iceman_w

I have a good theoretical understanding of the math behind deep learning and have broad understanding of the recent architectures (Transformers, etc.). I'm looking to improve my practical intuition around this. What are some good resources and/or project ideas to get my hands dirty with training/fine-tuning models that will have a fast iteration time so I can get frequent feedback? I'm particularly interested in multi-modal models.

Something other than the chat bots.

I'm trying to understand the incentive for engineers to join a startup as a 'founding engineer'. To me it seems like taking on all the risk that a founder takes (pre-pmf, pre-revenue, etc.) but getting a small fraction of the equity that a founder gets. The risk-reward ratio doesn't seem to add up.

Looking for insights from folks who have been in the startup world.

There's this narrative everywhere that having a diverse team is good for businesses. Instead of just forcing it on everyone, I think it would be very helpful to collect examples where having people from diverse backgrounds in a team helped ship a better product.

Example: YouTube figured out that many videos were being uploaded upside down from mobile phones because some left handed person in the company ran into it.

I was working on an express app with a friend and git auto merged a file after I ran 'git pull'. There were no merge conflicts, but git added duplicate functions to a file after merge. I spent an hour trying to figure our what the problem was before realizing that git had made a mistake while merging.

I had never seen such a thing before. How often does something like this happen?