Kuala Lumpur, MY · est. 2023

Perivitta Rajendran

Your daily dose of complex AI concepts made simple, practical, and accessible for everyone.

74 posts writing since 2023
Recent writing
Large Language ModelsRetrieval-Augmented GenerationAI AgentsFine-TuningDiffusion ModelsMixture of Experts Large Language ModelsRetrieval-Augmented GenerationAI AgentsFine-TuningDiffusion ModelsMixture of Experts

Recent writing

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Kimi K3 vs Claude Fable 5: Benchmarks, Pricing, and Which Model to Use blogpost
Jul 18, 2026 17 mins read Beginner

Kimi K3 vs Claude Fable 5: Benchmarks, Pricing, and Which Model to Use

Moonshot's Kimi K3 challenges Claude Fable 5 on agentic and frontend coding benchmarks at a third of the price. Where each model...

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GPT-5.6 Explained: Sol vs Terra vs Luna, Pricing, and How It Compares to Claude Sonnet 5 blogpost
Jul 11, 2026 14 mins read Beginner

GPT-5.6 Explained: Sol vs Terra vs Luna, Pricing, and How It Compares to Claude Sonnet 5

OpenAI's GPT-5.6 series splits into Sol, Terra, and Luna tiers. What each model is for, what it costs, and how the family...

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Claude Sonnet 5 Explained: What's New, Pricing, and Whether You Should Switch blogpost
Jul 4, 2026 18 mins read Beginner

Claude Sonnet 5 Explained: What's New, Pricing, and Whether You Should Switch

Claude Sonnet 5 is Anthropic's new default model for Free and Pro users. Here is what actually changed, what it costs, and...

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Structured learning · Courses

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Don't know where to start? Follow a full, structured course end to end: phases, modules, hands-on labs, and checkpoint quizzes.

01

Machine Learning Fundamentals

A complete, self-paced path through classical machine learning: statistics and problem framing, data wrangling and feature engineering, regression, evaluation, classification with ensembles, unsupervised learning, model-quality pitfalls, and a taste of reinforcement learning — finishing with a hands-on capstone project.

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02

Deep Learning

A hands-on, self-paced path through neural networks: start at a single neuron and backpropagation, work through optimization and regularization, build CNNs, RNNs, and attention/transformer architectures from scratch, and pick up the practical skills to train, debug, and avoid common pitfalls — finishing with a deep learning capstone project.

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