Forem: mehmet akar

Context7 MCP Tutorial

mehmet akar — Fri, 25 Apr 2025 12:46:07 +0000

Context7 and its tool Context7 MCP is so popular in AI-Vibe Coding world, nowadays. I want to mention it and create an article about it.

Context7 MCP is a tool that supercharges AI prompts with real-time, version-specific documentation and code examples. Whether you're using Claude, Cursor, VS Code, or another Model Context Protocol (MCP) client, Context7 helps eliminate hallucinated APIs and outdated examples by injecting live data into your LLM interactions.

We can see their popularity on github star graphics:

🌟 Why Use Context7?

❌ Without Context7:

Outdated examples based on old training data.
AI hallucinations about APIs that don’t exist.
Generalized help for outdated package versions.

✅ With Context7:

Accurate, live documentation and examples pulled from the actual library.
Instant, relevant answers based on real packages and versions.
All you have to do is add use context7 to your prompt.

Examples:

Create a basic Next.js project with app router. use context7

Create a script to delete the rows where the city is "" given PostgreSQL credentials. use context7

🛠️ Getting Started

Requirements

Node.js ≥ v18.0.0
One of the following MCP clients:
- Cursor
- Windsurf
- Claude Desktop
- VS Code
- Docker (optional)

📦 Installation Methods

1. Install via Smithery (for Claude Desktop)

npx -y @smithery/cli install @upstash/context7-mcp --client claude

🧭 Installation for Specific Clients

📍 Cursor

Steps:

Go to: Settings → Cursor Settings → MCP → Add new global MCP server
Or directly update ~/.cursor/mcp.json:

{
  "mcpServers": {
    "context7": {
      "command": "npx",
      "args": ["-y", "@upstash/context7-mcp@latest"]
    }
  }
}

Alternative Runtimes:

Using Bun

{
  "mcpServers": {
    "context7": {
      "command": "bunx",
      "args": ["-y", "@upstash/context7-mcp@latest"]
    }
  }
}

Using Deno

{
  "mcpServers": {
    "context7": {
      "command": "deno",
      "args": ["run", "--allow-net", "npm:@upstash/context7-mcp"]
    }
  }
}

🌊 Windsurf

Add to your windsurf.mcp.json:

{
  "mcpServers": {
    "context7": {
      "command": "npx",
      "args": ["-y", "@upstash/context7-mcp@latest"]
    }
  }
}

🧩 VS Code / VS Code Insiders

Install via VS Code MCP Docs

{
  "servers": {
    "Context7": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "@upstash/context7-mcp@latest"]
    }
  }
}

Install buttons:

🧠 Claude Code

claude mcp add context7 -- npx -y @upstash/context7-mcp@latest

💻 Claude Desktop

Update claude_desktop_config.json:

{
  "mcpServers": {
    "Context7": {
      "command": "npx",
      "args": ["-y", "@upstash/context7-mcp@latest"]
    }
  }
}

🐳 Using Docker

1. Dockerfile

Create a file named Dockerfile:

FROM node:18-alpine

WORKDIR /app

RUN npm install -g @upstash/context7-mcp@latest

CMD ["context7-mcp"]

2. Build the Docker Image

docker build -t context7-mcp .

3. Configure MCP Client

Example client_mcp_settings.json:

{
  "mcpServers": {
    "Context7": {
      "autoApprove": [],
      "disabled": false,
      "timeout": 60,
      "command": "docker",
      "args": ["run", "-i", "--rm", "context7-mcp"],
      "transportType": "stdio"
    }
  }
}

Note: The tag context7-mcp should match your docker build tag.

🧰 Available Tools

`resolve-library-id`

Resolves a general library name into a Context7-compatible ID.

libraryName (required)

`get-library-docs`

Fetch docs using Context7 ID.

context7CompatibleLibraryID (required)
topic (optional): e.g., "routing", "hooks"
tokens (optional, default 5000)

⚙️ Development

Clone the repo and install dependencies:

bun i

Build the project:

bun run build

Local config example:

{
  "mcpServers": {
    "context7": {
      "command": "npx",
      "args": ["tsx", "/path/to/folder/context7-mcp/src/index.ts"]
    }
  }
}

🔬 Test with MCP Inspector

npx -y @modelcontextprotocol/inspector npx @upstash/context7-mcp@latest

🧯 Context7 Troubleshooting

`ERR_MODULE_NOT_FOUND`?

Switch from npx to bunx:

{
  "mcpServers": {
    "context7": {
      "command": "bunx",
      "args": ["-y", "@upstash/context7-mcp@latest"]
    }
  }
}

Context7 MCP Errors?

Remove @latest from package name.
Try bunx.
Try deno.

Blender MCP: Seamless Integration of Blender with Claude AI

mehmet akar — Mon, 17 Mar 2025 05:24:28 +0000

Blender MCP (Blender Model Context Protocol) is a nice tool that connects Blender to Claude AI, allowing AI-driven 3D modeling, scene creation, and object manipulation. This integration makes 3D design faster, more intuitive, and highly interactive.

Why Use Blender MCP?

AI-powered modeling: Direct interaction with Claude AI for instant 3D modifications.
Seamless asset management: Access Poly Haven and Hyper3D Rodin for quick model generation.
Advanced material control: Modify object textures and materials effortlessly.
Script execution: Run Python scripts directly within Blender.

Key Features

Two-way communication between Blender and Claude AI
Object creation, deletion, and transformation
Material and texture application
Scene inspection and real-time information retrieval
AI-generated models via Hyper3D Rodin

Installation Guide

Prerequisites

To install Blender MCP, ensure you have:

Blender 3.0+
Python 3.10+
uv package manager (Essential for running MCP)

Installing UV

For Mac:

brew install uv

For Windows:

powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

Then, add to the system path:

set Path=C:\Users\nntra\.local\bin;%Path%

Full installation guide

⚠️ Do not proceed before installing UV

Setting Up MCP in Claude AI

Open Claude AI Desktop
Navigate to Settings > Developer > Edit Config
Add the following to claude_desktop_config.json:

{
    "mcpServers": {
        "blender": {
            "command": "uvx",
            "args": [
                "blender-mcp"
            ]
        }
    }
}

Installing Blender Addon

Download the addon.py file
Open Blender
Navigate to Edit > Preferences > Add-ons
Click Install... and select addon.py
Enable the addon (Checkbox: "Interface: Blender MCP")

How to Use Blender MCP

Connecting to Claude AI

Open Blender and press N to access the 3D View Sidebar
Navigate to the BlenderMCP tab
Enable Poly Haven API (Optional)
Click "Connect to Claude"
Ensure the MCP Server is running

Example Commands

You can now interact with Blender through Claude AI by using commands like:

Create a 3D scene: "Generate a dungeon scene with a dragon guarding gold"

Demo video

Modify textures: "Make this car red and metallic"
Use Poly Haven assets: "Create a beach scene using HDRIs and rock models from Poly Haven"

Demo video

AI-generated models: "Generate a 3D garden gnome using Hyper3D Rodin"
Adjust camera: "Set up isometric camera view"
Lighting setup: "Adjust lighting to a studio environment"

Using MCP with Cursor Integration

Open Cursor Settings > MCP
Paste the command:

uvx blender-mcp

Cursor setup guide video]

⚠️ Run MCP server on either Cursor or Claude, NOT both

Troubleshooting & FAQs

Common Issues & Fixes

Connection Issues: Ensure the Blender Addon Server is running and correctly configured on Claude.
Timeout Errors: Break complex requests into smaller commands.
Poly Haven API Issues: Refresh API settings if assets fail to load.
General Fix: Restart Blender and Claude AI.

