Shifting from debuggin code to debugging models. What actually happens when you train a model and why ? Read me latest blog while your model trains :)
Blog link in 🧵
Everyone is always rooting for you. Your parents want you to be a great son. Wife wants you to be a great husband. Your boss wants you to be a slam dunk hire. Every first date you’ve ever been on they’ve been rooting for you to get laid. Every time you started to tell a joke people hoped it would have a hilarious punch line. Your proximity to anyone is a reflection of themself, meaning the deck is never stacked against you, and your failures are completely your own
@HaoliYin This really hit home for me "exploration (research) versus exploitation (engineering)"
@HaoliYin , any advice for people wanting to work on teams that do both?
Running a company:
2020: can you survive a pandemic?
2021: still here? we’re going to give all of your competitors $100m series A rounds.
2022: wow, you made it? okay, all engineers cost $600,000/year now.
2023: nice job! okay, SVB failed and we’re going to take away your bank account.
2024: a survivor I see. but can you pivot from ai to crypto to defense tech back to ai-enabled defense tech in a 12 month period to stay relevant?
2025: unfortunately all of your competitors have raised $2b series B rounds. oh and only 500 engineers are relevant and they cost $100m/yr each.
2026: well, well, well. you’re still in business? let’s deploy the thunderclap of godlike LLMs from the heavens so all of your customers can rebuild your app in 2 hours. can you survive?
Bayes' theorem feels like magic when you translate it into natural language, but the numbers fight you the whole way.
I come back to it every now and then, still feeling the slight giddy joy of being able to understand something completely un-intuitive when in numbers but it logically makes sense when i use words to describe it!