my past 3 months were probably the best months that i’ve ever had. i wasn’t tied down to any commitments or any projects. i was able to say yes or no to anything without any self-doubt. just like a free bird.
a lot of my work over the past year has been fundamentally tied down somewhere in computer vision and self-driving. everything that i’ve built and researched was in that space. the only places when i explored other tech + fields were through newsletters that i occasionally read, not by exploring and allocating time to dive deeper into ideas that interested me.
i really like the connotation of being a free bird. you get to explore whatever you want without being held back. everything that i’ve done (even up until last month) was somewhere in my comfort zone. i built somewhere in computer vision whether it be GANs or end2end. there wasn’t room for explicit growth. the only growth that i was having was in a field where i already knew a lot about.
i stepped out of that zone this month. i looked into something fun, something challenging. something where i can make progress on my goal of solving self-driving. i spent the majority of this month building in reinforcement learning and understanding how it applies in self-driving.
and it was really, really fun. i had time allocated to learn about something that i’ve wanted to learn about for so long.
where did this take me?
i wrote an 80min article going into the science behind reinforcement learning and how it really works. why? there’s a difference between learning and communicating. technical understanding is linear but communicating is exponential.
that was one of the main intentions that i had when i was writing this article. i’ve learned so much technically. how do you showcase that? how can you showcase your understanding while creating something valuable for people?
for me, this was writing a really good article explaining everything you need to know to get started in reinforcement learning:
ok, i’ve learned so much theoretical knowledge. i now understand all the basics behind reinforcement learning. but do i, really?
there’s a fundamental difference between learning something and applying it. 90% of people can learn but only 10% can apply what they’ve really learned.
applying theoretical intuition via projects has been the best way for me to retain information while creating some sort of medium out of it. i decided to use an openai gym environment and have fun with using Deep Q Agents to learn how to drive:
the importance of meetings
i’ve met >75 people over the past year. one main data point that i’ve noticed when reflecting is that “i don’t ask the right questions.” sometimes i come in with questions that are too ambiguous and not thought out well enough.
i took a first-principles approach to having good meetings this month and boiled it down to 3 main points:
come in prepared
ask open-ended questions (that are not vague)
follow up on those questions
you don’t want to come in and end up having a qna, not a chat. one thing that ive been working on is speaking a lot slower and enunciating my words so that i can be more clear when i speak while having confidence.
also would recommend going through this lecture (h/t @Zayn) on how to speak (yes, literally):
some of my greatest meetings have had open-ended questions. why? because you can follow up. open-ended questions that have a bit of ambiguity allow you to turn a meeting into a chat. because now you develop a relationship with someone. now it doesn’t become a meeting, it becomes 2 people jamming out on something that interests them.
and that’s what i want to practice more. i want to have more meeting with smart people. so that i can learn and have meaningful conversations. my goal is to have 100 meaningful meetings (not total) where i can gain non-obvious insights. and learning how to speak is step one on this journey.
hey, thanks so much for reading this newsletter! would love to hear your thoughts and updates on what you’re working on, feel free to get in contact via twitter or linkedin! check out my personal website if you’d like to learn more about me.