📌 How to Make Claude Code Improve from its Own Mistakes

🗂 Category: AGENTIC AI

🕒 Date: 2026-03-24 | ⏱️ Read time: 7 min read

Supercharge Claude Code with continual learning

#DataScience #AI #Python

📌 From Dashboards to Decisions: Rethinking Data & Analytics in the Age of AI

🗂 Category: DATA SCIENCE

🕒 Date: 2026-03-24 | ⏱️ Read time: 7 min read

How AI agents, data foundations, and human-centered analytics are reshaping the future of decision-making

#DataScience #AI #Python

📌 Production-Ready LLM Agents: A Comprehensive Framework for Offline Evaluation

🗂 Category: AGENTIC AI

🕒 Date: 2026-03-24 | ⏱️ Read time: 18 min read

We’ve become remarkably good at building sophisticated agent systems, but we haven’t developed the same…

#DataScience #AI #Python

📌 The Complete Guide to AI Implementation for Chief Data & AI Officers in 2026

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2026-03-24 | ⏱️ Read time: 29 min read

How to leverage a framework to effectively prioritize AI Initiatives to rapidly accelerate growth and…

#DataScience #AI #Python

📌 Following Up on Like-for-Like for Stores: Handling PY

🗂 Category: DATA ANALYSIS

🕒 Date: 2026-03-25 | ⏱️ Read time: 7 min read

My last article was about implementing Like-for-Like (L4L) for Stores. After discussing my solution with…

#DataScience #AI #Python

📌 The Machine Learning Lessons I’ve Learned This Month

🗂 Category: MACHINE LEARNING

🕒 Date: 2026-03-25 | ⏱️ Read time: 5 min read

Proactivity, blocking, and planning

#DataScience #AI #Python

📌 Building Human-In-The-Loop Agentic Workflows

🗂 Category: AGENTIC AI

🕒 Date: 2026-03-25 | ⏱️ Read time: 10 min read

Understanding how to set up human-in-the-loop (HITL) agentic workflows in LangGraph

#DataScience #AI #Python

📌 My Models Failed. That’s How I Became a Better Data Scientist.

🗂 Category: DATA SCIENCE

🕒 Date: 2026-03-25 | ⏱️ Read time: 9 min read

Data Leakage, Real-World Models, and the Path to Production AI in Healthcare

#DataScience #AI #Python

📌 How to Make Your AI App Faster and More Interactive with Response Streaming

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2026-03-26 | ⏱️ Read time: 8 min read

In my latest posts, we’ve talked a lot about prompt caching as well as caching…

#DataScience #AI #Python

📌 Beyond Code Generation: AI for the Full Data Science Workflow

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2026-03-26 | ⏱️ Read time: 10 min read

Using Codex and MCP to connect Google Drive, GitHub, BigQuery, and analysis in one real workflow

#DataScience #AI #Python

📌 What the Bits-over-Random Metric Changed in How I Think About RAG and Agents

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2026-03-26 | ⏱️ Read time: 19 min read

Why retrieval that looks excellent on paper can still behave like noise in real RAG…

#DataScience #AI #Python

Classical filters & convolution: The heart of computer vision

Before Deep Learning exploded onto the scene, traditional computer vision centered on filters. Filters were small, hand-engineered matrices that you convolved with an image to detect specific features like edges, corners, or textures. In this article, we will dive into the details of classical filters and convolution operation - how they work, why they matter, and how to implement them.

More: https://www.vizuaranewsletter.com/p/classical-filters-and-convolution

📌 Building a Production-Grade Multi-Node Training Pipeline with PyTorch DDP

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2026-03-27 | ⏱️ Read time: 14 min read

A practical, code-driven guide to scaling deep learning across machines — from NCCL process groups…

#DataScience #AI #Python

📌 A Beginner’s Guide to Quantum Computing with Python

🗂 Category: QUANTUM COMPUTING

🕒 Date: 2026-03-27 | ⏱️ Read time: 7 min read

Simulate a quantum computer with Qiskit

#DataScience #AI #Python

📌 How ElevenLabs Voice AI Is Replacing Screens in Warehouse and Manufacturing Operations

🗂 Category: DATA SCIENCE

🕒 Date: 2026-03-27 | ⏱️ Read time: 10 min read

A warehouse picking operation is the process of collecting items from storage locations to fulfil…

#DataScience #AI #Python

📌 From NetCDF to Insights: A Practical Pipeline for City-Level Climate Risk Analysis

🗂 Category: CLIMATE CHANGE

🕒 Date: 2026-03-28 | ⏱️ Read time: 7 min read

Integrating CMIP6 projections, ERA5 reanalysis, and impact models into a lightweight, interpretable workflow

#DataScience #AI #Python