Multi Agent System - Hammer or Nail Gun

Everyone talks about multi-agent systems, but sometimes a simple hand-off (hammer) does the job. When workflows get complex, you’ll need orchestration (nail gun). The key is knowing when to keep it simple and when to scale. Use the right tool for the task.

Talk to your code

If you've ever wished you could just ask your codebase what it's doing—you're not alone. There are popular agent based solutions which are available like CursorAI , Replit and many more. ahh, they are paid. And now, thanks to a deceptively simple but incredibly

Agent Led Growth (ALG): Future of defining success

The article is more of a brainstorming that explores a new trajectory for achieving growth—one where it's not just sales or product that drive expansion, but automated, action-driven agents that actively work within the funnel to create a revenue machine.

It Gets More Creative as It Gets Hotter!

It is the Transformer based models. Almost all of these models have a feature to adjust their creativity. This article focuses on exploring these settings and their internal workings.

Optimizing retrievers for AI

Building an effective RAG system isn’t just about using a SOTA model—it’s about ensuring high-quality retrieval that provides the right context to the generation model.

Vertical AI - New Era of Boom

The article explores what Vertical AI is, how it's being adopted by the market, and the 7 steps formula to enter this rapidly evolving field.

Gandhi's 3 wise monkey's on AI

The philosophy of Gandhi's three wise monkeys—"See no evil, hear no evil, speak no evil"—originates from an ancient Japanese pictorial maxim. It was famously embraced by Mahatma Gandhi to emphasize moral integrity and self-discipline. The monkeys represent a guideline to live ethically by avoiding

AI's FLOPS Show!

FLOPS are fundamental to understanding AI performance, helping us grasp how AI hardware and precision choices impact cost, speed, and efficiency.

Add "FLAIR": Applying AI in FAIR Data Principles

The concept is to evolve FAIR into FLAIR by integrating Learning, making AI a native component of data handling and processing.

Language Model Series

Series covering till Transformer's "Multi Headed Attention"