AI & ML Research 8 Hours

Last updated: September 4, 2026, 2:27 PM ET

AI & ML Research

Default 50/50 splits in A/B tests are often suboptimal when variants carry different costs. Cost-based sampling weights can rebalance traffic toward cheaper treatments, improving statistical power per dollar spent. This approach is especially useful when a treatment is significantly more expensive than the control, as it avoids wasting budget on low-information trials.

GPU disaggregation—separating prefill and decode phases—only pays off at scale. Chunked prefill remains the better default below roughly a thousand GPUs, before three operational conditions make split serving viable. Understanding these thresholds is critical for hardware planning to avoid costly infrastructure mistakes.

Data Engineering

Microsoft's retirement of Power BI Premium has pushed developers onto Microsoft Fabric, a platform that changes capacity models and data warehousing defaults while retaining core DAX and Power Query workflows. The migration requires mapping old capacities to new compute tiers, with an emphasis on OneLake storage to unify lakehouse and warehouse paths. Existing skills stay relevant, but the mental model shifts.

AI in the Field

Drone footage from Ukraine has spawned a data marketplace where raw battlefield imagery is sold for AI training and reconnaissance models, raising ethical concerns around privacy and dual-use. Separately, AI systems are actively reshaping linguistic norms, as models increasingly dictate phrasing standards in enterprise and developer tools, shifting how we write and communicate.

Developer Workflow

Parallel coding agents like Claude Code demand significant local memory, but orchestrated remote sessions allow developers to run 10-plus concurrent agents using cloud instances with shared file systems—reducing hardware overhead and enabling low-cost scaling of agent-based development. This approach lets budget-conscious developers sidestep GPU purchases entirely.