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Introduction to benchmarking in Python
Benchmarking is one of the most valuable skills for Python developers aiming to write efficient and scalable code. This post introduces the fundamentals of benchmarking in Python, from basic timing techniques to powerful libraries like timeit and pytest-benchmark.
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Best practices for consistent style with PEP8
Consistent code style is not just about aesthetics — it is about clarity, maintainability, and collaboration. This post explores the key principles and best practices for adhering to Python’s PEP8 standard, along with tools like Black, Flake8, and Ruff for automation and enforcement.
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Expert: event-driven orchestration with EventBridge and Step Functions
In complex distributed architectures, orchestrating event-driven workflows reliably is a core challenge. This article explores how AWS EventBridge and Step Functions combine to deliver powerful, maintainable, and scalable event-driven orchestration.
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Tools: SHAP, LIME, InterpretML
Model interpretability is essential for building trust in machine learning. This post explores three leading interpretability tools — SHAP, LIME, and InterpretML — and how they help engineers and data scientists understand, debug, and explain complex models in 2025 and beyond.
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Topics Everyone Is Talking About No309
Why You Should Avoid UUIDv4 Primary Keys • U.S. Farmers Link Parkinsons to Toxic Pesticide • Will Turso Be the Better SQLite? • Virtualizing NVIDIA HGX B200 GPUs with Open Source Tools
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Topics Everyone Is Talking About No306
AI and the Ironies of Automation Part 2 • 2002: Last.fm and Audioscrobbler Pioneered the Social Web • Kimi K2 1T Model Runs on Dual 512GB M3 Ultras • TOON: Token-Oriented Object Notation for AI Data • Jubilant: Python Subprocess Meets Go Codegen
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Expert: designing ethical governance frameworks for AI
Artificial intelligence has moved from research labs into core business infrastructure, but governance has not kept pace. Designing ethical governance frameworks for AI requires blending technical understanding with organizational accountability, ensuring systems remain transparent, fair, and controllable. This post dives deep into the engineering, policy, and design principles behind AI governance in 2025 and beyond.
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