The web development world is huge, and an LLM can write the boilerplate for any of it. What it cannot do for you is choose. This post surveys the popular options, breaks any use case into twelve functionalities, and ends with a skill file you can hand to your own assistant.
Almost every analysis I have worked on ends in the same small moment: someone needs to see it. A chart in a notebook is often enough to convince me, but it rarely convinces a colleague or stakeholder who is never going to open that notebook. A small web app can. It lets them move a slider, filter to their own region, or type in a value and watch the model’s answer change, which feels very different from looking at a screenshot.
Sequence alignment on Rosalind looks like thirteen unrelated puzzles, until you notice most of them are the same dynamic programming recurrence wearing different clothes. This post traces how one DP engine, parameterized by a scoring function, a gap model, and four free-start/free-end flags, grows from counting point mutations into global, local, overlap, semiglobal, affine-gap, and multiple alignment.
A reusable OOP framework for optimization problems: Config, Problem, Builder, SolverStrategy, and Report as five composable layers, worked through two use cases, the Berth Allocation Problem and Citi Bike station rebalancing, each solved with a greedy heuristic and an exact MIP.
I’ve been deep in an optimization problem for work recently, formulating it as a mixed-integer program and building a few different ways to solve it. I won’t get into what the problem actually is here, that part is specific to my job, but the experience behind this post is not.
A practitioner’s reference built from two Udemy courses by Tom Phillips, one on GitHub Copilot Chat and one on Claude Code. Rather than treating them as two unrelated tools, this post maps both onto five underlying mechanisms, then preserves the full working reference for each, and closes with a combined workflow for using either one on a team.
I went through Tom Phillips’s two Udemy courses back to back, one on GitHub Copilot Chat, one on Claude Code, mostly because I wanted a working reference I could hand to my own team rather than a vague sense of “yeah, AI coding tools are good now.” I’ve been documenting both in our wiki as I go, and the more notes I took, the more obvious it became that the two tools aren’t really as different as their separate marketing pages suggest.
A ground-up walk through the mathematics of linear regression — least squares, variance partitioning, significance testing, maximum likelihood, and Bayesian regression — built from James V. Stone’s “Linear Regression with Python” and reframed through the lens of a working data scientist.
Linear regression is usually the first model anyone learns, and often the last one anyone really thinks about. It’s easy to fit a line with one call to sklearn and move on. But every time I’ve had to explain why a fitted line should be trusted to myself — I’ve realised how much of the intuition underneath gets skipped.