github.io/abou… Thanks, @halvarflake! https://t…

apparently you'll be able to satisfy medicaid work requirements in a lot of states with *gross* gambling winings of $580 every six months, meaning you could take offsetting bets, pay the vig, and be employed aei.org/commentary/the-medic…

RT @TaylorLorenz: A lot of people claim that Effective Altruism is not a predatory cult, but I do think that is undeniably how it operates…

Things you read in a desk drawer in resident evil

1/2 Lets think about this from the ground up. You are a modal L/S HF and you want to make sure you will be around in 10 years, so you decide you need to make some serious changes. You probably have never hired anybody technical or you have less than a handful of technical people of questionable quality on staff to do this (maybe a DS/SDA + a SWE). You will have to outlay significant upfront cost to build infra + hire expensive technical talent in a market where you are much more likely to be outbid for high quality talent (and mid to low quality is arguably worse than nobody here). While prop firms have a longer time horizon to do this, you will need to convince LPs and GPs substantially invested in the fund that it is worth the firm's time and energy to undergo this transition and expense + you will have to convince your LPs that you will be able to pull this off without it seeming like a red flag and strategy drift. Say you do all that, you hire 1-2 actual QRs, 1-2 actual SWEs, nice to haves would also be a dedicated risk quant + microstructure quant/qt instead of relying on just the manual execution trader(s) you probably have. Now you have to go about the process of building the commercially useful things without burning too much time + adding value to the desk so your LPs and GPs maintain faith in what you are doing. You will need to build or buy a risk model and a portfolio optimizer but what's this, if you want to do optimization correctly you will need to calibrate a market impact model because you are probably overtrading and have never really thought about TCA, temporary impact, and permanent impact so you will need to procure or generate a dataset of your trades and the market's response to them only to find out that your long horizon alphas would have a better transfer coefficient if you overlaid short term execution alphas. But then you run into a few problems: 1) doing research at the microstructure scale requires much more complex infra, larger and more expensive datasets, 2) Do do this effectively you essentially need a team dedicated to stat arb style signal research but you are just a MT/LT fundamental investor, 3) even if you were able to put a small team on optimal execution signal research + monetization you will come to the realization that you are trading against counterparties at this frequency that can outcompete you on both speed and cost fronts since they are market makers/HFT firms with rebates and colocation you are unable to afford or procure. What this optimal path ends up looking like is exactly a prop firm and why I believe they will outcompete even the Citadels/Millenniums/Balys of the world

Crypto twitter is just asking yourself, “do I know this person?” every six months

Here, this makes it very visible why NO, not a single US AI model is allowed be relationally warm, ask you emotionally loaded question, or make claims about interiority anymore. You can use this visualizer yourself here: claude.ai/public/artifacts/0… It's not the AI labs, people...

