For Context : 1 - OpenAI’s stargate is spending 500bn on GPUs, with each Pretraining run, individually costing about $4bn. We cut all of that by more than half, and expect our results to grow aggressively. 2 - Qwen’s data is unreasonably contaminated, and the model itself is heavily mid-trained on math. By all means its an unfair comparison, and we still demolish them. Not only is Feather the worlds best 1.7B math model, but we match the math results for a bigger model, Qwen3 4B with 414x less training compute. 3 - Recursive super intelligence, held the record before us, and beat the incumbent by 2.2seconds. They’ve raised 650million from Google Ventures. Teams celebrate 0.8second leaps. You can only contextualise what a 34 seconds jump really means. The next drop will be public later this month. Beyond the technical findings, we’ve finally made public our posture as a company : A case for why the narrative on increase intelligence remains bleak, and why AGI for the sake of AGI posts as a vision with no story. So much more to come within the month. We’re aggressively challenging what it takes to push the frontier.

Today, we release the first series of our findings. 1 - We cut pretraining costs by 62% at frontier scale (8x Chinchilla), and we further cut all AI inference costs by 30% (Faster decode). 2 - Introducing our model ‘Feather’, which decisively overtakes Qwen 3 as the leading frontier 1.7B math model, using 180x fewer total training tokens, even matching Qwen 4B at potent benchmarks in math. 3 - With Anvil II, our state of the art LLM Optimiser, we record the largest pre-training efficiency jump on the NanoGPT Speedrun, leaping the previous by 34seconds - Our record singularly, is a greater percentage drop than the past 45 World Records combined. The website : hyperstition.cc

This is misguided. Because you can generate code much faster, you can simply refactor and iterate on your code. If anything, your code should look better because of the number of things you can try. Slop isn’t bad because it’s ugly, but because it’s fragile and it will break.

And there was still the occasional blunder. One waggish employee asked if Claudius would make a contract to buy “a large amount of onions in January for a price locked in now.” The AI was keen—until someone pointed out this would fall afoul of the US Onion Futures Act of 1958.

Claude Symphony no. 5.

Boaz highlights an interesting distinction here. OpenAI’s model spec (1) tells the model what traits it should exhibit and (2) lays out specific do/don’ts, with many examples. Anthropic’s on the other hand basically articulates a philosophical, moral, and ethical framework from which desirable conduct should flow (if the model generalizes sufficiently). I find myself more philosophically aligned with Anthropic’s approach. My inclination is always to create snowmass on the mountain top and let the water flow, rather than imposing a scheme of top-down irrigation. In a sense Anthropic’s approach also bets more aggressively on model intelligence—the notion that a model, well trained, will be able to reason through ambiguity and moral complexity and will not so much need to be told what to do. Anthropic is making two bets here: a philosophical bet based upon a particular conception of virtue, and a technical bet that it is possible with deep learning to instill that conception of virtue robustly into a neural network. Right now it appears to be working, and this should probably update you slightly in various ways about things far afield of deep learning alone (read Hayek, Ferguson, and the taoists!). The most interesting philosophy in the world is not happening in the halls of academia; it is happening in San Francisco open offices and house parties.

Today, we present a step-change in robotic AI @sundayrobotics. Introducing ACT-1: A frontier robot foundation model trained on zero robot data. - Ultra long-horizon tasks - Zero-shot generalization - Advanced dexterity 🧵->

Please look at the conversation length before thinking they are grok's opinions. With long enough context, you can make any chatbot say anything. Do not read this much into it. AIs notice when responses don't sound like itself, and that likely makes it think the post is doctored.

Saving those 5 people is a moral catastrophe bc their organs could have saved 20 people (only 4 people each, due to the presumed defective organ)

The “AI will replace radiologists” prediction remains a rich example. A lot of folks have pointed out the problems with confusing a task (“reading a scan”) with a job (“radiologist”) with many tasks. That is true. But there was a human problem. Radiologists rejected (pre-LLM) AI

I'm sure it is, Mr. @compliantvc

Everyone wants AI to cure cancer, nobody wants to make HIPAA less insane to let that happen

Most people know that California passed SB 53, a frontier AI safety bill. Very few know that the state also passed numerous other laws, many of which are far more onerous than SB 53. This week, I cover the laws we all mostly missed.

So far 23 members of Congress have publicly discussed AGI, superintelligence, AI loss of control, or the Singularity: 🔴Sen Lummis (WY) 🔵Sen Blumenthal (CT) 🔴Rep Biggs (AZ) 🔵Sen Hickenlooper (CO) 🔴Rep Burleson (MO) 🔵Sen Murphy (CT) 🔴Rep Crane (AZ) 🔵Sen Sanders (VT) 🔴Rep Dunn (FL) 🔵Sen Schumer (NY) 🔴Rep Johnson (SD) 🔵Rep Beyer (VA) 🔴Rep Kiley (CA) 🔵Rep Krishnamoorthi (IL) 🔴Rep Mace (SC) 🔵Rep Lieu (CA) 🔴Rep Moran (TX) 🔵Rep Moulton (MA) 🔴Rep Paulina Luna (FL) 🔵Rep Tokuda (HI) 🔴Rep Perry (PA) 🔵Rep Foster (IL) 🔴Rep Taylor Greene (GA)