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

In the rapidly evolving landscape of artificial intelligence (AI), China’s DeepSeek has emerged as a formidable contender, challenging established players and redefining industry standards. This ascent is deeply intertwined with High-Flyer, an AI-driven quantitative hedge fund whose strategic investments and visionary leadership have propelled DeepSeek to the forefront of AI innovation.

Source: Internet

Founded in February 2016 by Liang Wenfeng, High-Flyer—officially known as Hangzhou Huanfang Technology Ltd Co.—quickly distinguished itself in the financial sector by leveraging AI models for investment decisions. By late 2017, AI systems managed the majority of High-Flyer’s trading activities, solidifying its reputation as a leader in AI-driven stock trading. The firm’s portfolio burgeoned to an impressive 100 billion yuan (approximately $13.79 billion), underscoring the efficacy of its AI-centric strategies.

AI Planet has introduced Buddhi-128K-Chat-7B, an open-source chat model distinguished by its expansive 128,000-token context window. This advancement enables the model to process and retain extensive contextual information, enhancing its performance in tasks requiring deep context understanding.

Source: Internet

Model Architecture

Buddhi-128K-Chat-7B is fine-tuned from the Mistral-7B Instruct v0.2 base model, selected for its superior reasoning capabilities. The Mistral-7B architecture incorporates features such as Grouped-Query Attention and a Byte-fallback BPE tokenizer, originally supporting a maximum of 32,768 position embeddings. To extend this to 128K, the Yet another Rope Extension (YaRN) technique was employed, modifying positional embeddings to accommodate the increased context length.

Coca-Cola is one of the most iconic brands in the world, with a history spanning over a century. It has become synonymous with soft drinks, creating a massive global presence. This case study explores Coca-Cola’s success story, key crises, and scandals, as well as how the company has managed to remain a market leader for so long.

Source: Internet

The Success Story

Origins and Early Growth

• Coca-Cola was invented in 1886 by Dr. John Stith Pemberton in Atlanta, Georgia.
• The formula was later bought by Asa Candler, who aggressively marketed it and expanded distribution.
• By 1895, Coca-Cola was sold across the United States.

Market Domination

• Coca-Cola became a household name through branding, advertising, and distribution.
• By 1919, the company was sold to a group of investors for $25 million.
• The introduction of the iconic contour bottle in 1915 helped distinguish Coca-Cola from competitors.
• The company’s famous Santa Claus campaign in the 1930s reinforced its association with happiness and celebration.
• By the 1950s, Coca-Cola had expanded into more than 100 countries.

Silicon Valley Bank (SVB) was one of the largest banks catering to the startup and venture capital ecosystem in the United States. Its sudden collapse in March 2023 sent shockwaves through the financial sector, prompting government intervention and raising concerns about the stability of other regional banks. This case study explores the factors that led to SVB’s failure, its impact, and key lessons learned.

Source: Internet

Background of Silicon Valley Bank

Founded in 1983, SVB grew into the 16th largest bank in the U.S., with assets exceeding $209 billion by the end of 2022. It specialized in serving technology startups, venture capital (VC) firms, and innovation-driven companies. SVB’s business model relied heavily on deposit funding from these clients and investments in long-duration U.S. government bonds.