Category: AI

  • Llama 4 is here, Meta’s Cutting-Edge Language Model

    What is Llama 4?

    Llama 4 is a large language model (LLM) built on the transformer architecture, which has revolutionized the field of natural language processing (NLP). This model is designed to process and generate human-like language, enabling a wide range of applications, from text classification and sentiment analysis to language translation and content creation.

    Key Features of Llama 4

    1. Advanced Language Understanding: Llama 4 boasts exceptional language understanding capabilities, allowing it to comprehend complex contexts, nuances, and subtleties.
    2. High-Quality Text Generation: The model can generate coherent, engaging, and context-specific text, making it an ideal tool for content creation, chatbots, and virtual assistants.
    3. Multilingual Support: Llama 4 supports multiple languages, enabling seamless communication and content generation across linguistic and geographical boundaries.
    4. Customizability: The model can be fine-tuned for specific tasks, industries, or applications, allowing developers to tailor its capabilities to their unique needs.
    5. Scalability: Llama 4 is designed to handle large volumes of data and traffic, making it an ideal solution for enterprise applications.

    Applications of Llama 4

    1. Chatbots and Virtual Assistants: Llama 4 can be used to build sophisticated chatbots and virtual assistants that provide personalized support and engagement.
    2. Content Generation: The model can generate high-quality content, including articles, blog posts, and social media updates, saving time and resources.
    3. Language Translation: Llama 4’s multilingual support enables seamless language translation, facilitating global communication and collaboration.
    4. Sentiment Analysis: The model can analyze text data to determine sentiment, helping businesses gauge public opinion and make informed decisions.
    5. Text Classification: Llama 4 can classify text into categories, enabling applications such as spam detection, topic modeling, and information retrieval.

    Technical Specifications

    1. Model Architecture: Llama 4 is built on the transformer architecture, with a focus on self-attention mechanisms and encoder-decoder structures.
    2. Training Data: The model was trained on a massive dataset of text from various sources, including books, articles, and websites.
    3. Model Size: Llama 4 has a large model size, enabling it to capture complex patterns and relationships in language.
    4. Compute Requirements: The model requires significant computational resources, including high-performance GPUs and large memory capacities.

    Getting Started with Llama 4

    To leverage the power of Llama 4, developers and enterprises can:

    1. Access the Model: Llama 4 is available through Meta’s API, allowing developers to integrate its capabilities into their applications.
    2. Fine-Tune the Model: Developers can fine-tune Llama 4 for specific tasks or industries, tailoring its capabilities to their unique needs.
    3. Build Applications: Llama 4 can be used to build a wide range of applications, from chatbots and virtual assistants to content generation and language translation tools.

    Key Technical Specifications:

    • Model Architecture: Llama 4 employs a sophisticated Mixture-of-Experts (MoE) architecture, which significantly improves parameter efficiency. This design allows for a large number of parameters (up to 400B in Llama 4 Maverick) while maintaining computational efficiency.
    • Multimodal Capabilities: The model features a native multimodal architecture, enabling seamless integration of text and image processing. This is achieved through an early fusion approach, where text and vision tokens are unified in the model backbone.
    • Context Window: Llama 4 Scout boasts an impressive 10M token context window, thanks to the innovative iRoPE architecture. This allows the model to process documents of unprecedented length while maintaining coherence.
    • Training Data: The model was trained on a massive dataset of 30+ trillion tokens, including text, image, and video data, covering 200 languages.

    Performance Benchmarks:

    • Multimodal Processing: Llama 4 demonstrates superior performance on multimodal tasks, outperforming GPT-4o and Gemini 2.0 Flash in image reasoning and understanding benchmarks.
    • Code Generation: The model achieves competitive results in code generation tasks, with Llama 4 Maverick scoring 43.4% on LiveCodeBench.
    • Long Context: Llama 4 Scout’s extended context window enables it to maintain coherence and accuracy across full books in the MTOB benchmark.

    API and Deployment:

    • API Pricing: Llama 4 models are available through multiple API providers, with varying pricing structures. For example, (link unavailable) offers Llama 4 Maverick at $0.27 per 1M input tokens and $0.85 per 1M output tokens.
    • Deployment Options: The model can be deployed on various hardware configurations, including single H100 GPUs and dedicated endpoints.

    Hardware Requirements:

    • GPU Requirements: Llama 4 Scout can run on a single H100 GPU, while Llama 4 Maverick requires a single H100 DGX host.
    • Quantization: The models support Int4 and Int8 quantization, allowing for efficient deployment .

    Llama 4 represents a significant advancement in the field of NLP, offering unparalleled language understanding and generation capabilities. As a powerful tool for developers and enterprises, Llama 4 has the potential to transform various industries and applications. By understanding its features, applications, and technical specifications, businesses can unlock the full potential of Llama 4 and drive innovation in the field of AI.

  • What Are Tokens in NLP?

    Tokens are the basic units we get when we split a piece of text, like a sentence, into smaller parts. In natural language processing (NLP), this process is called tokenization. Typically, tokens are words, but they can also be punctuation marks or numbers—basically anything that appears in the text.

