Walmart’s AI strategy is built on execution rather than experimentation, combining edge computing, cloud infrastructure, and retail-specific models to optimize inventory, logistics, and customer experience. This article explores the infrastructure behind Walmart’s AI flywheel and explains how Square Codex helps organizations replicate this level of operational AI by embedding nearshore engineering and data talent directly into execution-focused teams.

This article explains how Thales is launching AI Security Fabric to monitor AI applications in real time, protect RAG flows, and address emerging threats such as jailbreak attempts and data leaks.

Explore how major U.S. banks are using artificial intelligence to boost productivity, automate repetitive work, and reshape operational structures. It also explains how Square Codex supports financial institutions by providing nearshore engineering talent that strengthens AI integration, compliance, and production level reliability.

This article explains how IBM’s acquisition of Confluent reshapes the foundations of AI in production by strengthening real time data streaming, governance, and multi cloud connectivity. It also highlights why nearshore teams like Square Codex help companies turn complex streaming pipelines into reliable systems that support AI at scale.

Amazon’s second generation of Nova AI models introduces Nova Forge, a tool that allows companies to customize systems during pretraining. This shift aims to improve accuracy, reduce late-stage fixes, and embed domain rules from the beginning. The article explains how Nova Lite, Pro, Sonic, and Omni fit different workloads and how early customization compares with approaches from Google, OpenAI, and Anthropic. It also highlights how Square Codex supports projects built on Nova Forge with Costa Rican nearshore teams specializing in data pipelines, MLOps, and secure AI deployment.

Once again we are talking about Nvidia, a company that keeps surprising the computing world and one we at Square Codex follow closely. The message Jensen Huang sent this week from Washington was twofold and direct. On the one hand, the Nvidia chief executive confirmed he has spoken with Donald Trump about chip export restrictions and how that framework shapes the industry’s roadmap. On the other, he strongly criticized the idea of regulating artificial intelligence with different rules in every U.S. state, because in his view a fragmented regulatory patchwork would slow progress and raise costs for those building systems and services on that common foundation. These were not stray remarks in a hallway; they came in a public agenda that included a forum in Washington and interviews with the press, and they arrive at a time when the sector reads every political nuance as a signal of investment or risk.