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ffective resolutions up to 1536³, handling complex topologies that earlier methods struggled with. It also shipped with a PBR texturing model generating base color, roughness, and metallic maps. Ten months is an eternity in generative AI, yet Trellis.2 hasn’t just aged well, it has become foundational. Several open-source 3D models released since build directly on it, the most notable being Pixal3D whose implementation uses the Trellis.2 backbone. The community got there first As always, the Com

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inforcement-learning stack that brings programmable biped robotics down from $13K research hardware to less than a phone. Also today: ICE’s multimillion-dollar robot-dog plans and Meta putting robots to work inside its data centers. In today’s robotics rundown: Hugging Face’s cute open-source duck robot ICE is adding robot dogs to its arsenal Meta’s data centers are becoming robot test labs This robot swarm builds from the top down LATEST DEVELOPMENTS Image source: Hugging Face The Rundown: Hugg

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sly involved seven different vendor programs without a shared interface. A few nights of disrupted sleep are enough to cause widespread impairment: altered cognition, dysregulated metabolism, a weakened immune system. If this goes on long enough, sleep loss can even prove fatal. Yet we still don’t fully understand why. Part of the reason sleep is so hard to study is that it isn’t localized to any one organ. Because sleep is a whole-animal state, developing a mechanistic understanding of it requi

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as on pace to top 5M this quarter, with more than 80% of members renewing after year one. May's slimmer Ring 5 came with predictive-health software and pushes into women's health, metabolic tracking, AI coaching, and medical partnerships. Why it matters: Oura is getting bigger than its sleep-tracker roots: premium hardware, recurring subscriptions, and an expanding health-data business. With Whoop hitting a $10B valuation in March after adding hormone and blood-panel testing, investors are incre

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ond loading is zero-copy: the engine's param.data pointer is set directly to the IPC-mapped GPU tensor. No data is copied. To achieve this, the engine initializes the model on the meta device (no GPU/CPU memory allocation), then replaces each parameter's data pointer with the IPC-mapped tensor. Post-quantization parameters (e.g., weight scale from FP8 quantization) that were created by process weights after loading() are also cached by the daemon and mapped directly — no re-quantization needed.

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h) is unchanged. Our DSpark draft models for LFM2.5 ship with day-one support for llama.cpp (implementation builds on top of the official codebase , which we run with experimental metal kernels ) and SGLang ( implementation builds on the official SGLang implementation of DSpark ). We measure on-device throughput with llama.cpp and Metal on an M4 Max MacBook Pro using FP16 GGUF weights and up to 256 output tokens. We measure GPU throughput with SGLang on a single H100 80 GB in BF16. Both configur

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sor-parallel ranks, so the transfer layer slices and reassembles them for the destination layout. The same payload carries the MTP draft model's KV cache, hidden states, and top-k metadata, allowing speculative decoding to continue on the decode worker. When prefill and decode use different attention-sharding layouts, a GPU staging buffer coalesces the per-layer slices into one bulk RDMA transfer per chunk instead of issuing separate transfers for each slice. Radix Cache and HiCache Qwen3.8 uses

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open models continue to roll into the market, particularly from vendors from China, such as Moonshot AI’s Kimi K3, DeepSeek’s various models, and Alibaba’s Qwen family. Stateside, Meta Glimmer this week rolled out Muse Glimmer, the first in a promised set of open models. (Meaning open weights, but not necessarily open source code for the training algorithm for the models.) Briski noted that over the past year, Nvidia has rolled out its Nemotron Nano, Super, and Ultra, models, with each made for

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EMM produces the scores used for top-k selection, where small numerical changes can alter expert choices. The selected experts then process small and uneven token groups, allowing metadata construction, token movement, intermediate storage, and launch overhead to rival the expert GEMMs themselves. HPC-Ops addresses each stage with a dedicated operator: workload-aware scheduling for Attention, a precision-aware formulation for Router GEMM, and a fused pipeline for MoE that eliminates the standalo

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and a consumer pool. Producers schedule prompts across patched SGLang capture servers. SGLang writes feature tensors to Mooncake, while SpecForge sends only lightweight SampleRef metadata through the control plane. Trainer ranks resolve those references when they are ready to build a batch. Figure 1. The online-disaggregated training flow. Large tensors remain in the data plane; references and lifecycle state travel through the control plane. 1. The capture contract Every URL in deployment.disa