    Tokenizing “I love you 3000”

    When we tokenize the sentence “I love you 3000,” we split it into its individual components. Using a standard tokenizer (like the one from Python’s NLTK library), the result would be:

    • “I”
    • “love”
    • “you”
    • “3000”

    So, the tokens are: “I”, “love”, “you”, “3000”.

    Are Tokens Text or Numbers?

    Now, to the core question: are these tokens always text, or can they be numbers? In the tokenization process, tokens are always text, meaning they are sequences of characters (strings). Even when a token looks like a number, such as “3000,” it is still treated as a string of characters: “3”, “0”, “0”, “0”.

    For example:

    • In Python, if you tokenize “I love you 3000” using NLTK:
    import nltk
sentence = "I love you 3000"
tokens = nltk.word_tokenize(sentence)
print(tokens)

    The output is: [‘I’, ‘love’, ‘you’, ‘3000’]. Here, “3000” is a string, not an integer.

    Can Tokens Represent Numbers?

    Yes, tokens can represent numbers! The token “3000” is made up of digits, so it can be interpreted as the number 3000. However, during tokenization, it remains a text string. If you want to use it as an actual numerical value (like an integer or float), you’d need to convert it in a separate step after tokenization. For instance:

    • Convert “3000” to an integer: int(“3000”) in Python, which gives you the number 3000.

    What If I Want Numbers?

    If your goal is to work with “3000” as a number (not just a string), tokenization alone won’t do that. After tokenizing, you can:

    1. Identify which tokens are numbers (e.g., check if they consist only of digits).
    2. Convert them to numerical types (e.g., int(“3000”)).

    For example:

    • Tokens: [“I”, “love”, “you”, “3000”]
    • After conversion: You could process “3000” into the integer 3000 while leaving the other tokens as text.

    In the sentence “I love you 3000,” the tokens are all text: “I”, “love”, “you”, “3000”. The token “3000” is a string that represents a number, but as a token, it’s still text. Tokens are always text in the sense that they are sequences of characters produced by tokenization. If you need them to be numbers for some purpose, that’s a step you’d take after tokenization.

    So, to answer directly: tokens are always text, but they can represent numbers if they’re made of digits, like “3000” in this example.

  • OpenManus: FULLY FREE Manus Alternative

    OpenManus: FULLY FREE Manus Alternative

    First-Ever General AI Agent, Manus. But it’s restricted by the invite code and money. However, we haven’t got the prices yet. But it’s not gonna be free. So, what we do now. Well, let’s move to our saviours, the open-source community

    Well, guess what? OpenManus is like the answer to your prayers! It’s basically a free version of Manus that you can just download and use right now. It does all that cool AI agent stuff like figuring things out on its own, working with other programs, and automating tasks. And the best part? You don’t have to wait in line or pay anything, and you can see exactly how it’s built. Pretty awesome, huh?

    OpenManus is an open-source project designed to allow users to create and utilize their own AI agents without requiring an invite code, unlike the proprietary Manus platform. It’s developed by a team including members from MetaGPT and aims to democratize access to AI agent creation.

    Key Features

    • No Invite Code Required: Unlike Manus, OpenManus eliminates the need for an invite code, making it accessible to everyone.
    • Open-Source Implementation: The project is fully open-source, encouraging community contributions and improvements.
    • Integration with OpenManus-RL: Collaborates with researchers from UIUC on reinforcement learning tuning methods for LLM agents.
    • Active Development: The team is actively working on enhancements including improved planning capabilities, standardized evaluation metrics, model adaptation, containerized deployment, and expanded example libraries.

    Technical Setup and Run Steps

    Installation

    Method 1: Using Conda

    Create and activate a new conda environment:

    conda create -n open_manus python=3.12
conda activate open_manus

    Clone the repository:

    git clone https://github.com/mannaandpoem/OpenManus.git
cd OpenManus

    Install dependencies:

    pip install -r requirements.txt

    Method 2: Using uv (Recommended)

    Install uv:

    curl -LsSf https://astral.sh/uv/install.sh | sh

    Clone the repository:

    git clone https://github.com/mannaandpoem/OpenManus.git
cd OpenManus

    Create and activate a virtual environment:

    uv venv
source .venv/bin/activate  # On Unix/macOS
# Or on Windows:
# .venv\Scripts\activate

    Install dependencies:

    uv pip install -r requirements.txt

    Configuration:

    Create a config.toml file in the config directory by copying the example:cp config/config.example.toml config/config.toml

    Edit config/config.toml to add your API keys and customize settings:

    # Global LLM configuration
[llm]
model = "gpt-4o"
base_url = "https://api.openai.com/v1"
api_key = "sk-..."  # Replace with your actual API key
max_tokens = 4096
temperature = 0.0

# Optional configuration for specific LLM models
[llm.vision]
model = "gpt-4o"
base_url = "https://api.openai.com/v1"
api_key = "sk-..."  # Replace with your actual API key

    Running OpenManus

    After completing the installation and configuration steps, you can run OpenManus with a single command. The specific command may vary depending on your setup, but generally, you can execute:python main.py

    Then input your idea via the terminal when prompted.

    For the unstable version, you might need to use a different command as specified in the project documentation.

  • What is Infinite Retrieval, and How Does It Work?

