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models, like Google DeepMind’s Gemma. Check it out: The speed is important, she said, because “always-on” agents make thousands of models calls and latency matters. Lightning leverages Nvidia’s hybrid Mamba transformer architecture and multi-token prediction technology. According to the company, Lightning outperformed other open and proprietary models, as shown in the results below involving CodeRabbit, Harvey, and Lila in various tasks, and performed well against Nemotron 3 Super for CrowdStri

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Technical Blog sessionStorage.removeItem('facets.search.text'); sessionStorage.removeItem('facets.search.text-copy'); sessionStorage.removeItem('facets.search.filters'); Learn how NVIDIA NeMo Switchyard routes agent workloads across specialized and frontier models to balance performance, cost, and efficiency. AI-Generated Summary Powered by NVIDIA Nemotron. AI-generated content may summarize information incompletely. Verify important information. Learn more Building an AI agent does not end with

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-5.2. This and future open models will run on the same infrastructure, regional controls, and service commitments as Mistral models, so customers can broaden model choice without fragmenting where their AI runs. "Different workloads need different models, and that will keep changing as the frontier evolves. Mistral allows us to run open models under strict regional controls and service commitments, making it easy for us to maintain data residency and compliance requirements while furthering our

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agents locally, including Apple Silicon devices (Mac mini and M-series MacBook Pro), NVIDIA GeForce RTX 5090 GPUs, and the NVIDIA DGX Spark. On NVIDIA SM120 platforms, SGLang leverages its optimized GEMM and FlashInfer backends for high-throughput inference, while Apple Silicon devices use the native MLX backend to deliver high-performance local serving. Speculative Decoding with DFlash To achieve low-latency inference, use SGLang's implementation of DFlash: SGLang Native Optimizations Muse Gli

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guages. And the best English ASR is now an Indian one: across seven standard suites, from LibriSpeech and GigaSpeech to Svarah for Indian-accented speech, it records the lowest average word error rate of any system tested. Bulbul V4 (Research preview) Bulbul V4 is our most expressive text-to-speech model yet. Developers can shape how each line is spoken, guiding emotion, emphasis, pacing, and cues such as laughter as the audio is generated. This brings more control to voice agents, creator tools

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er population. If every solver succeeds, the task was too easy; if none succeed, it may be too difficult or invalid. The environment's learnability peaks at a 50% solve rate, encouraging the proposer to generate tasks that yield the maximum training signal for RL (inspired by Zhao et al. (2025), Absolute Zero). The interaction also requires role-aware credit assignment. Solver attempts should be compared with attempts on the same proposed problem, while proposer traces should be compared with ot

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d have been validated with Qwen3, Hy3, and LongCat workloads. Highlights Attention: On H20, HPC-Ops dynamic scheduling reaches 2.95× over its static split-KV schedule and is on average 2.25× faster than the best of FlashInfer and FlashAttention in each measured case. In upstream H200 validation, the integrated Hy3-FP8 path with FP8 KV cache improves output throughput by 3.7–5.9% over FlashAttention; Router GEMM: On H20, HPC-Ops is 1.30–3.22× faster than FP32 cuBLAS, while its maximum absolute er

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onsLocked":false,"hideComments":true}" AlessandroAle83 Aug 7 Thank you, beautiful work. I’ll take a closer look at it soon! Reply Edit Preview Upload images, audio, and videos by dragging in the text input, pasting, or clicking here . Tap or paste here to upload images Comment · Sign up or log in to comment Upvote 35 +23 Models mentioned in this article 3 deepseek-ai/DeepSeek-V4-Flash-0731 Text Generation • 304B • Updated Aug 1 • 4.49M • 4.01k moonshotai/Kimi-K3 Image-Text-to-Text • 2.8T • Updat

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els, and they do not reflect what frontier models can do today: fixed tool-calling schemas and context compaction force the model to work around its own scaffolding instead of leveraging it. Static, hand-engineered sub-agents, prompts, skills, and memory are set once at design time and never adapt to what the agent learns while running. We believe that harnesses should instead extrapolate on current model capabilities toward the next frontier of reasoning patterns. Prime Agent is built around th

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livery contract Captured hidden states can be large, so forwarding them through a Python queue or control database would quickly become a bottleneck. SpecForge separates tensor storage from sample coordination: SGLang writes feature tensors to Mooncake; The producer publishes tensor-free SampleRef records; FeatureDataLoader resolves references into tensor-carrying training batches; Trainers release feature objects after an optimizer-step acknowledgement The training loop depends on the FeatureSt

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raining data, the resulting model achieves strong results across several web-navigation benchmarks: 74.1% on WebVoyager, 67.0% on Online-Mind2Web, and 64.0% on DeepShop, for an average of 68.4%, as shown in Figure 1. These results place Orchard-GUI among the strongest open-source web agents to date while remaining competitive with larger proprietary models. The results also suggest that with the right training approach and environment, small open models can perform well on real-world web tasks.