Security Considerations

Python Execution Risks: The execute_blender_code function allows arbitrary code execution. Always save work before using it.
Large Asset Downloads: Disable Poly Haven integration if not needed.
Complex Commands: Break large operations into smaller steps for efficiency.

Contributing & Development

Blender MCP is an open-source project. Contributions are welcome! Submit a Pull Request or report issues on the GitHub repository: @blender-mcp

Final Thoughts

Blender MCP is a game-changer for 3D artists, allowing AI-assisted modeling, asset management, and interactive scene creation. By integrating Claude AI with Blender, this tool accelerates workflows and brings creative visions to life effortlessly.

What is Alpaca LLM?

mehmet akar — Sun, 16 Mar 2025 05:02:20 +0000

I want to talk about What is Alpaca LLM? as it is wondered heavily, nowadays.

What is Alpaca LLM? Introduction

Alpaca LLM is an advanced, open-source language model developed by researchers at Stanford University as a fine-tuned version of Meta’s LLaMA (Large Language Model Meta AI). Designed to be a cost-effective alternative to proprietary AI models like OpenAI’s GPT-4, Alpaca LLM enables developers and researchers to harness the power of large language models (LLMs) for various applications, including chatbots, content generation, and research.

In this article, we will explore what Alpaca LLM is, how it works, its features, advantages, limitations, and how you can deploy it for your AI applications.

What is Alpaca LLM?

Alpaca LLM is a fine-tuned version of Meta’s LLaMA 7B model. It was trained using self-instruction techniques inspired by OpenAI’s InstructGPT. The goal was to create a lightweight yet powerful AI model capable of generating human-like text while maintaining accessibility and affordability for developers and researchers.

Unlike proprietary models like GPT-4, Alpaca LLM is available as an open-source project, allowing developers to modify, optimize, and deploy the model in various applications.

How Does Alpaca LLM Work?

Alpaca LLM follows a supervised fine-tuning approach:

Base Model Selection: The model starts with Meta’s LLaMA 7B as its foundation.
Instruction Tuning: Researchers created 52,000 unique instruction-following samples using OpenAI’s text-davinci-003 model.
Fine-Tuning: The model is then fine-tuned with these instructions to improve performance in text generation, summarization, and conversational AI.
Inference & Deployment: Once trained, Alpaca LLM can generate responses to prompts in a manner similar to GPT-based models.

Key Features of Alpaca LLM

Open-Source: Unlike proprietary AI models, Alpaca LLM is freely available for developers and researchers.
Cost-Effective Training: Stanford researchers fine-tuned Alpaca for less than $600, proving that high-quality models can be trained at low cost.
Lightweight & Efficient: The model is designed to be compact and runs efficiently on consumer-grade hardware.
Instruction Following: It is optimized for following human instructions, making it suitable for chatbot applications.
Customizability: Developers can fine-tune the model further to meet specific needs.

Advantages of Using Alpaca LLM

Free & Open-Source: No licensing fees, making it accessible to everyone.
Flexible Deployment: Can be deployed on local machines, cloud servers, or edge devices.
Community Support: Being open-source, Alpaca LLM benefits from contributions and improvements from the AI research community.
Comparable Performance: Despite being lightweight, it achieves results similar to state-of-the-art language models.

Limitations of Alpaca LLM

Limited Scale: It is not as powerful as models like GPT-4 due to its smaller dataset and reduced computational resources.
Bias & Safety Issues: Like all AI models, it may inherit biases from training data.
Requires Technical Knowledge: Setting up and fine-tuning Alpaca LLM requires programming and AI expertise.

How to Install and Use Alpaca LLM

Prerequisites

Before installing Alpaca LLM, ensure you have the following:

A system with Linux or macOS (Windows users can use WSL)
Python 3.8 or later
CUDA-compatible GPU (for efficient inference)
Git and Python virtual environment tools installed

Step 1: Clone the Alpaca Repository

# Clone the Alpaca LLM repository
git clone https://github.com/tatsu-lab/stanford_alpaca.git
cd stanford_alpaca

Step 2: Set Up the Virtual Environment

# Create and activate a virtual environment
python3 -m venv alpaca_env
source alpaca_env/bin/activate  # On Windows: alpaca_env\Scripts\activate

Step 3: Install Dependencies

# Install required Python libraries
pip install -r requirements.txt

Step 4: Download LLaMA Model Weights

Since Alpaca is built on Meta’s LLaMA, you need to obtain access to LLaMA weights. You can request them from Meta’s official site:
https://ai.facebook.com/blog/large-language-model-llama-meta-ai/

Once you have the LLaMA weights, place them in the appropriate directory inside the Alpaca project.

Step 5: Fine-Tune Alpaca LLM

To fine-tune Alpaca with your custom dataset, use the following command:

python train.py --base_model '/path/to/llama/model' --data_path '/path/to/dataset.json' --output_dir './trained_model'

Step 6: Running Inference on Alpaca LLM

After training, you can generate text responses using:

python generate.py --model_path './trained_model' --prompt 'What is Alpaca LLM?'

Future of Alpaca LLM

The Alpaca LLM project demonstrates how cost-effective, high-quality AI models can be developed with minimal resources. With continuous improvements in open-source AI, we can expect better performance, increased accessibility, and more widespread adoption of lightweight LLMs in the future.

What is Alpaca LLM? Conclusion

Alpaca LLM is an exciting advancement in the field of open-source AI, offering a powerful, cost-effective alternative to proprietary large language models. With its open-source nature, efficiency, and customization options, it is an excellent choice for developers looking to integrate AI capabilities into their projects.

If you’re interested in AI development and want to experiment with a freely available LLM, Alpaca LLM is a great place to start. As AI technology continues to evolve, models like Alpaca will play a crucial role in democratizing access to artificial intelligence.

LCM vs. LLM

mehmet akar — Sat, 15 Mar 2025 22:52:09 +0000

LCM vs. LLM is a popular question, nowadays. Let me analyze this comparison.

With the rise of artificial intelligence and machine learning, two key terms that are often discussed are LCM (Large Concept Models) and LLM (Large Language Models). While they share similarities in being AI-driven models, they differ significantly in their approaches and applications. This article will explore their distinctions and use cases.

What is LCM (Large Concept Model)?

LCM, or Large Concept Model, is a new AI paradigm developed by Meta that shifts the focus from token-based processing to concept-level understanding. Unlike LLMs, which predict the next word based on tokenized text, LCMs operate at a higher level of abstraction by modeling entire concepts instead of individual words or tokens.

How Do LCMs Work?

LCMs use concept embeddings, which represent ideas rather than words, allowing them to generalize more effectively across languages and modalities. Their key components include:

Concept Encoding: Instead of breaking text into small units, LCMs encode entire sentences or ideas into a higher-dimensional embedding space.
Sequence Modeling: These models predict sequences of concept embeddings rather than individual words, enhancing long-term coherence.
Decoding: The predicted embeddings are then transformed back into readable text or other formats.

Advantages of LCMs:

Improved Coherence: By working with concepts rather than tokens, LCMs maintain better contextual consistency in long-form content.
Multilingual & Multimodal Capabilities: Since they operate at the concept level, LCMs can generalize across different languages and modalities (text, speech, images, etc.).
Higher-Level Reasoning: LCMs improve AI’s ability to understand abstract ideas and complex topics.