    Infinite Retrieval is a method to enhance LLMs Attention in Long-Context Processing.” The core problem it solves is that traditional LLMs, like those based on the Transformer architecture, struggle with long contexts because their attention mechanisms scale quadratically with input length. Double the input, and you’re looking at four times the memory and compute—yikes! This caps how much text they can process at once, usually to something like 32K tokens or less, depending on the model.

    The folks behind this (Xiaoju Ye, Zhichun Wang, and Jingyuan Wang) came up with a method called InfiniRetri. InfiniRetri is a trick that helps computers quickly find the important stuff in a giant pile of words, like spotting a treasure in a huge toy box, without looking at everything.

    It’s a clever twist that lets LLMs handle “infinite” context lengths—think millions of tokens—without needing extra training or external tools like Retrieval-Augmented Generation (RAG). Instead, it uses the model’s own attention mechanism in a new way to retrieve relevant info from absurdly long inputs. The key insight? They noticed a link between how attention is distributed across layers and the model’s ability to fetch useful info, so they leaned into that to make retrieval smarter and more efficient.

    Here’s what makes it tick:

    • Attention Allocation Trick: InfiniRetri piggybacks on the LLM’s existing attention info (you know, those key, value, and query vectors) to figure out what’s worth retrieving from a massive input. No need for separate embeddings or external databases.
    • No Training Needed: It’s plug-and-play—works with any Transformer-based LLM right out of the box, which is huge for practicality.
    • Performance Boost: Tests show it nails tasks like the Needle-In-a-Haystack (NIH) test with 100% accuracy over 1M tokens using a tiny 0.5B parameter model. It even beats bigger models, cuts inference latency, and computes overhead by a ton—up to a 288% improvement on real-world benchmarks.

    In short, it’s like giving your LLM a superpower to sift through a haystack the size of a planet and still find that one needle, all while keeping things fast and lean.

    What’s This “Infinite Retrieval” Thing?

    Imagine you’ve got a huge toy box—way bigger than your room. It’s stuffed with millions of toys: cars, dolls, blocks, even some random stuff like a sock or a candy wrapper. Now, I say, “Find me the tiny red racecar!” You can’t look at every single toy because it’d take forever, right? Your arms would get tired, and you’d probably give up.

    Regular language models (those smart computer brains we call LLMs) are like that. When you give them a giant story or a massive pile of words (like a million toys), they get confused. They can only look at a small part of the pile at once—like peeking into one corner of your toy box. If the red racecar is buried deep somewhere else, they miss it.

    Infinite Retrieval is like giving the computer a magic trick. It doesn’t have to dig through everything. Instead, it uses a special “attention” superpower to quickly spot the red racecar, even in that giant toy box, without making a mess or taking all day.

    How Does It Work?

    Let’s pretend the computer is your friend, Robo-Bob. Robo-Bob has these cool glasses that glow when he looks at stuff that matters. Here’s what happens:

    1. Big Pile of Words: You give Robo-Bob a super long story—like a book that’s a mile long—about a dog, a cat, a pirate, and a million other things. You ask, “What did the pirate say to the dog?”
    2. Magic Glasses: Robo-Bob doesn’t read the whole mile-long book. His glasses light up when he sees important words—like “pirate” and “dog.” He skips the boring parts about the cat chasing yarn or the wind blowing.
    3. Quick Grab: Using those glowing clues, he zooms in, finds the pirate saying, “Arf, matey!” to the dog, and tells you. It’s fast—like finding that red racecar in two seconds instead of two hours!

    The trick is in those glasses (called “attention” in computer talk). They help Robo-Bob know what’s important without looking at every single toy or word.

    Real-Time Example: Finding Your Lost Sock

    Imagine you lost your favorite striped sock at school. Your teacher dumps a giant laundry basket with everyone’s clothes in front of you—hundreds of shirts, pants, and socks! A normal computer would check every single shirt and sock one by one—super slow. But with Infinite Retrieval, it’s like the computer gets a sock-sniffing dog. The dog smells your sock’s stripes from far away, ignores the shirts and pants, and runs straight to it. Boom—sock found in a snap!

    In real life, this could help with:

    • Reading Long Books Fast: Imagine a kid asking, “What’s the treasure in this 1,000-page pirate story?” The computer finds it without reading every page.
    • Searching Big Videos: You ask, “What did the superhero say at the end of this 10-hour movie?” It skips to the end and tells you, “I’ll save the day!”

    Why’s It Awesome?

    • It’s fast—like finding your sock before recess ends.
    • It works with tiny robots, not just big ones. Even a little computer can do it!
    • It doesn’t need extra lessons. Robo-Bob already knows the trick when you build him.

    So, buddy, it’s like giving a computer a treasure map and a flashlight to find the good stuff in a giant pile—without breaking a sweat! Did that make sense? Want me to explain any part again with more toys or games?

  • Understanding LLM Parameters: A Comprehensive Guide

    Understanding LLM Parameters: A Comprehensive Guide

    Large Language Models (LLMs) have revolutionized the field of Artificial Intelligence, powering applications from chatbots to content generation. At the heart of these powerful models lie LLM parameters, numerical values that dictate how an LLM learns and processes information. This comprehensive guide will delve into what LLM parameters are, their significance in model performance, and how they influence various aspects of AI development.

