Welcome, AI Engineers!

Welcome, AI Engineers!

👋🏽 I'm Anup. I'm an AI and Software Engineer building production Agentic AI and Generative AI systems. I work on RAG pipelines, multi-agent architectures, and multi-cloud deployments.

My newsletter, The AI Engineering Brief, covers practical AI engineering for people shipping real systems. X/Twitter/Bluesky: @anup.

The AI Engineering Brief Newsletter

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Recommended Resources and Tools

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Zed

Zed

My new favourite code editor.
Claude Code

Claude Code

My pair programmer in the terminal.
Obsidian

Obsidian

Where my second brain lives. Daily notes go in, post outlines come out.
Wispr Flow

Wispr Flow

How I dictate notes and posts when typing would slow me down.

Recommended Podcasts

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AI for Humans

AI for Humans

Educational, light-hearted discussions breaking down AI concepts
Google DeepMind: The Podcast

Google DeepMind: The Podcast

Research-focused, neuroscience-inspired models, safe and ethical AI
Latent Space

Latent Space

Technical updates, real-world engineering, foundational models
NVIDIA AI Podcast

NVIDIA AI Podcast

In-depth interviews on AI transforming industries

Shipped

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LLM VRAM Calculator
VRAM Calculator: 2026 LLM Tool

LLM VRAM Calculator

A free VRAM calculator for self-hosted LLMs that picks the smallest fitting GPU instance (H100, B200, A100 ) across FP16, FP8, FP4.
Eliza Redux: A Voice AI crisis support Agent

Eliza Redux: A Voice AI crisis support Agent

Voice AI crisis support chatbot that responds in under a second. Built and deployed in 90 minutes for a hackathon win. Handles natural conversation, breathing exercises, safety checks, and can locate nearby emergency services.

Side Projects

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Vision Transformer (ViT) Paper PyTorch Implementation
ViT: 2024 - Paper Replication

Vision Transformer (ViT) Paper PyTorch Implementation

PyTorch replication of the Vision Transformer paper. Applied transformer architecture to image classification to deepen my understanding of attention over patches.
EfficientNetB2 Computer Vision Model
EfficientNetB2: 2024 - Paper Replication

EfficientNetB2 Computer Vision Model

An EfficientNetB2 feature extractor computer vision model to classify images of food as pizza, steak or sushi. The model performs at 95%+ accuracy, and can classify an image at 0.03 seconds inference time per image.