Activations are stacking up. Here’s where the StonkBrokers community currently stands: T0 Floor Trader — 1.00× T1 Analyst — 1.25× T2 Portfolio Manager — 1.60× T3 Managing Director — 2.00× T4 Partner — 3.33× 1,498 T0s. 161 T1s. 68 T2s. 59 T3s. 60 T4s. Every activation moves the community forward. Which tier are you aiming for?

.@e_woodford, Founder and CEO of @zerohashx, is increasingly bullish on on-chain lending through vaults. His reasoning: as we move toward T0 and atomic settlement, the traditional lending model may have to change.

What makes Gate Card stand out in 2026? Gate Card lets users spend USDT, BTC, ETH, and GT directly across around 150 million Visa merchants worldwide—without converting assets to fiat in advance. The latest T0–T5 rewards system offers 1%–8% cashback, with monthly rewards of up to 400 USDT. It also supports Apple Pay, Google Pay, virtual and physical cards, with no annual or monthly fees. Read the full guide:

Reconstruct a moving, deforming object in 4D (3D + time) from a single-camera video — even under heavy occlusion and large non-rigid motion 🎥 Title: Lift4D: Harmonizing Single-View 3D Estimation for 4D Reconstruction In-the-Wild URL: 🎥 Overview A test-time optimization framework combining single-view 3D prediction with deformable 3D Gaussian Splatting. It fuses learned geometry/appearance priors with the video to handle severe occlusion and non-rigid motion. ❓ Problem it solves ・Directly predicting 4D is limited by scarce training data and generalizes poorly ・Initializing 3D and refining with video alone breaks down on in-the-wild scenes with large deformation and occlusion 💡 Methodology Three components: ・Causal Latent Conditioning: aligns an image-to-3D model temporally without retraining, coupling adjacent latents at the ODE level, with t₀ trading off consistency vs per-frame fidelity ・Deformable Gaussians: sparse control nodes with time-varying SE(3) transforms + linear blend skinning deform a canonical representation across frames ・Occlusion-aware refinement: compares only visible pixels, while a view-conditioned diffusion prior completes unobserved surfaces 📊 Results ・Compared against STAG4D, PAD3R, L4GM, DreamMesh4D, V2M4, BANMo ・On Pexels data, point-tracking error EPE 0.072 (vs 0.119–0.211 for competitors) ・~30 min per 32-frame video on a single H200 GPU #3DReconstruction# #GaussianSplatting#

Hot take: the best Tesla feature isn’t 0-60. It’s a toss up between never hunting for a gas station on a road trip with kids, and not needing to drive. What’s the feature you didn’t know you needed until you had it?