AI Coding Live News

An Oct 30 NVIDIA blog post says Breakthrough Listen and the SETI Institute have brought agents into daily SETI work at the Allen Telescope Array (ATA, 42 dishes of 6 m each) in Northern California: from a single natural-language request, agents can steer the antennas, calibrate and focus, fix data errors, and run AI models on the live stream. Holoscan's DAQIRI library pulls sensor data straight into GPU memory, the SETI Institute's CyberEther handles signal processing, and the web-based orchestrator Nexus acts as the agent harness, handling up to 2.4 Tbps on premises on RTX PRO 6000 Blackwell GPUs. The pipeline has made the first real-time AI detection of a pulsar in the Crab Nebula, and the team is evaluating open models such as Nemotron. The @NVIDIAAI Halloween post on Oct 31 drew about 274 likes.

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On October 29, Cohere announced the initial close of a Series E targeting up to C$7 billion at a C$35.7 billion valuation, led by Schwarz Group with NVIDIA, Bell Ventures and others, which Cohere calls the largest private round in Canadian startup history. The same day Prime Minister Mark Carney said the government will make a staged equity investment of up to about C$1.6 billion; Cohere commits to keep its Canadian incorporation and headquarters and to invest more than C$4 billion and add 1,500+ jobs in Canada through 2031.

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AI data company micro1 released Dual-Arm Sim, which has 10 general-purpose frontier models control a two-arm Franka Panda robot in a simulated operating room built on NVIDIA Isaac Sim, using only camera images and robot sensors, across four tasks: swapping instruments, replacing a faulty drug cartridge, passing a sample between hands, and packing a surgical kit. Only 11 of 800 trials were completed, all by Gemini 4 Argon (9) and GPT-6 Astra (2). Models claimed "task complete" in 213 trials and were wrong every time. All 200 traces of the cartridge-swap task are public on Hugging Face.

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NVIDIA AI announced Nemotron 3.5 Transcribe on Oct 28, with weights on Hugging Face and ModelScope. It is an offline, instruction-driven speech model whose single checkpoint covers transcription in 40 languages, translation between 31 languages and English, speaker attribution in 13 languages, and audio question answering, on recordings up to an hour long with up to eight labeled speakers. NVIDIA says it ranks first among open-weight models on the Hugging Face Open ASR English leaderboard at 5.33% average WER, and Artificial Analysis measured a 2.8% AA-WER, tied for first among open models with Voxtral Small, which uses 24B active parameters. The weights, the Granary v2 training data and fine-tuning recipes are all released.

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On 2026-10-23 (16:53 UTC) the official @blocks account unveiled Dessa, and Jack Dorsey called it the most authentic full-duplex speech model ever built. The official page block.xyz/dessa says Dessa is a true speech-to-speech model trained only on recordings of human conversation, with no text and no pretrained models. It is full-duplex, so it listens and talks at the same time, cuts in and backchannels naturally, runs faster than real time, and runs locally on a MacBook Pro M4 Max or better. Block says it was trained on a single NVIDIA GB200 NVL72 and learned language from hundreds of times fewer words than LLMs. There is no public API or weights yet; a small group of AI safety researchers is getting early access.

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On 2026-10-23 (15:41 UTC) the official @NVIDIAAI account announced Nemotron 3.5 Bolt E4B 4GB, a small open model for local assistants, interactive characters and robots that can hold a conversation and use tools. The official Hugging Face model card says it is post-trained from Gemma-4-E4B-it, has 8B total parameters (4B effective), ships in Q4_K_M quantization, supports up to 64K input and 64K output tokens, and has a switchable thinking mode, plus a DFlash speculative-decoding drafter and a BF16 version. Weights, data and recipes are open under the OpenMDW-1.1 license, with commercial use allowed. On the model card the 4GB build scores 55.60 on MultiChallenge (Gemma4 E4B-it at the same quantization 39.36, Qwen3.5 4B 52.23), but trails Qwen3.5 4B on Tau2-Bench (52.32) and BFCL-v3 (67.31).

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LMSYS Org in collaboration with the NVIDIA Dynamo team officially unveiled Elastic Expert Parallelism (Elastic EP) with NIXL backend integration in SGLang (@lmsysorg). Addressing the critical vulnerability of large-scale MoE deployments (such as DeepSeek-V3/V4) where static EP causes entire cluster crashes upon single-node failures and prevents live capacity resizing, Elastic EP decouples rigid expert-to-GPU bindings. Leveraging NVIDIA NIXL device APIs and Mooncake orchestrators, SGLang enables live scaling of active MoE serving deployments (e.g. dynamically expanding from 20 to 24 GPUs) without draining traffic or restarting clusters, while delivering resilient fault tolerance.

