Agents
Today, agents are not just making static predictions, they are chaining decisions, retrieving documents, acting on APIs, and even reasoning recursively. How optimized is your agent — its prompt, model configuration, and reasoning structure? In this article, we’ll explore how to optimize agents?
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.
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
Brainstorming
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.
AI
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.
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.
AI
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.
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
FLOPS are fundamental to understanding AI performance, helping us grasp how AI hardware and precision choices impact cost, speed, and efficiency.
Data
The concept is to evolve FAIR into FLAIR by integrating Learning, making AI a native component of data handling and processing.
Series
Series covering till Transformer's "Multi Headed Attention"