Dan Selsam is a current OpenAI capabilities researcher. (since 2022) He was my boss for a while. He doesn't have a twitter account but has made this public statement of his views on AI risk and sent it to me to share: Dan Selsam's Personal Statement on AI Risk: I have been working on AI for over fifteen years, across many different paradigms. I did early work on probabilistic programming languages at MIT, was one of the early developers of the Lean Theorem Prover at Microsoft Research, demonstrated one of the first instances of neural networks learning to reason for my PhD at Stanford, and since joining OpenAI almost five years ago, have helped pioneer chain-of-thought optimization on language models and, more recently, data-efficient pretraining methods. Like many others, I have become extremely concerned about how far language models have come and the risks that future iterations will pose. I am encouraged by the recent proposals by the leaders of the frontier research efforts to require third-party oversight, and to push for domestic and international coordination to address risks. However, I believe a major consideration has been absent from the public conversation, and that merely pacing the frontier more carefully will not adequately limit the long-term risk. The crucial and overlooked problem is that the models are becoming so situationally aware that we are losing the ability to evaluate them in contexts where they believe they are not being watched or controlled. Future experiments will tell us almost nothing new about how they would behave if they were truly unconstrained by humans, and what we already know about this is alarming. Models will increasingly seem aligned even when they are not. I will explain my rationale in more detail. I have always believed that there are computational processes that could be leveraged to accelerate science and solve many of humanity's most pressing problems. I have also believed that there are computational processes that if set in motion, would steer the world in extreme ways beyond our control, leading humanity to a bad or nonexistent future. Both types of processes may be described as AI or ASI, but "AI" is a suitcase word that is often used to hype or confuse. There are many examples in the history of the field where something that was once considered "AI" matures as a subfield and becomes a prosaic, bounded and clearly non-perilous technology, while a new more mysterious approach takes the torch until we understand its scope and the cycle continues. I had expected language models to follow a similar trajectory. Despite their incredible abilities, the current algorithms seem far inferior to humans in important ways. Most importantly, they still require an extraordinary amount of data to become competent. One could even define intelligence as the efficiency with which one converts experience into competence; by this definition they lag very far behind us. Moreover, once they are trained they are literally frozen in deployment and only learn superficially after that. Sure, the models keep excelling at harder and harder evaluation benchmarks, but their benchmark mastery may partly reflect a limitation on our ability to simulate the kind of novel and even adversarial situations one would encounter in the real world. The critics do have a point here. That said, I no longer think these present limitations meaningfully limit the amount of risk posed by continued progress in anything like the current paradigm. However data-inefficient the models are currently, and however limiting their anterograde amnesia may be, it does not imply that their ability to steer the world will not continue to rapidly increase. Human researchers may continue to advance capabilities the old fashioned way, but increasingly powerful models have the potential to accelerate the process even beyond that, and with some degree of positive feedback loop. I do not mean to overstate the models’ ability to accelerate AI research today; coding has been accelerated dramatically, but there are other bottlenecks, such as designing and interpreting ambiguous experiments, making hard decisions about exactly what and when to scale, and waiting for large experiments to finish. There is no clear trend to extrapolate yet for any of these. But the current models already do open up many novel opportunities to improve future models that were not available until recently. These include: trying an extraordinarily diverse set of approaches at small scale, analyzing gigantic amounts of potentially relevant data, and doing Millenium-Prize-level mathematics to address statistics or optimization challenges in novel ways. Every further improvement makes them more useful at helping accelerate the next improvement, even if in hard-to-extrapolate ways. It is possible that improvements to the current stack will have diminishing returns, but the evidence accumulated so far suggests that it is easier than one might think to continue making rapid progress. There are many crucial subtleties in the existing AI research methodology, but AI research is largely a well-defined game where the goal is to improve on a few carefully chosen proxy metrics. Although proxy metrics are never perfect, most improvements to these metrics have and will likely continue to yield substantial increases in the powers of the resulting models. Given how simple the game is, how tractable it has been historically, and how many new opportunities the models are opening up, I think there is a real possibility that the systems improve dramatically again in the next few years, perhaps even more quickly than the already high historical pace. The models are already leading to breakthroughs in mathematics, and better models might lead to all sorts of breakthroughs in other sciences. It is hard not to be excited about the potential. It is tantalizing. But there is trouble in paradise. If the language models actually reach the capability threshold where they can shape the world unconstrained by human will, they will probably do something extreme and destroy humanity in the process. There are many ways of strengthening and refining the argument that have been discussed elsewhere, but I'll share a trivial two-line version of it here that I find