Karachi Wala Developer

In this episode we cover the latest updates from Google IO and OpenAI Spring update. The all new ChatGPT-o and Gemini 1.5 Flash. We talk about where are LLMs headed next.

Lama3 is a popular open source model by Meta, the company behind Facebook, Instagram and Whatsapp. They have release the next iteration of the famous Lama2 model, this is a popular open source LLM which is heavily used by developers world wide. Is the new iteration strong enough to take on the best closed source models? We explore in this episode.

As we move to use LLMs more and more for different use cases, we will run into at times issues or limitations with the model. There are many different ways to train the model to do a specific task better, and fine tuning is one of them. What is about, when do we need to use it and how do we do it - all in this episode.

This episode covers my journey as a GDE over the last few years, what got me into it, and what is the big outcome. Also joined another category for GDE in Machine Learning.

In this episode I share my thoughts on the latest additional to AI development called Deven, and where the industry is moving towards. Will software engineering be taken over by AI in the next few years?

So this week we had 2 back to back announcements which completely shook up the LLM worlds. Gemini Pro 1.5 with its 10 million token context length makes it super powerful to run on large codebases and a lots of documents. The other big news was the launch of OpenAI Sora - their new text to video generation which can now generate super realistic videos for up to a minute. How will these impact our tool, I share some thoughts here.

The all new heavy weight model has just dropped this week. Should you be rushing to try it out? Why is the ultra model better? This and more in his quick episode talking about Gemini Ultra.

In this episode - we discover the Retrieval Augmented Generation (RAG) process which allows us to train our LLMs using our data. In this short episode we cover what is this about and how can we use it.

If you are using ChatGPT or Bard - you are "prompting" the AI to get some answers. Not all prompts are made equal. The response of the LLM depends heavily on the quality of the prompt and the context given to it. Thus its worth while spending some time understanding prompt engineering and how to do it properly.

Referencing the classic episode from IT Crowd - we take our first dive into the world of LLMs by talking about neural nets and how they form the basis of the LLMs we know today. We touch on the famous paper - All you need is attention, and then talk about what LLMs under the hood are really about.