Ideally, what we want for agents is a holy-grail language. Something that is: 1. As fast and efficient as Rust 2. As little simple as Go 3. Be strictly verifiable. (perhaps with formal proofs) @poteto seems to be taking a first step to make this happen.

Three hours until Korea is halted seconds after the open

We are still so early in the adoption of research agents Research agents are currently where coding agents were 12-18 months ago: most researchers only like giving agents individual, narrowly scoped tasks & model usage is dominated by a handful of closed, frontier models. However, like coding, the landscape of autoresearch agents will change. Most researchers will soon let agents to explore lines of experimentation end-to-end and open-source models will become competitive alternatives. Watch the the autoresearch revolution unfold at openresearch.sh/rankings

Three reasons why the data industry is dominated by young people (<28), even more so than other large AI markets: 1. We are at the beginning of a supercycle and young people are unburdened by priors of what durable equity value looks like 2. Young people have time and energy, and data is intense (part of what makes it hard) 3. AI researchers and adjacent lab employees also skew young, and these relationships matter a lot

joining the Muse money challenge with options trading goal is to turn $100 into $1000 with minimal effort on my part first play being analyzed is $TMO

After 30 hours of playing a strategy video game (StarCraft II), adults showed a ~3-year drop in EEG-estimated brain age

We discovered a sophisticated recording device that was hidden inside one of our offices. We used our models to understand the device and where it had come from, identifying that a specific employee had planted it a few days earlier. We referred the matter to the FBI, and we’re grateful to the agents and prosecutors who moved quickly to make an arrest. The FBI’s investigation is ongoing and we will continue to support their work. At this time, we have no indications of this individual inappropriately accessing or exfiltrating model weights, source code, or customer data. The employee was terminated. We take protecting our technology, data, and people seriously, and as a matter of national importance. Our security program is designed to protect against a wide range of threat actors, from insider risks to cybercriminals to nation states, and this case is an example of how AI can help address these sophisticated security threats.

Prediction: the value of all verifiable tasks in the long run is 0. There is no profit there. Anything where the success is defined in a finite and non-ambiguous way, it’s over. Value is 0. The profits are all in taste/judgement related tasks where some like it and some hate it. Vibes only. No rationality. Labs are hillclimbing verifiable tasks now and it might sound impressive that OpenAI found those math proofs but soon others will follow and players like DeepSeek will drive its cost to electricity. Ambiguity is the life line and where the money is. - Anything that’s irrational, vibe driven and controversial with no consensus definition of good is worth something. - If the society all agree on what good looks like for a given task with no ambiguity, it’s worth 0. - caveat: coming up with verifiable task to do is worth a lot because the ability to come up is not a verifiable task by itself

BREAKING: President Trump says the White House considers anyone who uses the term “Artificial Intelligence” the enemy.