📰 Towards Data Science

Learn how to effectively present product ideas by building MVPs with coding agents The post How to Use Claude Code to Build a Minimum Viable Product appeared fi

A clear mental model and a practical foundation you can build on The post Grounding Your LLM: A Practical Guide to RAG for Enterprise Knowledge Bases appeared f

A practical system design combining open-source Bayesian MMM and GenAI for transparent, vendor independent marketing analytics insights. The post Democratizing

How a hybrid PyMuPDF + GPT-4 Vision pipeline replaced £8,000 in manual engineering effort, and why the latest models weren’t the answer The post From 4 Weeks to

How to optimize context, a precious finite resource for AI agents The post Context Engineering for AI Agents: A Deep Dive appeared first on Towards Data Science

Why does grand productivity promises never actually deliver? Is every product just bad, or is there something else hiding in the numbers? The post The Arithmeti

The geometric foundations you need to understand the dot product The post The Geometry Behind the Dot Product: Unit Vectors, Projections, and Intuition appeared

Learn how to apply coding agents in parallel to work more efficiently The post How to Run Claude Code Agents in Parallel appeared first on Towards Data Science

Why it doesn’t fit my workflow but still makes sense for beginners The post A Data Scientist’s Take on the $599 MacBook Neo appeared first on Towards Data Scien

Using modern tooling to identify defects earlier in the software lifecycle. The post Building a Python Workflow That Catches Bugs Before Production appeared fir

When we try to train a very deep neural network model, one issue that we might encounter is the vanishing gradient problem. This is essentially a problem where

Persistent AI memory without embeddings, Pinecone, or a PhD in similarity search. The post I Replaced Vector DBs with Google’s Memory Agent Pattern for my notes

The Vector View of Least Squares. The post Linear Regression Is Actually a Projection Problem (Part 2: From Projections to Predictions) appeared first on Toward

Workflows and encoding techniques in quantum machine learning The post How to Handle Classical Data in Quantum Models appeared first on Towards Data Science .

Run Quantum Experiments with Qiskit-Aer The post Quantum Simulations with Python appeared first on Towards Data Science .

A systems design diagnosis of hallucination, corrigibility, and the structural gap that scaling cannot close The post The Inversion Error: Why Safe AGI Requires

How I am adapting in my career in the age of AI, automation, and when everything moving faster than expected. The post What Happens Now That AI is the First Ana

Learn why embedding models are like a GPS for meaning. Instead of searching for exact words, it navigates a "Map of Ideas" to find concepts that share the same

Make your coding agent more efficient The post How to Make Claude Code Better at One-Shotting Implementations appeared first on Towards Data Science .

I’ve been so surprised by how fast individual builders can now ship real and useful prototypes. Tools like Claude Code, Google AntiGravity, and the growing ecos

What I learned about data wrangling, segmentation, and storytelling while building an application security report from scratch The post Turning 127 Million Data

What is p hacking, is it bad, and can you get ai to do it for you? The post How to Lie with Statistics with your Robot Best Friend appeared first on Towards Dat

Sara A. Metwalli on the rise of a promising new technology, the effects of LLM on her work, and more. The post Why Data Scientists Should Care About Quantum Com

SHAP needs 30 ms to explain a fraud prediction. That explanation is stochastic, runs after the decision, and requires a background dataset you have to maintain