Large Language Models

Today's Stories

1. 1

   Walmart Engineer: AI Is Reshaping Software Engineering

   Tianyu Zhang, a software engineer at Walmart, describes how AI coding tools evolved from experimental to deeply integrated across her roles, including work on Shop With Friends, Modular Intelligence, and now the Ship With Walmart team. AI tools like Wibey, Walmart's internal agent platform, now let engineers scaffold services, generate unit tests, and debug CI/CD pipelines with natural language prompts, dramatically accelerating productivity and lowering the barrier to development.

   Zhang predicts AI will become a core tool, shifting future demand toward system architects and engineers who can orchestrate complex systems, while emphasizing the continued need to validate generated code, especially for company-specific business logic.

2. 2

   Experian launches Agent OS with ServiceNow

   Experian has launched the Agent Operating System (Agent OS), a commercial agent-based platform, with ServiceNow as its first partner. It is designed to bring Experian's risk, identity, and decision-making capabilities into enterprise workflows. The platform enables customers to get high-quality information more quickly and helps Experian serve more clients. Initial target customers include insurers, financial services providers, and lenders, with early adopters primarily using model risk management services.

   Experian is in the enterprise scaling phase, not proof-of-concept. The platform uses a mix of commercial, open-weight, and open-source models, and can switch models based on the workload, potentially reducing costs for financial services tasks.

3. 3

   Yokohama Rubber adopts RAG-based generative AI

   Yokohama Rubber has introduced a generative AI system that uses RAG (a technique that lets an AI draw on a company's own documents to answer questions), in a giant warehouse housing 160 robots and 65,000 bins. The system is designed to help workers quickly find information across the vast warehouse operation, where managing tens of thousands of items and a large robot fleet makes fast, accurate answers valuable.

   The article does not specify a rollout date or cost, so the near-term focus is on how effectively the RAG-based AI improves daily retrieval tasks in this robot-heavy facility.

4. 4

   Aichi tourism policy blends data with intuition

   Aichi Prefecture's tourism bureau is promoting EBPM (evidence-based policy making) while recognizing that the traditional KKD approach (experience, intuition, courage) remains valuable. The hybrid style of combining both is reportedly increasing. Since OpenAI announced ChatGPT in November 2022, the tourism industry recognizes the need for generative AI but is still groping for practical uses. The author argues that AI's convincing answers lack real experience, so human judgment remains irreplaceable.

   The prefecture's official website is adapting to GEO (generative engine optimization) to become a trusted source for AI search answers. Aichi's approach of improving content quality and fairness may need adjustment in a few months.

5. 5

   Fujitsu rolls out Microsoft 365 Copilot to 100,000 staff

   Fujitsu has deployed Microsoft 365 Copilot to about 100,000 licenses, covering all employees, as part of a company-wide AI-driven business transformation initiative. The company says AI use is now embedded in daily work, with a company-wide utilization rate above 90%. In the service desk area, AI agents now handle about 75% of the process for first responses and assessments, going beyond individual productivity gains.

   Fujitsu is working with Microsoft Japan to deepen integration of Copilot as a business interface with AI agents and business systems, aiming for continued value creation and process transformation.

6. 6

   Sagamiko MORIMORI begins AI parking data trial

   A data-utilization demonstration experiment has begun at Sagamiko MORIMORI, combining parking congestion, visitor attributes, and generative AI. The trial suggests an effort to address parking congestion at the venue by applying AI to visitor data, potentially improving the visitor experience.

   The outcome of the experiment will indicate whether this data-driven approach effectively reduces congestion at the facility.