    We’ll explore this topic in a way that’s accessible to both beginners and those with a more technical background.

    How LLM Parameters Impact Performance

    The number of LLM parameters directly correlates with the model’s capacity to understand and generate human-like text. Models with more parameters can typically handle more complex tasks, exhibit better reasoning abilities, and produce more coherent and contextually relevant outputs.

    However, a larger parameter count doesn’t always guarantee superior performance. Other factors, such as the quality of the training data and the architecture of the model, also play crucial roles.

    Parameters as the Model’s Knowledge and Capacity

    In the realm of deep learning, and specifically for LLMs built upon neural network architectures (often Transformers), parameters are the adjustable, learnable variables within the model. Think of them as the fundamental building blocks that dictate the model’s behavior and capacity to learn complex patterns from data.

    • Neural Networks and Connections: LLMs are structured as interconnected layers of artificial neurons. These neurons are connected by pathways, and each connection has an associated weight. These weights, along with biases (another type of parameter), are what we collectively refer to as “parameters.”
    • Learning Through Parameter Adjustment: During the training process, the LLM is exposed to massive datasets of text and code. The model’s task is to predict the next word in a sequence, or perform other language-related objectives. To achieve this, the model iteratively adjusts its parameters (weights and biases) based on the errors it makes. This process is guided by optimization algorithms and aims to minimize the difference between the model’s predictions and the actual data.
    • Parameters as Encoded Knowledge: As the model trains and parameters are refined, these parameters effectively encode the patterns, relationships, and statistical regularities present in the training data. The parameters become a compressed representation of the knowledge the model acquires about language, grammar, facts, and even reasoning patterns.
    • More Parameters = Higher Model Capacity: The number of parameters directly relates to the model’s capacity. A model with more parameters has a greater ability to:
      • Store and represent more complex patterns. Imagine a larger canvas for a painter – more parameters offer more “space” to capture intricate details of language.
      • Learn from larger and more diverse datasets. A model with higher capacity can absorb and generalize from more information.
      • Potentially achieve higher accuracy and perform more sophisticated tasks. More parameters can lead to better performance, but it’s not the only factor (architecture, training data quality, etc., also matter significantly).

    Analogy Time: The Grand Library of Alexandria

    • Parameters as Bookshelves and Connections: Imagine the parameters of an LLM are like the bookshelves in the Library of Alexandria and the organizational system connecting them.
      • Number of Parameters (Model Size) = Number of Bookshelves and Complexity of Organization: A library with more bookshelves (more parameters) can hold more books (more knowledge). Furthermore, a more complex and well-organized system of indexing, cross-referencing, and connecting those bookshelves (more intricate parameter relationships) allows for more sophisticated knowledge retrieval and utilization.
      • Training Data = The Books in the Library: The massive text datasets used to train LLMs are like the vast collection of scrolls and books in the Library of Alexandria.
      • Learning = Organizing and Indexing the Books: The training process is analogous to librarians meticulously organizing, cataloging, and cross-referencing all the books. They establish a system (the parameter settings) that allows anyone to efficiently find information, understand relationships between different topics, and even generate new knowledge based on existing works.
      • A Small Library (Fewer Parameters): A small local library with limited bookshelves can only hold a limited collection. Its knowledge is restricted, and its ability to answer complex queries or generate new insightful content is limited.
      • The Grand Library (Many Parameters): The Library of Alexandria, with its legendary collection, could offer a far wider range of knowledge, support complex research, and inspire new discoveries. Similarly, an LLM with billions or trillions of parameters has a vast “knowledge base” and the potential for more complex and nuanced language processing.

    The Twist: Quantization and Model Weights Size

    While the number of parameters is the primary indicator of model size and capacity, the actual file size of the model weights on disk is also affected by quantization.

    • Data Types and Precision: Parameters are stored as numerical values. The data type used to represent these numbers determines the precision and the storage space required. Common data types include:
      • float32 (FP32): Single-precision floating-point (4 bytes per parameter). Offers high precision but larger size.
      • float16 (FP16, half-precision): Half-precision floating-point (2 bytes per parameter). Reduces size and can speed up computation, with a slight trade-off in precision.
      • bfloat16 (Brain Float 16): Another 16-bit format (2 bytes per parameter), designed for machine learning.
      • int8 (8-bit integer): Integer quantization (1 byte per parameter). Significant size reduction, but more potential accuracy loss.
      • int4 (4-bit integer): Further quantization (0.5 bytes per parameter). Dramatic size reduction, but requires careful implementation to minimize accuracy impact.
    • Quantization as “Data Compression” for Parameters: Quantization is a technique to reduce the precision (and thus size) of the model weights. It’s like “compressing” the numerical representation of each parameter.
    • Ollama’s 4-bit Quantization Example: As we saw with Ollama’s Llama 2 (7B), using 4-bit quantization (q4) drastically reduces the model weight file size. Instead of ~28GB for a float32 7B model, it becomes around 3-4GB. This is because each parameter is stored using only 4 bits (0.5 bytes) instead of 32 bits (4 bytes).
    • Trade-offs of Quantization: Quantization is a powerful tool for making models more efficient, but it often involves a trade-off. Lower precision (like 4-bit) can lead to a slight decrease in accuracy compared to higher precision (float32). However, for many applications, the benefits of reduced size and faster inference outweigh this minor performance impact.

