Article URL: https://twitter.com/claudeai/status/2072402636813607381 Comments URL: https://news.ycombinator.com/item?id=48752030 Points: 21 # Comments: 0
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Effective Power BI reporting relies not just on visuals but on a well-structured data model built from fact tables, dimension tables, and defined relationships. Fact tables store measurable business transactions and link to dimension tables that provide descriptive context such as product names, customer details, and dates. Schemas define how these tables are arranged, with the star schema being the most recommended design in Power BI due to its simplicity and performance benefits. Proper data modeling in Power BI's Model View ensures that visuals interact correctly, DAX calculations remain accurate, and dashboards stay manageable as data grows. Choosing the right schema and cardinality settings is therefore a foundational step before building any report or dashboard.
NVIDIA has unveiled a liquid-cooling design for AI data centers that it claims can eliminate virtually all on-site water consumption used for chip cooling. The system circulates warm coolant in a sealed loop directly against chips, rejecting heat to outside air via radiators rather than relying on water-evaporating methods. NVIDIA says the approach could also reduce energy use by allowing power-hungry chillers to be turned off for much of the year in suitable climates. Critics, including reports from TechCrunch and Fortune, note that the design only addresses water used inside the facility and does not account for water consumed by power plants generating the electricity that runs these data centers. The announcement, published on NVIDIA's blog on June 25, 2026, comes as AI's environmental footprint faces growing scrutiny from regulators and local communities.
In Apple's Grand Central Dispatch, a DispatchWorkItem can be cancelled via cancel(), but the mechanism is cooperative rather than preemptive. If cancel() is called before the work item begins executing, GCD checks the cancellation flag just prior to running the block and skips it entirely. If cancel() is called after the block has already started, it only sets isCancelled to true without interrupting the running code. The block itself must periodically check isCancelled and choose to exit early; otherwise it will run to completion regardless. Additionally, cancel() has no effect on the behavior of wait() or notify(), and a cancelled-but-running item will still block wait() until the block finishes on its own.
Managing AI spend limits for users is far more complex than it appears, involving edge cases around overage-only caps, promo credits, billing periods, and unit mapping. A common mistake is capping total spend rather than overage spend, which can block users from accessing features already included in their plan. Credit grants add another layer of ambiguity: whether credit-covered usage counts toward a cap depends on whether the cap is meant to protect margins or shield users from surprise bills. The open-source project Limitr addresses these issues by letting developers define spend caps with explicit flags for overage-only tracking and credit grant exclusions. A managed cloud version extends this with real-time alerting, billing integrations, and analytics built on the same engine.
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