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

AI guardrails are the policies, technical controls, and monitoring frameworks that constrain and guide an AI agent’s behavior to prevent harmful, unsafe, or unintended outcomes. They act like highway barriers that keep a car on track: they do not slow progress but reduce the risk of veering off into dangerous territory.

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.

An innovative evaluation technique in the field of artificial intelligence where one Large Language Model (LLM) is used to assess or "judge" the outputs generated by another LLM

The future of AI is not defined by larger models, but by better context and orchestration.

Modern neural networks can recognize faces, write stories, and even pass programming interviews — but they all share the same limitation: they stop learning once deployed.

A Deep Dive into Security Challenges, Ethical Implications, and Protective Strategies in Modern AI Systems.

Agentic AI begins a new era of autonomy. Now, AI systems will not just follow orders.

With a new upgrade, LSTM is here to challenge the LLM dominance.

AI's integration into space exploration has ushered in a new era of efficiency, innovation, and discovery.

The article provides a comprehensive overview on SORA text-to-video model.

1-bit LLMs offer a revolutionary approach by storing weights using only 1 bit (0 or 1) instead of multiple bytes.

The article contains an overview of fine tuning approches using PEFT and its implementation using pytorch, transformers and unsloth.

The article delves into Microsoft’s Phi-2 SLM, showcasing its fine-tuning for sentiment analysis using the PEFT and PyTorch.

Explore how Retrieval Augmented Generation (RAG) is revolutionizing the precision of responses from large language models (LLMs).