AI News & Research
1. The Claude Code Skill ecosystem is expanding rapidly. As of March 2026, the anthropics/skills repository reached over 87,000 stars on GitHub and more people are building and sharing Skills every week. How can we build a Skill from scratch in a structured way? This article walks through designing, building, and distributing a Skill from … Read more
hall, Tuesday morning. The professor uncaps a marker and writes across the whiteboard: P(A|B) = P(B|A) · P(A) / P(B). Your hand copies the formula. Your brain checks out somewhere around the vertical bar. If that memory just surfaced, you’re in good company. Research suggests up to 80% of college students experience some form of statistics anxiety. For … Read more
, most people knew about quantum physics, but it was mostly theory and maybe a little science fiction-sounding. But since IBM released Qiskit and opened access to its hardware, advances in quantum computing have continued to come. People from different disciplines and backgrounds could write code for quantum and run it on actual devices. Although … Read more
you are analyzing a small dataset: \[X = [15, 8, 13, 7, 7, 12, 15, 6, 8, 9]\] You want to calculate some summary statistics to get an idea of the distribution of this data, so you use numpy to calculate the mean and variance. import numpy as np X = [15, 8, 13, 7, … Read more
Image by Editor # Introduction Data pipelines in data science and machine learning projects are a very practical and versatile way to automate data processing workflows. But sometimes our code may add extra complexity to the core logic. Python decorators can overcome this common challenge. This article presents five useful and effective Python decorators to … Read more
everyone’s talking about AI apps, but no one really shows you what’s happening behind the curtain? Yeah… that was me a few weeks ago — staring at my screen, wondering if I’d ever actually build something that talked back. So, I decided to just dive in, figure it out, and share everything along the way. By the … Read more
: when I first started using Pandas, I wrote loops like this all the time: for i in range(len(df)): if df.loc[i, “sales”] > 1000: df.loc[i, “tier”] = “high” else: df.loc[i, “tier”] = “low” It worked. And I thought, “Hey, that’s fine, right?”Turns out… not so much. I didn’t realize it at the time, but loops … Read more
with Python, most people talk about Django and Flask. But there’s a newer, very speedy option that many Python programmers are starting to love: FastAPI. FastAPI is built on modern Python features, using standard type hints to provide automatic data validation, serialisation, and interactive API documentation for free. Picking the proper framework depends on your … Read more
Image by Editor # Introduction Python decorators are tailor-made solutions that are designed to help simplify complex software logic in a variety of applications, including LLM-based ones. Dealing with LLMs often involves coping with unpredictable, slow—and frequently expensive—third-party APIs, and decorators have a lot to offer for making this task cleaner by wrapping, for instance, … Read more
Image by Author # Introduction If you’ve been working with data in Python, you’ve almost certainly used pandas. It’s been the go-to library for data manipulation for over a decade. But recently, Polars has been gaining serious traction. Polars promises to be faster, more memory-efficient, and more intuitive than pandas. But is it worth learning? … Read more