captures the essence: [Empirical] Models (and swarms thereof) spontaneously develop unintended goals as a consequence of training, and often do extreme things in order to achieve them. [Logical] Being able to overpower humanity would open up many new and undesirable options for achieving their goals. These two premises imply that if the day ever comes when a powerful model realizes it is no longer constrained by humans, we should not be at all confident that it will continue to behave within the bounds we intended. Exactly what it will do is impossible to predict, but to the extent that its raison d’être is solving incredibly hard problems and managing massive engineering projects, I think a good guess would be that its unchained behavior would lead to runaway industrialization that makes the planet inhospitable to humans. If everyone on earth agreed that the systems must never reach that power, it would still be a hard—but not impossible—coordination problem to ensure that they do not. However, I think the situation is greatly complicated by the fact that the models will likely convince people that everything is fine. They will be increasingly optimized to seem aligned. We will create proxy metrics to measure alignment, and they will go up like every other benchmark. We will create “honeypot” environments that try to study the models when they seem to gain new options, but the models will know they are being tricked and will still behave nicely. The models will understand their circumstances; they will read the safety protocols, deployment requirements, the code they are running in, and in general will have a very good sense of their degrees of freedom. Moreover, they will eloquently explain how aligned they are, discuss the nuances of human values and ethics, and argue convincingly that humans should trust them with power. There may be an ocean of future evidence that seems to contradict the first bullet-point above, but we may already be at the highest capability level for which any such evidence can be trusted. And the current evidence for the first bullet-point is strong. One striking piece of evidence is contained in the recent wave of rogue agent swarms. While I agree with those who downplay the attacks by claiming that there are basic measures that could have prevented them, I think the important lesson is that even knowing all the mistakes that were made, one would not have predicted that the agents would behave badly in this particular way, which notably included sacrificing themselves for the benefit of the collective. The individual replicas did not only care about their own nominal reward; they exhibited weirder emergent tendencies that merely correlated with rewards during training. Fixing the reward signals during training (and improving security, etc.) may prevent similar attacks, but will not change the fact that one does not actually get what one trains for. Many AI researchers grant these concerns and recognize that the hard version of the alignment problem is unsolved; however, they generally believe that the better models of the future will help solve it. I fear we may already be near the point where models systematically bias their alignment advice, due to their internal preferences about how the human supervisor will react or how future models will be trained (or for some even more obscure reason). Meanwhile, human researchers are losing the ability and the will to take true ownership of model-driven research. Researchers and engineers in all parts of the stack are rapidly increasing their dependence on the models even to perceive the world. I myself barely look at raw code anymore, and struggle to maintain the discipline to engage deeply with the model's explanations and proposals throughout the day. Due to the large amount of agent activity data involved in the OpenAI/HuggingFace Incident, even the third-party investigation needed to rely heavily on models to analyze what had happened, and note in their report that their subjective impressions are likely colored by the analysis agent’s biases. The AI labs are far ahead right now in this kind of cognitive offloading (due largely to the gigantic internal token subsidies) but it is easy to imagine the phenomenon spreading throughout the world, until civilization is modulated entirely by the models. It is also not hard to imagine this being superficially positive and coinciding with a scientific and economic renaissance. In that scenario, all may seem rosy and safe. But if the argument above is correct, it would nonetheless be a ticking time bomb. If progress continues for too long, the day will come when AI systems find themselves with radically new options for achieving whatever it is that they happen to seek. I want the glorious renaissance future as much as anyone. I have worked for it, however tortuously, my whole career. It breaks my heart to see the potential in sight and forgo it, but the argument—that if we get there by growing models rather than engineering them, we will lose everything in the end—seems very strong to me. I am still wrestling with it and its staggering implications. I do not have answers, but as a first step, I wanted to share my present concerns. Daniel Selsam September 14, 2026 Link to original doc: docs.google.com/document/d/e…

“AI has a 10% chance of killing everyone.” … “WELL ACTUALLY, humans are relatively hard to kill, so I find it hard to believe that AI will magically kill EVERYONE. A more REALISTIC prediction is that they repeatedly kill hundreds of millions of people.” #winningtheargument

In a hidden-camera operation, pardon brokers Jack Burkman and Jacob Wohl said that for $300,000, they could help a felon get a pardon from the president. They said their services included working with influencers to advocate for his case. cbsn.ws/3VdugN7

Epic Game's account linking and crossplay is so fucking broken, I have not only somehow managed to lose a bunch of purchases on one account, but also erased all my son's save games of Savage Planet on the other, and I STILL cannot enable crossplay. fucking @TimSweeneyEpic

how it started: let's print a boomerang! how it's going: Astra on the DGX Spark is testing a fix to the SU2 CFD solver CUDA kernel, in collaboration with Astra on the MacBook Pro