Uber

Uber Pauses Most 2026 European Launches Amid Delivery Hero Bid

Uber has halted most of its planned 2026 expansion of food-delivery services in Europe, pausing entries in five of seven targeted countries, according to reporting that cites the Financial Times and Reuters. The company announced the original Uber Eats expansion plans in mid-February and had been preparing market launches in Austria, Norway, Greece, Denmark, Finland, the Czech Republic and Romania; it now views its Denmark and Finland launches as successful but will not proceed in several other markets. The strategic pause is reported to be linked to Uber’s ongoing takeover effort for German rival Delivery Hero: Delivery Hero disclosed a €33 per-share offer and Uber has raised its stake in Delivery Hero from about 25% to nearly 37% after buying shares from investor Aspex Management. The story was reported by manager magazin, citing the Financial Times and Reuters.

Agentic AI Costs Burn Budgets; Routing Cuts 74%

The article documents a fast-emerging cost crisis from "agentic" AI pipelines where single user requests translate into many LLM calls, growing context windows, and unexpectedly large bills — citing a Hacker News report that Uber exhausted its 2026 AI budget by April. It cites Forrester survey data that 22% of agent deployments report negative ROI driven by infrastructure spend. The author describes a practical multi-model routing pattern and token-optimization techniques (context trimming, structured outputs, delegation to cheaper models, response caching) that cut their pipeline costs by 74%. Code snippets and a minimal cost dashboard / budget-alerting pattern are provided. The piece also compares per-token pricing (Opus 4.7, GPT-5.5) and argues routing by task complexity and provider efficiency is critical to control agentic AI spend at scale. Publication date: 2026-07-04.

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.