About Me

The website's primary font, 'Bricolage Grotesque' and the etymology of the word 'bricolage' leads to a happy coincidence in how I now define myself. I built my skills as a developer on a decade-long foundation of marketing, data analytics, and creative strategy. 'Bricolage' in its original French context refers to building something with a diverse range of non-traditional elements, or 'improvisation.' In the context of entrepreneurship and social sciences (indulge my inner geek please):

"Bricolage is defined as a process in which new ventures adapt to challenges and pursue long-term goals by creatively utilizing available and undervalued resources, particularly in resource-constrained environments."

This has manifested in several ways throughout my life. The concept of 'jugaad' back in India (which loosely translates to the hustle culture here in the U.S.), my time as an undergrad at the University of Nebraska-Lincoln that exposed me to its ethos:

"Growing flexible, nimble and strong minds. That's how we do big things."

The growth mindset keeps me moving forward. A master's degree from Northwestern University to the sun-kissed shores of Southern California. And in full spirit of 'bricolage' I didn't wait for someone to build the tools I wanted, I went ahead and built them myself.

From Bots to Agents

Agents are LLM-powered systems that use dynamic reasoning to select tools and execute actions toward externally defined goals, unlike bots that rely on hard-coded rules and predetermined workflows. Quite the glow up right? You can see why it immediately triggered my urge to tinker.

Python became my programming language of choice partly because of its almost natural language syntax.

import pandas as pd

# Load CSV
df = pd.read_csv("data.csv")

# Basic filter
subset = df[df["age"] > 30]

# Simple aggregation
summary = df.groupby("department")["salary"].mean()

print(subset.head())
print(summary)

library(dplyr)

# Load CSV
df <- read.csv("data.csv")

# Basic filter
subset <- df %>% filter(age > 30)

# Simple aggregation
summary <- df %>%
  group_by(department) %>%
  summarise(avg_salary = mean(salary, na.rm = TRUE))

print(head(subset))
print(summary)

R comes across as gibberish to me. I can't/don't want to explain.
Anyway.

In 2020, I wanted to beat scalpers at their own game. I taught myself to build 'bots' so I could get a gaming console at retail value. Today, almost anyone with a subscription to Claude or ChatGPT (and the motivation) could spin one up in a few minutes. Since Python has stayed the de facto language of choice for most things machine learning and AI, I've gone from 'runs within my IDE' to 'deployed apps on personal VPS, Netlify, Railway and Render.'

I've worked in purely creative professions, data-driven strategy roles, and I've successfully shipped products. I've added DevOps and CloudOps skills to my arsenal as I continue to build and ship AI-enabled automations for marketing professionals. The only time I 'stay in my lane' is when I'm driving.

Or as someone wiser than me has stated:

"The future does not fit in the containers of the past"

I wish I had a Docker joke to add to this.