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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
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,
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
In this tutorial, we develop an end-to-end sentiment analysis workflow using the Stanford NLP IMDb Large Movie Review Dataset and compare classical machine learning with parameter-efficient transformer fine-tuning. We begin by establishing a reproducible environment and auditing the dataset for class ordering, review-length skew, duplicate leakage, and preprocessing artifacts before training a strong TF-IDF and
Mistral AI has released Shieldstral 1.0 3B, an open-weights, policy-adaptive multimodal safety classifier that treats content moderation as a single yes/no question rather than a fixed taxonomy of harm categories. Most guardrail models bake their category list into the weights, so re-targeting one to a new deployment context means retraining — and the same content
NVIDIA Labs has open-sourced NOOA (NVIDIA Object-Oriented Agents), a model-agnostic Python framework for building AI agents. Agent development today is split across prompt templates, tool schemas, callback code, and workflow graphs. NOOA collapses all of it into one Python class. Methods are the actions the model can take. Fields are agent state. Docstrings are prompts.
Learn how the SAS Viya MCP Server enables AI assistants like Claude Cowork to orchestrate governed, auditable banking analytics workflows while keeping model execution, governance and oversight within SAS Viya. The post Building a governed banking workflow with SAS® Viya® MCP Server and Claude Cowork appeared first on SAS Blogs.
Building a governed banking workflow with SAS® Viya® MCP Server and Claude Cowork Read More »
Meta AI has released Muse Code (in beta), a terminal coding agent in beta, powered by its new Muse Spark 1.2 model. Meta positions the pair as its next step toward the frontier, with larger models on the way. Muse Code targets complex software engineering across large repositories: it plans changes, writes code, and validates
The idea that AI agents need ‘eyes’ – real visual information from the real world, and not just paperwork and dashboards, have become a marker of advancement in construction technology. Yet mostly what has been referred to as an “agent” today is not actually acting based on what it sees. It narrates and alerts while
Introduction A reasoning LLM is a language model trained not just to predict the next word, but to work through a problem step by step and verify its own conclusions before answering the approach behind models like OpenAI’s o1 and DeepSeek’s R1. This guide condenses a practical path to building a compact 300–400M parameter GPT-style
Build a Reasoning LLM from Scratch: A Complete Guide to GRPO, RoPE & Pretraining. Read More »
