Status:Available for Q3 Projects // Specialized in SaaS & Scalable Architecture
I’m Mayur Pal. I build high-performance applications using Go, Next.js, and Postgres.
"I architect resilient digital infrastructure. From rapid MVP development to scaling distributed systems, I ensure your product stays up when your traffic spikes."
Engineered for Scale
Tech_Stack_Engine
Verifying_Production_Dependencies...
Documentation of architectural decisions and system design patterns.
In the final part of this series, I build a complete terminal AI chat app in TypeScript using Docker Model Runner. One binary called llm that anyone can run — streaming responses, conversation memory, preset modes, auto model detection, and history saved to disk. I walk through the full architecture, every design decision, and what I learned. Plus — why I'm migrating this to Go next.
In Part 1 we set up Docker Model Runner. Now we actually use it. In this post I walk through calling your local LLM programmatically using TypeScript and the OpenAI SDK — no API key, no cloud. We build three scripts from scratch: a single question chat, a streaming response that feels like ChatGPT, and a multi-turn conversation with memory. Plus — DMR's native endpoints for managing models directly from your code.
As a CS student, I wanted to experiment with LLMs without paying for API access or sending my data to the cloud. Turns out, if you already have Docker installed, you're 90% there. This is Part 1 of a series where I set up Docker Model Runner, explain the GenAI vocabulary that confuses everyone (parameters, quantization, context window), and get a 3B model running locally on my laptop — for free.
Currently accepting high-impact projects and full-stack engineering roles. Let's talk infrastructure.