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Tools: Evidently AI, WhyLabs
Evidently AI and WhyLabs are two leading tools shaping how teams monitor data drift and model health in production ML systems. This post explores their architectures, features, integrations, and best practices for using them together in modern data observability workflows.
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Best practices: scalable architectures for data systems
Scalable data architectures in 2025 demand more than adding servers—they require modularity, elasticity, and deep observability. This guide breaks down modern best practices, from event-driven designs and data mesh to storage-compute decoupling and AI-native architectures, with real-world case studies from Netflix, Uber, and Spotify.
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Topics Everyone Is Talking About No321
Jonathan Blows Decade-Long Journey to Craft 1,400 Intricate Puzzles • Irans Vanishing Water: How Mismanagement Drained an Ancient System • How Getting Richer Made Teenagers Less Free • Microsoft Retires IntelliCode to Push Developers Toward Paid Copilot • lightning-extra: PyTorch Lightning Plugins for Cloud-Native ML
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Topics Everyone Is Talking About No320
Secure Local Configuration for Kakoune • Ringspace: Rebuilding the Human Web • I Got Hacked: My Hetzner Server Started Mining Monero • Ask HN: Side Projects Making 500Month in 2025 • TOML 1.1.0 Released
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Topics Everyone Is Talking About No319
Genuary 2026: Creative Coding Prompts Unleashed • Selective Applicative Functors: Bridging Theory and Computation • Gut Bacteria from Reptiles Show Complete Tumor Elimination in Mice • A16z-Backed AI Startup Hacked, Exposing Synthetic Influencer Operations
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Empirical: algorithm benchmarks
Algorithm benchmarking defines the empirical backbone of modern computing. This article explores how high-performance teams measure, compare, and optimize algorithmic efficiency across CPUs, GPUs, and distributed systems — covering reproducibility, statistical rigor, and the tools that make empirical benchmarking a science rather than an art.
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Expert: interactive pipelines and parametrized runs
Interactive and parametrized pipelines are redefining workflow engineering in 2025. This article dives deep into dynamic configuration, runtime interactivity, and expert design strategies that allow modern data and ML pipelines to adapt, experiment, and respond in real time — with examples in Python using Dagster, Prefect, and other leading tools.
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