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Microsoft shipped Visual Studio Code 1.143. The Copilot harness, built on the Copilot SDK, is now the default for new sessions and works across the editor and Agents window. A Compare Models preview runs several models on one task with an optional Judge model, an experimental mode runs MAI Code 1.1 Flash on a local NVIDIA GPU, and large chats open much faster.

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During the GTC Berlin keynote, NVIDIA unleashed a sweeping array of open-weights domain-specific foundation models. The release highlights include the multimodal Nemotron 3.5 Super VL, the lightweight Nemotron 3.5 Bolt series, and the end-to-end Nemotron Retriever suite for embedding, reranking, and document OCR. For physical AI, NVIDIA debuted the Cosmos world models and Alpamayo 2 Edge autonomous driving models. In life sciences, NVIDIA and the Baker Lab released BioNeMo RFD4-Proteina, which establishes state-of-the-art accuracy across more than 10 protein design benchmarks while synthesizing 200-amino-acid binders roughly 13x faster than RFdiffusion3.

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At GTC Berlin, Mistral AI and NVIDIA announced a strategic partnership dedicated to advancing AI for industrial engineering, computational physics, and electronic design automation (EDA). The alliance couples Mistral frontier architectures with NVIDIA CUDA-X libraries, Warp physics simulation frameworks, and Omniverse digital twins. Mistral simultaneously revealed architectural specifics for its forthcoming flagship, Mistral Large 4: spanning roughly 1 trillion total parameters with 49 billion active parameters (MoE), trained on 3,800 Grace Blackwell GPUs, with open weights slated for official release at the end of October 2026.

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At GTC Berlin, NVIDIA unveiled the OpenShell Reference Architecture alongside the Agent Knowledge Foundation within its Agent Toolkit. Addressing critical enterprise vulnerabilities where autonomous tool-using agents succumb to prompt injections, hallucinated destructive commands, or unauthorized data exfiltration, OpenShell establishes hardware-isolated runtime boundaries. Operating through hardware roots of trust, OpenShell dynamically intercepts out-of-boundary tool calls and file access in real time with sub-microsecond latency, establishing an authoritative blueprint for secure, auditable enterprise agent deployments.

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At GTC Berlin on Oct 21, NVIDIA announced the OpenShell Reference Architecture: building on the open-source OpenShell secure runtime, it pairs Vera Rubin for inference, Vera CPUs for agent harnesses, and BlueField-4 DPUs running NVIDIA Sentry for in-silicon policy enforcement, so enterprises can build, contain, observe and govern agents on-prem and at the edge. NVIDIA says it is available now; partners named include Anthropic, OpenAI, Meta, Microsoft and ServiceNow. The software is Apache 2.0.

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The vLLM project team and Ant Group officially open-sourced Humming (humming-kernels), a specialized high-performance kernel library designed for serving quantized MoE and dense LLMs at massive scale. Humming natively supports WnA16, W8A8, W4A8, and W4A4 across INT1–INT8 and FP4–FP8 formats, spanning NVIDIA architectures from Turing through Blackwell as well as T-Head Zhenwu 810E silicon. In production benchmarks, Humming kernels outpace cuBLAS by 3–4x at small batch sizes and surpass Marlin by 2x on H200 clusters. Serving DeepSeek-V4.1-Flash across 8x H20 GPUs, Humming drives a +43.3% throughput gain under 64 concurrent requests while reducing time-to-first-token (TTFT) by 32%, pre-integrated directly into vLLM v0.29.0+.

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On Oct 21 the vLLM community and Ant Group open-sourced Humming (Apache 2.0), a quantized-inference library that JIT-compiles GEMM kernels on demand. It supports every NVIDIA architecture from Turing on plus T-Head's Zhenwu 810E, and covers weight and activation combinations from INT1-INT8 and FP4-FP8 for dense and MoE workloads. In vLLM's microbenchmarks it runs 2-5x faster than vLLM Marlin, FlashInfer or DeepGEMM in some cases; on 8 H20 GPUs, switching DeepSeek-V4.1-Flash MoE from Marlin W4A16 to Humming W4A8 raised throughput 14-43%. vLLM 0.29.0 ships it built in.

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