Lak Lakshmanan

An open-source, model-agnostic agentic framework that supports dependency injection Lak Lakshmanan · Follow Published in Towards Data Science · 12 min read · 10 hours ago — Ideally, you can evaluate agentic applications even as you are developing them, instead of evaluation being an afterthought. For this to work, though, you need to be able to mock both internal and external dependencies of the agent you are developing. I am extremely excited by PydanticAI because

A framework to select the simplest, fastest, cheapest architecture that will balance LLMs’ creativity and risk Lak Lakshmanan · Follow Published in Towards Data Science · 16 min read · 9 hours ago — Look at any LLM tutorial and the suggested usage involves invoking the API, sending it a prompt, and using the response. Suppose you want the LLM to generate a thank-you note, you could do: import openairecipient_name = “John Doe”reason_for_thanks = “helping

Using Gemini + Text to Speech + MoviePy to create a video, and what this says about what GenAI is becoming rapidly useful for Lak Lakshmanan · Follow Published in Towards Data Science · 10 min read · 1 day ago — Like most everyone, I was flabbergasted by NotebookLM and its ability to generate a podcast from a set of documents. And then, I got to thinking: “how do they do that, and where

Comparing Autogen and LangGraph from a developer standpoint Lak Lakshmanan · Follow Published in Towards Data Science · 10 min read · 12 hours ago — GenAI models are good at a handful of tasks such as text summarization, question answering, and code generation. If you have a business process which can be broken down into a set of steps, and one or more those steps involves one of these GenAI superpowers, then you will