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

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

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.

Designing the right context is what transforms powerful language models into reliable, grounded, and scalable AI systems. Context engineering shifts the focus from crafting better prompts to engineering how models see, reason, and act.

Large Language Models have the ability to recognize, summarize, translate, predict, and generate content by leveraging extensive datasets.