Smartproxy has been a promising proxy provider since its first days in 2018. They offer a wide selection of proxies (residential, shared and private datacenter proxies) and three full-stack scraping APIs with 100% success rates and a no-code scraper. It has an easy-to-use dashboard, public API, and flexible payment methods. Being awarded for its consumer-centric approach, Smartproxy continues to look for ways to make public data gathering as simple and accessible to everyone as possible.
The key things Smartproxy offers for you:
55M+ high-quality residential IPs with an average speed of <0.5 sec.
Global coverage – target 195+ locations, including cities and 50 US states.
Pay As You Go option - $3.5 per 1GB.
500K+ shared and dedicated datacenter IPs.
Unlimited connections and threads.
Full anonymity and security.
Smart Wallet for easy payment management.
Fantastic 24/7 support with 40s average response time.
User-friendly dashboard with a quick and easy proxy setup.
Detailed documentation and an informative blog.
A bunch of free tools with any plan subscription.
Free trial for all products..
Residential proxy is quite good. High success rate with their proxies even though it is quite expensive
Smartproxy, in my experience, is a solid choice for heavy-duty web scraping tasks. Been using it for bulk data mining. Their proxy pool's massive – good for avoiding blocks and captchas. Speed's decent, uptime mostly consistent. Setup? Straightforward. But yeah, pricing's a bit on the higher side, especially for large-scale operations. Overall, Smartproxy delivers, especially for those heavy scraping needs.
Based on our record, Jupyter seems to be a lot more popular than Smartproxy. While we know about 216 links to Jupyter, we've tracked only 6 mentions of Smartproxy. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Smarproxy is another proxy provider that offers a dedicated section for scraping e-commerce platforms including Amazon. Their Amazon API is known for its speed and quality and can be used just with a simple POST request. - Source: dev.to / 12 months ago
Smartproxy: Offers residential proxies with support for HTTP(S) and SOCKS5. It is known for its high success rate and 24/7 technical support. Smartproxy provides a balance between price and performance, making it accessible to less experienced users (Smartproxy). - Source: dev.to / about 1 year ago
For instance, imagine you are conducting market research and need to scrape data from various websites for analysis. By using a residential proxy service like anyip.io or Smart Proxy, you can ensure that your scraping activities are secure and undetectable while maintaining anonymity. Additionally, these services effortlessly handle throttling of threads and swiftly manage captchas. Source: over 1 year ago
Smartproxy Rotating residential IPs for multi-accounting. Large rotating residential proxy pool, 195+ countries, no subnets, an easy setup process. The IPs are from both desktop and mobile devices. Different tools for IG automation and affordable price plans ($75/5 GB ($15/GB). However the residential IPs are still P2P, so you have sticky sessions up to 30 min and then proxies rotate. - Source: dev.to / over 2 years ago
Smartproxy is one of the latest rotating residential proxy services to hit the market. Smartproxy offers its users immense features and functionality at affordable rates. If you’re looking for a long-term proxy service to stick to, it’s worth looking at Smartproxy’s unique offerings. - Source: dev.to / almost 4 years ago
Showcase and share: Easily embed UIs in Jupyter Notebook, Google Colab or share them on Hugging Face using a public link. - Source: dev.to / 2 months ago
LangChain wasn’t designed in isolation — it was built in the data pipeline world, where every data engineer’s tool of choice was Jupyter Notebooks. Jupyter was an innovative tool, making pipeline programming easy to experiment with, iterate on, and debug. It was a perfect fit for machine learning workflows, where you preprocess data, train models, analyze outputs, and fine-tune parameters — all in a structured,... - Source: dev.to / 3 months ago
Leverage versatile resources to prototype and refine your ideas, such as Jupyter Notebooks for rapid iterations, Google Colabs for cloud-based experimentation, OpenAI’s API Playground for testing and fine-tuning prompts, and Anthropic's Prompt Engineering Library for inspiration and guidance on advanced prompting techniques. For frontend experimentation, tools like v0 are invaluable, providing a seamless way to... - Source: dev.to / 4 months ago
Lately I've been working on Langgraph4J which is a Java implementation of the more famous Langgraph.js which is a Javascript library used to create agent and multi-agent workflows by Langchain. Interesting note is that [Langchain.js] uses Javascript Jupyter notebooks powered by a DENO Jupiter Kernel to implement and document How-Tos. So, I faced a dilemma on how to use (or possibly simulate) the same approach in... - Source: dev.to / 8 months ago
One of the most convenient ways to play with datasets is to utilize Jupyter. If you are not familiar with this tool, do not worry. I will show how to use it to solve our problem. For local experiments, I like to use DataSpell by JetBrains, but there are services available online and for free. One of the most well-known services among data scientists is Kaggle. However, their notebooks don't allow you to make... - Source: dev.to / 11 months ago
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