Authors: Ahsaas Bajaj and Benjamin S Knight ? We ran 134,400 simulations grounded in real production ML models to find out. The answer depends on what you’re optimizing for, and on a single diagnostic you can compute before fitting a model. If you’ve ever trained a linear model in scikit-learn, you’ve faced this question: RidgeCV, …
Which Regularizer Should You Actually Use? Lessons from 134,400 Simulations Read More »
Image by Editor # Introduction A harsh truth to begin with: textbook data science usually becomes a lie in the real world. Concepts and techniques are taught on finely curated, beautifully bell-curved data variables, but as soon as we venture into the wild of real projects, we are hit with lots of outliers, unduly skewed …
The “Robust” Data Scientist: Winning with Messy Data and Pingouin Read More »
In my first TDS post, I wrote about how to translate a real-world problem into an integer linear program. In my second, I wrote about how to make that program robust against uncertainty. Both were variations on the same idea: take a fuzzy real-world question, squeeze it into an LP, and let a solver do the rest. …
Image by Editor # The Self-Hosted LLM Problem(s) “Run your own large language model (LLM)” is the “just start your own business” of 2026. Sounds like a dream: no API costs, no data leaving your servers, full control over the model. Then you actually do it, and reality starts showing up uninvited. The GPU runs …
Self-Hosted LLMs in the Real World: Limits, Workarounds, and Hard Lessons Read More »
in a situation where you have plenty of ideas on how to improve your product, but no time to test them all? I bet you have. What if I told you that you no longer have to do it all on your own, you can delegate it to AI. It can run dozens (or even …
Image by Author # Introduction Building large language model (LLM) applications is very different from using consumer-facing tools like Claude Code, ChatGPT, or Codex. Those products are great for end users, but when you want to build your own LLM system, you need a lot more control over how everything works behind the scenes. That …
10 Python Libraries for Building LLM Applications Read More »
screening system checks a name against a watchlist, it faces a silent failure mode that nobody talks about. Type “Владимир Путин” into a system indexed on “Vladimir Putin” and most name-matching approaches return nothing. The two strings share zero characters, so edit distance is meaningless, phonetic codes fail (they assume Latin), and BM25 gives up …
Bytes Speak All Languages: Cross-Script Name Retrieval via Contrastive Learning Read More »
Image by Author # Introduction OpenClaw is quickly becoming one of the most talked-about open-source agent systems right now. But beyond the hype, the real question is simple: what are people actually using it for? At its core, OpenClaw helps turn AI from something you chat with into something that can actually do work for …
series about Reinforcement Learning (RL), following Sutton and Barto’s famous book “Reinforcement Learning” [1]. In the previous posts we finished dissecting Part I of said book, which introduces fundamental solution techniques which form the basis for many RL methods. These are: Dynamic Programming (DP), Monte Carlo methods (MC) and Temporal Difference Learning (TD). What separates …
Introduction to Approximate Solution Methods for Reinforcement Learning Read More »
Image by Editor # Introduction If you want to learn agent engineering by doing instead of just reading about it, the best way is still to fork real repos, run them locally, and change them for your own use. This is where the real learning happens. I’ve hand-picked the best 10 ones, the projects that …
AI Engineering Hub Breakdown: 10 Agentic Projects You Can Fork Today Read More »
