A routine that’s working for me is to use these tools in the context of podcasting and writing. Generating an image for a new post and experimenting with styles and prompts. It takes longer to get the actual post published, but you can always stop at “good enough” and iterate on the next one. Similarly, if you use ChatGPT for summarizing text with a standard prompt. It might work most of the time. But when it gives you unexpected results, you can spend some time to find out why and improve again.
It's pretty good advice Andreas. It is certainly what I trying to do as well. To incorporate the Generative AI into my pipeline, so it's actually useful for something rather than just an experimentation.
Currently, I don't want to overwhelmed myself with too much stack. So, I try to break down on two topics I want the most:
text-to-text (LLMs)
text-to-image
For both topic, I focus on learning with Transformers model (https://huggingface.co/docs/transformers/index). However, I try to active in reading new research as well (Usually my source is paperswithcode); that's why I sometimes write about anything exciting in Gen AI. I recently really interested in the Agent research as well, but it's not my focus for now.
A routine that’s working for me is to use these tools in the context of podcasting and writing. Generating an image for a new post and experimenting with styles and prompts. It takes longer to get the actual post published, but you can always stop at “good enough” and iterate on the next one. Similarly, if you use ChatGPT for summarizing text with a standard prompt. It might work most of the time. But when it gives you unexpected results, you can spend some time to find out why and improve again.
It's pretty good advice Andreas. It is certainly what I trying to do as well. To incorporate the Generative AI into my pipeline, so it's actually useful for something rather than just an experimentation.
What’s your current Gen AI stack? What stack you got skilled on during this time?
Currently, I don't want to overwhelmed myself with too much stack. So, I try to break down on two topics I want the most:
text-to-text (LLMs)
text-to-image
For both topic, I focus on learning with Transformers model (https://huggingface.co/docs/transformers/index). However, I try to active in reading new research as well (Usually my source is paperswithcode); that's why I sometimes write about anything exciting in Gen AI. I recently really interested in the Agent research as well, but it's not my focus for now.
For what I try to focus, I recently focusing on LLMs app development as well with LangChain (There is a good course: https://learn.activeloop.ai/courses/langchain).
There is a course before that I try to explore for text-to-image with diffusion (https://github.com/huggingface/diffusion-models-class).