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LiveRamp Launches Netflix CTV Campaign Amid Publicis Scrutiny

LiveRamp has launched its first brand campaign on connected TV, running 15- and 30-second spots on Netflix for five months and with additional activations on YouTube and social. The campaign positions LiveRamp as a trusted data and governance layer for AI-driven marketing, emphasizing identity, cleanroom technologies and data ethics. The effort arrives as Publicis announced plans in May to acquire LiveRamp for $2.2 billion, raising industry concerns about the platform’s neutrality and risking client pullback from groups like Omnicom and Dentsu. LiveRamp says the campaign was planned before the acquisition news and underscores its continued commitment to neutrality. The company recently introduced a program for partner-run AI agents and an arrangement with OpenAI enabling marketers to measure ChatGPT ads via LiveRamp’s Conversions API.

Data Engineer Interview Prep: Grind Right, Skip LeetCode

The article argues that typical LeetCode algorithm practice (trees, graphs, dynamic programming) is poorly aligned with what modern data engineering interviews actually assess. From 2023–2026 the role shifted toward real-time architecture, cloud cost optimisation, metadata governance and platform engineering. Hiring screens now prioritise SQL fluency (window functions, joins, deduplication), data-focused Python (Pandas, JSON handling, validation), and pipeline/system-design thinking (schema drift, slowly changing dimensions, idempotent upserts). The author recommends a targeted problem set (35–50 problems focused on arrays, hash maps, strings, sliding windows), and a prep time split weighted toward SQL, data-manipulation Python and system design. The piece cites industry examples (Airbnb, Meta, Google, Databricks, Uber, Stripe) changing interview formats and notes tensions introduced by AI in assessment practices.

Mapping the Figma-to-Code AI Pipeline

This article (published 2026-07-03) explains the practical layers and limits of current AI-driven design-to-code workflows. It defines a four-layer stack—(1) a Model Context Protocol (MCP) “pipe” that lets an LLM read design files, (2) project markdown files that capture rules and memory, (3) reusable "skills" (small recipes/scripts) that enforce repeatable tasks, and (4) a mapping layer (e.g., Code Connect) that wires Figma components to real code components in a codebase. The author highlights where each layer helps and what gaps remain (e.g., MCP reads design structure but not existing code components; markdown/skills can drift; mapping requires engineering and ongoing maintenance). The article gives guidance on which layers teams of different sizes should adopt and stresses that AI shortens the distance from design to code but does not replace product engineering or design judgment.