Huawei plans to become the computational backbone for drug discovery using AI, but will not manufacture drugs

Although artificial intelligence is capable of generating code at unprecedented speeds, the process of testing, verifying, and deploying it into a working environment remains a separate complex task. Engineering teams are increasingly using AI agents to write code, fix bugs, and create new features, but faster coding does not guarantee faster software delivery.

As code generation gains momentum, the volume of checks increases, testing slows down, and teams find it difficult to coordinate the growing amount of changes.

At the TechSparks 2026 conference, Tushar Matur, CEO of Forge Code, highlighted this issue in his session 'Anatomy of a Software Factory.' He explained how AI-focused engineering organizations can move beyond automating individual coding tasks and start creating systems that manage the entire Software Development Life Cycle (SDLC).

Matur noted that just because code generation has become easier and faster does not mean the overall SDLC will accelerate. He reminded that the traditional development approach focused on writing code efficiently, requiring teams to define requirements, prepare specifications, and design architecture to reduce the load on the implementation phase.

However, since AI takes on much of the coding work, bottlenecks shift to other areas. Matur described a situation where organizations face a huge number of pull requests awaiting review, longer testing cycles, and more errors than existing regression suites can detect.

He emphasized that solving the problem does not necessarily involve hiring more engineers or launching more AI agents. Additional staff can increase coordination costs, and adding agents without effective feedback mechanisms can lead to code that overwhelms the verification and testing systems.

'The reason for this is the lack of signal,' Matur explained, clarifying why simply connecting automated steps does not create an effective software factory.

Matur presented Forge Code as a software factory that coordinates AI agents throughout the development cycle. The process begins with sorting requests, creating specifications, and implementing features. Then, agents check the changes before they are deployed, with human engineers intervening when decisions or feedback are required.

The system also monitors in the working environment, using the gathered data to inform future tasks. This forms a feedback loop, allowing the factory to identify issues, improve its workflows, and generate new work based on the experience gained. During the presentation, Matur reported that Forge Code used about 100 agents in the background to work on features, compatibility, and bug detection.

He outlined six principles for building an effective software factory: autonomy, scalability, cost-effectiveness, measurability, self-improvement, and alignment with business goals. The last principle is particularly important.

The factory should not operate as a task of writing code for every request, but rather be aimed at achieving specific outcomes, such as increasing customer retention or meeting regulatory compliance. Teams can then prioritize features based on the value they bring. In this model, engineers spend less time managing each task and more time observing results, analyzing signals, and improving the system itself. Business leaders can also provide requests and priorities directly, allowing the factory to translate organizational goals into a development backlog.

Code writing speed is good, but creating better software is the real victory. For engineering teams, the key is transforming AI-driven speed into reliable products, measurable progress, and real business results.

Meituan Waimai, Meituan's food delivery division, has announced the provision of a free artificial intelligence-based manager for restaurant business locations across China. This was reported by Xue Bin, General Manager of Meituan Waimai, during the Meituan Food Delivery Industry Conference in 2026, where the company introduced the delivery version of its assistant, Miaoshou AI.

Meituan positions this product as an AI-powered tool specifically designed for managing restaurants operating through delivery. It covers four key functions: analyzing the current performance of the establishment, conducting regular checks, managing the dish assortment, and assisting new locations at the start of their operations. Instead of simply generating reports, this assistant integrates Meituan platform data with the seller's own data, suggesting specific actions, helping implement them, and tracking the resulting outcomes.

Meituan stated that thousands of small and medium-sized sellers have already used this tool in their daily operations prior to the nationwide launch, and it is being utilized by over 200 chain brands. As an example, a small chicken hot pot shop in Yuanxi County, Hebei Province, used the tool to diagnose its business, subsequently adjusted its menu and promotions, which led to an increase in weekly orders of over 30% compared to the previous week. Furthermore, Meituan noted that new locations of one of its partners showed better results in the first month of operation. It should be noted that these figures are internal data from Meituan based on selected test sellers and have not undergone independent verification.

Xue presented this AI tool as part of the so-called 'delivery efficiency triangle,' which includes innovations in products and new store formats. Regarding formats, Meituan promotes the concept of 'Super Kitchens'—larger kitchens focused on delivery—and provides support in selecting locations, products, kitchen layout, and workflows. The production planning system connects ingredient preparation, cooking, and order sorting. In one partner location, Meituan reported that order preparation time was reduced from approximately 18 minutes to 8 minutes.

Xue expressed hope that artificial intelligence can bring professional skills into the delivery sector for every location, providing each establishment with more detailed support tailored to its needs. This step places the assistant agent before a very large base of small restaurant owners, many of whom cannot afford full-time operational staff. The real test will be how well the system performs across diverse cuisines, menus, and districts after moving beyond the early testing group.

DAMO Academy from Alibaba unveiled the XuanTie E908A automotive processor IP at the RISC-V summit in Shenzhen, China, on October 19. This processor IP is designed for safety-related systems, domain controllers, and automotive gateways, and complies with automotive functional safety requirements ASIL-B and ASIL-D, according to DAMO Academy.

Furthermore, the company disclosed a processor roadmap intended for autonomous driving and embodied artificial intelligence. The planned automotive processor C950A is expected to deliver five times the performance of current RISC-V products for the automotive industry, with its release anticipated by the end of 2027.

DAMO Academy reported that the cumulative volume of chips utilizing the XuanTie processor IP has exceeded 6 billion units as the company expands its application from established areas into high-performance computing markets.