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ost is the exploration of your codebase, so a large codebase costs more. Load at least $20 onto your key when you log in. Can I eval against my own data? Yes, and it's highly encouraged. The more data you give Ori Eval about your use case, the more accurate the run is. Ori Eval asks you whether to include your data. The eval then runs every row and grades a candidate model against your current model on your own gold answers. How do I run evals in CI? Add ori eval to a GitHub Actions workflow. It

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rs or character pairs. So for example, a word like "daughter" in raw HTML might be rendered as "journalist" when it actually shows up to human viewers on the page. Here's what a paragraph written in the ShieldFont looks like as viewed on a web page (above), as opposed to what the raw HTML actually says (below): Even with that extended GSUB format, the font files are still quite small. We spoke with Amsterdam design studio Seneda & Abrucio, founded by Isaque Seneda and Gabriel Abrucio, the team b

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h now powers Dynamics 365 Contact Center, where customers like T-Mobile and EasyJet build their call center agents, reducing GPU costs by up to 89%; MAI-Transcribe-1.5 now serves Dragon Copilot’s multilingual workflow across 58 languages — a solution used by 170,000 medical providers that processed 28 million patient encounters last quarter, where our tests show a 50% relative in reduction transcription and language-identification error rates And what’s more, by co-designing our models with our

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Design draws from more than 600,000 real-world screens from over 1,000 apps to turn your idea into a finished app or site. Your brand, everywhere, in one click AI design today is fragmented. Prompt in one tool, refine in another, publish in a third. Every handoff strips something out: the colors drift, the type resets, the details that made it yours get lost in translation. What arrives at the end is a watered-down version of what you started with. Replit Design comes with design systems built i

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useful, apparently, because users can now ask it for additional details to “sharpen the answer,” and ask pointed follow-up questions to get a stronger result. Perplexity also encourages users to ask the Council for confidence levels to see what different models think of their collective answer. “Disagreements often come from different starting assumptions, so ask each model to state the assumptions behind its conclusions.” Of course, all of this comes at a price. Access to Model Council was prev

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Technical Blog sessionStorage.removeItem('facets.search.text'); sessionStorage.removeItem('facets.search.text-copy'); sessionStorage.removeItem('facets.search.filters'); Related Resources Simulation / Modeling / Design English 中文 Jul 28, 2026 Endoluminal Simulation Module enables real-time simulation of flexible catheter navigation through patient-specific vasculature using GPU-accelerated XPBD solvers. Surgical Simulation Module models soft-tissue deformation and surgical interactions with hapt

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et me zone districts, lay down roads, and grow a town with a live economy that reacts to what I build. Create a 3D physics playground: let me spawn spheres, boxes, and cylinders, drag them around, stack them into towers, and knock them over. Sliders for gravity, bounciness, and friction. Build a beat machine in my browser: a 16-step drum sequencer with classic, 808, techno, and lo-fi kits, tempo and swing controls, and genre presets. Let me beatbox into the mic and turn it into a pattern. Build

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released. If that leaves a gap in the middle, a block from the end is moved into it so the free region stays in one piece. This layout gives us flexible page sizes and no memory fragmentation: a 54 MB KDA state block and a 27 KB MLA KV block draw from the same bytes with no common page size forced on them, and the moves above keep each end packed at a negligible cost for state movement, so the free space is always one contiguous region usable by either kind. Capacity therefore follows the workl

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lf-Flow vs. Flow Matching (FM). Left: generation error (Fréchet distance) per modality, each normalized to FM = 100 (lower is better). Right: success rate on manipulation tasks averaged over four task groups through finetuning (higher is better). Scaling the world model Scaling laws remain true with Self-Flow, and FLUX 3 is the application of that: the scaled-up version of Self-Flow. It is trained on tens of millions of hours of general video content to learn world dynamics as broadly as possibl

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