The AI revolution is in full swing, promising a future transformed by intelligent machines. Every day, headlines tout new breakthroughs, and job boards overflow with “AI Specialist” roles. Yet, amidst this whirlwind of excitement and opportunity, a crucial question arises for aspiring professionals: how do you cut through the deafening hype to build a truly valuable, sustainable career in Large Language Models (LLMs)? If you’re ready to look beyond the buzzwords and commit to the real work, this guide is your starting point.
(more…)Pydantic Faker: How I Supercharged Test Data Generation and Mock API Creation with Python
Hello everyone! Today, I want to share the story behind my open-source project: pydantic-faker. Like many Python developers, I frequently use Pydantic for defining data models. It’s an incredibly convenient tool that brings type safety and validation to our projects. However, when it comes to testing, writing documentation, or prototyping a frontend, a common challenge arises: where do we get realistic data that conforms to these very Pydantic schemas?
(more…)Python Logging and f-strings: The Performance Trap You Might Have Forgotten
While Python’s f-strings offer convenient syntax, using them directly within logging calls (e.g., logger.debug(f”Processing {data}”)) hides a potential performance trap. This article explores how f-strings force immediate string formatting, consuming resources even if the log message’s level (like DEBUG) is disabled and the message is ultimately discarded. It contrasts this with the standard logging module’s optimized approach using %-style formatting (logger.debug(“Processing %s”, data)), which employs “lazy formatting” – deferring the actual formatting cost until after checking if the message needs to be emitted. Rediscover why this often-overlooked detail is crucial for efficiency, especially in high-volume or low-level logging scenarios, and how sticking to the recommended practice can significantly improve your application’s performance.
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