Perivitta Rajendran

May 25, 2026

OpenAI Codex Explained: How LLMs Learn to Write Code

OpenAI Codex powers GitHub Copilot and sparked the AI coding revolution. This post explains exactly how it works, the 54M-repo training corpus,...

artificial-intelligence· 31 mins read ·Intermediate

May 24, 2026

machine-learning

Random Forest: A Complete Beginner's Guide

Random Forest builds hundreds of deliberately different decision trees and takes a vote. This guide explains exactly why that works, covering bootstrap...

machine-learning· 44 mins read ·Intermediate

May 20, 2026

blogpost

Synthetic Data: How AI Trains Itself on AI-Generated Data

Real-world data is expensive, biased, and often private. Synthetic data lets AI systems generate their own training sets, and it is becoming...

blogpost· 17 mins read ·Intermediate

May 19, 2026

blogpost

AI for Drug Discovery: How AlphaFold Reinvented Biology

Developing a new drug used to take 12 years and cost over $1 billion. AI is compressing that timeline dramatically, from predicting...

blogpost· 23 mins read ·Advanced

May 12, 2026

blogpost

Neuromorphic Chips: The Brain-Inspired Hardware That Could Replace GPUs

GPUs brute-force intelligence with matrix math. Neuromorphic chips take the opposite approach, mimicking how biological neurons fire and wire to achieve AI...

blogpost· 21 mins read ·Advanced

Mar 27, 2026

blogpost

Multi-Agent Systems: Orchestration, Communication, and Collaborative AI

A single agent hits context and capability limits fast. Multi-agent systems distribute work across specialized roles with structured communication protocols. Orchestration patterns...

blogpost· 16 mins read ·Advanced

Mar 25, 2026

blogpost

LLM Inference Optimization: Quantization, KV Cache, and Serving at Scale

Serving a 70B model cheaply requires quantization, KV cache tuning, continuous batching, and the right serving stack. A systems-level breakdown of vLLM,...

blogpost· 27 mins read ·Advanced

Mar 22, 2026

blogpost

Embedding Models: Training, Fine-Tuning, and Optimization for Retrieval

Embedding quality determines what your retrieval system can find. How contrastive training works, when to fine-tune versus use off-the-shelf models, and what...

blogpost· 28 mins read ·Intermediate

Mar 21, 2026

blogpost

Advanced Prompt Engineering: Chain-of-Thought, ReAct, and Tree-of-Thoughts Explained

Chain-of-thought improves multi-step reasoning. ReAct adds tool use. Tree-of-thoughts explores multiple solution paths. When each technique earns its token cost — and...

blogpost· 24 mins read ·Advanced

Mar 19, 2026

blogpost

Structured Outputs in LLMs: JSON Mode, Function Calling, and Schema Validation

Free-form LLM output breaks parsing pipelines. JSON mode, function calling, grammar-constrained decoding, and Pydantic validation are the layers that make structured output...

blogpost· 20 mins read ·Intermediate

Perivitta Rajendran

Recent writing

OpenAI Agents SDK vs Anthropic SDK: A Technical Comparison

XGBoost: A Complete Beginner's Guide

Model Context Protocol (MCP): A Complete Beginner's Guide

From the archive

OpenAI Codex Explained: How LLMs Learn to Write Code

Random Forest: A Complete Beginner's Guide

Synthetic Data: How AI Trains Itself on AI-Generated Data

AI for Drug Discovery: How AlphaFold Reinvented Biology

Neuromorphic Chips: The Brain-Inspired Hardware That Could Replace GPUs

Multi-Agent Systems: Orchestration, Communication, and Collaborative AI

LLM Inference Optimization: Quantization, KV Cache, and Serving at Scale

Embedding Models: Training, Fine-Tuning, and Optimization for Retrieval

Advanced Prompt Engineering: Chain-of-Thought, ReAct, and Tree-of-Thoughts Explained

Structured Outputs in LLMs: JSON Mode, Function Calling, and Schema Validation

Recent writing

OpenAI Agents SDK vs Anthropic SDK: A Technical Comparison

XGBoost: A Complete Beginner's Guide

Model Context Protocol (MCP): A Complete Beginner's Guide

Not sure where to start? Just ask.

From the archive

OpenAI Codex Explained: How LLMs Learn to Write Code

Random Forest: A Complete Beginner's Guide

Synthetic Data: How AI Trains Itself on AI-Generated Data

AI for Drug Discovery: How AlphaFold Reinvented Biology

Neuromorphic Chips: The Brain-Inspired Hardware That Could Replace GPUs

Multi-Agent Systems: Orchestration, Communication, and Collaborative AI

LLM Inference Optimization: Quantization, KV Cache, and Serving at Scale

Embedding Models: Training, Fine-Tuning, and Optimization for Retrieval

Advanced Prompt Engineering: Chain-of-Thought, ReAct, and Tree-of-Thoughts Explained

Structured Outputs in LLMs: JSON Mode, Function Calling, and Schema Validation