I think a lot of these comments will highlight the lower level parts of ML, but what ML needs right now in my opinion is really smart people at the implementation level. As an analogy, there are way less “frontend” ML practitioners than “backend” ones.
Leveraging existing LLM technologies and putting them in software where regular people can use them and have a great experience is important, necessary work. When I studied CS in college the data structure kids were the “cool kids”, but I don’t think that’s the case in ML.
The daily practice is to sketch applications, configure prompts and function calls, learn to market what you create, and try to create zero to one type tools. Here’s two examples I made, one where I took the commonplace book technique of the era of Aristotle and put it in our modern embeddings era [1] and one where I really pushed to understand the pure MD spec and integrate streaming generative models into it [2]
Leveraging existing LLM technologies and putting them in software where regular people can use them and have a great experience is important, necessary work. When I studied CS in college the data structure kids were the “cool kids”, but I don’t think that’s the case in ML.
The daily practice is to sketch applications, configure prompts and function calls, learn to market what you create, and try to create zero to one type tools. Here’s two examples I made, one where I took the commonplace book technique of the era of Aristotle and put it in our modern embeddings era [1] and one where I really pushed to understand the pure MD spec and integrate streaming generative models into it [2]
[1] - https://github.com/bramses/commonplace-bot
[2] - https://github.com/bramses/chatgpt-md