Blog

The orchestrator-worker pattern brings scalable structure to agentic AI workflows by cleanly separating high-level planning from specialised task execution. Through a practical trip planning example, this article demonstrates how LLMs can dynamically coordinate expert agents, grounded in schema-driven logic and real-world data.

This article explores how to implement a reflection loop-an agentic AI pattern where a model generates, critiques, and iteratively improves its output - using image captioning as a practical example.

Unlock faster, more diverse reasoning by running multiple LLM prompts in parallel and aggregating their responses into a single, cohesive output.

A hands-on walkthrough for web developers to demystify large language models by actually building a mini Transformer from scratch.

Explore how to intelligently route AI queries using schema-guided function calling and contextual categorisation.

Learn how prompt chaining enables AI to tackle complex tasks through step-by-step reasoning, boosting both accuracy and interpretability.

In this article we take a look at how to see if an image has low-entropy for LCP calculation.

WebP, AVIF, JPEG XL, Progressive JPEG - how do these image formats compare in terms of their load speeds and how can we measure it? In this article we review a small app that gives us a visual answer.

A new performance insight panel has been added to Chrome which helps with tracking down performance bottlenecks in a much easier fashion compared to the classic performance panel

In this article we take a look at the differences between call(), apply() and bind().