    Calculating Approximate Model Weights Size

    To estimate the model weights file size, you need to know:

    1. Number of Parameters (e.g., 7B, 13B, 70B).
    2. Data Type (Float Precision/Quantization Level).

    Formula:

    • Approximate Size in Bytes = (Number of Parameters) * (Bytes per Parameter for the Data Type)
    • Approximate Size in GB = (Size in Bytes) / (1024 * 1024 * 1024)

    Example: Llama 2 7B (using float16 and q4)

    • Float16: 7 Billion parameters * 2 bytes/parameter ≈ 14 Billion bytes ≈ 13 GB
    • 4-bit Quantization (q4): 7 Billion parameters * 0.5 bytes/parameter ≈ 3.5 Billion bytes ≈ 3.26 GB (close to Ollama’s reported 3.8 GB)

    Where to Find Data Type Information:

    • Model Cards (Hugging Face Hub, Model Provider Websites): Look for sections like “Model Details,” “Technical Specs,” “Quantization.” Keywords: dtype, precision, quantized.
    • Configuration Files (config.json, etc.): Check for torch_dtype or similar keys.
    • Code Examples/Loading Instructions: See if the code specifies torch_dtype or quantization settings.
    • Inference Library Documentation: Libraries like transformers often have default data types and ways to check/set precision.

    Why Model Size Matters: Practical Implications

    • Storage Requirements: Larger models require more disk space to store the model weights.
    • Memory (RAM) Requirements: During inference (using the model), the model weights need to be loaded into memory (RAM). Larger models require more RAM.
    • Inference Speed: Larger models can sometimes be slower for inference, especially if memory bandwidth becomes a bottleneck. Quantization can help mitigate this.
    • Accessibility and Deployment: Smaller, quantized models are easier to deploy on resource-constrained devices (laptops, mobile devices, edge devices) and are more accessible to users with limited hardware.
    • Computational Cost (Training and Inference): Training larger models requires significantly more computational resources (GPUs/TPUs) and time. Inference can also be more computationally intensive.

    The “size” of an LLM, as commonly discussed in terms of billions or trillions, primarily refers to the number of parameters. More parameters generally indicate a higher capacity model, capable of learning more complex patterns and potentially achieving better performance. However, the actual file size of the model weights is also heavily influenced by quantization, which reduces the precision of parameter storage to create more efficient models.

    Understanding both parameters and quantization is essential for navigating the world of LLMs, making informed choices about model selection, and appreciating the engineering trade-offs involved in building these powerful AI systems. As the field advances, we’ll likely see even more innovations in model architectures and quantization techniques aimed at creating increasingly capable yet efficient LLMs accessible to everyone.

  • Never Start From Scratch: Persistent Browser Sessions for AI Agents

    Never Start From Scratch: Persistent Browser Sessions for AI Agents

    Building AI agents that interact with the web presents unique challenges. One of the most frustrating is the lack of persistent browser session for ai. Imagine an AI assistant that has to log in to a website every time it needs to perform a task. This repetitive process is not only time-consuming but also disrupts the flow of information and can lead to errors. Fortunately, there’s a solution: maintaining persistent browser sessions for your AI agents.

    The Problem with Stateless AI Web Interactions

    Without a persistent browser session, each interaction with a website is treated as a brand new visit. This means your AI agent loses all previous context, including login credentials, cookies, and browsing history. This “stateless” approach forces the agent to start from scratch each time, leading to:

    • Repetitive Logins: Constant login prompts hinder automation and slow down processes.
    • Loss of Context: Crucial information from previous interactions is lost, impacting the agent’s ability to perform complex tasks.
    • Inefficient Resource Use: Repeatedly loading websites and resources consumes unnecessary time and computing power.
    • Repetitive Logins: Constant login prompts hinder automation and slow down processes.
    • Loss of Context: Crucial information from previous interactions is lost, impacting the agent’s ability to perform complex tasks.
    • Inefficient Resource Use: Repeatedly loading websites and resources consumes unnecessary time and computing power.

    The Power of Persistent Browser Sessions for AI

    A persistent browser session for ai allows your agent to maintain a continuous connection with a website, preserving its state across multiple interactions. This means:

    • Eliminate Repetitive Logins: Your AI agent stays logged in, ready to perform tasks without interruption.
    • Preserve Context: Retain crucial information like cookies, browsing history, and form data for seamless task execution.
    • Streamline Workflow: Enable complex, multi-step automation without constantly restarting the process. This is crucial for tasks like web scraping, data extraction, and automated testing.

    How Browser-Use Enables Persistent Sessions

    Browser-Use offers a powerful solution for managing persistent browser context for ai. By leveraging its features, you can easily create and maintain browser sessions, allowing your AI agents to operate with maximum efficiency. This functionality is especially beneficial for long-running ai browser sessions that require continuous interaction with web applications.

    Installation Guide

    Prerequisites

    • Python 3.11 or higher
    • Git (for cloning the repository)

    Option 1: Local Installation

    Read the quickstart guide or follow the steps below to get started.

    Step 1: Clone the Repository

    git clone https://github.com/browser-use/web-ui.git
cd web-ui

    Step 2: Set Up Python Environment

    We recommend using uv for managing the Python environment.

