Gen AI

Three weeks of work. A fully functional AI chatbot, integrated into his company’s internal portal, built on top of GPT-4, with a clean UI and a snappy response time. His manager was impressed just from the screenshots.

If you have been coding for a while, you have probably heard of design patterns. Maybe you learned about the Gang of Four book

The API latency has suddenly increased. A few Kubernetes pods are restarting. Customers are reporting intermittent failures.

Artificial Intelligence has entered a transformative era. Large Language Models (LLMs) such as GPT, Llama, Gemini, Claude, and Mistral

It’s 11:45 PM. You have a tab open with the OpenAI API docs. Another one with a YouTube video on AI chatbots. And a half-written Python script that, for some reason, keeps giving you answers that are either too vague or completely off-topic.

Before an AI model can generate human-like responses, summarize documents, or translate languages

Ever wondered how a machine can look at a picture and instantly say “that’s a cat,” or listen to your voice and convert it into text?

Artificial Intelligence has evolved through a series of profound conceptual shifts rather than steady, incremental improvements.

It’s 12:18 AM.

Your laptop is still open, not because work is pending, but because something feels off. You’re scrolling through LinkedIn.