Sharge unveils loomys L1 smart glasses with titanium frame and active AI memory
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Sharge unveils loomys L1 smart glasses with titanium frame and active AI memory

Sharge Technology introduced its loomys L1 smart glasses in Wuhan on August 18. These glasses, priced from 2,699 RMB, are built around three core features: stylish design, comfort for extended wear, and an artificial intelligence-based active memory function.

Founder and CEO Zhang Bo referenced the company's past experience. In late 2024, Sharge released the A1 AI glasses at a collaborative price of 999 RMB, which sold over 50,000 units within 24 hours of domestic pre-orders and exceeded $2 million USD in international monthly crowdfunding. However, issues with Bluetooth, image calibration, battery life, and software upon delivery led to returns and refunds. Consequently, Sharge suspended production of tens of thousands of units and fully refunded early users.

Zhang stated that the goal of these smart glasses is to replace conventional eyeglasses, not smartphones. Most existing AI glasses resemble sunglasses and are designed for about three hours of daily use. In contrast, the loomys L1 targets users who wear glasses for more than 16 hours daily.

For construction, Sharge utilized an aerospace-grade titanium front frame and natural acetate plates. Titanium provides strength with low weight, while acetate is skin-safe. The design is inspired by the Danish eyewear brand Lindberg, where cables are positioned between the titanium and the plates. A proprietary spiral hinge with a flexible titanium rod eliminates the need for springs, supports frame width adjustment, maintains damping after 10,000 open-close cycles, and conceals wiring.

Thanks to the slim design, the camera, battery, and motherboard are housed in the temples, shifting the center of gravity behind the ears. The lightest model weighs only 43 grams, reducing pressure on the nose by 50%.

The loomys L1 operates on Sharge's proprietary active memory system, LoomOS, which differs from passive activation modes. Key functions include multi-scene recording that captures conversations, calls, and ambient sounds during music listening and voice chats; an AI digital diary that integrates recordings with phone messages to summarize encountered people and tasks; and an AI Live Photo feature that combines photos with notes.

Privacy concerns are addressed via a magnetic lens case. Sharge commits not to store raw audio and video materials in the cloud long-term, retaining only organized text content.

The hardware employs a dual-chip architecture: the main processor is the Qualcomm Snapdragon 4nm-class W5100 and the Bluetooth chip is the Hengxuan BES2700, both running on Android. The W5100 consumes 38% less power than in the AR1 AI memory mode. The camera is a custom 12-megapixel module with a 111-degree ultra-wide field of view, approximately 20% smaller. The audio system uses a Goertek 8mm speaker. The battery features a 5-second quick-swap architecture, with a 258mAh cell capacity and a foldable casing, providing up to 40 hours of total operation or about 12 hours in active AI memory mode.

In the loomys L1 ecosystem, Feishu is integrated via a command-line interface to connect agents, allowing voice control over messages, summaries, and scheduling, and it also connects to Tencent WorkBuddy for email management. Prices range from 2,699 RMB with Essilor Crizal 1.67 lenses to 2,999 RMB with Zeiss Duravance Platinum 1.67 lenses. Pre-orders are accepted until September 17th with an annual AI membership subscription costing 999 RMB. Large-scale delivery is expected by the end of 2026, with initial 24-hour pre-orders exceeding 25,000 units. This transition marks a fundamental shift, and the true test will come after delivery.

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EDA Giants Synopsys, Cadence, and Siemens EDA Launch AI Agency War in Chip Design
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EDA Giants Synopsys, Cadence, and Siemens EDA Launch AI Agency War in Chip Design

Automated chip design using artificial intelligence has become one of the most discussed topics in the industry. Last month, the Chinese company Kimi demonstrated a complete experiment where its K3 model independently designed a chip. This chip was tested on an EDA platform, running for 48 hours exclusively on AI, without using commercial software from Cadence or Synopsys.

However, a deeper analysis shows that replacing existing systems is still far from complete: the chip from Kimi corresponds to technologies from about 20 years ago, runs 20–30 times slower than modern chips, and open-source EDA tools still require commercial systems to perform real projects.

Nevertheless, agentic AI is truly transforming workflows, covering tasks from generating RTL code to creating test environments and invoking simulation, formal verification, and debugging tools.

At the DAC Chips to Systems 2026 conference, three leading EDA companies presented their different strategies. Synopsys, possessing the largest accumulation of AI, introduced a fully autonomous agent for design verification and an autonomous thermal simulation workflow based on Ansys Icepak. Furthermore, the company showcased an analog/mixed-signal workflow with a 3x efficiency increase and 20 GPU-accelerated tools. Synopsys proposed an AI autonomy ladder in the style of self-driving transport, from L1 to L5, which is currently at level L3, and launched AgentEngineer in 2026 after DSO.ai in 2020. In November 2025, NVIDIA invested two billion dollars in Synopsys, and Grace Blackwell is expected to provide 30 times higher performance for EDA workloads.

Cadence's strategy focuses on architectural completeness. The company introduced AuraStack—an AI-based superagent that joins ChipStack, InnoStack, and ViraStack to ensure coverage for verification, digital implementation, analog design, as well as printed circuit boards and packaging. This stack provides 20 times higher multiphysics analysis performance and a 15x workflow acceleration. NVIDIA engineers are already using this system. Cadence's most distinctive innovation is the Mental Model—a structured representation of knowledge that fixes design intentions to reduce hallucination risk, and its agents have been recognized by NVIDIA, Qualcomm, and Broadcom.

Siemens EDA adheres to a different approach—trusted autonomy. The core logic of their Fuse EDA AI agent uses self-checking, matching the results of large models with deterministic, physics-based assertion engines. The Solido Layout Analyzer tool helped the STMicroelectronics non-volatile memory team reduce debugging time by weeks, and through Intelligence Center X, Siemens is expanding agent planning to production processes and supply chains. Fuse uses the MCP protocol to connect tools from different vendors. All three giants rely on NVIDIA inference level, management level, and the Vera processor in EDA verification farms.

Chinese manufacturers view the current situation as a favorable moment. Xpeedic, together with Lenovo, presented its EDA agent as the only example of domestic EDA at DAC 2026. XEPIC launched the basic Agentic EDA XEPIC Intelligent System with intelligent verification and design, focusing on proof cycles that transform AI outputs into verifiable, traceable results ready for approval. UniVista released UDA 2.0—the first agentic EDA tool on a fully proprietary domestic architecture. Other solutions include Chips Z for advanced packaging, DVcrew and PDcrew agents from Banxin, and an LLM-based verification platform from Zhiwei Chuangxin called ChatDV, which reduces modular verification from months to hours. NVIDIA research shows that chip designers spend about 60% of their time on debugging—precisely the area targeted by agentic AI.

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