Essential Python Libraries for Data Science
Author(s): Raj kumar Originally published on Towards AI. In Part 1, we focused on how data is represented, transformed, and computed using NumPy and Pandas. By the end of that part, the dataset was clean, structured, and numerically stable. In Part 2, …
Why Your AI Product Isn’t Software: The New Rules of Uncertainty, Evidence, and Economics
Author(s): Sasha Apartsin Originally published on Towards AI. 1. Introduction: The Broken Promise of Logic Traditional software development is built on a comforting promise: if you implement the logic correctly, the system will behave correctly. In this deterministic world, failures are “bugs” …
Introducing pydantic-ai-skills: Composable Agent Skills for the Pydantic AI Ecosystem
Author(s): Douglas Trajano Originally published on Towards AI. Give your AI agents superpowers — without bloating their context window. The Agentic AI landscape is evolving fast. We went from simple chatbots to autonomous systems that plan, reason, and execute multi-step workflows. But …
Cutting Batch Release from 14 Days to 3: A Case Study in Multi-Agent AI for Pharmaceutical Manufacturing
Author(s): Saif Ali Kheraj Originally published on Towards AI. How agentic AI architectures can compress decision cycles in regulated manufacturing with a practical implementation blueprint using CrewAI. Every contract pharmaceutical manufacturer faces the same operational bottleneck. After a medicine batch completes production …
OpenClaw Architecture Deep Dive: Building Production-Ready AI Agents from Scratch
Author(s): Know-Island Originally published on Towards AI. Dissecting the agent framework that hit 100K GitHub stars in a week — and had 400+ malicious plugins. Architecture patterns for building agents that actually work. OpenClaw went from zero to 100,000 GitHub stars in …
Context Engineering: The 6 Techniques That Actually Matter in 2026 ( A Comprehensive Guide )
Author(s): Divy Yadav Originally published on Towards AI. Prompt engineering is dead. Context engineering is how production systems work now. Your RAG system returns perfect chunks. Your prompt is beautifully crafted But The LLM still hallucinates. Photo by AuthorThe article discusses the …
101 ML/LLM/Agentic AIOPS Interview Questions.
Author(s): Niraj Kumar Originally published on Towards AI. Image by Author Section 1: Technical & Hands-On (ML/AI & MLOps) These questions test your foundational knowledge of MLOps, regardless of the cloud platform. 1. Describe the most complex ML project you’ve taken from …
Securing the Autonomous Frontier: A Guide to AI Identity
Author(s): Niraj Kumar Originally published on Towards AI. Imagine an AI agent, tasked with “optimizing cloud costs,” deciding that the most efficient path is to delete an underutilized production database. In the shift toward 2026, we’ve moved from simple chat interfaces to …
Engineering the Semantic Layer: Why LLMs Need “Data Shape,” Not Just “Data Schema
Author(s): Shreyash Shukla Originally published on Towards AI. Image Source: Google Gemini The “Context Window” Economy In the world of Large Language Models (LLMs), attention is a finite currency. While context windows are expanding, the “Lost in the Middle” phenomenon remains a …
The Ghost in Your Machine: Why OpenClaw’s ‘Local-First’ Autonomy Beats the Cloud Every Time
Author(s): Anurag Jain Originally published on Towards AI. How running AI locally gives you autonomy, economic efficiency, and real agency over your digital life. OpenClaw vs. Claude Code We are reaching the end of the honeymoon phase with the cloud’s black-box oracles. …
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