Posts

REST vs gRPC: The Pragmatic, Developer-Friendly Guide TL;DR REST is universal, human-friendly, cacheable, and ideal for public APIs and browser clients. It uses HTTP semantics with resource-oriented design and JSON by default. gRPC is fast, strongly typed, and streaming-friendly—great for internal microservices and low-latency mobile/IoT. It uses HTTP/2 (or HTTP/3 in some stacks) and Protocol Buffers (Protobuf). Use REST for broad reach, compatibility, and simple CRUD; use gRPC for high-throughput, low-latency, streaming, or strongly typed interfaces across polyglot services. Hybrid patterns—REST for public, gRPC internally—often deliver the best of both worlds. Contents ...

REST vs. gRPC: Understanding the Differences and Choosing the Right Protocol As the landscape of application development continues to evolve, so too do the communication protocols that software developers rely upon. Two primary contenders in the realm of remote procedure call (RPC) architectures today are REST (Representational State Transfer) and gRPC (Google Remote Procedure Call). Both are powerful in their right and cater to different use cases based on project requirements. This article delves into the core differences between REST and gRPC, examining their strengths, weaknesses, and when to use each. ...

Understanding Software Architecture & Technology of Large-Scale Systems In the ever-expanding world of software development, creating applications that can support millions of users and handle large data volumes is a challenging task. When we’re talking about large-scale systems, we’re not just referring to an application’s size but also its complexity and ability to scale efficiently. This article will delve into the software architecture and technologies essential for building large-scale systems, exploring everything from architectural patterns to technology stacks and best practices. ...

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. ...

Understanding Hugging Face: A Comprehensive Guide Hugging Face has become a leading platform in the field of Natural Language Processing (NLP) and machine learning, especially known for its user-friendly tools and extensive community resources. In this blog, we’ll delve into what Hugging Face is, how it works, and the key libraries it offers, including transformers, datasets, accelerate, and hub. What is Hugging Face? Hugging Face started as a chatbot company but quickly shifted focus to NLP and now is a hub for state-of-the-art machine learning models. The platform is built around the community approach, enabling developers of all levels to collaborate and share pre-trained models, datasets, and innovations in machine learning. ...