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Topics Everyone Is Talking About No363
OpenAIs Cash Burn Will Be One of the Big Bubble Questions of 2026 • Show HN: 22 GB of Hacker News in SQLite • Replacing python-dateutil to Remove Six • 7 Practical std::chrono Calendar Examples C20 • runST Does Not Prevent Resources from Escaping
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Topics Everyone Is Talking About No361
Learn Computer Graphics from Scratch and for Free • CEOs Are Hugely Expensive So Why Not Automate Them? • As AI Devours Chips, Device Prices Are Set to Climb • Parsing Advances Building Safer and Smarter Parsers • 2D Distance Functions The Art and Math of Graphics
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Topics Everyone Is Talking About No359
AI Slop Takes Over: Up to a Third of YouTubes Feed May Be Low-Quality AI Content • C Says: We Have Try… Finally at Home • An Ounce of Silver Is Now Worth More Than a Barrel of Oil • How Bad Code Examples Can Corrupt Large Language Models • Solving Hi-Q with AlphaZero and…
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Topics Everyone Is Talking About No358
How a Fathers Fitness Rewrites the Genetic Playbook • Inside a Modern Neural Recommender System Architecture • On LLMs in Programming
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Expert: idioms for clean API and operator overloading
This deep-dive explores idiomatic Python API design and operator overloading. Learn how to use dunder methods, delegation, and context management to craft expressive, maintainable APIs. Includes modern best practices, code samples, and design principles inspired by frameworks like NumPy, SQLAlchemy, and Pydantic.
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Using matplotlib/plotly for infographic-style outputs
Infographic-style data visualization is reshaping how engineers communicate insights. This guide explores modern techniques using Matplotlib and Plotly to create polished, data-rich visuals that blend scientific accuracy with design precision. Learn when to use each tool, best practices for layout and color, and how to integrate both in automated workflows.
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Best practices for feature importance ranking
Feature importance ranking is central to explainable machine learning. This guide explores modern post-2024 best practices, including model-based, permutation, and SHAP methods, with code examples and interpretability tips. Learn how leading teams integrate explainability into CI/CD workflows for reliable, transparent, and ethical AI.
