What is MCP? 💭🤔
The Model Context Protocol (MCP) is a revolutionary open standard developed by Anthropic. It is transforming how AI interacts with the world. If you have ever felt that AI models are isolated or limited by what they can see and do, MCP is the only trusted solution. Before MCP, connecting an AI model to a new tool or database was difficult. Developers had to write unique custom code for every single integration. It moves the industry from siloed agents to a standardized system where everything can talk to each other.
The Architecture of MCP.
MCP works through a simple system that manages how information moves,
This communication happens using JSON-RPC 2.0. It is a standard message format that ensures everyone is speaking the same language.
The Three Core Building Blocks
An MCP server offers three main primitives or functions to an AI agent,
Real-World Applications
MCP is not just a concept. it is already being used in several ways,
By standardizing how AI interacts with the world, MCP makes AI more powerful, more secure and much easier for developers to build.
✍️ @TheInfinityAI
The Model Context Protocol (MCP) is a revolutionary open standard developed by Anthropic. It is transforming how AI interacts with the world. If you have ever felt that AI models are isolated or limited by what they can see and do, MCP is the only trusted solution. Before MCP, connecting an AI model to a new tool or database was difficult. Developers had to write unique custom code for every single integration. It moves the industry from siloed agents to a standardized system where everything can talk to each other.
The Architecture of MCP.
MCP works through a simple system that manages how information moves,
Host - The environment where AI lives, such as Claude Desktop or VS Code. It serves as the entry point for the user.
Client - acts as the connector. It manages the connection and sends specific requests to the servers.
Server - The provider that exposes tools and data to the AI. Developers build these servers to give the AI specific powers, like searching the web or reading a database.
This communication happens using JSON-RPC 2.0. It is a standard message format that ensures everyone is speaking the same language.
The Three Core Building Blocks
An MCP server offers three main primitives or functions to an AI agent,
Tools - executable functions that allow the AI to take action. For example, a tool might allow the AI to search the web or run a piece of code.
Resources - read-only data sources that provide context. This includes documents, database records or file contents that the AI can read but not change.
Prompts - reusable templates that help the AI complete tasks consistently. They guide the AI to handle specific requests
Real-World Applications
MCP is not just a concept. it is already being used in several ways,
AI Coding Assistants - Tools like Claude Code and Cursor.
Data Analysis - AI can connect to live databases to run SQL queries and analyze information securely.
Workflow Automation - AI agents can read your calendar, manage tasks and send emails through MCP servers.
RAG Systems - Agents can retrieve documents from PDFs and knowledge bases to provide more accurate answers.
By standardizing how AI interacts with the world, MCP makes AI more powerful, more secure and much easier for developers to build.
✍️ @TheInfinityAI
Telegram
𝐈𝐧𝐟𝐢𝐧𝐢𝐭𝐲 𝐂𝐒
❤3
In today’s fast-changing online media world, yt-dlp remains the leading open-source tool for downloading video and audio. Even as streaming platforms add stronger protections, this command-line tool is still trusted by developers.
The most significant evolution is how yt-dlp handles platform security. To bypass modern bot-detection and encrypted signatures, yt-dlp now supports external JavaScript runtimes. While it remains a Python-based tool, it uses engines like Deno or Node.js to solve complex server-side puzzles in real-time. This helps it access high-quality content, including 4K and 8K videos, which many basic tools cannot download.
yt-dlp is not just a downloader. It helps users keep control over their content. Since online media can be removed or locked behind paywalls at any time, yt-dlp allows users to save and manage their own copies. For users who are comfortable with terminal tools, it remains a powerful tool between online content and local storage.
✍️ @TheInfinityAI
Article 31: Policy Gradients and Actor-Critic – The Direct Strategy 🎭
Sometimes, calculating the value of every action is too hard. Instead of it, the agent learns the policy directly. We call it as Policy-Based learning.
1. Policy Gradients (Learning the Probability).
In Q-Learning, we pick the action with the highest number. In Policy Gradients, a Neural Network outputs a probability distribution.
The workflow;
2. The Actor-Critic Architecture
Policy Gradients can be noisy and slow. To fix this, we combine Value-Based and Policy-Based methods. This is the Actor-Critic model.
Think it as a movie set:
3. Advantage Function (A)
The critic uses a special math tool called the advantage function to help the Actor.
A(s, a) = Q(s, a) - V(s)
Summary 📝
Policy Gradients teach the agent a strategy (probabilities) directly. Actor-Critic models use two brains; one to act (Actor) and one to give feedback (Critic). The Advantage Function helps the agent understand if an action is better than the average choice.✨ 🙊 😁 .
In the next article (Article 32), we enter Phase 9: Deep Learning, where we study the secrets of Neural Networks and Backpropagation!🧠 🙊 😁
✍️ @TheInfinityAI
Sometimes, calculating the value of every action is too hard. Instead of it, the agent learns the policy directly. We call it as Policy-Based learning.
1. Policy Gradients (Learning the Probability).
In Q-Learning, we pick the action with the highest number. In Policy Gradients, a Neural Network outputs a probability distribution.
The workflow;
The network says there is a 70% chance that jumping is good and a 30% chance that running is good.
The agent picks an action based on these percentages.
