# Kalyan Mudumby

- **Website:** <https://kalyanmudumby.com>
- **Primary alias:** `kalyanmudumby.com`

## Description

Kalyan Mudumby is a personal technology and engineering brand focused on artificial intelligence, machine learning, and dependable software systems. Kalyan is an AI/ML engineer and developer pursuing an MS in Computer Science at Northeastern University, with interests spanning generative AI applications, production large language model systems, and ML infrastructure. His work and writing draw on tools and technologies including Python, LangChain, TensorFlow, Rust, and Kubernetes.

The site offers technical articles, project updates, and topic-based resources for readers interested in building and operating modern software. Recent posts explore reproducible environments with Nix and NixOS, the architecture of a self-hosted digital library, observability for Spring Boot applications, and privacy-focused network protection using AdGuard Home with HTTPS and TLS encryption. The blog also covers LLMs, Rust, observability, and infrastructure for machine learning. Visitors can browse articles by tags and series, explore projects, and connect through GitHub, LinkedIn, email, or RSS. Kalyan’s profile notes that he is open to co-op and internship opportunities for 2026.

## Industries

- 🖥 Computers Electronics and Technology
- 🖥 Artificial Intelligence and Machine Learning _(under Computers Electronics and Technology)_

## Links

- [github](https://github.com/theinhumaneme)

## Logos & icons

- logo _(primary)_ — PNG — [download](https://cdn.brandfetch.io/idf6inDDjc/w/180/h/180/theme/dark/logo.png?c=1bxid64Mup7aczewSAYMX&t=1790502787822)

## Colors

| Name | Hex | Theme |
| --- | --- | --- |
| Cobalt | #0550ae | accent |
| Shark | #24292e | dark |
| White | #ffffff | light |

## Fonts

- var(--font-sans) — asset _(custom)_
- var(--font-sans) — asset _(custom)_

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_Source: <https://brandfetch.com/kalyanmudumby.com>_
_See [/llms.txt](/llms.txt) for a full list of agent-readable pages, and [/auth.md](/auth.md) for how agents authenticate._