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Topics Everyone Is Talking About No270
Long-term safety confirmed: 4-year mortality data after mRNA vaccination • AV1 wins an Emmy open video codec that transformed the web • LISP Style Design • PostgreSQLs 1600-column limit why more isnt always better
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Topics Everyone Is Talking About No268
Django 6 • We Gave 5 LLMs 100K to Trade Stocks for 8 Months • Lookup Table vs. Enum Type: Which Wins in PostgreSQL?
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Expert: real-time feature stores and ML stream inference
Real-time feature stores are redefining machine learning architectures by enabling continuous and consistent feature computation for streaming inference. This post dives deep into how these systems operate, their architecture, key tools, and emerging trends in operational ML engineering.
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Tools: FastAPI, Docker, BentoML
FastAPI, Docker, and BentoML together form a powerful, production-grade stack for deploying machine learning models. This post explores how each tool fits into the MLOps pipeline, how to integrate them efficiently, and which best practices high-performing teams are using in 2025 to deploy models at scale.
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Empirical: coupling and cohesion analysis
Coupling and cohesion are core indicators of software quality. This article empirically examines how to measure them in Python projects using modern static analysis tools, benchmark data, and continuous integration practices. It connects theory with data-driven insights from 2025 codebases.
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Introduction to technical teaching and mentorship
Technical teaching and mentorship are vital skills for modern engineers. This article introduces the fundamentals of mentoring, communicating complex ideas, and building structured learning paths in engineering environments. It offers practical methods and examples to develop others effectively in 2025 and beyond.
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