Google Lens Tops 25 Billion Monthly Searches, With 20% Tied to Shopping

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Google Lens has surpassed 25 billion queries per month, up from nearly 20 billion reported in October 2024, according to Google's Think with Google search trends briefing. Approximately one in five of those searches carries commercial intent, signaling that visual search is increasingly relevant to product discovery and retail. The platform allows users to search using images rather than typed keywords, making it especially useful for visually distinctive or hard-to-name products. Google's broader Search strategy has been moving toward multimodal experiences that combine visual input with text and product data. The growth in Lens usage suggests businesses should ensure their product images, descriptions, and structured data are consistent and accurately represent what is being offered.

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Developer Builds Agentic Fraud Investigator Powered by TigerGraph Graph Database

A developer has built an agentic fraud investigation tool using TigerGraph, a graph database platform, as part of the HHGOA2026 hackathon. The tool is designed to mimic how a human analyst investigates fraud by leveraging relational data connections. The project is built on the premise that fraud is inherently relational, making graph databases a natural fit for detecting it. The application is deployed via Streamlit and the source code has been made publicly available on GitHub.

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FraudGraph AI Uses TigerGraph and GraphRAG to Build Explainable Fraud Investigations

A team built FraudGraph AI, an agentic fraud investigation platform, as part of a recent hackathon project. The system moves beyond single transaction scoring by mapping relationships across customers, cards, merchants, and historical cases using TigerGraph as its graph investigation layer. GraphRAG supplies structured graph context to a downstream reasoning pipeline, enabling the system to trace connected entities and identify non-obvious fraud patterns. A dedicated Risk and Uncertainty Engine separately evaluates fraud probability, confidence, and uncertainty to avoid premature or irreversible decisions. The platform also includes a Next Best Action Engine that produces deterministic, policy-aligned recommendations such as monitoring, blocking, or requesting verification.

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Developer Open Sources Repairo, a Free Automated API Auto-Repair Tool

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AI Boom Mirrors Railway and Dot-Com Eras: Real Tech, Real Bubble Risk

Analysts drawing on historical parallels argue that 'real technology' and 'speculative bubble' are not mutually exclusive — railways and the internet were both transformative and financially ruinous for many early investors. The four largest US hyperscalers are collectively spending hundreds of billions of dollars annually on AI data centres, with capital expenditure revised upward nearly every quarter, mirroring the massive physical infrastructure bets of the railway age. A key concern is that Nvidia, the dominant AI chip supplier, has taken stakes in and provided financing to some of its own largest customers, creating a reflexive loop where chip purchases and valuations reinforce each other rather than reflecting independent outside demand. This dynamic echoes the 1720 South Sea Bubble, in which the company lent buyers money to purchase its own shares, making rising prices appear as validation of the scheme. The more useful question, historians and analysts suggest, is not whether AI is a bubble, but which parts represent durable infrastructure build-out and which parts are speculative financing structures dressed in technological clothing.

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