If the action leads to a high reward, the machine increases the probability of that action for the future.
If the action leads to a penalty, it decreases the probability.
2. The Actor-Critic Architecture
Policy Gradients can be noisy and slow. To fix this, we combine Value-Based and Policy-Based methods. This is the Actor-Critic model.
Think it as a movie set:
The Actor - this is a neural network that learns the Policy. It decides which Action to take.
The Critic - This is a second neural network that learns the value. It watches the actor and critiques the action. It tells to the actor if the action was better or worse than expected.
3. Advantage Function (A)
The critic uses a special math tool called the advantage function to help the Actor.
A(s, a) = Q(s, a) - V(s)
V(s) - This is the average reward we expect from this state.
Q(s, a) - This is the actual reward we got from a specific action.
Result - If A is positive, the action was better than average. The actor learns to do it more. If A is negative, the actor learns to do it less.
Summary 📝
Policy Gradients teach the agent a strategy (probabilities) directly. Actor-Critic models use two brains; one to act (Actor) and one to give feedback (Critic). The Advantage Function helps the agent understand if an action is better than the average choice.
In the next article (Article 32), we enter Phase 9: Deep Learning, where we study the secrets of Neural Networks and Backpropagation!
Please open Telegram to view this post
VIEW IN TELEGRAM
Telegram
𝐈𝐧𝐟𝐢𝐧𝐢𝐭𝐲 𝐂𝐒
❤1
Which library is used for basic plotting in Python?
Anonymous Quiz
25%
NumPy
27%
Pandas
42%
Matplotlib
6%
Tensorflow
Data breach Alert ☠️
ශ්රී ලංකාවේ රාජ්ය පරිපාලන, පළාත් සභා සහ පළාත් පාලන අමාත්යංශයේ නිල වෙබ් අඩවිය (pubad.gov.lk) සහ එහි පද්ධතිවලට එල්ල කරලා තියෙන සයිබර් ප්රහාරයක් හේතුවෙන් රාජ්ය සේවකයන් දහස් ගණනකගේ සංවේදී දත්ත අන්තර්ජාලයට මුදාහැර ඇති බවට වාර්තා වෙනවා. wh6am නැමති Threat Actor විසින් මේ දත්ත Dark Web අඩවි හරහා අලෙවි කිරීමට උත්සාහ කරන බවට තොරතුරු Dark web Intelligence වාර්තා කරනවා.
මේ විකුණන්න තියෙන Data අතර,
වගේ දේවල් තියෙනවා. මේක තවම unconfirmed. නමුත් Source කීපයකින්ම මේක වාර්තා කරනව. මෙ තොරතුරු Dark Web හරහා ඩොලර් 200 වැනි සුළු මුදලකට අලෙවි වෙනවා. රාජ්ය සේවකයින් ඉදිරි කාලවලදී Phishing Attack වලට අහු වෙන්න පුළුවන් මේ නිසා. (Targeted Phishing, Identity Theft, Social Engineering)
Sources
▪️ Source 01
▪️ Source 02
▪️ Source 03
✍️ @TheInfinityAI
ශ්රී ලංකාවේ රාජ්ය පරිපාලන, පළාත් සභා සහ පළාත් පාලන අමාත්යංශයේ නිල වෙබ් අඩවිය (pubad.gov.lk) සහ එහි පද්ධතිවලට එල්ල කරලා තියෙන සයිබර් ප්රහාරයක් හේතුවෙන් රාජ්ය සේවකයන් දහස් ගණනකගේ සංවේදී දත්ත අන්තර්ජාලයට මුදාහැර ඇති බවට වාර්තා වෙනවා. wh6am නැමති Threat Actor විසින් මේ දත්ත Dark Web අඩවි හරහා අලෙවි කිරීමට උත්සාහ කරන බවට තොරතුරු Dark web Intelligence වාර්තා කරනවා.
මේ විකුණන්න තියෙන Data අතර,
▪️ Full names (first and last, initials)
▪️ Email addresses (personal and work)
▪️ Phone numbers (mobile, office, home)
▪️ Physical addresses (home and work)
▪️ National ID numbers (NIC)
▪️ Job titles and designations
▪️ Employer names and department details
▪️ Usernames and hashed passwords
▪️ User registration dates and last activity timestamps
▪️ Internal government circulars and service minutes (PDF files)
වගේ දේවල් තියෙනවා. මේක තවම unconfirmed. නමුත් Source කීපයකින්ම මේක වාර්තා කරනව. මෙ තොරතුරු Dark Web හරහා ඩොලර් 200 වැනි සුළු මුදලකට අලෙවි වෙනවා. රාජ්ය සේවකයින් ඉදිරි කාලවලදී Phishing Attack වලට අහු වෙන්න පුළුවන් මේ නිසා. (Targeted Phishing, Identity Theft, Social Engineering)
Sources
▪️ Source 01
▪️ Source 02
▪️ Source 03
✍️ @TheInfinityAI
Telegram
𝐈𝐧𝐟𝐢𝐧𝐢𝐭𝐲 𝐂𝐒
Which activation function is most commonly used in hidden layers of deep neural networks? ✴️
Anonymous Quiz
22%
Sigmoid
20%
Tanh
30%
ReLU
28%
Softmax