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Topics Everyone Is Talking About No350
Rob Pike Goes Nuclear over GenAI • TurboDiffusion: 100200 Acceleration for Video Diffusion Models • High Schooler Discovers 1.5M New Astronomical Objects with AI • Ancient Greek Geometry
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Topics Everyone Is Talking About No349
NVIDIA Open-Sources CUDA Tile for MLIR-Based GPU Optimization • MiniMax M2.1 Empowers AI Agents for Complex Real-World Tasks • Building an NES Emulator in Haskell: Functional Meets Retro
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Topics Everyone Is Talking About No348
Maybe the Default Settings Are Too High • Automating What Backblaze Lifecycle Rules Dont Do Instantly • Microsoft Finally Retires the RC4 Encryption Algorithm
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Intro to scaling ML inference
Scaling machine learning inference efficiently is as critical as training a good model. As models grow larger and more complex, the challenge shifts from accuracy to throughput, latency, and cost optimization. This post introduces practical strategies, architectures, and tools used in 2025 to scale ML inference across CPUs, GPUs, and distributed environments.
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Best practices: reactive and event-driven observer systems
Learn how to design robust, event-driven observer systems using reactive principles. This guide covers best practices for architecture, error handling, observability, and performance optimization, with examples in Python, JavaScript, and modern reactive frameworks.
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Tools: Apache Beam, Flink, Dataflow
Apache Beam, Apache Flink, and Google Cloud Dataflow form the backbone of modern data processing. This article compares their architectures, use cases, and integration best practices for high-scale batch and streaming workloads in 2025.
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Expert: Bayesian regularization and priors
Bayesian regularization introduces principled uncertainty into machine learning models through probabilistic priors. By combining prior knowledge with observed data, Bayesian methods balance overfitting and generalization more effectively than traditional penalties. This deep dive explores the mathematical foundations, regularization mechanisms, and implementation of Bayesian priors in modern ML.
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