Artificial Intelligence

Author(s): Towards AI Editorial Team Originally published on Towards AI. Good morning, AI enthusiasts! Your next AI system is probably too complicated, and you haven’t even built it yet. This week, we co-published a piece with Paul Iusztin that gives you a mental model for catching overengineering before it starts. Here’s what’s inside: Agent or workflow? Getting it wrong is where most production headaches begin. Do biases amplify as agents get more autonomous? What actually changes and how to c

Author(s): Youssef Hosni Originally published on Towards AI. 15 Tips to Use Claude Code More Effectively from Boris Cherny (Creator of Claude Code) Most developers use Claude Code for simple tasks, but it can do much more than that. Once you start exploring its advanced features, it becomes a powerful tool for automating workflows, managing codebases, and speeding up daily work. I came across these tips from Boris Cherny (creator of Claude Code), and they completely change how you can use it. In

Last Updated on April 2, 2026 by Editorial Team Author(s): Sefa Bilicier Originally published on Towards AI. Disclaimer: This article is only for educational purposes. We do not encourage anyone to scrape websites, especially those web properties that may have terms and conditions against such actions. Introduction The internet contains an enormous wealth of information, from product prices and news articles to social media posts and research data. But how do we efficiently extract and utilize t

Last Updated on April 2, 2026 by Editorial Team Author(s): DrSwarnenduAI Originally published on Towards AI. 512,000 lines of TypeScript. A secret AI pet. An always-on daemon that dreams. A mode that hides from you that it’s AI. All of it now public, because someone forgot one line in a config file. March 31, 2026. 4:23 AM Eastern Time. Image related to the code leak incidentThe article discusses the significant leakage of Anthropic’s Claude Code, detailing how a single error led to the exposure

Last Updated on April 2, 2026 by Editorial Team Author(s): Rajendra Kumar Yadav, M.Sc (CS) Originally published on Towards AI. Scaffold production-ready FastAPI, LangChain, and full-stack Python projects in seconds — powered by uv. You have a great idea. You open your terminal, create a new folder, and then… you spend the next 60–90 minutes doing the same thing you always do. ai generate imageThe article introduces app-generator-cli, a command-line tool designed to eliminate the repetitive boile

Last Updated on April 2, 2026 by Editorial Team Author(s): Sefa Bilicier Originally published on Towards AI. Introduction In today’s digital economy, data has become the new oil. But unlike oil, which requires drilling and refining, data requires a different kind of extraction: data mining. Everyday, organizations generate massive amounts of information from customer interactions, business operations, social media, and countless other sources. The challenge isn’t collecting data anymore — it’s m

Last Updated on April 2, 2026 by Editorial Team Author(s): Julia Originally published on Towards AI. Why is everyone obsessed with YOLO? And no I don’t talk about the 2012 mantra “You Only Live Once”. For years, computers struggled to “see” the world. Object detection, the task of finding and identifying objects in images, was slow and complex. Traditional models used a multi-step process. They scanned an image, proposed regions, and then classified those regions. This was accurate but painfully

Last Updated on April 2, 2026 by Editorial Team Author(s): Yelpin Sergey Originally published on Towards AI. Agentic UX is the next step in the evolution of interfaces. Services are learning to listen to the user, understand intent, and act on their own — moving beyond familiar buttons and forms. This article explores what agentic interaction is, what skills designers now need, how to design system behavior, what mistakes to avoid, and how to integrate the AX approach into your workflow. Traditi

Last Updated on April 2, 2026 by Editorial Team Author(s): Raj kumar Originally published on Towards AI. Data quality issues are the silent killers of production systems. A single malformed record can crash your pipeline. A gradual drift in data distributions can slowly degrade model performance. Missing values that sneak through validation can corrupt downstream analytics. The cost of poor data quality is measured not just in failed jobs, but in wrong business decisions, customer frustration, a

Last Updated on April 2, 2026 by Editorial Team Author(s): Bram Nauts Originally published on Towards AI. AI will not transform because it’s deployed – it will transform because the way of operating is redesigned. The tricky part? Transformations rarely fail at the start, they fail in the middle – when organisations try to scale. In a previous article I defined the concept of the AI-native bank. A bank where decisions, processes and customer interactions are continuously driven by AI. Since publ

