In the ever-evolving field of artificial intelligence, large language models (LLMs) like GPT-4, Claude, Gemini, and LLaMA have reshaped how machines understand and generate human language. Behind the impressive capabilities of these models lies a deceptively simple but foundational step: tokenization.

In this blog, we will dive deep into the concept of tokenization, understand its types, why it’s needed, the challenges it solves, how it works under the hood, and where it’s headed in the future. This is a one-stop technical deep-dive for anyone looking to fully grasp the backbone of language understanding in LLMs.

What is Tokenization?

At its core, tokenization is the process of converting raw text into smaller units called tokens that a language model can understand and process. These tokens can be:

• Characters
• Words
• Subwords
• Byte-pair sequences
• WordPieces
• SentencePieces
• Byte-level representations

Each model has its own strategy, depending on design goals like efficiency, vocabulary size, multilingual handling, and memory constraints.

BluSmart was once a symbol of India’s clean energy aspirations — an all-electric ride-hailing platform backed by marquee investors and government lenders. With its zero-emissions fleet and no-surge pricing model, it quickly gained popularity in cities like Delhi and Bengaluru.

But behind the scenes, the startup’s success story unraveled into one of the most serious corporate fraud cases in India’s startup ecosystem. At the center of this financial maze was Gensol Engineering Ltd, a publicly listed company, controlled by the same promoters behind BluSmart. The ₹262 crore scandal that emerged in 2025 now implicates not just BluSmart, but Gensol’s board, finances, and investors.

Source: Internet

In the annals of modern financial history, few names have sparked as much controversy, disbelief, and chaos as Futures Exchange (FTX). Once hailed as a shining star of the cryptocurrency world, FTX’s meteoric rise and catastrophic fall stunned investors, regulators, and the general public alike. By the end of 2023, the scandal surrounding FTX and its founder Sam Bankman-Fried had cemented its place as one of the largest and most complex financial frauds of the 21st century.

This blog dives into the rise and fall of FTX, examining the events that led to its collapse, the financial and human toll it took, and the key takeaways from a debacle that shook the entire crypto industry to its core.

Source: Internet

The Rise of FTX: From Start-up to Crypto Juggernaut

FTX was founded in 2019 by Sam Bankman-Fried (commonly referred to as SBF), a former Wall Street quant with a background from MIT and a reputation for genius-level intellect. The exchange was created as a more sophisticated platform for cryptocurrency derivatives and quickly attracted traders looking for advanced features, high leverage, and innovative products.

By 2021, FTX had:

• Raised over \$1.8 billion from prominent investors including Sequoia Capital, SoftBank, and Tiger Global.
• Claimed over 1 million users and processed billions of dollars in trades daily.
• Achieved a staggering \$32 billion valuation, making it the third-largest crypto exchange globally.

SBF’s influence extended well beyond the company. He was a frequent guest on financial talk shows, lobbied in Washington, and was dubbed the “JP Morgan of crypto” after bailing out other struggling crypto firms in 2022. But behind the charismatic image and philanthropic posturing was a house of cards waiting to collapse.

In today’s dynamic business environment, efficient case management is paramount. Enter Smartcase Engine, an advanced case management framework designed to streamline complex case handling through real-time tracking, efficient workflows, and automated decision-making processes.

What is Smartcase Engine?

Smartcase Engine is a modular, microservices-based platform tailored for managing intricate case workflows. It offers:

• Real-Time Case Tracking: Monitor cases as they progress through various stages.
• Efficient Workflows: Automate and optimize the sequence of tasks involved in case resolution.
• Automated Decision-Making: Leverage predefined rules and AI to make informed decisions without manual intervention.

Source: Rishijeet Mishra’s Blog

As the AI landscape rapidly evolves, the demand for systems that support modular, context-aware, and efficient orchestration of models has grown. Enter the Model Context Protocol (MCP) — a rising standard that enables dynamic, multi-agent AI systems to exchange context, manage state, and chain model invocations intelligently.

In this article, we’ll explore what MCP is, why it matters, and how it’s becoming a key component in the infrastructure stack for advanced AI applications. We’ll also walk through a conceptual example of building an MCP-compatible server.

What is the Model Context Protocol (MCP)?

MCP is a protocol designed to manage the contextual state of AI models across requests in multi-agent, multi-model environments. It’s part of a broader effort to make LLMs (Large Language Models) more stateful, collaborative, and task-aware.

At its core, MCP provides:

• A way to pass and maintain context (like conversation history, task progress, or shared knowledge) across AI agents or model calls.
• A standardized protocol to support chained inference, where multiple models collaborate on subtasks.
• Support for stateful computation, which is critical in complex reasoning or long-running workflows.

Source: Internet