Applied machine learning. Developer tools. Clear explanations.
Repositories ·
Open-source contributions
I'm Shashank Thota, an AI/ML developer building practical machine-learning projects and tools that make code easier to understand. I work across the model, interface, and explanation, and learn by building, testing, and contributing to open source.
shashank@github:~$ cat principles.txt
> Build useful things.
> Keep the human in control.
> Explain how they work.
Turns selected source files from a GitHub pull request into a grounded four-panel visual explanation. Connects a Chrome side panel, a Next.js app, read-only GitHub access, private storage, and background jobs.
TypeScript · Next.js · Chrome Extension · GitHub Apps · Supabase · Trigger.dev
A tabular ML demo with reproducible training, risk bands, evaluation, and batch scoring. Its outreach workflow uses rule-based, operator-approved email reminders.
Python · Pandas · scikit-learn · Random Forest
03 / New Life — concept notebook
An exploratory concept for maternal-health awareness: reminders, possible risk indicators, and escalation to a doctor. Concept only; not a deployed medical product.
| Area | Working with |
|---|---|
| Languages | Python · TypeScript |
| Machine learning | Pandas · scikit-learn · model evaluation |
| Product development | Next.js · React · Chrome Extensions |
| Tools & infrastructure | Git · GitHub Apps · Supabase · Vercel |
| Practice | LeetCode · CodeChef · Codeforces |
Public data, refreshed daily. Language shares measure code volume in non-fork repositories, not proficiency.

