Hierarchical Navigable Small World (HNSW) is a state-of-the-art algorithm used for an approximate search of nearest neighbours.

From pass@k to prompt injection: everything you need to evaluate an AI agent before it touches production.

This article examines A2A from a systems-engineering perspective: its architecture, communication model, protocol objects, task lifecycle, relationship with MCP, practical implementation patterns, limitations, and security requirements.

A decision model is a machine learning model designed to select, classify, rank, or score outcomes based on an input state and a defined decision task.

Instead of relying exclusively on a more powerful model, can we make the system surrounding a model progressively better at solving different problems?

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

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.

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.

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

Running Large Language Models Locally, Simply and Securely

Imagine an AI that goes beyond answering questions—one that remembers what matters to you, learns from each interaction, and continuously adapts to become more intuitive, relevant, and personalized over time.

Demystifying the Compression of Large Language Models(LLMs)

Large Language Models (LLMs) like GPT-5, Gemini, and Claude have demonstrated remarkable reasoning and generative capabilities. However, their scale comes with trade-offs: high latency, massive compute requirements, privacy concerns, and limited deployability on edge devices.This has led to the rise of Small Language Models (SLMs)—compact, efficient models designed to deliver strong performance under tight resource constraints.

A hands-on walkthrough of a hotel cancellation agent using ReAct and LangGraph. Learn how reasoning, tools, memory, and safety come together in a real production scenario.

Manifold-Constrained Hyper-Connections (mHC), introduced by DeepSeek, propose a principled evolution of residual connectivity—one that preserves stability while dramatically increasing expressive power. This innovation could redefine how future large language models (LLMs) are trained and architected.

How LLMs bring reasoning, context, and human understanding to the heart of autonomous driving.

Moving beyond similarity search to true knowledge reasoning using GraphRAG