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  1. ElevenLabs Blog65

    Introducing Scribe v3 and v3 Live, our most accurate speech to text models

    ElevenLabs launched Scribe v3 for recorded audio, which makes 27% fewer word errors than Scribe v2 across 102 languages and has the lowest diarization error of any model tested, and Scribe v3 Live for realtime audio, which makes 43% fewer semantic word errors than Scribe v2 Realtime and adds speaker diarization and improved turn detection. Scribe v3 Live is available today in ElevenAgents, and both models are available in ElevenAPI, where switching from Scribe v2 is a one-line change to the model ID (scribe_v3 or scribe_v3_live). Both run in the US by default with isolated EU, India, and Singapore environments, Zero Retention Mode, and SOC 2, ISO 27001, PCI DSS Level 1, and HIPAA-eligible coverage.

    Why it matters: Useful for teams choosing speech-to-text models, since it details measured accuracy, diarization, and latency trade-offs plus deployment and compliance options.

  1. Hugging Face Blog75

    NVIDIA Kumo Relational Outperforms Supervised Machine Learning on Relational Data, No Training Required

    NVIDIA introduces Kumo Relational, a pre-trained relational foundation model that predicts directly on enterprise databases via in-context learning in a single forward pass, requiring no task-specific training, feature engineering, or table flattening. Across 41 tasks on 15 databases, it beats supervised relational deep learning models and expert-engineered gradient-boosted tree pipelines, and on the SAP SALT benchmark it surpasses AutoGluon ensembles by 8% in mean reciprocal rank (MRR). It scales to databases with 500B+ rows and is available through NVIDIA's open-source structured-data-models and kumo-engine libraries or as an NVIDIA NIM.

    Why it matters: Useful for ML teams weighing whether to replace production XGBoost pipelines with a pre-trained relational model, though the reported gains come from NVIDIA's own paper and benchmarks.

  1. Mistral AI80

    Voxtral 3

    Mistral introduces Voxtral 3, a family of speech-to-text and text-to-speech models plus a LiveKit-based voice agent SDK that combines listening, reasoning, tool calling and speaking in one real-time streaming pipeline. It includes Voxtral Small Transcribe 3 for multilingual transcription with diarization and context biasing, Voxtral Mini TTS 3 for expressive speech in 35+ languages and 50+ dialects, and Pro Voice adaptation from 30 minutes of audio. It is available via Mistral's EU-hosted API or self-deployment, with pricing from $0.0035/min for batch transcription to $13 per 1M characters for TTS.

    Why it matters: The announcement details concrete model capabilities, benchmarks and pricing for teams evaluating real-time voice agents and sovereign speech deployment.

  1. Mistral AI67

    Introducing Timestral

    Mistral releases Timestral, its first family of time-series foundation models in four sizes from 5M to 209M parameters, with the largest ranking first on GIFT-Eval, TIME and fev-bench among pre-trained zero-shot models. Timestral produces probabilistic multivariate forecasts, supports past-only and known-future covariates, and runs on CPU without a GPU. It combines synthetic-first pre-training, covariate-aware attention and decoupled horizon forecasting, and can be fine-tuned on proprietary data through Mistral Forge.

    Why it matters: Useful for teams weighing time-series foundation models, since it reports benchmark standings, a size-versus-accuracy trade-off and CPU latency figures alongside the adaptation options.

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