I've always found this conversation between Wittgenstein and Turing (reprinted in Andrew Hodges's Turing biography) endlessly fascinating: "WITTGENSTEIN: … Think of the case of the Liar. It is very queer in a way that this should have puzzled anyone – much more extraordinary than you might think. … Because the thing works like this: if a man says ‘I am lying’ we say that it follows that he is not lying, from which it follows that he is lying and so on. Well, so what? You can go on like that until you are black in the face. Why not? It doesn’t matter. … it is just a useless language-game, and why should anybody be excited? TURING: What puzzles one is that one usually uses a contradiction as a criterion for having done something wrong. But in this case one cannot find anything done wrong. WITTGENSTEIN: Yes – and more: nothing has been done wrong. … where will the harm come? TURING: The real harm will not come in unless there is an application, in which a bridge may fall down or something of that sort. WITTGENSTEIN: … The question is: Why are people afraid of contradictions? It is easy to understand why they should be afraid of contradictions in orders, descriptions, etc., outside mathematics. The question is: Why should they be afraid of contradictions inside mathematics? Turing says, ‘Because something may go wrong with the application.’ But nothing need go wrong. And if something does go wrong – if the bridge breaks down – then your mistake was of the kind of using a wrong natural law. … TURING: You cannot be confident about applying your calculus until you know that there is no hidden contradiction in it. WITTGENSTEIN: There seems to me to be an enormous mistake there. … Suppose I convince Rhees of the paradox of the Liar, and he says, ‘I lie, therefore I do not lie, therefore I lie and I do not lie, therefore we have a contradiction, therefore 2 × 2 = 369.’ Well, we should not call this ‘multiplication’, that is all. … TURING: Although you do not know that the bridge will fall if there are no contradictions, yet it is almost certain that if there are contradictions it will go wrong somewhere. WITTGENSTEIN: But nothing has ever gone wrong that way yet. …"

Introducing DeepRSI, a new industry standard for measuring recursive self-improvement. On preexisting benchmarks, tasks are contributor-based and short-horizon (~12 hrs). In DeepRSI, we push agents to work 48 hours on ML tasks actually derived from ongoing proprietary research.

We’re on a mission to build a billion AI models, not trained by us, but by you. Introducing @bryel_labs we help your company build its in-house AI lab and give you the software to make model development and continual improvement simpler. We are backed by @ycombinator and have trained models at Apple, Framer, and F500s. We’ve enabled companies to train: • Classification models that are 20x faster • Search models that cost 5x less. • Long horizon agent models that match the frontier at 5-8x lower cost. Are you interested in partnering with us? Reach out. bryel.ai/

How do you build the most diverse UMI dataset in robotics? In the last 8 weeks, we scaled an international UMI data collection operation from 5 to 90 operators, built out a dataset of over 1M+ unique tasks, and continue to capture over 45k+ new tasks each day. Robotic World Models require much higher diversity of tasks than imitation learning. Here's the why and how behind our operations. 🧵

Introducing Blender Bench v1 (BBv1): a benchmark for frontier LLMs on 20 realistic 3D production workflows including animation, modeling, rigging, cloth & surfacing. GPT-6 Astra leads overall; Claude Opus 5.5 wins modeling & cloth simulation.

iteratively hillclimbing an artificial wall, v5 over v2 unequivocally suggests gains in long-horizon endurance

With data, you can/should train both model weight & harness. 🐳: arxiv.org/abs/2609.00196 A full blown thread is on its way 🙂

Demystifying Reinforcement Learning Post-Training of Language Models A very useful exploration of RLVR in a simplified setup, lots of good insights... "Result 1: sparse RL cannot find what the model never samples" "Result 2: dense rewards break that ceiling" "Result 3: "spurious rewards" are a story about the prompt set" "Post-training is best understood as redistributing probability mass inside the pretrained distribution. That framing suggests the measurement: track the probability the model assigns to the behavior you want, and track the entropy of its output distribution, throughout training." project page: minjang10.github.io/demystif… code: github.com/sankarh-1/demysti… link: arxiv.org/abs/2608.24949