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Models

Qualcomm launches Snapdragon Reality Elite for on-device AI and spatial computing

Qualcomm has introduced the Snapdragon Reality Elite platform to support on-device artificial intelligence and spatial computing. This new hardware architecture is designed to handle complex AI workloads directly on the device to facilitate more responsive mixed reality experiences without a constant cloud connection.

Why it matters

This development allows enterprise developers to build applications that process sensitive data locally, which is critical for maintaining privacy and reducing latency in industrial environments. It signals a move towards more capable edge computing for spatial interfaces and advanced computer vision.

Key points

  • Focuses on high-performance on-device AI for spatial computing environments.
  • Enables local processing of complex workloads to support mixed reality.
  • Optimised for power efficiency in wearable enterprise hardware.

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OpenAI disbands preparedness team responsible for assessing catastrophic ai risks

OpenAI has reportedly dissolved its preparedness team, the group tasked with evaluating and mitigating potential catastrophic risks from advanced AI models. This internal restructuring follows several high profile departures from the company safety and alignment divisions.

Models

Deepseek raises V4 API pricing by up to eleven times ahead of reported IPO

DeepSeek has implemented a substantial price increase for its V4 API, with some costs rising by up to eleven times as of 16 August 2026. This shift signals the end of the low cost strategy the company utilised to disrupt the market in 2025. The adjustment coincides with industry reports suggesting the firm is preparing for an initial public offering.

Models

Writer releases enterprise-optimised GLM-5.2 variant with token-saving technology

Writer has launched a new variant of the open-source GLM-5.2 model, specifically post-trained to meet enterprise reliability standards. This release introduces a specialised harness designed to significantly reduce token consumption, directly addressing the high operational costs associated with large-scale model deployment.

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