Seizing the Moment: The Hidden Silhouette of Data | Towards Data Science
Towards Data Science - Mediumby Aniruddha Karajgi·23d ago
Section 0: Abstract If you’ve spent any time in statistics or machine learning, you’ve met the usual suspects: the mean and the variance.
How Many Labeled Examples Does a Text Classifier Actually Need? I Measured It. | Towards Data Science
Towards Data Science - Mediumby Himanshu Sharma·23d ago
The question teams skip "Just use an LLM for it" has become the default answer to almost any text classification problem — routing support tickets, tagging feedback, sorting incoming requests.
Your Model’s MSE Is Lying to You | Towards Data Science
Towards Data Science - Mediumby Waleed Esmail·23d ago
Two models, one number, opposite realities Imagine you have a sensor recording something you care about, for example seismic background at a detector site, electrical load on a grid, or strain in a...
When to Use One Model and When to Use a Team of Agents | Towards Data Science
Towards Data Science - Mediumby Naveen Goel·24d ago
On a recent AI capacity buildout, I scheduled a control plane node into an early cutover wave because the 2 week traffic snapshot I had of it looked effectively isolated. It was not.
Graph Engineering for AI Agents: From Prompts and Loops to Workflows | Towards Data Science
Towards Data Science - Mediumby Nhu Hoang·24d ago
1. Introduction: From a viral X fight to a graph you can build by hand this week In mid-2026, Peter Steinberger — the founder of OpenClaw — posted a twelve-word question on X: "Are we still talking...
From Static to Dynamic Skills: A Different Model for Agent Knowledge | Towards Data Science
Towards Data Science - Mediumby Tomer Mesika·24d ago
Most agent skills today are static. Someone reads the truth once, writes it into a markdown file, and ships it.
Your Model Isn't Done Until Someone Else Can Call It | Towards Data Science
Towards Data Science - Mediumby Ibrahim Salami·25d ago
To give you context, I got curious about what goes into building and deploying machine learning models. So, rather than learn theory.
Your AI Adoption Lift Is a Selection Effect | Towards Data Science
Towards Data Science - Mediumby William Gieng·25d ago
Somewhere in your company there is a slide that says something like this: customers who enabled the AI assistant retain 15 points better than customers who did not. It has a bar chart.
One Capital Letter Was Silently Breaking My AI Support Bot, and It Wasn't in the New Model | Towards Data Science
Towards Data Science - Mediumby Abdullahi Dattijo·26d ago
Picture a support inbox for a bank. Every message that comes in needs to be sorted into a category, a lost card, a refund request, a wrong charge, and sent to the right team.
Software Design in the Age of AI | Towards Data Science
Towards Data Science - Mediumby Devesh Rajadhyax·27d ago
Introduction AI can now write significant amounts of software code, including increasingly large and complex systems.
The 95% Illusion: Why Your Confidence Interval Isn't What You Think It Is | Towards Data Science
Towards Data Science - Mediumby Ananya Bhattacharyya·27d ago
A product analyst presents an A/B test dashboard. The treatment variant’s conversion rate improved by 0.4 percentage points, and the 95% confidence interval for the lift excludes zero.
Demystifying Anthropic's J-Space: A Mathematical Primer | Towards Data Science
Towards Data Science - Mediumby Pirmin Lemberger·27d ago
In Verbalizable Representations Form a Global Workspace in Language Models, Anthropic researchers introduce the J-space — an LLM analogue (demonstrated on Claude Opus 4.6) to the global workspace...
How to 5x Your Communication Effectiveness with Claude Code | Towards Data Science
Towards Data Science - Mediumby Eivind Kjosbakken·28d ago
A substantial issue I've noticed when interacting with Claude Code, Codex, or any other coding agent is that I sometimes find it hard to read all of the information they present inline in the...
Optimizing LLM Inference Costs in Multi-Agent Systems with Adaptive Model Routing | Towards Data Science
Towards Data Science - Mediumby Partha Sarkar·28d ago
An Adaptive Model Router in front of your LLM pipeline can cut inference costs by up to 90%, without updating any of your agent logic.
Who Questions What Works: When Should We Retest Our Assumptions? | Towards Data Science
Towards Data Science - Mediumby Erika Gomes-Gonçalves·28d ago
“What giants?” asked Sancho Panza. “Those you see over there,” replied his master, “with the long arms; sometimes they are almost two leagues long.” “Look, your grace,” Sancho responded, “those...
Getting started with dbt | Towards Data Science
Towards Data Science - Mediumby Thomas Reid·29d ago
As a contract data engineer, I sometimes experience — ahem — let's just say, periods of inactivity.
The Symmetry That Breaks Neural Network Averaging | Towards Data Science
Towards Data Science - Mediumby Ananya Bhattacharyya·29d ago
If you have ever tried neural network weight averaging by training the same neural network architecture twice on the same data, changing nothing but the random seed, you have probably assumed the...
When One Process Becomes Too Much: Splitting a Pipeline into MCP Services | Towards Data Science
Towards Data Science - Mediumby Priyansh Bhardwaj·29d ago
The usual way to build a multi-part pipeline is to put every part in the same process and call between them with function calls.
10 Statistical Traps We Often Overlook | Towards Data Science
Towards Data Science - Mediumby Sara A. Metwalli·29d ago
We have all been there, sitting in a college class where a professor is writing down a collection of formulas on the board, asking us to memorize it “because that is what is going to be on the...
How to Maximize GPT-6 Astra | Towards Data Science
Towards Data Science - Mediumby Eivind Kjosbakken·8 Sept 2026
GPT-6 Astra was recently released. I got access to it on Friday evening, European time, and have been using it extensively ever since.
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