Applications of LCMs:

Advanced summarization
Content creation and reasoning tasks
Multilingual and multimodal AI applications

What is LLM (Large Language Model)?

LLM, or Large Language Model, is a deep learning-based AI model designed to process and generate human-like text. LLMs rely on tokenization, predicting the next token in a sequence based on vast amounts of text data.

How Do LLMs Work?

Tokenization: Text is broken down into tokens (words or subwords).
Training: Models learn linguistic patterns by analyzing massive datasets and adjusting their internal parameters to minimize prediction errors.
Generation: LLMs construct sentences word by word, relying on statistical patterns.

Applications of LLMs:

Text completion and generation
Machine translation
Chatbots and conversational AI
Sentiment analysis and code generation

Limitations of LLMs:

Struggles with Long-Range Context: Token-by-token processing makes it difficult to maintain coherence over long texts.
Language-Specific Limitations: Requires extensive training in each language, making multilingual support more complex.
Lack of True Understanding: Predicts words statistically rather than understanding concepts like LCMs.

Key Differences Between LCMs and LLMs

Aspect	Large Language Models (LLMs)	Large Concept Models (LCMs)
Processing Unit	Tokens (words or subwords)	Concepts (sentences or higher-level ideas)
Abstraction Level	Operate at a granular level, focusing on individual tokens	Function at a higher abstraction level, dealing with entire concepts
Language Dependency	Often tailored to specific languages; multilingual capabilities require extensive training	Designed to be language-agnostic, leveraging embedding spaces that support multiple languages and modalities
Context Handling	May struggle with long-term coherence due to token-by-token processing	Better equipped for maintaining context over extended content by focusing on broader concepts
Generation Approach	Sequential token prediction, constructing sentences word by word	Predicts and generates entire concepts, allowing for more holistic and coherent content creation
Training Paradigm	Requires vast amounts of token-level data; training involves learning probabilities of token sequences	Trained on sequences of concept embeddings, enabling the model to grasp and generate higher-level semantic structures
Applications	Suitable for tasks requiring detailed token-level manipulation, such as precise text editing or code generation	Ideal for applications involving abstract reasoning, summarization, and content creation across different languages and formats
Limitations	- May lack deep understanding of context - Can produce less coherent long-form content - Language and modality limitations due to token-based processing	- Emerging technology with ongoing research - Requires robust concept encoding and decoding mechanisms - Potential challenges in defining and standardizing what constitutes a "concept" across various applications and domains

LCM vs. LLM: Conclusion

While both LCMs and LLMs are crucial AI advancements, they serve different purposes. LLMs are ideal for text-based generation tasks, whereas LCMs take a broader, concept-driven approach that enhances coherence, multilingual adaptability, and abstract reasoning. As AI technology evolves, LCMs may overcome many of the limitations of traditional LLMs, offering a more holistic approach to natural language understanding.

What is LLM in AI

mehmet akar — Sat, 15 Mar 2025 18:47:54 +0000

What is LLM in AI? This is the most asked questions for people who wonder ai development.

What is LLM in AI? in some words?

Large Language Models (LLMs) have gained significant attention for their ability to understand and generate human-like text. But what exactly is an LLM in AI, and how does it work? This guide will explore everything you need to know about LLMs, their applications, benefits, and limitations.

What is an LLM in AI?

A Large Language Model (LLM) is a type of artificial intelligence algorithm designed to process and generate text by analyzing vast amounts of language data. These models are built using deep learning techniques, particularly neural networks with billions of parameters. Some well-known LLMs include OpenAI’s GPT-4, Google’s Bard, and Meta’s LLaMA.

How Do LLMs Work?

LLMs are trained on extensive datasets consisting of books, articles, and online content. They leverage Natural Language Processing (NLP) techniques to:

Understand Context: Recognizing the meaning of words and sentences based on their placement and usage.
Predict Words: Generating text based on probabilities derived from training data.
Mimic Human Responses: Producing coherent and contextually relevant replies to prompts.

Key Features of Large Language Models

Scalability: LLMs can process vast amounts of text and generate responses in real-time.
Multitasking Capability: They can handle diverse tasks such as translation, summarization, and question-answering.
Self-Improvement: Many LLMs leverage reinforcement learning techniques to refine their outputs over time.

Applications of LLMs in Various Industries

1. Content Creation

LLMs can generate high-quality articles, blog posts, and marketing content with minimal human intervention.

2. Customer Support

AI chatbots powered by LLMs improve customer service by providing instant responses to queries.

3. Healthcare

They assist in analyzing medical records, summarizing patient data, and even generating medical reports.

4. Software Development

LLMs like GitHub Copilot help developers by auto-completing code and offering debugging suggestions.

Benefits of LLMs

Time-Saving: Automates repetitive text-based tasks.
Cost-Effective: Reduces the need for manual content generation and data analysis.
Enhanced Personalization: Provides tailored responses and recommendations.

Challenges and Limitations

Despite their advantages, LLMs come with some challenges:

Bias in Data: They may inherit biases present in their training data.
High Computational Costs: Training and deploying LLMs require substantial computational resources.
Potential Misinformation: Not all AI-generated content is accurate or reliable.

The Future of LLMs

The future of LLMs looks promising with advancements in:

More Efficient Models: Reducing energy consumption while maintaining performance.
Better Context Understanding: Enhancing their ability to interpret and generate more accurate responses.
Ethical AI Development: Addressing bias and improving transparency in AI decision-making.

What is LLM in AI? Conclusion

Large Language Models (LLMs) are revolutionizing the way we interact with AI, offering numerous applications across various industries. However, it is crucial to understand their capabilities and limitations to use them effectively. As AI technology continues to evolve, LLMs will become even more sophisticated, shaping the future of communication and automation.

By understanding the fundamentals of LLMs, businesses and individuals can harness their power to improve productivity, enhance user experiences, and drive innovation.

LLM vs NLP

mehmet akar — Fri, 14 Mar 2025 08:04:20 +0000

LLM vs NLP is one of the most asked comparisons in the ai world.
Let me analyze their differences, advantages, disadvantages.

LLM vs NLP Introduction

In recent years, advancements in artificial intelligence (AI) have led to significant progress in the field of natural language processing (NLP) and the rise of large language models (LLMs). While these terms are often used interchangeably, they have distinct differences in functionality, scope, and applications. This article provides a comprehensive comparison of LLMs vs NLP, exploring their differences, similarities, and use cases.

What is NLP (Natural Language Processing)?

NLP is a branch of AI focused on enabling computers to understand, interpret, and generate human language. It involves various techniques, including machine learning, deep learning, linguistic rules, and statistical methods to process and analyze text or speech data.

Core Components of NLP:

Tokenization – Breaking text into words or subwords.
Part-of-Speech (POS) Tagging – Identifying grammatical categories.
Named Entity Recognition (NER) – Detecting entities like names, places, and dates.
Sentiment Analysis – Determining the emotional tone of a text.
Text Classification – Categorizing text into predefined groups.
Machine Translation – Converting text from one language to another.

What is an LLM (Large Language Model)?

An LLM is a type of AI model designed to process and generate human-like text. These models are built using deep learning techniques, primarily transformers, and are trained on vast datasets.

Features of LLMs:

Pretrained on Large Datasets – LLMs learn from massive text corpora before fine-tuning for specific tasks.
Contextual Understanding – Unlike traditional NLP models, LLMs consider extensive context for better coherence.
Generative Capabilities – They can create human-like text, answer questions, and even write code.
Zero-shot and Few-shot Learning – LLMs can perform tasks with minimal training data.
Multimodal Integration – Some advanced LLMs support text, image, and audio processing.