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rces, or otherwise reject it; We have no data stores in the critical path of sandbox creation at all, which improves scalability and reliability. While we do need to write sandbox metadata and results to durable storage, we do so largely asynchronously; Other than sandbox creations, we have no RPCs which are O(sandboxes). Workers batch control messages for multiple sandboxes together in single RPCs, in the spirit of ideas from data-oriented design The result is that the sandbox creation path req

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apture most of the forward pass once, then replay it without re-dispatching Python each step. Both still have to fall back to eager execution wherever an op needs live per-request metadata, and Inkling has four ShortConv sites per layer doing exactly that, on top of attention's own. Each site's eager fallback is a real, multi-level Python call stack, not a single kernel launch: Figure 2: Under BCG, each of Inkling's four ShortConv sites (K stream, V stream, attention-output, MLP/MoE-output) fall

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t Foundry is a platform for building and operating agentic AI applications. Foundry starts with the widest model selection on any cloud — models from Microsoft, OpenAI, Anthropic, Meta, Mistral, DeepSeek, Hugging Face, and others, spanning frontier, open-source, and custom weights — all accessible through a single endpoint and a single set of SDKs in Python, C , JavaScript, and Java. On top of those models sits the Foundry Agent Service : multi-agent orchestration with built-in memory, knowledge

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encies with their own install steps; each docs page has the exact recipe, and every benchmark ships a ready-made Docker image if you'd rather skip the setup. Together with LIBERO, Meta-World, and NVIDIA IsaacLab-Arena, that makes nine benchmark families under one roof, and a new Adding a New Benchmark guide documents exactly how to plug in yours. Evaluation also got faster: parallel eval now defaults to async vectorized environments, benchmarked at up to 2x faster. Training & inference lerobot-r

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128-x86 64-windows: rejected (CPU (x86 64) does not match system CPU (aarch64)) torch211-cu128-x86 64-windows: rejected (CPU (x86 64) does not match system CPU (aarch64)) torch212-metal-aarch64-darwin: rejected (OS (darwin) does not match system OS (linux)) torch211-metal-aarch64-darwin: rejected (OS (darwin) does not match system OS (linux)) torch210-metal-aarch64-darwin: rejected (OS (darwin) does not match system OS (linux)) torch29-metal-aarch64-darwin: rejected (OS (darwin) does not match s

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reuses attention kernels the backend already has — on DeepSeek-V4 the model's own sparse-MLA path (flash mla), with no new kernel; each supported backend just rebuilds its varlen metadata from the packed layout on graph replay. Figure 2. Fitting a batch with per-request-variable verify lengths into a captured CUDA graph. A fixed-shape graph pads every request to the full block width (N x W); the ragged path front-packs the scheduled tokens and rounds only the total up to the nearest captured ti

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r, Erlang, C, Kotlin, and Dart. Only Devin found three critical vulnerabilities that other tools missed: a PHP sandbox bypass via template injection, an argument injection through metadata value parsing, and an overly broad deserialization surface in Spring Kafka. Scan profiles Devin can generate scan profiles directly from your existing threat model documentation, tailor them to specific attacker personas, and apply them across your entire organization without per-repo configuration or CI setup

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me.” Shader effects and fills Customer snapshot: Anna Zhang Creative technologist Anna Zhang built her own shaders to remix her own photography with collage, marbling, light leak, metal emboss, and prism effects. “It’s a way to compound what I’ve been doing in the past or build a reusable workflow,” she says. “There’s a lot of expressive potential from a single starting point.” Anna focused on the functionality while letting the agent handle the UI: “It was a back-and-forth dialogue between me a

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d analysis before waiting for LLM calls to complete. 2. Anatomy of an analysis plan This analysis plan starts with a DQL query to select successful runs by filtering the agent run metadata to resolved=1. After that, it passes the results to a reading step, which scores runs for cheating. A reading step has the following components: A prompt template with parameters. To detect cheating, we create a rubric that defines suspiciousness, taking one agent run as a parameter. The Docent UI shows the fu

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erlap scheduler. In particular, there are two key opportunities for overlap: host-side pop and process cleanup after the GPU finishes batch N-1 (e.g. stop token detection, request metadata updates) can overlap with GPU work on batch N; host KV allocation (in prepare for decode) for batch N can overlap with GPU work on batch N-1 Under V2 with these optimizations, performance improved by over 33%, from 11.4 ktok/s to 15.3 ktok/s, when running Qwen 3-8B on a single B200 at concurrency 32 (details h

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