    Using uv (recommended):

    uv venv --python 3.11

    Activate the virtual environment:

    • Windows (Command Prompt):
    .venv\Scripts\activate
    • Windows (PowerShell):
    .\.venv\Scripts\Activate.ps1
    • macOS/Linux:
    source .venv/bin/activate

    Step 3: Install Dependencies

    Install Python packages:

    uv pip install -r requirements.txt

    Install Playwright:

    playwright install

    Step 4: Configure Environment

    1. Create a copy of the example environment file:
    • Windows (Command Prompt):
    copy .env.example .env
    • macOS/Linux/Windows (PowerShell):
    cp .env.example .env
    1. Open .env in your preferred text editor and add your API keys and other settings

    Option 2: Docker Installation

    Prerequisites

    Installation Steps

    1. Clone the repository:
    git clone https://github.com/browser-use/web-ui.git
cd web-ui
    1. Create and configure environment file:
    • Windows (Command Prompt):
    copy .env.example .env
    • macOS/Linux/Windows (PowerShell):
    cp .env.example .env

    Edit .env with your preferred text editor and add your API keys

    1. Run with Docker:
    # Build and start the container with default settings (browser closes after AI tasks)
docker compose up --build
    # Or run with persistent browser (browser stays open between AI tasks)
CHROME_PERSISTENT_SESSION=true docker compose up --build
    1. Access the Application:
    • Web Interface: Open http://localhost:7788 in your browser
    • VNC Viewer (for watching browser interactions): Open http://localhost:6080/vnc.html
      • Default VNC password: “youvncpassword”
      • Can be changed by setting VNC_PASSWORD in your .env file

    Docker Setup

    Environment Variables:

    All configuration is done through the .env file

    Available environment variables:

    # LLM API Keys
OPENAI_API_KEY=your_key_here
ANTHROPIC_API_KEY=your_key_here
GOOGLE_API_KEY=your_key_here

# Browser Settings
CHROME_PERSISTENT_SESSION=true   # Set to true to keep browser open between AI tasks
RESOLUTION=1920x1080x24         # Custom resolution format: WIDTHxHEIGHTxDEPTH
RESOLUTION_WIDTH=1920           # Custom width in pixels
RESOLUTION_HEIGHT=1080          # Custom height in pixels

# VNC Settings
VNC_PASSWORD=your_vnc_password  # Optional, defaults to "vncpassword"

    Platform Support:

    Supports both AMD64 and ARM64 architectures

    For ARM64 systems (e.g., Apple Silicon Macs), the container will automatically use the appropriate image

    Browser Persistence Modes:

    Default Mode (CHROME_PERSISTENT_SESSION=false):

    Browser opens and closes with each AI task

    Clean state for each interaction

    Lower resource usage

    Persistent Mode (CHROME_PERSISTENT_SESSION=true):

    Browser stays open between AI tasks

    Maintains history and state

    Allows viewing previous AI interactions

    Set in .env file or via environment variable when starting container

    Viewing Browser Interactions:

    Access the noVNC viewer at http://localhost:6080/vnc.html

    Enter the VNC password (default: “vncpassword” or what you set in VNC_PASSWORD)

    Direct VNC access available on port 5900 (mapped to container port 5901)

    You can now see all browser interactions in real-time

    Persistent browser sessions are essential for building efficient and robust AI agents that interact with the web. By eliminating repetitive logins, preserving context, and streamlining workflows, you can unlock the true potential of AI web automation. Explore Browser-Use and discover how its persistent session management can revolutionize your AI development process. Start building smarter, more efficient AI agents today!

  • 2025: Best and free platform to deploy python application like vercel

    2025: Best and free platform to deploy python application like vercel

    Best and free platform to deploy python application
    Best and free platform to deploy Python application

    Several platforms offer free options for deploying Python applications, each with its own features and limitations. Here are some of the top contenders:

    • Render: Render is a cloud service that allows you to build and run apps and websites, with free TLS certificates, a global CDN, and auto-deploys from Git[1]. It supports web apps, static sites, Docker containers, cron jobs, background workers, and fully managed databases. Most services, including Python web apps, have a free tier to get started[1]. Render’s free auto-scaling feature ensures your app has the necessary resources, and everything hosted on Render gets a free TLS certificate. It is a user-friendly Heroku alternative, offering a streamlined deployment process and an intuitive management interface.
    • PythonAnywhere: This platform has been around for a while and is well-known in the Python community[1]. It is a reliable and simple service to get started with[1]. You get one web app with a pythonanywhere.com domain for free, with upgraded plans starting at $5 per month.
    • Railway: Railway is a deployment platform where you can provision infrastructure, develop locally, and deploy to the cloud[1]. They provide templates to get started with different frameworks and allow deployment from an existing GitHub repo[1]. The Starter tier can be used for free without a credit card, and the Developer tier is free under $5/month.
    • GitHub: While you can’t host web apps on GitHub, you can schedule scripts to run regularly with GitHub Actions and cron jobs. The free tier includes 2,000 minutes per month, which is enough to run many scripts multiple times a day.
    • Anvil: Anvil is a Python web app platform that allows you to build and deploy web apps for free. It offers a drag-and-drop designer, a built-in Python server environment, and a built-in Postgres-backed database.