Key Differences Between LLM and NLP

Feature	NLP	LLM
Definition	A field of AI focused on processing human language.	A type of AI model designed to generate and understand language at scale.
Scope	Includes a broad range of techniques and methods.	A subset of NLP using deep learning-based models.
Data Processing	Uses rule-based, statistical, and machine learning approaches.	Uses massive datasets and neural networks.
Training	Can be task-specific or general-purpose.	Typically pre-trained on large corpora and fine-tuned.
Examples	SpaCy, NLTK, BERT, TF-IDF	GPT-4, LLaMA, Claude, Gemini
Usage	Sentiment analysis, machine translation, chatbots	Conversational AI, content generation, reasoning tasks

LLM vs NLP Use Cases and Applications

When to Use NLP?

Text Preprocessing: Cleaning and structuring data for machine learning.
Information Retrieval: Search engines and document indexing.
Spam Detection: Filtering unwanted messages.
Voice Assistants: Speech-to-text and text-to-speech applications.

When to Use LLMs?

Content Creation: Writing articles, emails, and social media posts.
Chatbots and Virtual Assistants: AI-powered customer service.
Code Generation: Writing and debugging code using AI.
Data Analysis: Summarizing and extracting insights from large text datasets.

LLM vs NLP: Which is Better?

When comparing LLM vs NLP which is better, the answer depends on the use case. Traditional NLP techniques are still valuable for structured tasks like sentiment analysis, while LLMs excel at open-ended language generation. Businesses looking for advanced conversational AI or how do large language models work should consider LLMs. However, NLP remains essential for practical applications that require rule-based processing and computational efficiency.

Future of LLMs and NLP

As AI research progresses, NLP and LLMs will continue evolving. Some anticipated trends include:

Hybrid Models: Combining rule-based NLP with deep learning for better accuracy.
Personalized AI: Models tailored to individual users for improved responses.
Ethical AI Development: Addressing biases and making AI more transparent.
Multimodal AI: Integrating text, images, and voice for seamless interactions.

LLM vs NLP: The Final Verdict

While NLP and LLMs are interconnected, they serve different purposes in AI-driven language processing. NLP encompasses a broader field with various methods, whereas LLMs represent a specialized subset with advanced deep-learning capabilities. Understanding their differences and applications will help businesses and developers choose the right technology for their needs.

Best Open Source LLM

mehmet akar — Fri, 14 Mar 2025 07:47:29 +0000

I want to mention some best open source llm in 2025.

Best Open Source LLM: Introduction

Open-source Large Language Models (LLMs) are becoming increasingly powerful, offering flexible and cost-effective alternatives to proprietary models. These models enable developers, researchers, and enterprises to integrate AI capabilities into their applications without relying on closed-source platforms. This article provides an overview of the best open-source LLMs in 2025, including their capabilities, use cases, licensing, and performance comparisons.

Top Open-Source LLMs of 2025

1. LLaMA 3.1

Developer: Meta AI
Release Date: July 23, 2024
Parameter Size: 405B, 70B, 8B
Use Cases: General text generation, multilingual processing, code generation, long-form content, enterprise AI.
License: Llama Community License
Highlights: LLaMA 3.1 is Meta’s latest high-performance model, designed for large-scale enterprise applications and advanced AI research.

2. DeepSeek-R1

Developer: DeepSeek
Release Date: January 2025
Parameter Size: 671B
Use Cases: General-purpose AI, scalable applications, chatbots, education, data analysis.
License: DeepSeek License
Highlights: DeepSeek-R1 is one of the largest open-source LLMs, optimized for efficiency and adaptability across various industries.

3. Qwen 2.5 72B

Developer: Alibaba Cloud
Release Date: September 19, 2024
Parameter Size: 72B
Use Cases: Multilingual and multimodal tasks, enterprise AI, international team collaboration.
License: Qwen License
Highlights: This model supports multiple languages and image-text interactions, making it ideal for global AI applications.

4. Mistral 7B & Mistral Large 2

Developer: Mistral AI
Release Dates: Mistral 7B (2023), Mistral Large 2 (July 2024)
Parameter Sizes: 7B, 123B
Use Cases: Edge AI, personal assistants, scalable AI, research and development.
License: Apache 2.0, Mistral Research License
Highlights: Mistral 7B is lightweight for on-device AI, while Mistral Large 2 is optimized for enterprise-scale processing.

5. Falcon 180B

Developer: Technology Innovation Institute (TII)
Release Date: September 2024
Parameter Size: 180B
Use Cases: Financial analysis, legal AI, healthcare applications.
License: TII Falcon License
Highlights: Falcon 180B is one of the most advanced open-source LLMs for deep reasoning and high-performance AI tasks.

6. DeepSeek-MoE 16B

Developer: DeepSeek
Release Date: January 9, 2024
Parameter Size: 16B (2.7B activated per token)
Use Cases: Domain-specific AI, efficient training, custom AI solutions.
License: DeepSeek License
Highlights: A Mixture of Experts (MoE) model that reduces computational costs while maintaining high adaptability.

7. PaLM 2

Developer: Google
Release Date: May 2023
Parameter Size: 340B
Use Cases: Multimodal AI, translation, advanced reasoning, research.
License: Gemma License
Highlights: PaLM 2 is designed for cross-modal tasks, including text, image, and audio understanding.

8. Grok-1

Developer: xAI
Release Date: November 2023
Parameter Size: 314B
Use Cases: Creative applications, humor, personalized AI.
License: Open-source
Highlights: Grok-1 specializes in generating engaging and humorous content for entertainment industries.

9. Gemma 2

Developer: Google
Release Date: 2025
Parameter Size: 2B, 9B, 27B
Use Cases: General text generation, question answering, summarization, code generation.
License: Gemma License
Highlights: Gemma 2 models are lightweight yet powerful, making them ideal for both research and commercial applications.

10. Yi

Developer: 01.AI
Release Date: 2025
Parameter Size: 6B, 9B, 34B
Use Cases: Bilingual AI, code generation, mathematical reasoning.
License: Apache 2.0
Highlights: Yi models are optimized for high-performance bilingual AI and code-related tasks.

Performance & Benchmark Comparisons

Processing Speed: DeepSeek-R1 and Falcon 180B lead in speed for large-scale tasks.
Memory Efficiency: DeepSeek-MoE 16B and Mistral 7B are optimized for resource-constrained environments.
Multimodal Abilities: Qwen 2.5 and PaLM 2 excel in text-image interactions.
Creative AI: Grok-1 is the best performer in creative and entertainment-focused applications.
Enterprise Use: LLaMA 3.1 (405B) and DeepSeek-R1 (671B) are top-tier choices for high-performance AI deployment.

Conclusion

Choosing the best open-source LLM depends on specific needs:

For enterprise-scale AI: LLaMA 3.1, DeepSeek-R1, and Falcon 180B.
For edge computing and lightweight AI: Mistral 7B and DeepSeek-MoE 16B.
For multimodal tasks: Qwen 2.5 and PaLM 2.
For creative and personalized AI: Grok-1.

These open-source LLMs provide developers with a broad selection of tools for research, business applications, and personal projects, ensuring innovation continues in the AI ecosystem.

Postgresql vs. MongoDB

mehmet akar — Fri, 14 Mar 2025 07:44:20 +0000

PostgreSQL vs. MongoDB is the one of the most wondered comparisons in the database world. Let me compare them in a concise way.

