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Daily GitHub Project Recommendation: Stagehand – A New Paradigm for AI-Driven Browser Automation!

Browser automation is a crucial part of the daily work of many developers and businesses. Whether it’s data scraping, automated testing, or repetitive tasks, we are always looking for more efficient and intelligent solutions. Today, I bring you a hot GitHub project – browserbase/stagehand – which is redefining how we interact with browsers.

Project Highlights

Stagehand is hailed as an “AI browser automation framework,” cleverly blending the flexibility of artificial intelligence with the precision of traditional code to thoroughly address the pain points of existing tools. Traditional automation tools either require extensive low-level coding (like Selenium, Playwright), which is time-consuming and laborious; or they rely on high-level AI agents that perform unsatisfactorily and unpredictably in production environments. Stagehand emerges precisely to resolve this dilemma:

  • Perfect Blend of Code and AI: This is Stagehand’s core highlight. When you know the exact steps, you can leverage Playwright’s power to write precise code; when faced with unfamiliar or dynamically changing pages, you can entrust the AI to navigate and interact using natural language. This hybrid model ensures automation is both efficient and reliable.
  • Intelligent Preview and Caching: AI agent operations are no longer a “black box”! Stagehand allows you to preview actions executed by the AI, ensuring they meet expectations. Additionally, it can cache repeatable actions, significantly saving time and API call costs, thus lowering operational expenses.
  • One-Click Integration of SOTA AI Models: With just one line of code, you can integrate top-tier computer usage models like OpenAI and Anthropic, empowering the browser with more complex reasoning and operational capabilities.
  • Precise Data Extraction: Using the page.extract() function, you can easily extract structured data from a page, whether it’s author, title, or other information, precisely according to a predefined Schema.

Technical Details and Use Cases

Stagehand is primarily built on TypeScript and deeply integrates with Playwright, ensuring its technology stack is modern and reliable. This means developers can leverage TypeScript’s type safety and Playwright’s powerful features to build robust and easily maintainable automation scripts. It is highly suitable for scenarios requiring the handling of complex, dynamic web pages, such as content scraping, automated testing, intelligent data entry, and even building AI-powered web agents. If you are looking for a browser automation solution that runs stably in production and also possesses AI intelligence, Stagehand is definitely your top choice.

How to Get Started

Want to experience Stagehand’s charm firsthand? Getting started is incredibly simple! You just need to run one command to quickly kickstart a new project:

npx create-browser-app

For more detailed information and complete documentation, please visit its official documentation: docs.stagehand.dev or explore the GitHub repository directly: github.com/browserbase/stagehand .

Call to Action

Stagehand has currently garnered 15K+ Stars and added 350+ Stars today, a testament to its community’s enthusiasm and the project’s potential! If you are interested in AI and browser automation, I highly recommend you explore Stagehand immediately. Try using it to solve your real-world problems, contribute to the project, or share your insights within the community. Let’s witness together how AI empowers our development work!

Daily GitHub Project Recommendation: Ollama - Your Local AI Large Model Runner!

Today, we recommend a star project on GitHub with over 149,000 stars – ollama/ollama . If you aspire to run various advanced Large Language Models (LLMs) locally and privately, bidding farewell to cloud API dependencies and high costs, then Ollama is definitely your go-to choice!

Project Highlights

Ollama aims to make running large models locally simpler and more efficient than ever before, benefiting both developers and AI enthusiasts alike:

  • Local Deployment, Privacy Control: Ollama allows you to run various mainstream large models, including Gemma, DeepSeek-R1, Llama, and more, directly on your own computer. This means your data remains entirely local, greatly ensuring data privacy and security.
  • Extremely Convenient Experience: The project provides native installers for macOS, Windows, Linux, and even supports Docker deployment. With just a simple command-line instruction, such as ollama run gemma3, you can quickly launch a model and begin your AI conversation journey.
  • Rich Model Ecosystem: Ollama features a vast built-in model library, ranging from lightweight 1B parameter models to high-performance tens of billions parameter models, covering various application scenarios such as text generation, code assistance, and multi-modality. You simply need to choose and download based on your hardware conditions (a 7B model is recommended to have at least 8GB of RAM).
  • Highly Customizable and Extensible: Beyond running official models, Ollama also supports importing custom models in formats like GGUF, and easily adjusting model parameters, setting system prompts, or even building personalized AI assistants via Modelfile. Its powerful REST API and multi-language client libraries (e.g., Python, JavaScript, Go) also offer developers infinite integration possibilities.
  • Active Community Ecosystem: A robust community has formed around Ollama, giving rise to numerous Ollama-based web/desktop UIs, mobile applications, various plugins, and integration tools. Whether it’s a WebUI, desktop application, or command-line tool, you can find comprehensive supporting resources to help you easily build and apply local AI.

