Skip to main content

back to basics

it's been quite sometime since i performed musically. now, it's haunting me. i was a music student in the early part of this decade and now i'm one of the few who just want to live life to the fullest. it was a roller coaster ride from where it started and believe me it's one heck of a ride. the years are sinking in my thoughts as i wander in this uncertain route called life. but it's all worth it.


getting dizzy now.


i was talking to a bunch of new found friends discussing life while fixing the 93 corolla that we're fixing. (yup! i wreck one - but that's a whole different story.) we all got a few things in common - cars, music and living life. my only difference is that im older than them - way lot older. the good thing is i never felt it. it's not that big of'a deal. i don't even notice it when im with friends. (yes, they're around twenty's.) i feel the same inspite of everything. but all of it rushed down last night, filling my head with still photographs of everything that happened through the years. it felt like i left myself 10 years ago and now it's catching up.

it's all good! i don't have any regrets and i feel fantastic.
Labels:

Popular

Understanding Large Language Models (LLMs) Using First-Principles Thinking

Instead of memorizing AI jargon, let’s break down Large Language Models (LLMs) from first principles —starting with the most fundamental questions and building up from there. Step 1: What is Intelligence? Before we talk about AI, let’s define intelligence at the most basic level: Intelligence is the ability to understand, learn, and generate meaningful responses based on patterns. Humans do this by processing language, recognizing patterns, and forming logical connections. Now, let’s apply this to machines. Step 2: Can Machines Imitate Intelligence? If intelligence is about recognizing patterns and generating responses, then in theory, a machine can simulate intelligence by: Storing and processing vast amounts of text. Finding statistical patterns in language. Predicting what comes next based on probability. This leads us to the core function of LLMs : They don’t think like humans, but they generate human-like text by learning from data. Step 3: How Do LLMs Wor...
Read more

Contextual Stratification - Chapter 8: Scales

  The Microscope Analogy Imagine looking at a painting. Stand close, inches from the canvas and you see individual brushstrokes, texture, the physical application of paint. Step back a few feet, and you see the image: a face, a landscape, a composition. Step back further, across the room, and you see how the painting relates to its frame, the wall, the space it occupies. Step back outside the building, and the painting disappears entirely into the larger context of the museum, the city, the culture. Same painting. Different scales of observation. And at each scale, different features become visible while others disappear. The brushstrokes that dominated up close are invisible from across the room. The composition that emerged at medium distance fragments into meaningless marks up close. Neither view is "wrong". They're both accurate descriptions of what's observable at that scale. This is what scale means in contextual stratification: the resolution of observation, th...
Read more

Contextual Stratification - Chapter 6: A Different Possibility

The Uncomfortable Question We've spent five chapters documenting a pattern: frameworks work brilliantly within their domains, then break down at boundaries. Physics, economics, psychology, medicine, mathematics; everywhere we look, the same story. We've examined why the standard explanations fail to account for this pattern. Now we must ask the question that makes most scientists uncomfortable: What if the boundaries are real? Not artifacts of incomplete knowledge. Not gaps waiting to be filled. Not temporary inconveniences on the road to unified understanding. What if reality itself is genuinely structured into domains, each operating under different rules, each requiring different frameworks to understand? This is not the answer we want. We want unity. We want simplicity. We want one elegant equation that explains everything from quarks to consciousness. The history of science seems to promise this; each generation unifying more, explaining more with less, moving toward that ...
Read more