Models
Audio Interaction: A New Open-Weights Model for Continuous Voice AI
A new Apache 2.0 open-weights model enables continuous listening and real-time voice interaction, potentially ending the era of clumsy VAD wrappers.
Latest
127 stories in the archive
Models
Alibaba’s Qwen3.7-Plus: Evaluating the Potential of Multimodal AI Agents
An analysis of Alibaba’s Qwen3.7-Plus, examining its agentic capabilities, hardware requirements for local deployment, and the implications of its licensing.
Industry
The End of Tokenmaxxing: Why AI Cost Management is Now Critical
The AI industry is shifting from reckless token consumption to sustainable engineering as the financial cost of monolithic models becomes unsustainable.
Industry
NVIDIA Dynamo Snapshot: Reducing AI Inference Cold Starts on Kubernetes
NVIDIA introduces a CRIU-based system to snapshot vLLM workers, drastically reducing the time it takes to scale AI models on Kubernetes.
Models
NVIDIA Nemotron 3 Ultra: A Deep Dive into the 550B MoE Hybrid Model
NVIDIA’s Nemotron 3 Ultra combines Mamba and Transformer architectures to enable efficient 1M-token context windows for long-running enterprise agents.
Research
Huawei Releases KVarN: A Native vLLM Backend for KV-Cache Quantization
Huawei’s KVarN reduces VRAM usage in vLLM by quantizing the KV cache, allowing for larger batch sizes and longer context windows.
Research
Solving Long-Form Coherence in Small Open-Weight LLMs
An analysis of the POLARIS paper and its approach to preventing quality degradation and structural collapse in long-form creative writing for small models.
Models
MisoTTS: Analyzing the 8B Emotive Text-to-Speech Model
An analysis of MisoTTS’s 8B parameter architecture, RVQ implementation, and the implications of its open-weights release for local TTS.
Models
Google Gemma 4 12B: The Ideal Balance for Local LLM Deployment
Google’s new 12B model targets the gap between 8B and 70B models, offering high reasoning capabilities for 16GB RAM devices.
Research
AURA: Solving the KV Cache Problem for Continuous Embodied AI
AURA introduces action-gated memory to prevent VRAM bloat in robots, allowing long-term policies to run indefinitely without crashing or hallucinating.