Tools

Table of Contents

  1. Background
  2. General Tools
    1. AI Tools
    2. Machine Learning Tools
    3. Editors + IDES
    4. Terminal
    5. Testing
    6. Linters, Formatters
    7. Container Environments
    8. Version Control
    9. Prototyping
    10. Writing + Publishing
    11. Package Managers
    12. Datasets
  3. Programming Languages
    1. Python Libraries
  4. Data
    1. Core Libraries
    2. Notebooks
    3. Machine Learning
      1. Deep Learning
    4. Visualization
    5. Dimensionality Reduction
    6. Language: Computational Linguistics, Information Retrieval
    7. Image Analysis and Computer Vision
    8. Audio
  5. Interactive Visualization
    1. D3
      1. D3 extensions
    2. JavaScript Frameworks
  6. Systems Programming + Infrastructure
    1. Software Frameworks
    2. API
    3. AI Infrastructure
    4. Data Processing
    5. Databases
      1. rdbms
      2. nosql
    6. ML Interfaces, Deployment, etc.
  7. Things I wish I had time to explore and learn more about
    1. Creative Coding

Background

This page is a place where I keep track of what libraries and tools I have used/currently using. This page is inspired by uses this, the missing semester of your cs education, the pragmatic programmer, strangeloop, eyeo festival.

I owe most of my engineering knowledge to the following people: Tommy Ngyuen, Sundar Rajan, Ron Minnich, James Wexler, Ryan Stutsman, Jakob Johnson, Peter Jensen, Jeff Phillips and Varun Shankar whose teaching, office hours, and conversations gave me a foundation for not only understanding how to debug and navigate all levels of the technical stack but also core engineering practices - environment/tool setup, experimentation and monitoring setup, command line hacks, numerical programming, and the mental fortitutde you need for finding and fixing bugs.

In the age of chatgpt and llms their advice still holds - the only way to become a better engineer is to spend countless hours debugging, experimenting and writing code.

Note: This page is getting constantly updated - Last update: October 17, 2025.

General Tools

AI Tools

Machine Learning Tools

Editors + IDES

  • vscode, opensource version: vscodium. GitHub copilot and its autocomplete is such a nuisance that I have turned it off. If I need AI help, Gemini/Claude is more useful.
  • vim
  • intellij

Terminal

Testing

Linters, Formatters

Container Environments

Version Control

Prototyping

Writing + Publishing

Package Managers

Datasets

Programming Languages

  • Python - machine learning, scripting, prototyping and almost all of my development these days
  • Bash - compile and running programs, experiments etc.
  • Java - information retrieval, data engineering
  • JavaScript - interfaces, creative coding, interactive visualization

    Python Libraries

  • tqdm
  • rich
  • pathlib

Data

Core Libraries

Notebooks

Machine Learning

Deep Learning

Visualization

The python visualization ecosystem is fragmented and choosing the right visualization library depends on the project and audience. Many of the python visualization libraries build off of matplotlib. Matplotlib while tedious is super power powerful and versatile, with the ability to render interactive 3d plots, conic sections and do image processing work. Seaborn is great for statistical charts with an aesthetic similar to ggplot but the syntax can get gnarly. Altair is an easy to learn member of the Vega-Lite ecosystem but requires deep knowledge of the grammar of graphics and gets knarly with interaction and customizability.

Dimensionality Reduction

Language: Computational Linguistics, Information Retrieval

Image Analysis and Computer Vision

Audio

Interactive Visualization

D3

D3 - a library that has caused many ups and down for me but still I am excited about its capabilities. This is highly opinionated but D3 is done. Svelte, JavaScript, charting libraries(chart.js) and frontend engineering have slowly taken and improved D3’s magic to the point that you can actually implement many features with the Canvas API, CSS, and frameworks. D3 was built for the 2009-2021 era but now the api design seems stuck in time as it clashes with frameworks that need to utilize its capabilities. To that end, with some engineering tricks and staring at the source code it is possible to integrate d3 into a framework project but it is now treated more as a module library.

At the time of this writing, d3’s capabilities are unmatched for interactive visualization thus d3 is still needed for interactive visualization and building interfaces that interface between users and ml models.

JavaScript Frameworks

Not learning a JS framework was the biggest regret I had from my Utah days. For choosing a framework, it really depends on how well you know javascript and your project goals (see the framework documentaries in the talks page). Since I’m interested in interactive visualization for machine learning, svelte seems to be the most widely adopted based on the tools and research published by Anthropic, Apple AIML Google DeepMind PAIR and OpenAI hiring Jay Wang. I wouldn’t count out vue and react because Catherine Yeh built AttentionVis with vue and the Polo Club for Data Science publishes a wide range of ml-vis tools using svelte, vue and react.

Systems Programming + Infrastructure

Software Frameworks

API

AI Infrastructure

Data Processing

Databases

rdbms

ML Interfaces, Deployment, etc.

Things I wish I had time to explore and learn more about

These are things that I wish I had time to explore

Creative Coding

I first heard about this at the EYEO Festival. I started with Dan Shiffman’s Coding Train but shifted to Kevin Workman’s Happy Coding and then stopped. It was a lot of fun to use p5 and it explained some of the javascript quirks I struggled with. After seeing all the cool stuff put out by Martin Wattenberg, Fernanda Viegas, Golan Levin, Ravi Chugh, and Andrew McNutt I want to pick it up again and try to implement some of the projects from Code as a Creative Medium.