Nootropics are cognitive enhancers, boosting brainpower and mental abilities, while ergogenic variants focus on elevating physical performance, creating a powerful combo for unleashing your potential.
+I’ve got my core habits dialed down now (sleep, exercise, nutrition) and have some good routines in place to actually start converging upon a diet that suits my goal of clocking in stable, moderately high levels of physical and mental performance throughout the day.
The Premise I was initially reluctant on using generative AI for my writing process.
That being said, I was quite aware of the potential of large language models (generically addressed as LLMs in here henceforth) - especially true in the case of content creators and/or eccentrically curious individuals.
I, therefore, decided to clarify how I’ll be using generative AI for my ideation process.
The Promise Before we get onto that, as promised by the title, distilling the over-arching skills needed to extract good insights from a conversation with an LLM (an el-el-em; please don’t read it as large, please.
So, I’ve been reading a book on prompt engineering and decided to practice a little…
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Reading sets you up for an involved existence. If you read vastly, you’ll be a force to be reckoned with.
Over the span of 3 years (2019 - 2022), I read around a 100 books ranging from the classics, biographies, auto-biographies, physics, computer science, anthropology, data science, etymology, theology, game theory, mathematics, history, how-to-books, nutrition, physiology , fitness, health, psychology, neuroscience, linguistics, business (okay, don’t judge - we all do this), economics, writing, self help (guilty as charged), spirituality, humor (guides to being funny and similar stuff), young adult (only 1 - please don’t chastise me - I could not handle the cringe), philosophy (a lot - please don’t chastise me) and meta books (speed reading, how to consume books, taking notes, etc).
I took this up when I had to setup a bunch of NLP pipelines for work and this does stand true to its name - it is a quick and practical index into approaches, introductory theory and useful libraries for the same.
I don’t like reading text books at a stretch due to several reasons:- “cross a bridge, when you get to it” is something that has stood the test of time for me when it comes to reading practical books.
What makes lisp so unique is the way its code is structured - you definitely can’t miss all those parentheses. In this section of the series, I discuss the cause for such a representation and how that makes lisp unique in terms of how it views its code as data as code (aka homo-iconicity).
-Further reading Python disassembler Homoiconity Byte Code S-expressions Common Operator Notation Abstract Syntax Tree The Blub Paradox Call to collaborate If you’re someone who shares the dream of making lisp popular and mainstream so that we can use it for our jobs and don’t have to switch to blubs to make a living (without denting its charm of course) , consider contributing to the notes and hit me up via mail or any of the other media I’m present on.
This is an auxilliary post collating resources for the recent video I posted …
-The Pipeline All the ideas, resources that I want to process, any miscellaneous questions I have, are fed into the input-queue in the buffer All the manipulation takes place in these buffers - they’re org-files and I use org-roam to maintain the connections whenever a node set ripens and is worth sharing, I write a post or publish a video.