PostgreSQL vs. MongoDB: A Concise Comparison

PostgreSQL and MongoDB are two of the most popular database management systems. PostgreSQL is an open-source relational database, while MongoDB is a NoSQL document-oriented database. Both have their unique strengths, and choosing between them depends on various factors such as data structure, scalability needs, and performance requirements. This article will compare them based on installation, benchmarks, pricing, and key performance metrics.

Installation Tutorials

Installing PostgreSQL on Ubuntu

Update the Package List:

   sudo apt update

Install PostgreSQL:

   sudo apt install postgresql postgresql-contrib

Verify Installation:

   psql --version

Installing MongoDB on Ubuntu

Import the Public Key:

   wget -qO - https://www.mongodb.org/static/pgp/server-5.0.asc | sudo apt-key add -

Create a List File for MongoDB:

   echo "deb [ arch=amd64,arm64 ] https://repo.mongodb.org/apt/ubuntu focal/mongodb-org/5.0 multiverse" | sudo tee /etc/apt/sources.list.d/mongodb-org-5.0.list

Update the Package List:

   sudo apt update

Install MongoDB:

   sudo apt install -y mongodb-org

Start and Enable MongoDB:

   sudo systemctl start mongod
   sudo systemctl enable mongod

PostgreSQL vs. MongoDB: Performance Benchmarks

Transaction Performance

According to Airbyte:

PostgreSQL processed over 20,000 transactions per second, whereas MongoDB handled fewer than 2,000 transactions per second.

OLTP Workloads

PostgreSQL was found to be 2.7 to 3.2 times faster in in-memory tests.
With larger datasets (2TB), PostgreSQL outperformed MongoDB by 25 to 40 times.

OLAP Workloads

PostgreSQL performed 35% to 53% faster than MongoDB in JSON-based analytical queries.

PostgreSQL vs. MongoDB: Pricing Models

PostgreSQL

PostgreSQL is free and open-source.
Managed PostgreSQL services start at $15/month on platforms like DigitalOcean.

MongoDB

MongoDB provides a free Community Edition and a paid Enterprise Edition.
Managed MongoDB Atlas services start at $15.23/month on DigitalOcean.

PostgreSQL vs. MongoDB: Other Performance Metrics

Scalability

MongoDB supports horizontal scaling with built-in sharding.
PostgreSQL primarily scales vertically, though sharding can be manually implemented.

Data Modeling

MongoDB is schema-less and flexible for unstructured data.
PostgreSQL follows a strict schema model for structured data integrity.

Query Language

MongoDB uses MongoDB Query Language (MQL).
PostgreSQL uses SQL, a widely adopted query language.

PostgreSQL vs. MongoDB: Conclusion

Both PostgreSQL and MongoDB are powerful databases with different strengths.

Choose PostgreSQL if you require ACID compliance, complex transactions, and structured data.
Choose MongoDB if you need high scalability, flexibility, and quick development for unstructured data.

Selecting the right database depends on your specific use case, workload requirements, and budget.

RAG Vector Database - Use Cases & Tutorial

mehmet akar — Thu, 13 Mar 2025 11:05:59 +0000

RAG Vector Database is one of the first main terms RAG geeks are looking for. Let me dive in!

RAG Vector Database: A Comprehensive Guide: Table of Contents

- RAG Vector Database: Introduction

- What is RAG?

- Vector Databases Explained

- What is RAG Vector Database?

- Popular Vector Databases

- Implementing RAG with Vector Databases

- Tutorials and Examples

- Best Practices

- Future Trends

- RAG Vector Database: Conclusion

RAG Vector Database: Introduction

Retrieval Augmented Generation (RAG) with vector databases has revolutionized how AI systems access and utilize information. This comprehensive guide explores the technology, implementation, and best practices for building powerful RAG systems.

What is RAG?

RAG (Retrieval Augmented Generation) is an AI architecture that combines:

Information retrieval from a knowledge base
Large Language Model (LLM) generation capabilities
Vector embeddings for semantic search

Benefits:

Improved accuracy with up-to-date information
Reduced hallucinations
Better context awareness
Verifiable responses

Vector Databases Explained

Vector databases are specialized systems that store and retrieve high-dimensional vectors representing data. Key features:

Vector Embeddings: Mathematical representations of data
Similarity Search: Fast nearest neighbor search
Scalability: Efficient handling of millions of vectors
Index Types: HNSW, IVF, etc.

What is RAG Vector Database?

A RAG (Retrieval-Augmented Generation) vector database is a specialized type of database used in AI applications, particularly in Large Language Models (LLMs), to enhance their knowledge retrieval and response generation capabilities. It combines vector databases with retrieval-augmented generation (RAG) techniques to improve the quality, accuracy, and relevance of responses.

How It Works

Data Storage in Vector Format:
- Documents, text, and other information are converted into vector embeddings using embedding models (e.g., OpenAI's Ada, Cohere, Sentence Transformers).
- These embeddings capture semantic meaning and are stored in a vector database like Pinecone, Weaviate, Milvus, Qdrant, or PostgreSQL with pgvector.
Retrieval (R in RAG):
- When a user inputs a query, the system converts the query into a vector and searches for the most similar embeddings in the database using approximate nearest neighbors (ANN) search.
Augmenting the LLM (A in RAG):
- The retrieved text is fed into a language model (e.g., GPT-4, LLaMA, Claude) as context.
- The LLM generates a response by leveraging both the retrieved data and its own pre-trained knowledge.
Final Response Generation (G in RAG):
- The model integrates the retrieved information and produces a more context-aware and accurate response.

Benefits of RAG with Vector Databases

Better Accuracy: Helps reduce hallucinations by grounding responses in real data.
Efficient Information Retrieval: Uses vector-based similarity instead of keyword-based search.
Scalability: Can handle large-scale unstructured data (documents, PDFs, research papers, etc.).
Customization: Allows businesses to fine-tune AI models with their proprietary data without retraining LLMs.

Use Cases

Chatbots & Virtual Assistants: Providing better responses by integrating domain-specific knowledge.
Enterprise Search: Improving search efficiency in company databases.
Legal & Financial AI: Summarizing documents with high precision.
Medical AI: Ensuring responses are based on verified medical sources.
E-commerce & Recommendations: Enhancing product search and recommendations.

Popular RAG Vector Databases

1. Pinecone

import pinecone

pinecone.init(api_key="your-api-key")
index = pinecone.Index("example-index")

# Insert vectors
index.upsert([
    ("id1", [0.1, 0.2, 0.3], {"metadata": "example"}),
])

2. Weaviate

import weaviate

client = weaviate.Client("http://localhost:8080")

# Create schema
class_obj = {
    "class": "Document",
    "vectorizer": "text2vec-transformers"
}
client.schema.create_class(class_obj)

3. Milvus

from pymilvus import Collection, connections

connections.connect()
collection = Collection("documents")

# Search vectors
results = collection.search(
    query_vectors,
    "embeddings",
    param={"metric_type": "L2"}
)

4. Chroma

import chromadb

client = chromadb.Client()
collection = client.create_collection("documents")

# Add documents
collection.add(
    documents=["content"],
    embeddings=[[0.1, 0.2, 0.3]],
    ids=["id1"]
)

Implementing RAG with Vector Databases

Basic RAG Pipeline

from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA

# Initialize components
embeddings = OpenAIEmbeddings()
vectorstore = Chroma(embedding_function=embeddings)
llm = OpenAI()

