the AI roll-up is only getting started Long Lake CEO @alextaubman has bought 30+ real-economy, non-tech companies and just took the largest corporate travel platform private (Amex GBT, $6.3B) its a bet that AI can better transform legacy industries and that someone has to turn capex into real economic growth "You see the hundreds of billions of CapEx the labs are investing. Somebody's got to take that and turn it into GDP growth. That's what Long Lake was formed to do."

If you have to pay more interest bc you raise rates esp as you move towards increasing t bills (reverse operation twist) then you worsen your own credit because youre accelerating both the timing and prob of default at same time-> higher roll risk higher term premium The end

🚨 FINAL CALL for @pendle_fi users: Apyx's August market expires TOMORROW, August 27. If you're holding PTs or LPs tied to Apyx, roll your position now to maintain exposure. Don't let your capital sit idle. 👀

Triumph is continuing the celebration of their 50th Anniversary with Leg 2 of The Rock & Roll Machine Reloaded Tour with April Wine. Tickets for the newly announced dates go on sale Thursday, August 27 at 10AM local time. See you there.

Game world models tend to generate pixels directly, but pose and geometry drift the longer you roll them out. Marionette simply gives up the parts that must be exact — to a renderer. Title: Marionette: Predicting World States, Rendering Geometry, Painting Appearance URL: A world model that delegates geometry, occlusion, and motion to a deterministic renderer and lets the neural net paint only appearance. Three highlights. 🎮 Highlight 1: Predict an explicit 276-D world state Instead of pixels, it predicts a 3D state of joints, root trajectory, and rotations via two-stage autoregression. Control is just overwriting an action token (like a player's button press). Forcing a wrong action shifts the pose by ~31% — proof the control truly bites. 📐 Highlight 2: A zero-parameter deterministic renderer Six closed-form operations turn state into pose-control video, so world consistency, occlusion order, and metric scale hold by construction. Long-horizon failures are fixed by rules (e.g. a terrain collider) without retraining appearance — cutting penetration by 66%. 🎨 Highlight 3: Video diffusion for appearance only A Wan2.2-Fun-5B diffusion paints photorealistic RGB on top of exact geometry. Even via predicted state, FVD is 831 (799 from recorded state, 975 for pixel-autoregressive) — no detectable fidelity loss. Exactness to deterministic compute, appearance to the neural net — a clean division of labor. #WorldModels# #GameAI#

We dissect the $SPCX acquisition of $TSLA in our analysis at Predictions: - Roadster demo will show the world the magic that a @SpaceX - @Tesla engineering collab can produce. - Acquisition probably not announced announced before second half of 2027, after share distros. - Must be a $SPCX acquisition of $TSLA so that Elon retains control & voting mechanism, even after some future dilution to pay for 27-30 CAPEX plan (which will be >100bln). - Makes sense to roll in @boringcompany and potentially @neuralink because A- the more @SpaceX engineers can tackle other problems the better, and B- Musk owns a larger percentage of these Co.s & the transaction(s) would (likely) be accretive. Investors may want to have exposure to those other co.'s for an eventual accretive roll-up.

NEW SKILL: This team take humanoid parkour to the next level! This project involves @zhenkirito123, @Yuanhang__Zhang, @pabbeel, @carlo_sferrazza, @GuanyaShi, and others. Called Perceptive Humanoid Parkour (PHP), it lets a Unitree G1 humanoid (29 DoF, 1.3 m) run long-horizon, vision-based parkour and decide on its own whether to step over, climb onto, vault, or roll off obstacles, using only onboard depth sensing (a 30 Hz camera) plus a discrete 2D velocity command from the operator. It builds the motions by motion matching, a nearest-neighbor search in a 27-dimensional feature space (imported from the video-game industry) that stitches retargeted atomic human clips into long-horizon kinematic trajectories through a shared Locomotion to Skill to Locomotion manifold. It then trains per-motion RL tracking experts (with a privileged height scan) and distills them into one depth-based multi-skill student via DAgger combined with RL. It replaces hand-scripted skill sequencing and operator-triggered skill selection. The onboard depth-only student holds 0.95 to 1.00 success across obstacle heights in simulation, while a naive end-to-end depth policy collapses from 0.95 to 0.07/0.08 at 58 and 76 cm. The upside comes from distilling privileged-height-scan experts and from motion-matching composition, not from training onboard from scratch. The operator sends only a coarse 2D velocity (W/A/D keys), and the robot itself picks which skill to run from the depth image. The training data contains only single-obstacle traversals, so the ability to chain skills across a multi-obstacle course is generalization the policy was never explicitly taught. Transitions are constructed in kinematics, not rediscovered by learning. Every skill enters and exits through a shared locomotion manifold, so no hand-captured skill-to-skill transition clip is needed. Motion matching densifies a sparse library into smooth long-horizon trajectories before any RL. The entire parkour library is only about 66 seconds of mocap, and each skill is just a few seconds. Ablating approach-distance variety ("Extreme Distances") drops the 76 and 94 cm climbs to 0.62 and 0.64, and "Half Density" drops the 76 cm climb to 0.32. Motion matching's job is to manufacture stride-phase and approach variety out of seconds of data. It clears a 1.25 m wall (96 percent of its 1.3 m height) in 3.63 s, and a cat-vault peaks at 3.41 m/s clearing a 0.4 m by 0.5 m obstacle in 0.8 s. A single 30 Hz depth stream drives both traversal and runtime re-planning, shown by displacing obstacles about 0.5 m mid-run.