I’ve scraped websites for data, for price monitoring, for lead generation, and honestly just because I was curious what a page looked like parsed into JSON. Python is still the best language for it, and I’ve settled on a handful of patterns that I reuse everywhere. Here are
I keep seeing people write try/finally blocks for file handles, database connections, and locks. Every. Single. Time. Python has context managers for this, and they are not complicated ( honestly, they are the cleanest abstraction in the language ). If you have ever used with open('file.txt') as
I've spent the better part of a decade staring at broken code, confused logs, and systems that worked yesterday but don't today. After a while, you stop throwing darts and start developing actual mental models — patterns of thinking that consistently get you closer to the bug.
For years, my relationship with regular expressions went something like this: encounter a regex I needed, Google it, copy it from Stack Overflow, test it once, and move on. If it broke, I'd Google a new one. I treated regex like incantations — mysterious symbols that somehow worked, as
I have a problem. Every time I come back from shooting — whether it's the coast at La Paloma, the streets of Montevideo, or drone footage over the countryside — I end up with hundreds of photos across two cameras and an SD card that desperately needs formatting. For years,
I first played with GPT-2 back in 2019, when OpenAI released the 1558M parameter model and the whole NLP world lost its mind. I ran it in a Colab notebook, typed in a prompt about Montevideo, and watched it hallucinate streets that don't exist. It was messy, it
Every production system eventually faces the same uncomfortable truth: things fail. Networks partition, services crash, databases timeout, and disk space runs out at 3 AM. The difference between a system that survives these events and one that doesn't comes down to one word: resilience. Why Resilience Matters Imagine
Meta Description: Discover how to get started with data science using Python. Learn key concepts, essential libraries, practical tips, and real code examples to begin your journey into data-driven insights. Data science is revolutionizing industries by turning raw data into actionable insights. At the heart of this transformation lies Python,
When building a web application or API with Python, choosing the right framework is crucial. Django, Flask, and FastAPI are three of the most popular Python frameworks, each offering unique advantages depending on the project’s needs. In this article, we’ll compare these three frameworks based on various factors
FastAPI is a modern, fast (high-performance) web framework for building APIs with Python. It is designed to be easy to use while providing the power of asynchronous programming and automatic validation. Built with Python type hints, FastAPI enables developers to create robust APIs quickly and efficiently. Why Choose FastAPI? 1.