    When choosing a platform, consider the specific needs of your application, including the required resources, dependencies, and traffic volume. Some platforms may have limitations on outbound internet access or the number of projects you can create.

  • Build Your Own and Free AI Health Assistant, Personalized Healthcare

    Build Your Own and Free AI Health Assistant, Personalized Healthcare

    Imagine having a 24/7 health companion that analyzes your medical history, tracks real-time vitals, and offers tailored advice—all while keeping your data private. This is the reality of AI health assistants, open-source tools merging artificial intelligence with healthcare to empower individuals and professionals alike. Let’s dive into how these systems work, their transformative benefits, and how you can build one using platforms like OpenHealthForAll 

    What Is an AI Health Assistant?

    An AI health assistant is a digital tool that leverages machine learning, natural language processing (NLP), and data analytics to provide personalized health insights. For example:

    • OpenHealth consolidates blood tests, wearable data, and family history into structured formats, enabling GPT-powered conversations about your health.
    • Aiden, another assistant, uses WhatsApp to deliver habit-building prompts based on anonymized data from Apple Health or Fitbit.

    These systems prioritize privacy, often running locally or using encryption to protect sensitive information.

    Why AI Health Assistants Matter: 5 Key Benefits

    1. Centralized Health Management
      Integrate wearables, lab reports, and EHRs into one platform. OpenHealth, for instance, parses blood tests and symptoms into actionable insights using LLMs like Claude or Gemini.
    2. Real-Time Anomaly Detection
      Projects like Kavya Prabahar’s virtual assistant use RNNs to flag abnormal heart rates or predict fractures from X-rays.
    3. Privacy-First Design
      Tools like Aiden anonymize data via Evervault and store records on blockchain (e.g., NearestDoctor’s smart contracts) to ensure compliance with regulations like HIPAA.
    4. Empathetic Patient Interaction
      Assistants like OpenHealth use emotion-aware AI to provide compassionate guidance, reducing anxiety for users managing chronic conditions.
    5. Cost-Effective Scalability
      Open-source frameworks like Google’s Open Health Stack (OHS) help developers build offline-capable solutions for low-resource regions, accelerating global healthcare access.

    Challenges and Ethical Considerations

    While promising, AI health assistants face hurdles:

    • Data Bias: Models trained on limited datasets may misdiagnose underrepresented groups.
    • Interoperability: Bridging EHR systems (e.g., HL7 FHIR) with AI requires standardization efforts like OHS.
    • Regulatory Compliance: Solutions must balance innovation with safety, as highlighted in Nature’s call for mandatory feedback loops in AI health tech.

    Build Your Own AI Health Assistant: A Developer’s Guide

    Step 1: Choose Your Stack

    • Data Parsing: Use OpenHealth’s Python-based parser (migrating to TypeScript soon) to structure inputs from wearables or lab reports.
    • AI Models: Integrate LLaMA or GPT-4 via APIs, or run Ollama locally for privacy.

    Step 2: Prioritize Security

    • Encrypt user data with Supabase or Evervault.
    • Implement blockchain for audit trails, as seen in NearestDoctor’s medical records system.

    Step 3: Start the setup

    Clone the Repository:

    git clone https://github.com/OpenHealthForAll/open-health.git
cd open-health

    Setup and Run:

    # Copy environment file
cp .env.example .env

# Add API keys to .env file:
# UPSTAGE_API_KEY - For parsing (You can get $10 credit without card registration by signing up at https://www.upstage.ai)
# OPENAI_API_KEY - For enhanced parsing capabilities

# Start the application using Docker Compose
docker compose --env-file .env up

    For existing users, use:

    docker compose --env-file .env up --build
    1. Access OpenHealth: Open your browser and navigate to http://localhost:3000 to begin using OpenHealth.

    The Future of AI Health Assistants

    1. Decentralized AI Marketplaces: Platforms like Ocean Protocol could let users monetize health models securely.
    2. AI-Powered Diagnostics: Google’s Health AI Developer Foundations aim to simplify building diagnostic tools for conditions like diabetes.
    3. Global Accessibility: Initiatives like OHS workshops in Kenya and India are democratizing AI health tech.

    Your Next Step

    • Contribute to OpenHealth’s GitHub repo to enhance its multilingual support.
  • OmniHuman-1: AI Model Generates Lifelike Human Videos from a Single Image

    OmniHuman-1: AI Model Generates Lifelike Human Videos from a Single Image

    OmniHuman-1 is an advanced AI model developed by ByteDance that generates realistic human videos from a single image and motion signals, such as audio or video inputs. This model supports various visual and audio styles, accommodating different aspect ratios and body proportions, including portrait, half-body, and full-body formats. Its capabilities extend to producing lifelike videos with natural motion, lighting, and texture details.

    OmniHuman-1 by ByteDance

    As of now, ByteDance has not released the OmniHuman-1 model or its weights to the public. The official project page states, “Currently, we do not offer services or downloads anywhere. Please be cautious of fraudulent information. We will provide timely updates on future developments.”