# Create RAG chain
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vectorstore.as_retriever()
)

Tutorials and Examples

1. Basic RAG Implementation

# 1. Prepare documents
documents = [
    "Document 1 content",
    "Document 2 content"
]

# 2. Create embeddings
embeddings = OpenAIEmbeddings()
doc_embeddings = embeddings.embed_documents(documents)

# 3. Store in vector database
vectorstore.add_documents(documents)

# 4. Query
response = qa_chain.run("Your question here")

2. Advanced RAG with Hybrid Search

# Combine keyword and semantic search
from langchain.retrievers import BM25Retriever
from langchain.retrievers import EnsembleRetriever

# Create retrievers
bm25_retriever = BM25Retriever.from_documents(documents)
vector_retriever = vectorstore.as_retriever()

# Combine retrievers
ensemble_retriever = EnsembleRetriever(
    retrievers=[bm25_retriever, vector_retriever],
    weights=[0.5, 0.5]
)

Best Practices

Data Preparation
- Clean and preprocess text
- Split documents appropriately
- Remove duplicates
Vector Database Selection
- Consider scale requirements
- Evaluate hosting options
- Compare performance metrics
Optimization
- Use appropriate chunk sizes
- Implement caching
- Monitor and tune performance
Security
- Implement access controls
- Encrypt sensitive data
- Regular security audits

Future Trends

Multi-modal RAG systems
Improved context compression
Hybrid search techniques
Real-time updating capabilities
Enhanced privacy features

RAG Vector Database: Conclusion

RAG vector databases represent a powerful approach to enhancing AI systems with accurate, retrievable knowledge. By following this guide and implementing best practices, you can build robust RAG systems for various applications.

Important Note: I update this article regularly to reflect the latest developments in RAG and vector database technologies.

Manus AI Invitation Code

mehmet akar — Thu, 13 Mar 2025 09:20:03 +0000

Manus AI Invitation Code is asked heavily by the AI development geeks, nowadays. As you realize, Manus AI does not accept direct sign-up. It accepts users via giving invitation code after filling a application form.

I'll guide you through the process of applying for getting Manus AI invitation code process.

Manus AI Invitation Code: How to get it?

There are 4 steps for applying for Manus AI Invitation Code.

Step 1: Click on "Get Started"

First, you should visit manus ai home page. As you see below, you should click on the upper-right "get started" button.

Step 2: Click on "Apply for access"

After clicking on "get started", the code activation popup window opens. You should click on "Apply for access" because you have not get Manus AI invitation code yet. See below:

Step 3: Filling Manus AI Invitation Code Application Form

After clicking on "Apply for access", Manus AI Invitation Code application form page opens. In this page, Manus AI want to know your plans for using Menus AI. See Below:

In this stage, we should be careful. Manus AI has some use cases as they have stated in their website. We can mention one of these use cases in that application form.

If you don't know why you use Manus AI, I created an example with the use cases provided by Manus AI.

Be Careful: You should keep the paragraph of one of the use cases and remove others as you see below:

Manus AI Invitation Code Application Form Content Sample:

Subject: Invitation Code Request

Dear Manus Team,

Hello!

My name is [Your Name], and I am excited about the potential of integrating Manus AI into my work and daily life. Given the wide-ranging capabilities of Manus, I see immense value in leveraging its AI-driven solutions for my specific needs.

One of my primary use cases for Manus involves trip planning and travel research. I have an upcoming trip to Japan in April and would love to use Manus to generate a personalized itinerary, complete with travel insights, cultural recommendations, and an optimized schedule to enhance my experience. A custom travel handbook tailored to my interests would greatly streamline my planning process. (Keep one, remove others)

Additionally, I am deeply involved in financial market analysis, particularly focusing on Tesla stock performance. Manus’s ability to provide in-depth, visually interactive dashboards with real-time financial insights would be instrumental in making informed investment decisions. (Keep one, remove others)

Another area where I see Manus adding value is in education and content creation. As someone passionate about teaching, I would like to explore how Manus can generate engaging, video-based educational content to explain complex topics like the momentum theorem in a way that is easy to grasp for middle school students. (Keep one, remove others)

Moreover, I frequently compare and assess insurance policies and financial products. I believe Manus can streamline this process by generating structured comparison tables, helping me analyze key differences between policies and make well-informed choices. (Keep one, remove others)

On a professional level, I am actively involved in B2B supplier sourcing and industry research, particularly in finding high-quality suppliers and evaluating AI-driven solutions for various industries. With Manus’s expertise in compiling comprehensive research reports and conducting competitive analysis, I am eager to leverage its capabilities to enhance my market insights. (Keep one, remove others)

Lastly, I am interested in Manus’s ability to optimize e-commerce operations. As someone who manages an online store, I see Manus as an invaluable tool for analyzing sales data, generating performance reports, and identifying actionable strategies to boost conversion rates. (Keep one, remove others)

I have high expectations for Manus and am eager to explore its AI-driven insights across multiple domains. I am confident that its powerful data-processing capabilities will significantly enhance my decision-making and efficiency.

Thank you for considering my application. If I am fortunate enough to receive an invitation code, I will fully utilize Manus to optimize my workflows, research, and personal projects.

Applicant: [Your Name]

[Application Date]

Step 4: Finishing Apply Process

As you see below, "Thanks for your interest for Manus" page opens.

It says it will return to your email.

Step 5: Taking and Submitting Manus AI Invitation Code.

After finishing the applying process, you will get an email from Manus AI and see an invitation code if you are accepted by Manus AI.

After taking Manus AI Invitation Code, you should again click on get started button on home page and submit the code in the code activation page.

That's it! You can now experience Manus AI!

Thanks for reading. See you on more tutorials!

Model Context Protocol (MCP) Tutorial

mehmet akar — Thu, 13 Mar 2025 06:36:14 +0000

Model Context Protocol (MCP) is actually a concept that is a few months old, but getting so hot nowadays as AI development evolves day by day.
Let me look at what is Model Context Protocol (MCP) and deep dive into Model Context Protocol (MCP) with tutorial with implementation code examples.

What is Model Context Protocol (MCP)

Model Context Protocol (MCP), an open source project run by Anthropic, is an emerging framework that enables structured and dynamic context management for machine learning models, particularly in applications involving conversational AI, large language models (LLMs), and multi-modal systems.

This protocol is designed to provide better control, efficiency, and adaptability in real-time AI applications by managing the context dynamically based on user interactions and data flows.

Key Features of Model Context Protocol

Dynamic Context Switching – Enables AI models to switch between different contexts based on predefined rules or user interactions.
Memory Management – Efficiently handles long-term and short-term memory for contextual conversations.
Optimized for LLMs – Reduces token usage by maintaining only relevant context.
Multi-Agent Coordination – Enables collaboration between multiple AI agents.
Fine-Tuned Context Retrieval – Ensures only necessary context is fetched, improving response relevance.

Use Cases of Model Context Protocol

Conversational AI – Enhances chatbots and voice assistants by dynamically managing conversation memory.
Code Generation and Assistance – Allows AI coding assistants to remember user-specific coding preferences.
Customer Support AI – Manages ongoing support cases by maintaining relevant conversation history.
Medical AI Assistants – Helps AI-powered diagnostic tools maintain patient history context.
Multimodal AI Applications – Enables AI models to understand and switch between text, images, and audio contexts.

Model Context Protocol in Action

Let's explore MCP through practical examples with Python-based implementations.

