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Novo Nordisk and AWS bring agentic AI into drug discovery

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Novo Nordisk is expanding its use of AWS artificial intelligence tools across drug discovery, including AI agents for target identification, therapy design, and research workflows. Under the agreement announced recently, AWS will become Novo Nordisk’s preferred cloud provider and strategic AI partner. The companies have also created a co-innovation hub at Novo Nordisk’s existing London […]

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webAI Releases TwIL-LM: A 1.7B and 3B Formal-Logic Model Family for Autoformalization on Local Hardware

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webAI has released TwIL-LM, a two-model family of formal-logic reasoners at 1.7B and 3B parameters. The 3B member, TwIL-LM3, is a merged fine-tune of SmolLM3-3B; the 1.7B member is a PEFT LoRA adapter for SmolLM2-1.7B-Instruct. Both target autoformalization: translating English into first-order logic and checking whether a conclusion follows from its premises. Both run locally,

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Meta AI Releases Muse Glimmer: A 30B Open-Weights Agentic Model That Runs on One Consumer GPU

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Meta has released Muse Glimmer, a 30-billion-parameter multimodal model distilled from Muse Spark. It is tuned for always-on local agent workflows, and ships under Apache 2.0. A 30B model normally needs over 55 GB of memory at full precision. Meta compresses it to roughly 4-bit, then adds block-level speculative decoding so it answers fast enough

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Meta Muse Glimmer brings local AI agents to consumer GPUs

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Meta is releasing Muse Glimmer under an Apache 2.0 licence for local AI agents that can run on a consumer GPU. The company’s  Superintelligence Labs has released the 30-billion-parameter model’s weights on Hugging Face. Meta says developers can use it for local coding, function calling, local agents, and LLM-as-a-judge evaluation. The release targets an operational

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What do AI leaders think comes next?

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AI is evolving quickly, but where is it headed next? At SAS Innovate 2026, we asked customers, partners and industry leaders a simple question: What do you think the future of AI looks like? Their answers touched on everything from agentic AI and trustworthy AI to the role of human […] The post What do

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ByteDance Seed Introduces SeedRealtime: a Native Audio-Visual Full-Duplex LLM That Watches, Listens and Speaks in One Model

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ByteDance’s Seed team has introduced SeedRealtime, a native audio-visual full-duplex LLM. The model fuses audio, video and text in a single unified architecture. It interacts in real time over continuous multimodal streams, rather than one turn at a time. Seed positions it as a step toward omni-modal interaction, and claims three breakthroughs: joint audio-visual understanding,

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NVIDIA Releases NemotronLabs VoiceChat 11B: An Open Full-Duplex Speech-to-Speech Model with ~450 ms Turn-Taking and Live Tool Calling

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NVIDIA has released NemotronLabs VoiceChat 11B, an open 11B end-to-end speech-to-speech model for real-time, full-duplex conversation. Instead of chaining ASR, an LLM, and TTS, it performs streaming speech understanding and speech generation in one unified network. That removes the multi-model orchestration and API handoffs a cascaded stack requires, and cuts end-to-end latency: measured smooth turn-taking

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Top LLM Observability and Evaluation Platforms in 2026: Langfuse, LangSmith, Braintrust, Arize, and More Compared

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LLM applications fail in ways traditional software does not. The same prompt can produce different outputs. A retrieval step can return the wrong document while every HTTP status reads 200. An agent can loop through fourteen tool calls, burn thousands of tokens, and deliver a confidently wrong answer. Standard application performance monitoring (APM) alone does

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Meet Shepherd: An Open-Source Python Substrate That Lets Meta-Agents Fork, Replay, and Revert Any Agent Run

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Long-running agents accumulate state that no transcript captures. A coding agent at step 10 holds edited files, a running dev server, installed packages, and a warm prompt cache. When it misreads a traceback and rewrites a file that was already correct, neither available recovery path is cheap: patching forward grows the context and the token

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Pokee AI Releases Pokee-Isaac 28B: A 10M-Token Context Agentic Model Built to Run Inside the Customer Boundary

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Long-horizon agents accumulate context faster than they resolve tasks. Every tool output, observation, and intermediate reasoning step stays in the window, and the two capabilities that matter — holding that context and staying coherent across it — have so far been available almost exclusively from cloud endpoints. That excludes regulated industries, public-sector institutions, and on-device

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