Part 8: Data Manipulation in Grouping and Aggregation
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 …
Part 7: Data Manipulation in Date and Time Handling
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 …
Does Water Break Math? DeepMind’s Physics-Informed Search for the $1,000,000 Singularity
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 …
I Built My Own Local AI Agent with OpenClaw + Obsidian: What Nobody Tells You
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 …
MCP (Model Context Protocol): Explained Simply
Author(s): Nisarg Bhatt Originally published on Towards AI. Here is something that does not get talked about enough. The AI tools you use every day, including ChatGPT, Claude, Cursor, whatever your favourite is, they all have the same quiet limitation. They know …
From Monolith to Microservices: A Developer’s Survival Guide in 2026
Author(s): FutureLens Originally published on Towards AI. From Monolith to Microservices: A Developer’s Survival Guide in 2026 The journey starting from monolithic architecture to microservices is a very big challenging nowadays yet rewarding one. As we are explore in the ins and …
TAI #195: GPT-5.4 and the Arrival of AI Self-Improvement?
Author(s): Towards AI Editorial Team Originally published on Towards AI. What happened this week in AI by Louie Two stories dominated this week that look unrelated but tell the same story. On Wednesday, OpenAI released GPT-5.4, its most work-oriented frontier model to …
Oracle Is Firing 30,000 People to Pay for AI It Hasn’t Built Yet
Author(s): Menna Adly Originally published on Towards AI. If your company is “pivoting to AI,” your job might be funding the pivot. One desk. One cut badge. And a data center full of chalk outlines where the servers were supposed to go. …
Code Ships in Minutes. Everything Else Takes Weeks.
Author(s): Know-Island Originally published on Towards AI. AI 100x’d the speed of writing code. We’ve done nothing about the 3 days it takes to review, build, approve, and deploy it. AI can write a feature in 3 minutes. Then you wait 2 …
Claude Cowork: The Future of AI Collaboration at Work
Author(s): Shubham Choudhary Originally published on Towards AI. Claude Cowork: The Future of AI Collaboration at Work Artificial intelligence tools have rapidly evolved from simple chatbots to powerful productivity assistants. Tools like ChatGPT, GitHub Copilot, and Microsoft Copilot already help users generate …
The Footnote That Runs the World-Johan Jensen Died in 1925. He’d Never Seen a Computer. Stable Diffusion Runs His Math Every Second
Author(s): DrSwarnenduAI Originally published on Towards AI. The Footnote That Runs the World His name was Johan. Lets pay our homage today!This article explores the significant yet often unrecognized contributions of Johan Jensen, a telephone engineer whose mathematical insights have become foundational …
The Video Frontier: When AI Stopped Watching and Started Understanding
Author(s): Ampatishan Sivalingam Originally published on Towards AI. Part IV of the Multimodal Intelligence Series · The model learned to see. Then it learned to remember what it saw. This stack did not exist in 2023. The U-Net diffusion models that produced …
LLMOps Guide: The End-to-End Pipeline for Reliable AI Applications
Author(s): Divy Yadav Originally published on Towards AI. For developers who have just built an LLM, RAG, or agentic system and are wondering what comes next. Most teams celebrate when their AI application finally works. The demo looks good, the feature ships. …
Part 6: Data Manipulation in String and Text Processing
Author(s): Raj kumar Originally published on Towards AI. If you’ve ever worked with real-world data, you know the struggle. Names come in all caps when they should be title case. Email addresses have trailing spaces. Phone numbers show up in a dozen …
Part 5: Data Manipulation in Data Transformation
Author(s): Raj kumar Originally published on Towards AI. By the time we reach transformation in a data pipeline, the dataset usually appears stable. It has been imported with structure, inspected with skepticism, selected with intent, and cleaned through deliberate intervention. At this …
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