1. Implementing a Basic Model Context Protocol

Here’s how you can implement a simple context-aware AI model using Python:

class ModelContextProtocol:
    def __init__(self):
        self.context = {}

    def update_context(self, user, message):
        if user not in self.context:
            self.context[user] = []
        self.context[user].append(message)
        if len(self.context[user]) > 5:  # Limit memory to the last 5 messages
            self.context[user].pop(0)

    def get_context(self, user):
        return self.context.get(user, [])

# Example Usage
mcp = ModelContextProtocol()
mcp.update_context("User1", "Hello, AI!")
mcp.update_context("User1", "Can you help me with Python?")
print(mcp.get_context("User1"))  # Output: Last 5 interactions

2. Implementing Context-Aware LLM using OpenAI GPT

MCP can be used to enhance OpenAI’s GPT models by feeding only the relevant conversation history.

import openai

class ContextAwareGPT:
    def __init__(self, api_key):
        self.api_key = api_key
        self.context = {}

    def update_context(self, user, message):
        if user not in self.context:
            self.context[user] = []
        self.context[user].append(message)
        if len(self.context[user]) > 5:  # Limit history
            self.context[user].pop(0)

    def generate_response(self, user, new_message):
        self.update_context(user, new_message)
        context_str = " ".join(self.context[user])

        response = openai.ChatCompletion.create(
            model="gpt-4",
            messages=[{"role": "system", "content": "You are an AI assistant."}] + 
                     [{"role": "user", "content": msg} for msg in self.context[user]]
        )

        return response["choices"][0]["message"]["content"]

# Example Usage
gpt_context = ContextAwareGPT("your-api-key")
response = gpt_context.generate_response("User1", "How do I install NumPy?")
print(response)

3. Implementing Multi-Agent Model Context Protocol

In complex applications, multiple AI agents can collaborate using shared contexts.

class MultiAgentMCP:
    def __init__(self):
        self.contexts = {}

    def update_agent_context(self, agent, message):
        if agent not in self.contexts:
            self.contexts[agent] = []
        self.contexts[agent].append(message)
        if len(self.contexts[agent]) > 10:  # Limit memory
            self.contexts[agent].pop(0)

    def get_agent_context(self, agent):
        return self.contexts.get(agent, [])

# Example Usage
mcp_agents = MultiAgentMCP()
mcp_agents.update_agent_context("Agent1", "Analyzing data patterns.")
mcp_agents.update_agent_context("Agent2", "Generating a summary.")
print(mcp_agents.get_agent_context("Agent1"))  # Output: Context history for Agent1

Advanced MCP: Using Vector Databases for Context Storage

Instead of storing plain text, a vector database like Pinecone or FAISS can be used to store and retrieve context efficiently.

Example: Using FAISS for Context Retrieval

import faiss
import numpy as np

class VectorizedContext:
    def __init__(self, dim=128):
        self.index = faiss.IndexFlatL2(dim)  
        self.context_data = {}

    def add_context(self, user, vector):
        self.index.add(np.array([vector]).astype('float32'))
        self.context_data[len(self.context_data)] = (user, vector)

    def retrieve_context(self, vector, k=3):
        _, indices = self.index.search(np.array([vector]).astype('float32'), k)
        return [self.context_data[idx] for idx in indices[0] if idx in self.context_data]

# Example Usage
context_manager = VectorizedContext(dim=128)
context_manager.add_context("User1", np.random.rand(128))  # Adding a random vector as context
print(context_manager.retrieve_context(np.random.rand(128)))  # Retrieve similar contexts

Future of Model Context Protocol

Integration with Blockchain – Ensuring secure context storage.
Real-Time Context Compression – Optimizing large-scale AI models.
Personalized AI Models – Tailoring responses based on user behavior.

Model Context Protocol (MCP) : Final Words

Model Context Protocol is transforming AI by improving how models understand, store, and retrieve context dynamically. Whether for chatbots, LLMs, multi-agent AI, or vectorized knowledge bases, MCP enhances efficiency and responsiveness.

Openai Agents SDK, Responses Api Tutorial

mehmet akar — Wed, 12 Mar 2025 07:28:14 +0000

Openai Agents SDK, Responses Api is the new tools provided by Openai. I want to talk about OpenAI's New Tools for Building Intelligent Agents

Openai Agents SDK, Responses API

OpenAI has introduced a suite of new tools aimed at empowering developers and enterprises to build more sophisticated and reliable AI agents. These enhancements mark a significant step in making AI-driven applications more adaptable, efficient, and accessible.

Openai Responses API

The Responses API is OpenAI’s new API primitive for leveraging built-in tools to build agents. It combines the simplicity of Chat Completions with the tool-use capabilities of the Assistants API. The API provides a more flexible foundation for developers by enabling multi-tool use and multiple model turns within a single API call.

Supports web search, file search, and computer use as built-in tools.
Offers a unified item-based design for easier management.
Allows intuitive streaming events and simplified SDK access via response.output_text.
Enables better data storage and evaluation capabilities for agent performance analysis.

The Responses API is available today, with standard pricing applying to tokens and tools used.

OpenAI’s Responses Api in action:

Openai Agents SDK

A major highlight is the release of an advanced agent-building framework, which simplifies the integration of AI into complex workflows. The new tools allow for:

Seamless orchestration of multiple AI models.
Memory capabilities for agents to retain information across interactions.
Better security and compliance features to ensure enterprise-grade usage.
Multi-agent workflows that allow developers to integrate agents that work collaboratively.
Configurable handoffs between AI agents based on task requirements.

For example, Coinbase used the Agents SDK to prototype and deploy AgentKit, which enables AI agents to interact with crypto wallets and other on-chain activities in just a few hours.

OpenAI’s Agents SDK in action:

New Built-in Tools for Agents

Web Search

Developers have already been utilizing web search for a variety of use cases, including shopping assistants, research agents, and travel booking agents—any application that requires timely information from the web. for a variety of use cases including shopping assistants, research agents, and travel booking agents—any application that requires timely information from the web.

One example is Hebbia, which leverages the web search tool to help asset managers, private equity and credit firms, and law practices quickly extract actionable insights from extensive public and private datasets. to help asset managers, private equity and credit firms, and law practices quickly extract actionable insights from extensive public and private datasets. By integrating real-time search capabilities into their research workflows, Hebbia delivers richer, context-specific market intelligence and continuously improves the precision and relevance of their analyses, outperforming current benchmarks.

The web search tool in the API is powered by the same model used for ChatGPT search. Benchmarks such as SimpleQA, which evaluates the accuracy of LLMs in answering short, factual questions, show that GPT‑4o search preview and GPT‑4o mini search preview achieve scores of 90% and 88%, respectively.. On SimpleQA, a benchmark that evaluates the accuracy of LLMs in answering short, factual questions, GPT‑4o search preview and GPT‑4o mini search preview score 90% and 88% respectively.

SimpleQA Accuracy Benchmark

Responses generated with web search in the API include links to sources, such as news articles and blog posts, allowing users to engage with more information while also giving content owners opportunities to reach a broader audience., such as news articles and blog posts, giving users a way to learn more. With these clear, inline citations, users can engage with information in a new way, while content owners gain new opportunities to reach a broader audience.

Additionally, websites and publishers have the option to appear in web search results within the API, enhancing visibility for their content.

The web search tool is available to all developers in preview in the Responses API. Developers also have direct access to OpenAI’s fine-tuned search models via gpt-4o-search-preview and gpt-4o-mini-search-preview in the Chat Completions API. Pricing starts at \$30 per 1,000 queries for GPT‑4o search and \$25 per 1,000 queries for GPT‑4o-mini search.

