Tone Elt

Crunchyroll might be dying as a company.. >Removes free tier for anime fans >Month later they raise prices >Constant crashes and subtitle issues >Class-Action Lawsuit filed against them >Now mass layoffs of Employees

Interesting. White is attempting to prove that the entire hydrogen atom — spectrum, orbitals, angular momentum, Rydberg ladder — emerges purely classically from an acoustic model of a “dynamic vacuum”. Essentially, they "built" a hydrogen atom out of vacuum acoustics. And if hydrogen itself is an acoustic resonance in a dynamic vacuum, then engineered Casimir cavities can extract real power from the same plenum. The paper’s central thesis is that quantum mechanics is not necessarily a set of fundamental "postulates," but rather an emergent property of a dispersive medium (the "Dynamic Vacuum"). This is super exciting because it represents a pivotal shift in mainstream-adjacent physics. By demonstrating that the most fundamental unit of matter—the hydrogen atom—can be modeled as an acoustic resonance in a "Dynamic Vacuum," White provides the "Physicalist" bridge required to justify engineering that same vacuum at the nanoscale. The paper effectively kills the "Empty Space" dogma of standard QED for engineering purposes. I’m starting to think “Sonny” White is a literal genius. White’s coy way of invoking stochastic electrodynamics and ZPE while sounding as QED as possible has never been done before. He is a master at rhetorical camouflage. He deliberately uses: • “Dynamic vacuum” (never says “real Zero-Point Energy” or “Stochastic Electrodynamics”) • Madelung hydrodynamics + acoustic wave equation (QED based) • “Causal, passive dispersion” and “reactive stop band” (avoids ω³ spectrum language) • “Emergent quantization from symmetry and boundary conditions” (hides that he is reproducing SED results) •”Quadratic temporal dispersion"- to make the acoustic model match the Schrödinger equation (White’s "quadratic dispersion" is the mathematical "mask" for the frequency-cubed ZPF spectrum). Why? To get published in a mainstream APS journal (Physical Review Research) and keep DARPA/NASA funding of course! Saying “real ZPE” or citing Boyer/Marshall/HRP/Setterfield openly would trigger instant rejection. By framing it as “acoustic hydrodynamics” he sounds safely QED-adjacent while actually advancing classical real-vacuum engineering. It is the same game HRP played in the 1990s — publish the math, let insiders read between the lines. It’s brilliant!