Last Updated on April 2, 2026 by Editorial Team Author(s): Divy Yadav Originally published on Towards AI. The practical guide to monitoring, debugging, and governing AI agents before they become a liability You shipped an AI agent. It worked in staging. Photo by authorFollowing the introduction, the article delves into the challenges faced by teams operating AI agents in production, emphasizing the inadequacy of traditional monitoring systems that fail to capture the nuanced failures of these ag

Last Updated on April 2, 2026 by Editorial Team Author(s): Nagaraj Originally published on Towards AI. No boilerplate here. No DI container, nothing-no middleware whatsoever. Just results I have dedicated several months to developing artificial intelligence backends using C# which includes building Semantic Kernel and HttpClient and custom middleware and dependency injection systems. The installation process for the Anthropic Python SDK required four lines of code. Source : AuthorThis article ex

Last Updated on April 2, 2026 by Editorial Team Author(s): CreateMoMo Originally published on Towards AI. How AI Agents Work: The OpenClaw Case This note uses OpenClaw as an example to explain how AI Agents work. While technology is evolving rapidly — and some details may differ from the latest developments, this does not affect the fundamental understanding of how AI Agents operate. Contents:The article discusses the functionality and evolution of AI Agents, using OpenClaw as a primary example.

Last Updated on March 11, 2026 by Editorial Team Author(s): Krishnan Srinivasan Originally published on Towards AI. (A practical, end-to-end guide to building a ground-truth-based evaluation pipeline, complete with synthetic data and runnable SQL on Snowflake) In the earlier parts of this Agentic AI series, we explored how AI systems can reason, use tools, retrieve knowledge, and orchestrate complex workflows. But as AI systems become more capable and autonomous, an equally important question st

Last Updated on March 11, 2026 by Editorial Team Author(s): DrSwarnenduAI Originally published on Towards AI. Nobody Invented Attention. A Frustrated PhD Student Ran Out of Other Options. Dzmitry Bahdanau was not trying to invent the architecture that would eventually run inside every large language model on earth. Completely gibberish at this stage!!!!wait!!!!!! Be with me!!!!!The article discusses the journey of Dzmitry Bahdanau, who, while trying to improve long sentence translations with neu

Last Updated on March 11, 2026 by Editorial Team Author(s): Raj kumar Originally published on Towards AI. Every analysis that combines data from multiple sources faces the same fundamental question: how should these datasets align? Which records match? What happens when they don’t? These aren’t just technical decisions. They shape what your analysis says and what it hides. Data merging is where careful analysis can quietly become corrupted analysis. Not through malicious intent, but through defa

Last Updated on March 11, 2026 by Editorial Team Author(s): Raj kumar Originally published on Towards AI. Every business decision starts with a question. What are our total sales by region? Which product categories generate the most revenue? How do customer segments compare in profitability? These questions all share something in common: they require grouping data and calculating aggregates. Grouping and aggregation are the backbone of business intelligence. They transform raw transactions into

Last Updated on March 11, 2026 by Editorial Team Author(s): Raj kumar Originally published on Towards AI. Time is the invisible thread that runs through almost every dataset you’ll encounter. Sales happen on specific dates. Transactions occur at precise moments. Events unfold across hours, days, and years. Yet despite how fundamental time is to data analysis, working with dates and times often trips up even experienced analysts. The challenge is not just technical. It’s conceptual. Time zones sh

Last Updated on March 11, 2026 by Editorial Team Author(s): DrSwarnenduAI Originally published on Towards AI. Does Water Break Math? DeepMind’s Physics-Informed Search for the $1,000,000 Singularity There is a prize. Not the proof. Not the $1 million.The article discusses how DeepMind employed a Physics-Informed Neural Network to explore the Navier-Stokes equations, a long-standing mathematical problem tied to fluid dynamics. It highlights the $1 million prize for solving the equations, and how

Last Updated on March 11, 2026 by Editorial Team Author(s): Moun R. Originally published on Towards AI. A real field report on a VM Ubuntu setup: Docker, Telegram, persistent memory, guardrails, config errors, and genuinely useful lessons. Three weeks ago, I decided to stop paying for AI subscriptions I only use 10 minutes a day. I cloned OpenClaw, ran ./docker-setup.sh, and spent the next 4 hours debugging permission errors. This guide is everything I wish I'd read first. This isn’t an official