“NVIDIA AI shared a guide from @MiaAI_lab on chaining DGX Sparks together to run recently released models.”

#15 𝕏 NVIDIA AI shared a guide from @MiaAI_lab on chaining DGX Sparks together to run recently released models. The specific models and number of DGX Sparks were not identified. #16 𝕏 clem 🤗 commented that the new AI model framework treats APIs from providers such as Anthropic and OpenAI differently from open weights, calling the distinction “very good policy.”

“NVIDIA AI shared an AI Model Co-Design post explaining why long-context serving speed is largely determined before training.”

#7 𝕏 NVIDIA AI shared an AI Model Co-Design post explaining why long-context serving speed is largely determined before training. It highlights four architecture choices—group size, head dimension, KV-cache size, and parallelism strategy—that shape system throughput and per-user responsiveness.

“NVIDIA AI launched Spatial-IQ, a diagnostic benchmark that breaks 3D object counting into nine perceptual sub-tasks and shows humans score 82.1% vs. 17.7% for off-the-shelf models.”

#5 𝕏 NVIDIA AI launched Spatial-IQ, a diagnostic benchmark that breaks 3D object counting into nine perceptual sub-tasks and shows humans score 82.1% vs. 17.7% for off-the-shelf models. Training Qwen2.5-VL-32B on those tasks raised its accuracy from 2.9% to 62.

“𝕏 NVIDIA AI unveiled Cosmos-Dreams, a neural closed-loop simulator demoing world models as the data engine of physical AI at SIGGRAPH 2026. They highlighted that “data stops being something you collect; it becomes something you compute.””

#3 𝕏 NVIDIA AI unveiled Cosmos-Dreams, a neural closed-loop simulator demoing world models as the data engine of physical AI at SIGGRAPH 2026. They highlighted that “data stops being something you collect; it becomes something you compute.” #4 𝕏 NVIDIA AI thanks @perplexity_ai for open-sourcing Numbat, an agent-detection and response layer, to the Open Secure AI Alliance.

“NVIDIA AI demos Nemotron Labs’ workflow for training open-source models with reinforcement learning on the Prime Intellect platform, covering the end-to-end pipeline and optimization strategies.”

#14 𝕏 NVIDIA AI demos Nemotron Labs’ workflow for training open-source models with reinforcement learning on the Prime Intellect platform, covering the end-to-end pipeline and optimization strategies. #15 𝕏 NVIDIA AI rolled out an end-to-end workflow in the Jetson AI Lab showing how to run GenAI models locally on NVIDIA Jetson devices, leveraging TensorRT and CUDA optimizations for real-time, low-latency edge inference.

“𝕏 NVIDIA AI launched Model Express, a gRPC-powered pipeline built on NGC and Triton that shards, compresses, and parallel-streams model artifacts for near–real-time global distribution.”

𝕏 NVIDIA AI launched Model Express, a gRPC-powered pipeline built on NGC and Triton that shards, compresses, and parallel-streams model artifacts for near–real-time global distribution. #5 ▶️ Most Valuable Skill of 2026: Managing AI Agents Greg Isenberg Ryan Carson uses Cognition’s Devon cloud VMs to run five to ten parallel AI agent sessions, shipping 22–40 PRs per day (about 50% from his iPhone), and automates QA with a $60 thrice-weekly end-to-end browser-based signup test.

“NVIDIA AI ran a hosted RL loop on PrimeIntellect Lab to boost Nemotron 3 Nano’s math accuracy from 22% to 91% for under $5, yielding a downloadable LoRA adapter.”

#17 𝕏 NVIDIA AI ran a hosted RL loop on PrimeIntellect Lab to boost Nemotron 3 Nano’s math accuracy from 22% to 91% for under $5, yielding a downloadable LoRA adapter. The same workflow scales to Nemotron 3 Super and Ultra with just one line change. #18 📝 Mario Zechner advanced-context-engineering-for-coding-agents/wsff.md at main · humanlayer/advanced-context-engineering-for-coding-agents - StrongDM has promoted a "lights-off" software factory and OpenAI's Ryan Lopopolo has described their harness-engineering system Symphony, even as companies report outages and codebases degrading.

“NVIDIA AI ’s Nemotron 3 Embed models swept the LMEB leaderboard, with the 8B and 1B variants ranking #1 and #2.”

#3 𝕏 NVIDIA AI ’s Nemotron 3 Embed models swept the LMEB leaderboard, with the 8B and 1B variants ranking #1 and #2. This demonstrates their top-tier performance on memory-intensive, long-context conversational embedding tasks. #4 𝕏 NVIDIA AI NeMo AutoModel now supports @huggingface Diffusers, extending its open-source fine-tuning library from Transformers to image and video models. It offers ready-to-run full and LoRA recipes to streamline distributed training.

“#2 𝕏 NVIDIA AI released Nemotron 3 Embed 8B, which secured the #1 spot on the RTEB retrieval accuracy benchmark.”

#2 𝕏 NVIDIA AI released Nemotron 3 Embed 8B, which secured the #1 spot on the RTEB retrieval accuracy benchmark. This top ranking highlights its superior context retrieval, enabling agents to deliver more accurate responses. #14 𝕏 NVIDIA AI launched three new ways to try the Thinking Machines Inkling model: free GPU-accelerated endpoints, a downloadable NVIDIA NIM container, and an NVIDIA Dynamo deployment recipe. Also covered by: @Julien Chaumond

“NVIDIA AI released DeepStream 9.1, which adds 13 agentic skills—including Multi-View 3D Tracking and AutoMagicCalib—for building video analytics pipelines via plain-language prompts with coding agents like Claude Code or Codex.”

#6 𝕏 NVIDIA AI released DeepStream 9.1, which adds 13 agentic skills—including Multi-View 3D Tracking and AutoMagicCalib—for building video analytics pipelines via plain-language prompts with coding agents like Claude Code or Codex. It also brings JetPack 7. #7 𝕏 Google DeepMind warns that while AI agents can now propose hypotheses and design experiments to accelerate scientific discovery, real-world validation remains the toughest challenge; its new essay diagnoses this growing “validation bottleneck” and lays out four key priorities...