From static retrieval pipelines to intelligent, self-correcting AI systems

Proximal Policy Optimization (PPO) was introduced in 2017 for simulated robots and Atari games, it became the optimization engine behind reinforcement learning from human feedback (RLHF) for large language models.

Designing Agentic AI Systems: 20 Patterns That Matter

Building the Brain of a Robot: A Deep Dive into Physical AI

A practical guide to how hierarchical document trees and LLM-guided navigation find precise evidence in long documents—without relying on chunk embeddings or vector search.

A practical deep dive into planning, subagents, context engineering, memory, tools, and autonomous execution.

How AI is evolving from predicting patterns to understanding, simulating, and acting in the world.

From agents that simply act to agents that learn, adapt, and continuously improve from their own experience.

How eight specialised AI agents, two LangGraph state machines, a Tavily MCP search pipeline, and the 40-year-old SM-2 memory algorithm combine to create a tutor that gets smarter every session.

How I Built Voyager — A Multi-Agent Architecture Using LangGraph

How machine learning, deep neural networks, and probabilistic Artificial Intelligence are reshaping our understanding of the atomic nucleus — from fusion reactors to particle accelerators.

At its core, an agent harness is the execution runtime that governs how an AI agent behaves across an entire lifecycle of interaction