Provides fast, up-to-date answers with citations from the web.
Available in GPT-4o and GPT-4o-mini.
Enables shopping assistants, research agents, and travel booking bots.

Example:

const response = await openai.responses.create({
    model: "gpt-4o",
    tools: [ { type: "web_search_preview" } ],
    input: "What was a positive news story that happened today?",
});
console.log(response.output_text);

File Search

Retrieves relevant information from large document volumes.
Supports query optimization, metadata filtering, and reranking.
Helps with customer support, legal research, and technical documentation queries.

Example:

const productDocs = await openai.vectorStores.create({
    name: "Product Documentation",
    file_ids: [file1.id, file2.id, file3.id],
});
const response = await openai.responses.create({
    model: "gpt-4o-mini",
    tools: [{
        type: "file_search",
        vector_store_ids: [productDocs.id],
    }],
    input: "What is deep research by OpenAI?",
});
console.log(response.output_text);

Computer Use Automation

Developers can use the computer use tool to automate browser-based workflows like performing quality assurance on web apps or executing data-entry tasks across legacy systems.

One example is Unify, a system designed to streamline revenue growth through AI agents. By leveraging OpenAI’s computer use tool, Unify’s agents can access previously unreachable data via APIs—such as verifying a business’s real estate expansion through online maps. This analysis serves as a custom trigger for personalized outreach, allowing go-to-market teams to engage buyers with greater accuracy and efficiency.

Another example is Luminai, which has integrated the computer use tool to automate operational workflows for large enterprises struggling with legacy systems lacking API support. In a pilot project with a major community service organization, Luminai automated the application processing and user enrollment process in just days, something traditional robotic process automation (RPA) systems had failed to achieve in months.

Before launching Computer-Using Agent (CUA) in Operator last year, OpenAI conducted extensive safety testing and red teaming, addressing three primary risks: misuse, model errors, and frontier risks. With the introduction of CUA in the API, OpenAI has implemented additional safeguards, including:

Safety checks to guard against prompt injections.
Confirmation prompts for sensitive actions.
Tools to help developers isolate execution environments.
Enhanced detection of potential policy violations.

Despite these mitigations, the model is still susceptible to errors, particularly in non-browser environments. Benchmark tests show that CUA’s performance on OSWorld is 38.1%, indicating the need for human oversight in real-world automation scenarios.

Benchmark Comparison for OSWorld, WebArena, and WebVoyager

Starting today, the computer use tool is available as a research preview in the Responses API for select developers in usage tiers 3-5. Pricing is set at \$3 per 1M input tokens and \$12 per 1M output tokens.

Developers can now build AI-powered tools that perform computer automation tasks, including:

Capturing mouse and keyboard actions.
Automating workflows across multiple software applications.
Enhancing enterprise productivity through AI-driven task execution.

Example:

const response = await openai.responses.create({
    model: "computer-use-preview",
    tools: [{
        type: "computer_use_preview",
        display_width: 1024,
        display_height: 768,
        environment: "browser",
    }],
    truncation: "auto",
    input: "I'm looking for a new camera. Help me find the best one.",
});
console.log(response.output);

4. AI Safety and Governance Enhancements

Recognizing the importance of responsible AI use, OpenAI has reinforced its safety measures with:

Stronger bias detection algorithms.
More transparent AI decision-making processes.
Advanced compliance tracking tools for regulatory requirements.
Additional safety evaluations for automation-related risks.

Openai Agents SDK Tutorial

OpenAI has also provided new code examples for integrating AI into applications efficiently:

Example: Using the Agents SDK to build AI agents

from agents import Agent, Runner, WebSearchTool, function_tool

@function_tool
def submit_refund_request(item_id: str, reason: str):
    return "success"

support_agent = Agent(
    name="Support & Returns",
    instructions="You are a support agent who can submit refunds",
    tools=[submit_refund_request],
)

shopping_agent = Agent(
    name="Shopping Assistant",
    instructions="You are a shopping assistant who can search the web",
    tools=[WebSearchTool()],
)

triage_agent = Agent(
    name="Triage Agent",
    instructions="Route the user to the correct agent.",
    handoffs=[shopping_agent, support_agent],
)

output = Runner.run_sync(
    starting_agent=triage_agent,
    input="What shoes might work best with my outfit so far?",
)
print(output)

Deprecation of the Assistants API

OpenAI has announced that the Assistants API will be deprecated by mid-2026 in favor of the Responses API. Developers are encouraged to migrate applications to the Responses API, which offers:

Full feature parity with Assistants API.
Support for Assistant-like and Thread-like objects.
Better flexibility, speed, and efficiency.

A detailed migration guide will be provided by OpenAI closer to the sunset date.

Openai Agents SDK, Responses API: Final Thoughts

OpenAI continues to push the boundaries of what’s possible with artificial intelligence. With these new tools, businesses and developers can create smarter, safer, and more scalable AI solutions. As these advancements roll out, the AI landscape is set to become more innovative and impactful than ever before.

For more details, visit OpenAI’s official announcement here:openai.com/news

Forem: mehmet akar

Context7 MCP Tutorial

🌟 Why Use Context7?

❌ Without Context7:

✅ With Context7:

Examples:

🛠️ Getting Started

Requirements

📦 Installation Methods

1. Install via Smithery (for Claude Desktop)

🧭 Installation for Specific Clients

📍 Cursor

Alternative Runtimes:

🌊 Windsurf

🧩 VS Code / VS Code Insiders

🧠 Claude Code

💻 Claude Desktop

🐳 Using Docker

1. Dockerfile

2. Build the Docker Image

3. Configure MCP Client

🧰 Available Tools

resolve-library-id

get-library-docs

⚙️ Development

🔬 Test with MCP Inspector

🧯 Context7 Troubleshooting

ERR_MODULE_NOT_FOUND?

Context7 MCP Errors?

Blender MCP: Seamless Integration of Blender with Claude AI

Why Use Blender MCP?

Key Features

Installation Guide

Prerequisites

Installing UV

Setting Up MCP in Claude AI

Installing Blender Addon

How to Use Blender MCP

Connecting to Claude AI

Example Commands

Using MCP with Cursor Integration

Troubleshooting & FAQs

Common Issues & Fixes

Security Considerations

Contributing & Development

Final Thoughts

What is Alpaca LLM?

What is Alpaca LLM? Introduction

What is Alpaca LLM?

How Does Alpaca LLM Work?

Key Features of Alpaca LLM

Advantages of Using Alpaca LLM

Limitations of Alpaca LLM

How to Install and Use Alpaca LLM

Prerequisites

Step 1: Clone the Alpaca Repository

Step 2: Set Up the Virtual Environment

Step 3: Install Dependencies

Step 4: Download LLaMA Model Weights

Step 5: Fine-Tune Alpaca LLM

Step 6: Running Inference on Alpaca LLM

Future of Alpaca LLM

What is Alpaca LLM? Conclusion

LCM vs. LLM

What is LCM (Large Concept Model)?

How Do LCMs Work?

Advantages of LCMs:

Applications of LCMs:

What is LLM (Large Language Model)?

How Do LLMs Work?

Applications of LLMs:

Limitations of LLMs:

Key Differences Between LCMs and LLMs

LCM vs. LLM: Conclusion

What is LLM in AI

What is LLM in AI? in some words?

What is an LLM in AI?

How Do LLMs Work?

Key Features of Large Language Models

Applications of LLMs in Various Industries

`resolve-library-id`

`get-library-docs`

`ERR_MODULE_NOT_FOUND`?