MIC Electronics Limited has signed a memorandum of understanding (MoU) with Singapore-based VC firm TOP2 Pte Ltd to boost India’s semiconductor manufacturing capacity. 

Under the agreement, TOP2 will help MIC identify and finalise a fabrication partner from Taiwan, aiming to start wafer production in India with a capacity of 25,000 to 30,000 wafers per month.

The collaboration supports India’s $10 billion semiconductor mission, which seeks to reduce reliance on imports and develop domestic chipmaking capabilities. India currently meets most of its semiconductor demand through imports, making access to established wafer lines a quicker and cost-effective option than building new facilities.

To Reduce Imports

MIC Electronics will share technical expertise, define fabrication needs and engage in discussions with potential partners. “By partnering with TOP2, we will be able to tap into proven global expertise and move at a faster pace on our journey to reduce import dependence and strengthen India’s position in the global semiconductor landscape,” said Rakshit Mathur, CEO of MIC Electronics.

TOP2 has already assisted three Indian firms this year in securing wafer production lines from the US, Europe and Japan. The company plans to bring two to three additional lines annually, citing growing demand driven by electric vehicles, 5G, AI and consumer electronics. 

“This partnership with MIC allows us to bring together international expertise and local strength to make sure there is faster execution and greater resilience,” said Rao Panidapu, founding partner of TOP2.

The agreement underscores a shared commitment to technology transfer and sustainable growth, aligning with India’s semiconductor vision. Both companies see the MoU as a step towards making India more self-reliant and globally competitive in the semiconductor sector.

Adobe has announced the general availability of its AI agents designed to help businesses build, deliver, and optimise customer experiences. The launch is anchored in the Adobe Experience Platform (AEP) and its new Agent Orchestrator, which enables companies to manage, customise, and connect AI agents across Adobe and third-party ecosystems.

The company said the agents are capable of understanding context, planning multi-step actions, and refining responses with human oversight.

“Adobe’s agentic AI innovations are redefining customer experience orchestration in the era of AI, enabling businesses to unlock productivity with agent orchestration, reimagine longstanding processes and deliver personalised experiences at scale to drive business growth,” said Anjul Bhambhri, senior vice president of engineering at Adobe Experience Cloud in a statement.

Over 70% of AEP customers are already using Adobe’s AI Assistant, the conversational interface for interacting with agents. Brands including The Hershey Company, Lenovo, Merkle, Wegmans Food Markets, and Wilson Company have adopted the technology to enhance capabilities across marketing and customer engagement.

The release includes out-of-the-box AI agents across Adobe’s enterprise applications. 

These range from Audience Agent for audience creation and optimisation, to Journey Agent for campaign orchestration, Experimentation Agent for analysing performance, Data Insights Agent for customer analytics, Site Optimisation Agent for website performance, and Product Support Agent for troubleshooting.

Adobe is also preparing to roll out Experience Platform Agent Composer, which will let businesses customise AI agents with brand guidelines and policy controls. New developer tools such as an Agent SDK and Agent Registry are also in the pipeline.

Additionally, Adobe announced partnerships with Cognizant, Google Cloud, Havas, Medallia, Omnicom, PwC, and VML to expand industry-specific use cases.

Alibaba’s Qwen team has introduced Qwen3-Next, a new large language model architecture designed to improve efficiency in both training and inference for ultra-long context and large-parameter settings.

At its core, Qwen3-Next combines a hybrid attention mechanism with a highly sparse mixture-of-experts (MoE) design, activating just three billion of its 80 billion parameters during inference. 

The announcement blog explains that the new mechanism allows the base model to match, and in some cases outperform, the dense Qwen3-32B, while using less than 10% of its training compute. In inference, throughput surpasses 10x at context lengths beyond 32,000 tokens.

Two post-trained versions are being released: Qwen3-Next-80B-A3B-Instruct and Qwen3-Next-80B-A3B-Thinking. The Instruct model performs close to the 235B flagship and shows clear advantages in ultra-long context tasks of up to 2,56,000 tokens. The thinking model, aimed at complex reasoning, outperforms mid-tier Qwen3 variants and even the closed-source Gemini-2.5-Flash-Thinking on several benchmarks.

Among the key technical innovations are Gated DeltaNet mixed with standard attention, stabilised training via Zero-Centred RMSNorm, and Multi-Token Prediction for faster speculative decoding. These designs also address stability issues typically seen in reinforcement learning training with sparse MoE structures.

Pretrained on a 15-trillion-token dataset, Qwen3-Next demonstrates not just higher accuracy but also efficiency, requiring only 9.3% of the compute cost of Qwen3-32B. Its architecture enables near-linear scaling of throughput, delivering up to 7x speedup in prefill and 4x in decode stages at shorter contexts.

The models are available via Hugging Face, ModelScope, Alibaba Cloud Model Studio and NVIDIA API Catalog, with support from inference frameworks like SGLang and vLLM. According to the company, this marks a step towards Qwen3.5, targeting even greater efficiency and reasoning capabilities.