Technical Details and Use Cases

Ollama is written in high-performance Go, ensuring runtime efficiency and stability. It not only provides an intuitive command-line interface but also exposes a full-featured REST API, meaning you can easily integrate Ollama as the local AI inference backend for your existing applications, enabling various advanced functionalities such as local RAG (Retrieval-Augmented Generation), code assistance, and document summarization. For individuals and enterprises pursuing data sovereignty, offline work, or looking to explore large model capabilities without relying on the internet, Ollama is undoubtedly the ideal choice.

How to Get Started

Eager to experience the charm of local AI large models?

  1. Visit the Ollama official website to download the installer suitable for your operating system: https://ollama.com
  2. Or go directly to its GitHub repository for more information: https://github.com/ollama/ollama
  3. After installation, open your terminal and try running your first local model: ollama run gemma3

Call to Action

Ollama represents a revolutionary endeavor in the realm of local AI large model execution. If you are also passionate about local AI, data privacy, and technological innovation, why not try it out immediately, give this project a ⭐ Star, join their community discussions, or even contribute your code or ideas to collectively drive the future development of local AI!

Daily GitHub Project Recommendation: vLLM - Unlocking Efficient Deployment of Large Language Models!

Today’s GitHub treasure brings you vllm-project/vllm, a project that has caused a sensation in the field of Large Language Model (LLM) inference and deployment. With over 54K stars and 9K forks, vLLM is a true star project, aiming to enable everyone to deploy LLMs easily, quickly, and cost-effectively.

Project Highlights

vLLM’s core value lies in its exceptional high throughput and memory efficiency. It’s not just a serving engine, but a powerful performance optimization tool tailor-made for LLMs. Whether deploying complex language models or handling massive concurrent requests, vLLM delivers state-of-the-art inference throughput.

  • Technical Depth: vLLM introduces the revolutionary PagedAttention technology, an innovation that effectively manages Key and Value memory within the attention mechanism, significantly reducing memory footprint and improving efficiency. Furthermore, it supports continuous batching, leverages CUDA/HIP Graph for rapid model execution, and integrates various quantization schemes like GPTQ, AWQ, INT4/8, FP8, as well as optimized CUDA kernels such as FlashAttention, ensuring extreme running speed.
  • Application Value: For developers and enterprises, vLLM translates to lower deployment costs and higher service quality. It provides an OpenAI-compatible API server, greatly simplifying integration with existing applications. At the same time, the project supports distributed inference modes such as tensor, pipeline, data, and expert parallelism, making it possible to deploy ultra-large models on massive clusters. Whether your model is Transformer-like, Mixture-of-Expert (e.g., Mixtral), an embedding model, or a multimodal LLM (e.g., LLaVA), vLLM offers seamless support and is compatible with various hardware platforms including NVIDIA, AMD, and Intel.

Technical Details and Use Cases

vLLM is primarily written in Python, relying on powerful underlying GPU acceleration technology, providing an ideal solution for scenarios requiring the deployment of large language models. If you are building AI chatbots, intelligent customer service systems, content generation platforms, or any application requiring efficient, real-time LLM inference, vLLM will be an indispensable tool for you. It not only allows your LLM services to run at high speed but also significantly reduces operational costs.

How to Get Started

Want to experience the extreme performance brought by vLLM? Installation is very simple:

pip install vllm

For more detailed information and usage guides, please visit its official documentation: https://docs.vllm.ai .

Project address: https://github.com/vllm-project/vllm

Call to Action

vLLM’s powerful capabilities benefit from its thriving community. If you are interested in large model deployment and performance optimization, consider exploring vLLM’s source code, contributing your expertise, or trying to use it in your projects. Don’t forget to give this excellent project a star and share it with more friends who might need it!