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Topics Everyone Is Talking About No300
OpenAI Introduces Skills in ChatGPT and Codex CLI • The Stunning Symmetry of Abelian Sandpiles • The Checkerboard: A Legal Puzzle of Public Land Access • Ask HN: How Can I Get Better at Using AI for Coding? • Measuring the Science of Cognitive Fatigue
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Topics Everyone Is Talking About No299
Why Lightweight Code Still Matters on Modern Machines • My Python Workflow, December 2025 Edition • Rethinking Array Indices: Points Between Elements • Concrete Syntax Matters, Actually • Indexed Reverse Polish Notation: A Smarter Alternative to ASTs
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Introduction to GRASP design principles
GRASP (General Responsibility Assignment Software Patterns) defines how to distribute responsibilities across classes and objects for maintainable, scalable software. This article introduces the nine GRASP principles with real-world examples and modern framework applications for engineers in 2025.
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Tools: abc, dataclasses, strategy helpers
In modern Python, creating clean, extensible architectures often revolves around three foundational tools: abc for defining contracts, dataclasses for concise data modeling, and strategy helpers for dynamic behavior switching. This article explores how these tools integrate to produce elegant, maintainable, and scalable systems used by teams across industries.
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Expert: model capacity and overfitting trade-offs
Understanding model capacity and overfitting trade-offs is essential for designing high-performing, generalizable machine learning systems. This article explores how model capacity affects bias and variance, how overfitting manifests in deep models, and what strategies expert practitioners use to optimize complexity without sacrificing generalization.
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Best practices for ML API design
Designing APIs for machine learning systems requires combining software engineering rigor with data science insight. This article explores best practices for building scalable, maintainable, and reproducible ML APIs, covering versioning, schema management, performance, and lifecycle integration used by top tech companies.
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Introduction to stream processing concepts
Stream processing is at the core of real-time analytics and event-driven architectures. This article introduces stream processing concepts, explains key differences from batch processing, and highlights tools like Apache Kafka, Flink, and Materialize that enable continuous computation on live data streams.
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