
MarkTechPost: Soniox STT v6 Adds Agent Text Input and Echo Filtering for Voice Agents
Soniox has released Soniox STT v6, the latest generation of its speech-to-text API. Soniox STT v6 is a speech-to-text model that reads what a voice agent says while hearing the caller. It uses that text to recognize short replies and to drop the agent’s own voice. The release ships as stt-rt-v6 for streaming and stt-async-v6 for files, across 60+ languages. TL;DR Size: Soniox STT v6 is a proprietary hosted model, no parameter count published. Runs on: Soniox cloud API only (WebS
A speech-to-text API has been updated with two new capabilities: it can now read the text of what a voice agent is saying while simultaneously hearing a caller, and it can filter out the agent's own voice when that audio leaks back through speakerphones or laptops. This matters because voice agents previously could only hear the caller and had no way to suppress their own audio, leading to transcription errors on short answers and agents interrupting themselves. The update is available across 60+ languages at $0.12 per hour for streaming and $0.10 per hour for files, with diarization included in the price.
Want to go deeper than the news? Explore live, cohort-based AI courses taught by practitioners.
Browse AI courses on Maven
Anthropic’s Frontier Red Team has released FlashProve, an open-source research prototype for provable inference. FlashProve is a zkSNARK system from Anthropic that proves a reply came from a committed model. The proof reveals nothing about the model weights. On Llama 4 Maverick, a 400B mixture-of-experts model, it proves about 110 tokens/second on 1 H100. For developers, this targets a real gap: today you cannot check which model actually answered your API call. TL;DR Size: Proves

Here's a question straight out of sci-fi: What if AI could predict what you were going to do before you decided to, even in a completely new situation?
Also reported by SiliconANGLE

Mistral says its new model can afford organizations a kind of control over its cyber defenses that closed models can't.

Deep Principle has released Hydrogen-1.0, an open-weight scientific model for chemistry, materials science and life sciences. It is built by post-training Z.ai’s GLM-5.3 on domain data, laboratory records and agent discovery trajectories. For developers, it offers open weights that report a FrontierScience-Research win over GPT-6-Astra. The weights ship under the MIT License. TL;DR Size: 753B parameters (MoE, ~40B active per the GLM-5.3 base), 1M-token context, 128K max output.

OpenAI has updated its Model Spec, restructuring its AI behavior guidelines around five core values, including faithfulness to human intent and correctability. GPT models should now draw on conversation history to figure out what users actually want and disclose their own assumptions. Whether the models follow these rules is another matter. The article OpenAI sets new behavioral guidelines for AI models that have already proven hard to control appeared first on The Decoder.

Designers could save hours on repetitive layout work if AI learns to match their specific visual systems and brand guidelines.