Image by Editor   #  Introduction   Data has become an easier commodity to store in the current digital era. With the advantage of having abundant data for business, analyzing data to help companies gain insight has become more critical than ever. In most businesses, data is stored within a structured database, and SQL is…

Image by Author   #  Introduction   If you’ve used LLMs for different tasks, you’ve probably noticed that the response often depends on how you write the prompt. This is what we call prompt engineering. The way you give instructions can be the difference between a vague reply and a precise, actionable answer. I know…

Image by Editor   #  Introducing ChatGPT Study Mode   Among the unending supply of AI-powered tools and features of late, ChatGPT Study Mode has captured the attention of students, educators, and lifelong learners. It promises to revolutionize study habits with personalized learning, interactive exercises, and on-demand explanations. Yet, as with any new technology, the…

Image property of Marvel Comics   #  Introduction   If you've ever tried to assemble a team of algorithms that can handle messy real world data, then you already know: no single hero saves the day. You need claws, caution, calm beams of logic, a storm or two, and occasionally a mind powerful enough to…

Image by Editor | ChatGPT   #  Introduction   Hallucinations — the bane of the language model (LM) and its users — are the plausible-sounding but factually incorrect statements produced by LMs. These hallucinations are problematic because they can erode user trust, propagate misinformation, and mislead downstream decisions even when the output is expressed with…

Image by Author | Ideogram   #  Introduction   You know the basics of Python's standard library. You’ve probably used functions like zip() and groupby() to handle everyday tasks without fuss. But here's what most developers miss: these same functions can solve surprisingly “uncommon” problems in ways you've probably never considered. This article explains some…

Image by Author | Ideogram   #  Introduction   You just pushed your Python app to production, and suddenly everything breaks. The app worked perfectly on your laptop, passed all tests in CI, but now it's throwing mysterious import errors in production. Sound familiar? Or maybe you're onboarding a new developer who spends three days…

Image by Author | Canva   #  Introduction   Writing classes in Python can get repetitive really fast. You’ve probably had moments where you’re defining an __init__ method, a __repr__ method, maybe even __eq__, just to make your class usable — and you're like, "Why am I writing the same boilerplate again and again?" That’s…

Image by Author   Data science projects are notorious for their complex dependencies, version conflicts, and "it works on my machine" problems. One day your model runs perfectly on your local setup, and the next day a colleague can't reproduce your results because they have different Python versions, missing libraries, or incompatible system configurations. This…

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