    ByteDance, the parent company of TikTok, has recently unveiled OmniHuman-1, an advanced AI model capable of generating realistic human videos from a single image and motion signals such as audio or video inputs. This development marks a significant leap in AI-driven human animation, offering potential applications across various industries.

    Key Features of OmniHuman-1

    • Multimodal Input Support: OmniHuman-1 can generate human videos based on a single image combined with motion signals, including audio-only, video-only, or a combination of both. This flexibility allows for diverse applications, from creating talking head videos to full-body animations.
    • Aspect Ratio Versatility: The model supports image inputs of any aspect ratio, whether they are portraits, half-body, or full-body images. This adaptability ensures high-quality results across various scenarios, catering to different content creation needs.
    • Enhanced Realism: OmniHuman-1 significantly outperforms existing methods by generating extremely realistic human videos based on weak signal inputs, especially audio. The realism is evident in comprehensive aspects, including motion, lighting, and texture details.

    Current Availability

    As of now, ByteDance has not released the OmniHuman-1 model or its weights to the public. The official project page states, “Currently, we do not offer services or downloads anywhere. Please be cautious of fraudulent information. We will provide timely updates on future developments.”

    Implications and Considerations

    The capabilities of OmniHuman-1 open up numerous possibilities in fields such as digital content creation, virtual reality, and entertainment. However, the technology also raises ethical considerations, particularly concerning the potential for misuse in creating deepfake content. It is crucial for developers, policymakers, and users to engage in discussions about responsible use and the establishment of guidelines to prevent abuse.

    OmniHuman-1 represents a significant advancement in AI-driven human animation, showcasing the rapid progress in this field. While its public release is still pending, the model’s demonstrated capabilities suggest a promising future for AI applications in creating realistic human videos. As with any powerful technology, it is essential to balance innovation with ethical considerations to ensure beneficial outcomes for society.

  • How to Install and Run Virtuoso-Medium-v2 Locally: A Step-by-Step Guide

    How to Install and Run Virtuoso-Medium-v2 Locally: A Step-by-Step Guide

    Virtuoso-Medium-v2 is here, Are you ready to harness the power of Virtuoso-Medium-v2 , the next-generation 32-billion-parameter language model? Whether you’re building advanced chatbots, automating workflows, or diving into research simulations, this guide will walk you through installing and running Virtuoso-Medium-v2 on your local machine. Let’s get started!

    Virtuoso-Medium-v2

    Why Choose Virtuoso-Medium-v2?

    Before we dive into the installation process, let’s briefly understand why Virtuoso-Medium-v2 stands out:

    • Distilled from Deepseek-v3 : With over 5 billion tokens worth of logits, it delivers unparalleled performance in technical queries, code generation, and mathematical problem-solving.
    • Cross-Architecture Compatibility : Thanks to “tokenizer surgery,” it integrates seamlessly with Qwen and Deepseek tokenizers.
    • Apache-2.0 License : Use it freely for commercial or non-commercial projects.

    Now that you know its capabilities, let’s set it up locally.

    Prerequisites

    Before installing Virtuoso-Medium-v2, ensure your system meets the following requirements:

    1. Hardware :
      • GPU with at least 24GB VRAM (recommended for optimal performance).
      • Sufficient disk space (~50GB for model files).
    2. Software :
      • Python 3.8 or higher.
      • PyTorch installed (pip install torch).
      • Hugging Face transformers library (pip install transformers).

    Step 1: Download the Model

    The first step is to download the Virtuoso-Medium-v2 model from Hugging Face. Open your terminal and run the following commands:

    # Install necessary libraries
pip install transformers torch

# Clone the model repository
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "arcee-ai/Virtuoso-Medium-v2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

    This will fetch the model and tokenizer directly from Hugging Face.


    Step 2: Prepare Your Environment

    Ensure your environment is configured correctly:
    1. Set up a virtual environment to avoid dependency conflicts:

    python -m venv virtuoso-env
source virtuoso-env/bin/activate  # On Windows: virtuoso-env\Scripts\activate

    2. Install additional dependencies if needed:

    pip install accelerate

    Step 3: Run the Model

    Once the model is downloaded, you can test it with a simple prompt. Here’s an example script:

    from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the model and tokenizer
model_name = "arcee-ai/Virtuoso-Medium-v2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Define your input prompt
prompt = "Explain the concept of quantum entanglement in simple terms."
inputs = tokenizer(prompt, return_tensors="pt")

# Generate output
outputs = model.generate(**inputs, max_new_tokens=150)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

    Run the script, and you’ll see the model generate a concise explanation of quantum entanglement!

    Step 4: Optimize Performance

    To maximize performance:

    Use quantization techniques to reduce memory usage.

    Enable GPU acceleration by setting device_map="auto" during model loading:

    model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

    Troubleshooting Tips

    • Out of Memory Errors : Reduce the max_new_tokens parameter or use quantized versions of the model.
    • Slow Inference : Ensure your GPU drivers are updated and CUDA is properly configured.

    With Virtuoso-Medium-v2 installed locally, you’re now equipped to build cutting-edge AI applications. Whether you’re developing enterprise tools or exploring STEM education, this model’s advanced reasoning capabilities will elevate your projects.

    Ready to take the next step? Experiment with Virtuoso-Medium-v2 today and share your experiences with the community! For more details, visit the official Hugging Face repository .