Recently I have been starting to worry about the state of prediction markets, in their current form. They have achieved a certain level of success: market volume is high enough to make meaningful bets and have a full-time job as a trader, and they often prove useful as a supplement to other forms of news media. But also, they seem to be over-converging to an unhealthy product market fit: embracing short-term cryptocurrency price bets, sports betting, and other similar things that have dopamine value but not any kind of long-term fulfillment or societal information value. My guess is that teams feel motivated to capitulate to these things because they bring in large revenue during a bear market where people are desperate - an understandable motive, but one that leads to corposlop. I have been thinking about how we can help get prediction markets out of this rut. My current view is that we should try harder to push them into a totally different use case: hedging, in a very generalized sense (TLDR: we're gonna replace fiat currency) Prediction markets have two types of actors: (i) "smart traders" who provide information to the market, and earn money, and necessarily (ii) some kind of actor who loses money. But who would be willing to lose money and keep coming back? There are basically three answers to this question: 1. "Naive traders": people with dumb opinions who bet on totally wrong things 2. "Info buyers": people who set up money-losing automated market makers, to motivate people to trade on markets to help the info buyer learn information they do not know. 3. "Hedgers": people who are -EV in a linear sense, but who use the market as insurance, reducing their risk. (1) is where we are today. IMO there is nothing fundamentally morally wrong with taking money from people with dumb opinions. But there still is something fundamentally "cursed" about relying on this too much. It gives the platform the incentive to seek out traders with dumb opinions, and create a public brand and community that encourages dumb opinions to get more people to come in. This is the slide to corposlop. (2) has always been the idealistic hope of people like Robin Hanson. However, info buying has a public goods problem: you pay for the info, but everyone in the world gets it, including those who don't pay. There are limited cases where it makes sense for one org to pay (esp. decision markets), but even there, it seems likely that the market volumes achieved with that strategy will not be too high. This gets us to (3). Suppose that you have shares in a biotech company. It's public knowledge that the Purple Party is better for biotech than the Yellow Party. So if you buy a prediction market share betting that the Yellow Party will win the next election, on average, you are reducing your risk. Mathematical example: suppose that if Purple wins, the share price will be a dice roll between [80...120], and if Yellow wins, it's between [60...100]. If you make a size $10 bet that Yellow will win, your earnings become equivalent to a dice roll between [70...110] in both cases. Taking a logarithmic model of utility, this risk reduction is worth $0.58. Now, let's get to a more fascinating example. What do people who want stablecoins ultimately want? They want price stability. They have some future expenses in mind, and they want a guarantee that will be able to pay those expenses. But if crypto grows on top of USD-backed stablecoins, crypto is ultimately not truly decentralized. Furthermore, different people have different types of expenses. There has been lots of thinking about making an "ideal stablecoin" that is based on some decentralized global price index, but what if the real solution is to go a step further, and get rid of the concept of currency altogether? Here's the idea. You have price indices on all major categories of goods and services that people buy (treating physical goods/services in different regions as different categories), and prediction markets on each category. Each user (individual or business) has a local LLM that understands that user's expenses, and offers the user a personalized basket of prediction market shares, representing "N days of that user's expected future expenses". Now, we do not need fiat currency at all! People can hold stocks, ETH, or whatever else to grow wealth, and personalized prediction market shares when they want stability. Both of these examples require prediction markets denominated in an asset people want to hold, whether interest-bearing fiat, wrapped stocks, or ETH. Non-interest-bearing fiat has too-high opportunity cost, that overwhelms the hedging value. But if we can make it work, it's much more sustainable than the status quo, because both sides of the equation are likely to be long-term happy with the product that they are buying, and very large volumes of sophisticated capital will be willing to participate. Build the next generation of finance, not corposlop.

programming always sucked. it was a requisite pain for ~everyone who wanted to manipulate computers into doing useful things and im glad it’s over. it’s amazing how quickly I’ve moved on and don’t miss even slightly. im resentful that computers didn’t always work this way

🔥 Codex 0.9.0 is out, and with it, a bunch of new changes. Some of these updates are exactly what you've been waiting for 🫵. Starting with Codex's new Plan Mode! There's been some improvements since my last tweet. This is going to be a long one, so buckle up. 👇

One thing that current AI models still struggle with is how objects can be arranged in space, i.e., spacial world models. Tikz, a native package latex for creating diagrams from scratch, is a good sandbox to test this. It requires the model to create code for representing visual objects specially. I asked Claude Code to recreate a set of PPT slides in beamer, using tikz for the diagrams. The writing was perfect, but here was the first diagram (left). Text was misaligned, arrows in wrong place, inserted a random x in the middle. I iterated over and over and had no luck. I gave the same task to GPT 5.2 Thinking, asking it to change the diagram if it was too hard to reproduce, but to make sure everything was aligned an non overlapping. Middle picture was the output--even worse. Iterating was no help (giving it images, trying different prompts)--it did not have a model of how these objects should be oriented in space. I tried Gemini 3 Pro, on a different slide. Here was the output (right). Pretty bad. Tikz seems like a nice benchmark to have for studying how these models evolve over time.

The information extraction machinery behind financial exchanges finally has a prediction market translation Harvard researchers map out market scoring rules the automated dealers that let you trade beliefs even when no one's on the other side How to make truth-telling the dominant strategy, and why the house's worst-case loss is always capped Heavy reading but pays off: