Shubham Raizada’s Blog

How LLMs Work, Part 4: Using the Trained Model A while back I wrote a post on TurboQuant, about compressing the KV cache to make inference cheaper.

How LLMs Work, Part 3: From Toy Model to GPT In Part 1 I covered how text gets tokenized, embedded, and processed through the transformer architecture.

How LLMs Work, Part 2: How LLMs Learn In Part 1, I covered tokenization and the forward pass: how text becomes numbers, and how those numbers flow through a transformer to produce predictions.

How LLMs Work, Part 1: How LLMs Process Text I have been working as a software engineer building distributed systems for several years, and have been using LLMs extensively in my day-to-day work.

Java Virtual Threads: The Pinning Problem, the Deadlock, and the Fix in Java 24 I ran into this in an internal Atlassian engineering writeup.

TurboQuant and Vector Quantization: From Shannon to KV Cache Compression Google Research recently published a blog post titled TurboQuant: Redefining AI efficiency with extreme compression.

Garbage Collection: From First Principles to Modern Collectors in Java, Go and Python Over the last few years I have gone from Java to Go to Rust and now back to Java.

This is not a technical post.

Setting Up Claude Code as a Context-Aware Development Collaborator I’ve been experimenting with Claude Code(Anthropic’s terminal-based AI agent) to see how useful it can be as a coding assistant that actually understands the conventions and constraints of…

Evaluating Claude’s C Compiler Against GCC Anthropic recently announced that 16 instances of Claude Opus 4.6, running in parallel as autonomous agents, built a C compiler from scratch.