AI

UV: The High-Performance Python Package & Project Manager Built in Rust The Python ecosystem has no shortage of packaging tools—pip, virtualenv, pipenv, poetry, conda, rye, and others—but it has lacked something essential: a unified, extremely fast, modern, user-friendly solution that handles installation, environment management, project workflows, dependency resolution, and Python version management all in one place, without the typical performance issues of Python-based tooling. Enter UV: a next-generation Python package and project manager from Astral (formerly Astral Software), written entirely in Rust for maximum speed, safety, and modern ergonomics. ...

JSON-RPC: A Complete and In-Depth Guide to the Lightweight Remote Procedure Call Protocol Remote Procedure Call (RPC) systems have been part of software engineering for decades, long before REST, GraphQL, or gRPC entered the scene. Among the many RPC variants, JSON-RPC stands out for its simplicity, language neutrality, and minimal overhead. In an increasingly interconnected digital landscape, where microservices, distributed back ends, IoT platforms and blockchain ecosystems depend on clean and efficient interfaces, JSON-RPC has seen a strong resurgence. ...

AI Dictionary: An Essential Glossary for Modern Artificial Intelligence Artificial Intelligence evolves quickly, and keeping up with terminology is essential for anyone working in technology, business, or research. This dictionary summarises the key terms used across machine learning, deep learning, natural language processing, generative AI, and applied systems. A AGI (Artificial General Intelligence) A theoretical form of AI capable of understanding, learning, and applying intelligence across any task at human level or beyond. ...

The Model Context Protocol (MCP) and Its Role in Modern AI Systems Artificial Intelligence (AI) has rapidly moved from research labs to enterprise production environments. Large Language Models (LLMs) — the engines behind chatbots, assistants, and generative systems — are now used to automate knowledge work, improve productivity, and enhance decision-making. However, there’s a persistent architectural problem: how do we connect models safely, efficiently, and consistently to real-world systems, tools, and data? ...

Understanding Agentic AI: A Comprehensive Exploration Agentic AI is a captivating concept at the intersection of artificial intelligence and cognitive science, transcending traditional AI systems with its focus on autonomy, initiative, and decision-making capabilities. In this article, we’ll delve into what defines Agentic AI, its theoretical foundations, practical applications, associated challenges, and future directions. Whether you’re a developer keen on the latest AI paradigms or simply curious about the evolution of intelligent systems, this comprehensive guide will give you a solid grounding in Agentic AI. ...

Understanding Base vs Instruct Variants in Machine Learning Models As machine learning continues to evolve, developers frequently encounter different model variants suited for diverse tasks. Two common types you’ll often come across are base models and instruct models (also known as instruct-tuned models). Understanding the key differences between these variants can help you better select and tailor models for specific applications. In this blog post, we will take a closer look at these two variants, exploring their unique characteristics, applications, and nuances. ...

Fine-Tuning a Closed Source LLM like ChatGPT: A Step-by-Step Guide In the realm of machine learning, fine-tuning a language model can significantly enhance its ability to perform specific tasks or understand particular contexts. In this blog post, we’ll explore how to fine-tune a closed-source language model like OpenAI’s ChatGPT. While direct access to the model’s parameters isn’t available as it might be with open-source models, fine-tuning it using your own dataset is still achievable. ...

Understanding NLP, LLMs, Linear Regression, and the Landscape of Machine Learning Machine Learning (ML) has reshaped modern technology — powering everything from recommendation systems to self-driving cars. Within this field, Natural Language Processing (NLP) and Large Language Models (LLMs) have become particularly prominent due to the rise of generative AI. In this blog post, we’ll demystify the connections between these areas, explore the role of Linear Regression, and look at how they fit into the broader ML ecosystem. ...

Techniques for Customizing Large Language Models (LLMs) As large language models (LLMs) continue to evolve, the need for customization becomes increasingly important to tailor their capabilities to specific use cases. This blog post delves into several prominent techniques for LLM customization: prompting (including multi-shot and chaining), utilizing tools, Retrieval-Augmented Generation (RAG), and fine-tuning. Each technique has its own pros and cons, and understanding them will equip developers to make informed decisions in their projects. ...

Understanding Retrieval Augmented Generation (RAG) Retrieval Augmented Generation (RAG) is an innovative machine learning architecture that combines the strengths of information retrieval and text generation. As developers and machine learning practitioners, understanding RAG can elevate the capabilities of your AI models, enabling them to provide more relevant and context-aware responses. In this blog post, we’ll explore the workings of RAG, the importance of vectors, and how they facilitate efficient operations within this powerful framework. ...