Welcome to M5BMachine 5-Minute Briefing

Learn how to use Marimo for interactive data analysis with Python, Pandas, and Altair, and turn a reactive notebook into a simple shareable dashboard.

Get work done, and have seconds added back to your life since you won't be writing second, third or — *gasp!* — fourth line follow-ups to your impeccable typed line numero uno.

Learn how to use Julius AI to analyze Excel data, verify key findings, and turn them into an editable PowerPoint presentation.

Here's how each got to where it is, a real use case and working code for both, and a side-by-side on the numbers that actually matter.

When Numba code disappoints, it's nearly never the compiler, and usually ends up being the boundary around the compiled code: not crossing it, not making it wide enough, or crossing it during every run.

We finish off our short series on SLM optimization with the third entry, focused on batching by length instead of looping item by item.

Leveling up rarely means new syntax. It means learning what the language already promised you.

KDnuggets team member Shittu Olumide tested out DeepSeek Harness. Here's what he found.

Six habits that keep a notebook runnable after you close the laptop.

Learn about private AI browsing for data professionals.

Learn how to build a Python AI agent with the OpenAI Agents SDK, using tool calling and function tools to automate multi-step workflows.

Almost every slow Polars script lacks in terms of one of these two: its expression engine written and executing in Rust across every core at its disposal, and its query optimizer that rewrites your work before any of it runs.

In this second article in our short series on SLM optimization techniques we focus on the reuse of the prompt prefix with a key-value cache.

Learn how to build effective evals for AI agents, from designing clear tasks and choosing the right graders to building reliable eval harnesses and tracking changes over time.

Senior Python practice, watched up close, is mostly surprise reduction. These seven habits surface the surprises before production does.

DeepSeek-V4.1-Flash shows how Causal Encoder-Decoder architecture, MoE, KV cache compression, CSA2, cheaper prefill, and efficient decoding can make powerful open-source AI models far more efficient to run.

Learn how to refactor messy Python code into clean, maintainable functions.

If you’re paying for ChatGPT, Claude, and another AI tool simultaneously, this review is for you. It covers what an AI platform like Abacus AI actually includes, how the credit system works in practice, and whether it genuinely replaces your current stack or just adds to it.

FDEs are becoming some of the most in-demand engineers in AI. Here’s the 7-step roadmap to becoming one in 2026.

The review pass fixed a row count and approved two wrong conclusions.

A clean run proves the process executed. It says nothing about what the pipeline learned, from which rows, in what state, or whether the saved result can be trusted anywhere else.

It's about the mistakes that make a running program wrong. Below are seven of them. For each one you get the hidden cause, plus the first thing worth checking.

Hey, Google Engineers: What prompt do you personally refuse to work without, and why?

If you're responsible for turning data and AI strategy into enterprise reality, this is a conversation worth being part of.