I recently started dabbling in robotics, and a thought hit me. 🤖💭 Robots, AI agents, and business systems (ERP/WMS) all end up talking past each other in completely different data, don't they? Robots emit point clouds and sensor values on a millisecond scale 📡, AI agents want text with sources 📄, and business systems hold structured records on an hour-to-month scale 📊. Wiring these three together with bespoke integrations gets unrealistic fast — the number of pairs explodes as N×(N−1). 💥 So I ran an experiment: what if everyone dumps their data into the same search system and pulls out what they need by searching? Can search become a common protocol? I called it Multi-World Search (MWS). 🔍 Inside MuJoCo on a single MacBook 💻, I let a Gemini agent drive end to end across 7 business scenarios — a maintenance handoff 🔧, reconciling physical inventory against stale records 📦, and my personal favorite: three robots pinning down a defective lot without any direct communication 🐜. To avoid faking the "it worked!", I attached falsification tests that fail when you change the input. ✅ I'd be glad if it became a starting point for anyone just getting into robotics. Take a peek. 🙏✨ 📝 Blog: #Robotics# #AIAgents#

PyTorch 2.13 is here, with 3,328 commits from 526 contributors and updates across FlexAttention, CuTeDSL, nn.LinearCrossEntropyLoss, torchcomms, FSDP2, Python 3.15 wheels, ROCm, Arm, and XPU. The release blog and notes cover FlexAttention on Apple Silicon with up to ~12x speedup over SDPA on sparse patterns, a deterministic backward path on CUDA, the CuTeDSL "Native DSL" backend for Inductor, nn.LinearCrossEntropyLoss to reduce peak GPU memory by up to 4x, torchcomms for large-cluster training, and FSDP2 communication overlap improvements. On July 22 at 11 a.m. PT, join @albanDesmaison (@Meta), Andrey Talman (@Meta), Piotr Bialecki (@NVIDIA), and Chris Gottbrath for a live 2.13 Q&A. 🔗 Read the release blog, and register for the live Q&A:

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The Hessian matrix H(f) of a function f: Rⁿ → R is the n×n matrix of all second partial derivatives. For f(x,y): [ fxx fxy ] [ fyx fyy ] Definition: [ ∂²f/∂x₁² ∂²f/∂x₁∂x₂ ... ∂²f/∂x₁∂xₙ ] [ ∂²f/∂x₂∂x₁ ∂²f/∂x₂² ... ∂²f/∂x₂∂xₙ ] H(f) = [ : : : ] [ ∂²f/∂xₙ∂x₁ ∂²f/∂xₙ∂x₂ ... ∂²f/∂xₙ² ] In compact form: [H(f)]ᵢⱼ = ∂²f / ∂xᵢ∂xⱼ Named after German mathematician Otto Hesse (1811–1874). Used to study curvature, convexity, and classify critical points in multivariable calculus & optimization.