The ReAct pattern — short for Reasoning and Acting — is one of the most influential agent architectures in practical AI development. Published in a 2022 paper by Yao et al., it describes a simple but powerful idea: interleave the model’s reasoning steps with its action steps, making both visible and traceable. Rather than having … Read more
The sub-3B parameter range has become one of the most competitive and practically useful tiers in the small language model landscape. Models in this range run comfortably on CPU, fit on smartphones and edge devices, and are cheap to fine-tune on consumer hardware — while delivering quality that would have required a 13B+ model just … Read more
Building an AI agent from scratch in Python means writing the orchestration loop yourself rather than relying on a framework like LangChain or AutoGen to handle it. This gives you full control over how the agent decides what to do, how it calls tools, how it handles errors, and when it stops. For production agents … Read more
Small language models — typically defined as models with fewer than 7 billion parameters — have become one of the most practically significant areas of AI development in 2025–2026. While headlines focus on ever-larger frontier models, the rapid improvement in smaller models has quietly made high-quality AI capability available on laptops, smartphones, edge devices, and … Read more
VS Code added native MCP server support in version 1.99 (April 2025), bringing the same external tool connectivity that Cursor and Claude Desktop offer to the world’s most widely used code editor. With MCP in VS Code, GitHub Copilot’s chat can query your database while you write queries, fetch GitHub issues while you implement features, … Read more
TorchServe is PyTorch’s official model serving framework, designed to take a trained PyTorch model from a .pt or .pth file to a production HTTP endpoint with minimal configuration. It handles the serving infrastructure — request batching, multi-model management, logging, metrics, and health checks — so you can focus on your model and handler logic rather … Read more
Cursor’s MCP support lets you connect external tools — databases, APIs, file systems, web search, internal services — directly to the AI inside your IDE. Once connected, Cursor’s AI can query your database while reviewing code, search internal documentation while writing a feature, or read a GitHub issue while implementing a fix — all without … Read more
ONNX Runtime is a high-performance inference engine for machine learning models in the ONNX (Open Neural Network Exchange) format. It accelerates model inference across CPUs, GPUs, and specialised hardware, with optimisations that often produce 2–10x speedups over running the same model in its original framework. For teams deploying ML models in production — whether on … Read more
The MCP Web Search Server gives AI assistants the ability to search the web during a conversation, pulling in current information that goes beyond their training data cutoff. Instead of the AI saying “I don’t have information about recent events,” it can search, read results, and answer with up-to-date data. For developers, analysts, and researchers … Read more
The MCP Database Server connects AI assistants directly to your database, letting them query data, inspect schemas, and answer questions about your data in natural language without requiring you to write SQL or export data manually. Instead of asking “how do I write a query to find all users who signed up last month and … Read more
